From 09d10084e3036d22c30419a899217587a85979d7 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Fri, 16 Aug 2024 08:09:14 -0700 Subject: [PATCH 1/5] update development version number --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 24f5339..170ce7a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: SSN2 Title: Spatial Modeling on Stream Networks -Version: 0.2.0 +Version: 0.2.1 Authors@R: c( person(given = "Michael", family = "Dumelle", From a3f2f6691270db789585e1d35d4186a0b6d319d9 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Fri, 16 Aug 2024 08:09:47 -0700 Subject: [PATCH 2/5] enhance stability of deviance in beta regression #23 --- NEWS.md | 6 ++++++ R/get_model_stats_glm.R | 4 +++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index 33c2ec4..aa8f94a 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,9 @@ +# SSN2 0.2.1 + +## Minor Updates + +* Enhanced numeric stability of deviance and pseudo R-squared for `ssn_glm()` models when `family = "beta"` [(#23)](https://github.com/USEPA/SSN2/issues/23). + # SSN2 0.2.0 ## Major Updates diff --git a/R/get_model_stats_glm.R b/R/get_model_stats_glm.R index 2073c44..1a73774 100644 --- a/R/get_model_stats_glm.R +++ b/R/get_model_stats_glm.R @@ -445,7 +445,9 @@ get_deviance_glm <- function(family, y, fitted_response, size, dispersion) { } else if (family == "inverse.gaussian") { half_deviance_i <- 0.5 * (y - fitted_response)^2 / (y * fitted_response^2) } else if (family == "beta") { - constant <- log(gamma(fitted_response * dispersion)) + log(gamma((1 - fitted_response) * dispersion)) - log(gamma(y * dispersion)) - log(gamma((1 - y) * dispersion)) + # has NA problem for large dispersion + # constant <- log(gamma(fitted_response * dispersion)) + log(gamma((1 - fitted_response) * dispersion)) - log(gamma(y * dispersion)) - log(gamma((1 - y) * dispersion)) + constant <- lgamma(fitted_response * dispersion) + lgamma((1 - fitted_response) * dispersion) - lgamma(y * dispersion) - lgamma((1 - y) * dispersion) half_deviance_i <- constant + (y - fitted_response) * dispersion * log(y) + ((1 - y) - (1 - fitted_response)) * dispersion * log(1 - y) } deviance_i <- 2 * half_deviance_i From 27025b63cafd39841854cab685217063afd6e11b Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Tue, 27 Aug 2024 16:02:37 -0700 Subject: [PATCH 3/5] update to fix AIC and AICc link with spmodel v0.8.0 changes --- NEWS.md | 1 + cran-comments.md | 3 ++- man/reexports.Rd | 2 +- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/NEWS.md b/NEWS.md index aa8f94a..c45ee40 100644 --- a/NEWS.md +++ b/NEWS.md @@ -3,6 +3,7 @@ ## Minor Updates * Enhanced numeric stability of deviance and pseudo R-squared for `ssn_glm()` models when `family = "beta"` [(#23)](https://github.com/USEPA/SSN2/issues/23). +* Updated `reexport.Rd` to reflect changes in `spmodel v0.8.0`'s handling of `AIC()` and `AICc()`. # SSN2 0.2.0 diff --git a/cran-comments.md b/cran-comments.md index 759ca03..09be54b 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,4 +1,5 @@ -This is a minor update that adds a few small features and bug fixes. Thank you. +This is a minor update that enhances stability and includes updates to `reexports.Rd` +to reflect changes in `spmodel v0.8.0`'s handling of `AIC()` and `AICc()`. Thank you. ------- diff --git a/man/reexports.Rd b/man/reexports.Rd index 4d86b31..e4197d7 100644 --- a/man/reexports.Rd +++ b/man/reexports.Rd @@ -25,6 +25,6 @@ below to see their documentation. \describe{ \item{generics}{\code{\link[generics]{augment}}, \code{\link[generics]{glance}}, \code{\link[generics]{tidy}}} - \item{spmodel}{\code{\link[spmodel:AIC.spmodel]{AICc}}, \code{\link[spmodel]{covmatrix}}, \code{\link[spmodel]{dispersion_initial}}, \code{\link[spmodel]{dispersion_params}}, \code{\link[spmodel]{glances}}, \code{\link[spmodel]{loocv}}, \code{\link[spmodel]{pseudoR2}}, \code{\link[spmodel]{randcov_initial}}, \code{\link[spmodel]{randcov_params}}, \code{\link[spmodel]{varcomp}}} + \item{spmodel}{\code{\link[spmodel]{AICc}}, \code{\link[spmodel]{covmatrix}}, \code{\link[spmodel]{dispersion_initial}}, \code{\link[spmodel]{dispersion_params}}, \code{\link[spmodel]{glances}}, \code{\link[spmodel]{loocv}}, \code{\link[spmodel]{pseudoR2}}, \code{\link[spmodel]{randcov_initial}}, \code{\link[spmodel]{randcov_params}}, \code{\link[spmodel]{varcomp}}} }} From fe4ccc85373b22be35f773619a66c6bee8a5e3ca Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Tue, 27 Aug 2024 17:20:58 -0700 Subject: [PATCH 4/5] update JOSS badge link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index da72cda..0b9c775 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![cran checks](https://badges.cranchecks.info/worst/SSN2.svg)](https://cran.r-project.org/web/checks/check_results_SSN2.html) [![R-CMD-check](https://github.com/USEPA/SSN2/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/USEPA/SSN2/actions/workflows/R-CMD-check.yaml) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![status](https://joss.theoj.org/papers/66fd932526762f8ccd8bd9c3954e0e3d/status.svg)](https://joss.theoj.org/papers/66fd932526762f8ccd8bd9c3954e0e3d) +[![status](https://joss.theoj.org/papers/10.21105/joss.06389/status.svg)](https://joss.theoj.org/papers/10.21105/joss.06389) # SSN2: Spatial Modeling on Stream Networks From 20850c15729ff109a9682ff190627145758c4e39 Mon Sep 17 00:00:00 2001 From: Michael Dumelle Date: Wed, 28 Aug 2024 10:56:37 -0700 Subject: [PATCH 5/5] website updates; downgrade to pkgdown 2.0.9 and boostrap 3 for toc --- _pkgdown.yml | 4 +- docs/404.html | 126 ++-- docs/CONTRIBUTING.html | 105 ++-- docs/LICENSE.html | 99 +-- docs/articles/index.html | 101 ++-- docs/articles/introduction.html | 316 +++++----- docs/authors.html | 135 +++-- docs/bootstrap-toc.css | 60 ++ docs/bootstrap-toc.js | 159 +++++ docs/docsearch.css | 148 +++++ docs/docsearch.js | 85 +++ docs/index.html | 182 +++--- docs/news/index.html | 119 ++-- docs/pkgdown.css | 384 ++++++++++++ docs/pkgdown.js | 176 ++---- docs/pkgdown.yml | 8 +- docs/reference/AIC.SSN2.html | 164 ++--- docs/reference/MiddleFork04.ssn.html | 127 ++-- docs/reference/SSN2-package.html | 124 ++-- docs/reference/SSN_to_SSN2.html | 138 +++-- docs/reference/Torgegram.html | 161 ++--- docs/reference/anova.SSN2.html | 167 +++--- docs/reference/augment.SSN2.html | 176 +++--- docs/reference/coef.SSN2.html | 149 +++-- docs/reference/confint.SSN2.html | 147 +++-- docs/reference/cooks.distance.SSN2.html | 151 +++-- docs/reference/copy_lsn_to_temp.html | 134 +++-- docs/reference/covmatrix.SSN2.html | 138 +++-- docs/reference/create_netgeom.html | 145 +++-- docs/reference/deviance.SSN2.html | 144 +++-- docs/reference/fitted.SSN2.html | 153 +++-- docs/reference/formula.SSN2.html | 142 +++-- docs/reference/glance.SSN2.html | 144 +++-- docs/reference/glances.SSN2.html | 147 +++-- docs/reference/hatvalues.SSN2.html | 151 +++-- docs/reference/index.html | 661 +++++++++------------ docs/reference/influence.SSN2.html | 151 +++-- docs/reference/labels.SSN2.html | 140 +++-- docs/reference/logLik.SSN2.html | 140 +++-- docs/reference/loocv.SSN2.html | 153 +++-- docs/reference/mf04p.html | 127 ++-- docs/reference/model.frame.SSN2.html | 144 +++-- docs/reference/model.matrix.SSN2.html | 144 +++-- docs/reference/plot.SSN2.html | 154 +++-- docs/reference/plot.Torgegram.html | 146 +++-- docs/reference/predict.SSN2.html | 166 +++--- docs/reference/print.SSN.html | 134 +++-- docs/reference/print.SSN2.html | 148 +++-- docs/reference/pseudoR2.SSN2.html | 146 +++-- docs/reference/reexports.html | 125 ++-- docs/reference/residuals.SSN2.html | 159 ++--- docs/reference/ssn_create_distmat.html | 150 +++-- docs/reference/ssn_get_data.html | 148 +++-- docs/reference/ssn_get_netgeom.html | 136 +++-- docs/reference/ssn_get_stream_distmat.html | 151 +++-- docs/reference/ssn_glm.html | 198 +++--- docs/reference/ssn_import.html | 153 +++-- docs/reference/ssn_import_predpts.html | 144 +++-- docs/reference/ssn_initial.html | 171 +++--- docs/reference/ssn_lm.html | 194 +++--- docs/reference/ssn_names.html | 133 +++-- docs/reference/ssn_params.html | 164 ++--- docs/reference/ssn_put_data.html | 152 +++-- docs/reference/ssn_simulate.html | 183 +++--- docs/reference/ssn_split_predpts.html | 161 ++--- docs/reference/ssn_subset.html | 149 +++-- docs/reference/ssn_update_path.html | 145 +++-- docs/reference/ssn_write.html | 145 +++-- docs/reference/summary.SSN.html | 138 +++-- docs/reference/summary.SSN2.html | 148 +++-- docs/reference/tidy.SSN2.html | 150 +++-- docs/reference/varcomp.SSN2.html | 143 +++-- docs/reference/vcov.SSN2.html | 142 +++-- docs/sitemap.xml | 272 ++++++--- 74 files changed, 6925 insertions(+), 4592 deletions(-) create mode 100644 docs/bootstrap-toc.css create mode 100644 docs/bootstrap-toc.js create mode 100644 docs/docsearch.css create mode 100644 docs/docsearch.js create mode 100644 docs/pkgdown.css diff --git a/_pkgdown.yml b/_pkgdown.yml index d71acfb..1069461 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,4 +1,4 @@ -url: ~ +url: https://usepa.github.io/SSN2/ template: - bootstrap: 5 + bootstrap: 3 diff --git a/docs/404.html b/docs/404.html index dbe33f7..70fc951 100644 --- a/docs/404.html +++ b/docs/404.html @@ -4,78 +4,112 @@ - + Page not found (404) • SSN2 - - - - - + + + + + + + - - Skip to contents - - -
-
-
+ +
+ -

Page not found (404)

+ +
+
+
+

Page not found (404)

Content not found. Please use links in the navbar. -
-
+
+ -
-

Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

-
-

Site built with pkgdown 2.1.0.

+ + +
+

+

Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

-
+
+

+

Site built with pkgdown 2.0.9.

+
+ + + diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html index e79a6c5..7f50c97 100644 --- a/docs/CONTRIBUTING.html +++ b/docs/CONTRIBUTING.html @@ -1,39 +1,59 @@ -Contributing to SSN2 development • SSN2 - Skip to contents - - -
-
-
- +
  • + Changelog +
    • +
    +
    +
    + + + +
    +
    +

    Contributing to SSN2 development

    - Source: CONTRIBUTING.md
    @@ -53,23 +73,30 @@

    GitHub Pull Request

    +
    + -
    + + diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 5dbd091..e289cbf 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -1,39 +1,59 @@ -GNU General Public License • SSN2 - Skip to contents +GNU General Public License • SSN2 + +
    +
    -
    -
    -
    + +
    +
    +

    GNU General Public License

    - Source: LICENSE.md
    @@ -220,23 +240,30 @@

    How to Apply These Terms

    -
    +
    + -
    -

    Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

    -
    -

    Site built with pkgdown 2.1.0.

    + + +
    +

    Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

    -
    +
    +

    Site built with pkgdown 2.0.9.

    +
    +
    + + diff --git a/docs/articles/index.html b/docs/articles/index.html index 793cb48..d1faf46 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -1,63 +1,86 @@ -Articles • SSN2 - Skip to contents - - -
    -
    -
    - +
  • + Changelog +
    • +
    +
    +
    + + + +
    +
    +

    Articles

    All vignettes

    -
    +

    An Introduction to Spatial Stream Network Modeling in R Using SSN2
    -
    +
    + -
    -

    Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

    +
    +

    Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

    -
    -

    Site built with pkgdown 2.1.0.

    +
    +

    Site built with pkgdown 2.0.9.

    -
    - +
    + + diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index f31929d..d04f4a1 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -4,65 +4,89 @@ - + An Introduction to Spatial Stream Network Modeling in R Using SSN2 • SSN2 - - - - - + + + + + + + + - - Skip to contents - - -
    - - - + +
    + -
    -
    + -

    An Introduction to Spatial Stream Network Modeling in R Using SSN2

    +
    +
    +
    +

    An Introduction to Spatial Stream Network +Modeling in R Using SSN2

    Michael Dumelle, Erin Peterson, Jay M. Ver Hoef, Alan Pearse, and Dan Isaak

    Source: vignettes/introduction.Rmd -
    introduction.Rmd
    + +
    @@ -155,7 +179,7 @@

    Background
    -library(SSN2)

    +library(SSN2)

    If you use SSN2 in a formal publication or report, please cite it. Citing SSN2 lets us devote more resources to it in the future. We view the SSN2 citation by @@ -434,44 +458,29 @@

    Linear SSN Models𝐲\mathbf{y} -have spatially dependent random errors and are often parameterized -as

    -𝐲=𝐗𝛃+𝛕tu+𝛕td+𝛕eu+𝛜,\begin{equation*} -\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\tau}_{tu} + \boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu} + \boldsymbol{\epsilon}, -\end{equation*}

    where -𝐗\mathbf{X} -is a matrix of explanatory variables (usually including a column of 1’s -for an intercept), -𝛃\boldsymbol{\beta} -is a vector of fixed effects that describe the average impact of -𝐗\mathbf{X} -on -𝐲\mathbf{y}, -𝛕tu\boldsymbol{\tau}_{tu} -is a vector of spatially dependent (autocorrelated) tail-up random -errors, -𝛕td\boldsymbol{\tau}_{td} -is a vector of spatially dependent (autocorrelated) tail-down random -errors, -𝛕eu\boldsymbol{\tau}_{eu} -is a vector of spatially dependent (autocorrelated) Euclidean random -errors, and -𝛜\boldsymbol{\epsilon} -is a vector of spatially independent (uncorrelated) random errors. The -spatial dependence of each -𝛕\boldsymbol{\tau} -term is explicitly specified using a spatial covariance function that -incorporates the variance of the respective -𝛕\boldsymbol{\tau} -term, often called a partial sill, and a range parameter that controls -the behavior of the respective spatial covariance. The variance of -𝛜\boldsymbol{\epsilon} -is often called the nugget (or nugget effect). Sometimes several -unconnected networks are modeled simultaneously. For two observations on -separate networks, tail-up and tail-down random errors are uncorrelated -but Euclidean random errors can be autocorrelated.

    +vector \(\mathbf{y}\) have spatially +dependent random errors and are often parameterized as

    +\[\begin{equation*} +\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\tau}_{tu} + +\boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu} + \boldsymbol{\epsilon}, +\end{equation*}\] +

    where \(\mathbf{X}\) is a matrix of +explanatory variables (usually including a column of 1’s for an +intercept), \(\boldsymbol{\beta}\) is a +vector of fixed effects that describe the average impact of \(\mathbf{X}\) on \(\mathbf{y}\), \(\boldsymbol{\tau}_{tu}\) is a vector of +spatially dependent (autocorrelated) tail-up random errors, \(\boldsymbol{\tau}_{td}\) is a vector of +spatially dependent (autocorrelated) tail-down random errors, \(\boldsymbol{\tau}_{eu}\) is a vector of +spatially dependent (autocorrelated) Euclidean random errors, and \(\boldsymbol{\epsilon}\) is a vector of +spatially independent (uncorrelated) random errors. The spatial +dependence of each \(\boldsymbol{\tau}\) term is explicitly +specified using a spatial covariance function that incorporates the +variance of the respective \(\boldsymbol{\tau}\) term, often called a +partial sill, and a range parameter that controls the behavior of the +respective spatial covariance. The variance of \(\boldsymbol{\epsilon}\) is often called the +nugget (or nugget effect). Sometimes several unconnected networks are +modeled simultaneously. For two observations on separate networks, +tail-up and tail-down random errors are uncorrelated but Euclidean +random errors can be autocorrelated.

    Suppose we are interested in studying summer mean temperature (Summer_mn) on the stream network. We can visualize the distribution of summer mean temperature (overlain onto the stream @@ -664,22 +673,17 @@

    Model SummariesAREAWTMAP represents the decrease in average summer mean stream temperature with a one unit (mm) increase in precipitation. There is strong evidence that average summer mean stream temperature decreases -with elevation -(pp-value -<0.001< 0.001), -while there is moderate evidence that average summer mean stream -temperature decreases with precipitation -(pp-value -≈\approx -0.05). A pseudo r-squared is also returned, which quantifies the -proportion of variability explained by the fixed effects. The -coefficients table of covariance parameters describes the model’s -dependence. The larger the de parameter, the more -variability in the process is attributed to the relevant effect. Here, -most of the model’s random variability comes from the tail-up and -tail-down portions of the model. The larger the range -parameter, the more autocorrelated nearby observations are with respect -to the relevant effect.

    +with elevation (\(p\)-value \(< 0.001\)), while there is moderate +evidence that average summer mean stream temperature decreases with +precipitation (\(p\)-value \(\approx\) 0.05). A pseudo r-squared is also +returned, which quantifies the proportion of variability explained by +the fixed effects. The coefficients table of covariance parameters +describes the model’s dependence. The larger the de +parameter, the more variability in the process is attributed to the +relevant effect. Here, most of the model’s random variability comes from +the tail-up and tail-down portions of the model. The larger the +range parameter, the more autocorrelated nearby +observations are with respect to the relevant effect.

    We directly compare the sources of variability in the model using the varcomp function:

    @@ -726,8 +730,7 @@ 
    Model Summariesn: The sample size.
 
  • 
 p: The number of fixed effects (linearly independent
-columns in
-𝐗\mathbf{X}).
    • 
+columns in \(\mathbf{X}\)).
 
  • 
 npar: The number of estimated covariance
 parameters.
    • 
@@ -766,7 +769,7 @@ 
    Model Summariesssn_mod) indicates it is a better fit to the data
 (than ssn_mod2). Outside of glance() and
 glances(), the functions AIC(),
-AICc(), logLik(), deviance(), and
+AICc(), logLik(), deviance(), and
 pseudoR2() are available to compute the relevant
 statistics. Note that additive is only required when the
 tail-up covariance is specified. We are able to compare AIC
@@ -1149,31 +1152,26 @@ 
    Advanced FeaturesGeneralized Linear SSN Models
 
    
 
    Generalized linear spatial stream network models for a response
-vector
-𝐲\mathbf{y}
-have spatially dependent random errors and are often parameterized
-as
    
-g(𝛍)=𝐗𝛃+𝛕tu+𝛕td+𝛕eu+𝛜,\begin{equation*}
-g(\boldsymbol{\mu}) = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\tau}_{tu} + \boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu} + \boldsymbol{\epsilon},
-\end{equation*}
    where
-𝛍\boldsymbol{\mu}
-is the mean of
-𝐲\mathbf{y},
-g(⋅)g(\cdot)
-is a link function that “links”
-𝛍\boldsymbol{\mu}
-to a linear function of the predictor variables and random errors, and
-all other terms are the same as those defined for linear spatial stream
-network models. Rather than assuming
-yy
-is normally (Gaussian) distributed as is often the case with linear
-spatial stream network models, generalized linear spatial stream network
-models assume
-𝐲\mathbf{y}
-follows one of many distributions and has a corresponding link function.
-Below we summarize the families of generalized linear spatial stream
-network models supported by SSN2 their link functions, and
-the type of data typically associated with these families. For more on
+vector \(\mathbf{y}\) have spatially
+dependent random errors and are often parameterized as
    
+\[\begin{equation*}
+g(\boldsymbol{\mu}) = \mathbf{X} \boldsymbol{\beta} +
+\boldsymbol{\tau}_{tu} + \boldsymbol{\tau}_{td} + \boldsymbol{\tau}_{eu}
++ \boldsymbol{\epsilon},
+\end{equation*}\]
+
    where \(\boldsymbol{\mu}\) is the
+mean of \(\mathbf{y}\), \(g(\cdot)\) is a link function that “links”
+\(\boldsymbol{\mu}\) to a linear
+function of the predictor variables and random errors, and all other
+terms are the same as those defined for linear spatial stream network
+models. Rather than assuming \(y\) is
+normally (Gaussian) distributed as is often the case with linear spatial
+stream network models, generalized linear spatial stream network models
+assume \(\mathbf{y}\) follows one of
+many distributions and has a corresponding link function. Below we
+summarize the families of generalized linear spatial stream network
+models supported by SSN2 their link functions, and the type
+of data typically associated with these families. For more on
 generalized linear models more generally, see McCullagh and Nelder (1989), Myers et al. (2012), and Faraway (2016).
    
 
    The ssn_glm() function is used to fit generalized linear
 spatial stream network models and bears many similarities to
@@ -1202,7 +1200,8 @@ 
    Generalized Linear SSN Models
 Gaussian
-g(𝛍)=𝛍g(\mathbf{\mu}) = \mathbf{\mu}
+\(g(\mathbf{\mu}) =
+\mathbf{\mu}\)
 Identity
 Continuous
 
@@ -1211,42 +1210,48 @@ 
    Generalized Linear SSN Models
 Binomial
-g(𝛍)=log(𝛍/(1−𝛍))g(\mathbf{\mu}) = \log(\mathbf{\mu} / (1 - \mathbf{\mu}))
+\(g(\mathbf{\mu}) = \log(\mathbf{\mu} / (1
+- \mathbf{\mu}))\)
 Logit
 Binary; Binary Count
 ssn_glm()
 
 
 Beta
-g(𝛍)=log(𝛍/(1−𝛍))g(\mathbf{\mu}) = \log(\mathbf{\mu} / (1 - \mathbf{\mu}))
+\(g(\mathbf{\mu}) = \log(\mathbf{\mu} / (1
+- \mathbf{\mu}))\)
 Logit
 Proportion
 ssn_glm()
 
 
 Poisson
-g(𝛍)=log(𝛍)g(\mathbf{\mu}) = \log(\mathbf{\mu})
+\(g(\mathbf{\mu}) =
+\log(\mathbf{\mu})\)
 Log
 Count
 ssn_glm()
 
 
 Negative Binomial
-g(𝛍)=log(𝛍)g(\mathbf{\mu}) = \log(\mathbf{\mu})
+\(g(\mathbf{\mu}) =
+\log(\mathbf{\mu})\)
 Log
 Count
 ssn_glm()
 
 
 Gamma
-g(𝛍)=log(𝛍)g(\mathbf{\mu}) = \log(\mathbf{\mu})
+\(g(\mathbf{\mu}) =
+\log(\mathbf{\mu})\)
 Log
 Skewed (positive continuous)
 ssn_glm()
 
 
 Inverse Gaussian
-g(𝛍)=log(𝛍)g(\mathbf{\mu}) = \log(\mathbf{\mu})
+\(g(\mathbf{\mu}) =
+\log(\mathbf{\mu})\)
 Log
 Skewed (positive continuous)
 ssn_glm()
@@ -1319,15 +1324,10 @@ 
    Generalized Linear SSN Modelspp-value
-<0.001< 0.001),
-while there is moderate evidence that log average summer mean
-temperature decreases with precipitation
-(pp-value
-≈\approx
-0.03). Recall that the covariance parameter estimates are on the link
-(here, log) scale.
    
+with elevation (\(p\)-value \(< 0.001\)), while there is moderate
+evidence that log average summer mean temperature decreases with
+precipitation (\(p\)-value \(\approx\) 0.03). Recall that the covariance
+parameter estimates are on the link (here, log) scale.
    
 
    The Poisson model assumes that each observations mean and variance
 are equal. Often with ecological or environmental data, the variance is
 larger than the mean – this is called overdispersion. The negative
@@ -1444,7 +1444,7 @@ 
    Function GlossaryAIC(): Compute the AIC.
 
  • 
-AICc(): Compute the AICc.
    • 
+AICc(): Compute the AICc.
 
  • 
 anova(): Perform an analysis of variance.
    • 
 
  • 
@@ -1474,8 +1474,7 @@ 
    Function Glossaryloocv(): Perform leave-one-out cross validation and
 compute relevant statistics.
    • 
 
  • 
-model.matrix(): Return the model matrix
-(𝐗\mathbf{X}).
    • 
+model.matrix(): Return the model matrix (\(\mathbf{X}\)).
 
  • 
 plot(): Create fitted model plots.
    • 
 
  • 
@@ -1578,7 +1577,7 @@ 
    From SSN to SSN2
 AIC()
 
-AIC(); AICc()
+AIC(); AICc()
 
 
 
@@ -1713,7 +1712,7 @@ 
    The Future of SSN2R Code Appendix
 
    
 
    -library(SSN2)
+library(SSN2)
 citation(package = "SSN2")
 path <- system.file("lsndata/MiddleFork04.ssn", package = "SSN2")
 copy_lsn_to_temp()
@@ -2015,26 +2014,35 @@ 
    References
-
    
+
    • + - +

    + + diff --git a/docs/authors.html b/docs/authors.html index c27d9b7..d01ea77 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -1,71 +1,94 @@ -Authors and Citation • SSN2 - Skip to contents - - -
    -
    -
    - -

    Authors and Citation

    +Authors and Citation • SSN2 + + +
    +
    +
    +
    +
    +

    Authors and Citation

    +
    + +

    • Michael Dumelle. Author, maintainer.

    • -

      Jay M. Ver Hoef. Author. +

      Jay M. Ver Hoef. Author.

    • -

      Erin Peterson. Author. +

      Erin Peterson. Author.

    • -

      Alan Pearse. Contributor. +

      Alan Pearse. Contributor.

    • -

      Dan Isaak. Contributor. +

      Dan Isaak. Contributor.

    +
    +
    +

    Citation

    + Source: inst/CITATION +
    +
    -
    -

    Citation

    -

    Source: inst/CITATION

    -

    Dumelle M, Peterson EE, Ver Hoef JM, Pearse A, Isaak DJ (2024). SSN2: The next generation of spatial stream network modeling in R. Journal of Open Source Software, 9(99), 6389, https://doi.org/10.21105/joss.06389

    - 
    @Article{,
+    
    Dumelle M, Peterson EE, Ver Hoef JM, Pearse A, Isaak DJ (2024). SSN2: The next generation of spatial stream network modeling in R. Journal of Open Source Software, 9(99), 6389, https://doi.org/10.21105/joss.06389
    
+    
    @Article{,
   title = {{SSN2}: The next generation of spatial stream network modeling in {R}},
   author = {Michael Dumelle and Erin E. Peterson and Jay M. {Ver Hoef} and Alan Pearse and Daniel J. Isaak},
   journal = {Journal of Open Source Software},
@@ -77,25 +100,27 @@ 
    Citation
    
   url = {https://doi.org/10.21105/joss.06389},
   publisher = {The Open Journal},
 }
    
-
    - -
    +
    -
    -

    Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

    -
    -

    Site built with pkgdown 2.1.0.

    + + +
    +

    Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

    -
    +
    +

    Site built with pkgdown 2.0.9.

    +
    +
    + + diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css new file mode 100644 index 0000000..5a85941 --- /dev/null +++ b/docs/bootstrap-toc.css @@ -0,0 +1,60 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ + +/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ + +/* All levels of nav */ +nav[data-toggle='toc'] .nav > li > a { + display: block; + padding: 4px 20px; + font-size: 13px; + font-weight: 500; + color: #767676; +} +nav[data-toggle='toc'] .nav > li > a:hover, +nav[data-toggle='toc'] .nav > li > a:focus { + padding-left: 19px; + color: #563d7c; + text-decoration: none; + background-color: transparent; + border-left: 1px solid #563d7c; +} +nav[data-toggle='toc'] .nav > .active > a, +nav[data-toggle='toc'] .nav > .active:hover > a, +nav[data-toggle='toc'] .nav > .active:focus > a { + padding-left: 18px; + font-weight: bold; + color: #563d7c; + background-color: transparent; + border-left: 2px solid #563d7c; +} + +/* Nav: second level (shown on .active) */ +nav[data-toggle='toc'] .nav .nav { + display: none; /* Hide by default, but at >768px, show it */ + padding-bottom: 10px; +} +nav[data-toggle='toc'] .nav .nav > li > a { + padding-top: 1px; + padding-bottom: 1px; + padding-left: 30px; + font-size: 12px; + font-weight: normal; +} +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 29px; +} +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 28px; + font-weight: 500; +} + +/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ +nav[data-toggle='toc'] .nav > .active > ul { + display: block; +} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js new file mode 100644 index 0000000..1cdd573 --- /dev/null +++ b/docs/bootstrap-toc.js @@ -0,0 +1,159 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ +(function() { + 'use strict'; + + window.Toc = { + helpers: { + // return all matching elements in the set, or their descendants + findOrFilter: function($el, selector) { + // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ + // http://stackoverflow.com/a/12731439/358804 + var $descendants = $el.find(selector); + return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); + }, + + generateUniqueIdBase: function(el) { + var text = $(el).text(); + var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); + return anchor || el.tagName.toLowerCase(); + }, + + generateUniqueId: function(el) { + var anchorBase = this.generateUniqueIdBase(el); + for (var i = 0; ; i++) { + var anchor = anchorBase; + if (i > 0) { + // add suffix + anchor += '-' + i; + } + // check if ID already exists + if (!document.getElementById(anchor)) { + return anchor; + } + } + }, + + generateAnchor: function(el) { + if (el.id) { + return el.id; + } else { + var anchor = this.generateUniqueId(el); + el.id = anchor; + return anchor; + } + }, + + createNavList: function() { + return $('
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      `, then `

      `, etc.) that has more than one element. Defaults to 1 (for `

      `). + getTopLevel: function($scope) { + for (var i = 1; i <= 6; i++) { + var $headings = this.findOrFilter($scope, 'h' + i); + if ($headings.length > 1) { + return i; + } + } + + return 1; + }, + + // returns the elements for the top level, and the next below it + getHeadings: function($scope, topLevel) { + var topSelector = 'h' + topLevel; + + var secondaryLevel = topLevel + 1; + var secondarySelector = 'h' + secondaryLevel; + + return this.findOrFilter($scope, topSelector + ',' + secondarySelector); + }, + + getNavLevel: function(el) { + return parseInt(el.tagName.charAt(1), 10); + }, + + populateNav: function($topContext, topLevel, $headings) { + var $context = $topContext; + var $prevNav; + + var helpers = this; + $headings.each(function(i, el) { + var $newNav = helpers.generateNavItem(el); + var navLevel = helpers.getNavLevel(el); + + // determine the proper $context + if (navLevel === topLevel) { + // use top level + $context = $topContext; + } else if ($prevNav && $context === $topContext) { + // create a new level of the tree and switch to it + $context = helpers.createChildNavList($prevNav); + } // else use the current $context + + $context.append($newNav); + + $prevNav = $newNav; + }); + }, + + parseOps: function(arg) { + var opts; + if (arg.jquery) { + opts = { + $nav: arg + }; + } else { + opts = arg; + } + opts.$scope = opts.$scope || $(document.body); + return opts; + } + }, + + // accepts a jQuery object, or an options object + init: function(opts) { + opts = this.helpers.parseOps(opts); + + // ensure that the data attribute is in place for styling + opts.$nav.attr('data-toggle', 'toc'); + + var $topContext = this.helpers.createChildNavList(opts.$nav); + var topLevel = this.helpers.getTopLevel(opts.$scope); + var $headings = this.helpers.getHeadings(opts.$scope, topLevel); + this.helpers.populateNav($topContext, topLevel, $headings); + } + }; + + $(function() { + $('nav[data-toggle="toc"]').each(function(i, el) { + var $nav = $(el); + Toc.init($nav); + }); + }); +})(); diff --git a/docs/docsearch.css b/docs/docsearch.css new file mode 100644 index 0000000..e5f1fe1 --- /dev/null +++ b/docs/docsearch.css @@ -0,0 +1,148 @@ +/* Docsearch -------------------------------------------------------------- */ +/* + Source: https://github.com/algolia/docsearch/ + License: MIT +*/ + +.algolia-autocomplete { + display: block; + -webkit-box-flex: 1; + -ms-flex: 1; + flex: 1 +} + +.algolia-autocomplete .ds-dropdown-menu { + width: 100%; + min-width: none; + max-width: none; + padding: .75rem 0; + background-color: #fff; + background-clip: padding-box; + border: 1px solid rgba(0, 0, 0, .1); + box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); +} + +@media (min-width:768px) { + .algolia-autocomplete .ds-dropdown-menu { + width: 175% + } +} + +.algolia-autocomplete .ds-dropdown-menu::before { + display: none +} + +.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { + padding: 0; + background-color: rgb(255,255,255); + border: 0; + max-height: 80vh; +} + +.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { + margin-top: 0 +} + +.algolia-autocomplete .algolia-docsearch-suggestion { + padding: 0; + overflow: visible +} + +.algolia-autocomplete .algolia-docsearch-suggestion--category-header { + padding: .125rem 1rem; + margin-top: 0; + font-size: 1.3em; + font-weight: 500; + color: #00008B; + border-bottom: 0 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { + float: none; + padding-top: 0 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { + float: none; + width: auto; + padding: 0; + text-align: left +} + +.algolia-autocomplete .algolia-docsearch-suggestion--content { + float: none; + width: auto; + padding: 0 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--content::before { + display: none +} + +.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { + padding-top: .75rem; + margin-top: .75rem; + border-top: 1px solid rgba(0, 0, 0, .1) +} + +.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { + display: block; + padding: .1rem 1rem; + margin-bottom: 0.1; + font-size: 1.0em; + font-weight: 400 + /* display: none */ +} + +.algolia-autocomplete .algolia-docsearch-suggestion--title { + display: block; + padding: .25rem 1rem; + margin-bottom: 0; + font-size: 0.9em; + font-weight: 400 +} + +.algolia-autocomplete .algolia-docsearch-suggestion--text { + padding: 0 1rem .5rem; + margin-top: -.25rem; + font-size: 0.8em; + font-weight: 400; + line-height: 1.25 +} + +.algolia-autocomplete .algolia-docsearch-footer { + width: 110px; + height: 20px; + z-index: 3; + margin-top: 10.66667px; + float: right; + font-size: 0; + line-height: 0; +} + +.algolia-autocomplete .algolia-docsearch-footer--logo { + background-image: url("data:image/svg+xml;utf8,"); + background-repeat: no-repeat; + background-position: 50%; + background-size: 100%; + overflow: hidden; + text-indent: -9000px; + width: 100%; + height: 100%; + display: block; + transform: translate(-8px); +} + +.algolia-autocomplete .algolia-docsearch-suggestion--highlight { + color: #FF8C00; + background: rgba(232, 189, 54, 0.1) +} + + +.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { + box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) +} + +.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { + background-color: rgba(192, 192, 192, .15) +} diff --git a/docs/docsearch.js b/docs/docsearch.js new file mode 100644 index 0000000..b35504c --- /dev/null +++ b/docs/docsearch.js @@ -0,0 +1,85 @@ +$(function() { + + // register a handler to move the focus to the search bar + // upon pressing shift + "/" (i.e. "?") + $(document).on('keydown', function(e) { + if (e.shiftKey && e.keyCode == 191) { + e.preventDefault(); + $("#search-input").focus(); + } + }); + + $(document).ready(function() { + // do keyword highlighting + /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ + var mark = function() { + + var referrer = document.URL ; + var paramKey = "q" ; + + if (referrer.indexOf("?") !== -1) { + var qs = referrer.substr(referrer.indexOf('?') + 1); + var qs_noanchor = qs.split('#')[0]; + var qsa = qs_noanchor.split('&'); + var keyword = ""; + + for (var i = 0; i < qsa.length; i++) { + var currentParam = qsa[i].split('='); + + if (currentParam.length !== 2) { + continue; + } + + if (currentParam[0] == paramKey) { + keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); + } + } + + if (keyword !== "") { + $(".contents").unmark({ + done: function() { + $(".contents").mark(keyword); + } + }); + } + } + }; + + mark(); + }); +}); + +/* Search term highlighting ------------------------------*/ + +function matchedWords(hit) { + var words = []; + + var hierarchy = hit._highlightResult.hierarchy; + // loop to fetch from lvl0, lvl1, etc. + for (var idx in hierarchy) { + words = words.concat(hierarchy[idx].matchedWords); + } + + var content = hit._highlightResult.content; + if (content) { + words = words.concat(content.matchedWords); + } + + // return unique words + var words_uniq = [...new Set(words)]; + return words_uniq; +} + +function updateHitURL(hit) { + + var words = matchedWords(hit); + var url = ""; + + if (hit.anchor) { + url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; + } else { + url = hit.url + '?q=' + escape(words.join(" ")); + } + + return url; +} diff --git a/docs/index.html b/docs/index.html index a16e903..085c599 100644 --- a/docs/index.html +++ b/docs/index.html @@ -4,75 +4,101 @@ - + Spatial Modeling on Stream Networks • SSN2 - - - - - - + + + + + + + - - Skip to contents + + +
      +
      +
      -
      -
      -
      -

      SSN2: Spatial Modeling on Stream Networks

      -

      SSN2 is an R package for spatial statistical modeling and prediction on stream networks, including models based on in-stream distance. Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included. It is the successor to the SSN R package. See the SSN2 website for more: https://usepa.github.io/SSN2/.

      +

      SSN2 is an R package for spatial statistical modeling and prediction on stream networks, including models based on in-stream distance. Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included. It is the successor to the SSN R package. See the SSN2 website for more: https://usepa.github.io/SSN2/.

      Citation

      If you use SSN2 in a formal publication or report, please cite it. Citing SSN2 lets us devote more resources to it in the future. View the SSN2 citation by running

       citation(package = "SSN2")
      -
      #>
+
      #> 
 #> To cite SSN2 in publications use:
-#>
+#> 
 #>   Dumelle M, Peterson EE, Ver Hoef JM, Pearse A, Isaak DJ (2024). SSN2:
 #>   The next generation of spatial stream network modeling in R. Journal
 #>   of Open Source Software, 9(99), 6389,
 #>   https://doi.org/10.21105/joss.06389
-#>
+#> 
 #> A BibTeX entry for LaTeX users is
-#>
+#> 
 #>   @Article{,
 #>     title = {{SSN2}: The next generation of spatial stream network modeling in {R}},
 #>     author = {Michael Dumelle and Erin E. Peterson and Jay M. {Ver Hoef} and Alan Pearse and Daniel J. Isaak},
@@ -99,7 +125,7 @@ 
      Installation Instructions# install the most recent approved version from CRAN
 install.packages("SSN2")
 # load the most recent approved version from CRAN
-library(SSN2)
      +library(SSN2)

      Install and load the most recent version ofSSN2 from GitHub by running

       # Installing from GitHub requires you first install the remotes package
@@ -108,7 +134,7 @@ 
      Installation Instructions# install the most recent version from GitHub
 remotes::install_github("USEPA/SSN2", ref = "main")
 # load the most recent development version from GitHub
-library(SSN2)
      +library(SSN2)

      Install and load the most recent development version ofSSN2 from GitHub by running

       # Installing from GitHub requires you first install the remotes package
@@ -117,7 +143,7 @@ 
      Installation Instructions# install the most recent version from GitHub
 remotes::install_github("USEPA/SSN2", ref = "develop")
 # load the most recent development version from GitHub
-library(SSN2)
      +library(SSN2)

      Contributing to SSN2 @@ -138,10 +164,10 @@

      Getting Help

      Example Usage

      -

      Below we provide a brief example showing how to use SSN2. For a thorough introduction to the software, see our introductory vignette linked here. For a list of all functions available in SSN2, see our function reference linked here.

      +

      Below we provide a brief example showing how to use SSN2. For a thorough introduction to the software, see our introductory vignette linked here. For a list of all functions available in SSN2, see our function reference linked here.

      We load SSN2, copy the .ssn object that comes with SSN2 to the temporary directory, and create stream distance matrices used for modeling by running

      -library(SSN2)
+library(SSN2)
 copy_lsn_to_temp()
 path <- paste0(tempdir(), "/MiddleFork04.ssn")
 mf04p <- ssn_import(path, predpts = "pred1km")
@@ -157,26 +183,26 @@ 
      Example Usage= "afvArea"
 )
 summary(ssn_mod)
      -
      #>
+
      #> 
 #> Call:
-#> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p,
-#>     tailup_type = "exponential", taildown_type = "spherical",
+#> ssn_lm(formula = Summer_mn ~ ELEV_DEM + AREAWTMAP, ssn.object = mf04p, 
+#>     tailup_type = "exponential", taildown_type = "spherical", 
 #>     euclid_type = "gaussian", additive = "afvArea")
-#>
+#> 
 #> Residuals:
-#>     Min      1Q  Median      3Q     Max
-#> -3.6393 -2.0646 -0.5952  0.2143  0.7497
-#>
+#>     Min      1Q  Median      3Q     Max 
+#> -3.6393 -2.0646 -0.5952  0.2143  0.7497 
+#> 
 #> Coefficients (fixed):
-#>              Estimate Std. Error z value Pr(>|z|)
+#>              Estimate Std. Error z value Pr(>|z|)    
 #> (Intercept) 76.195041   7.871574   9.680  < 2e-16 ***
 #> ELEV_DEM    -0.026905   0.003646  -7.379  1.6e-13 ***
-#> AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 *
+#> AREAWTMAP   -0.009099   0.004461  -2.040   0.0414 *  
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-#>
+#> 
 #> Pseudo R-squared: 0.6124
-#>
+#> 
 #> Coefficients (covariance):
 #>               Effect     Parameter   Estimate
 #>   tailup exponential  de (parsill)  3.800e+00
@@ -192,7 +218,7 @@ 
      Example Usage#> # A tibble: 3 × 7
 #>   term        estimate std.error statistic  p.value conf.low conf.high
 #>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
-#> 1 (Intercept) 76.2       7.87         9.68 0         60.8    91.6
+#> 1 (Intercept) 76.2       7.87         9.68 0         60.8    91.6     
 #> 2 AREAWTMAP   -0.00910   0.00446     -2.04 4.14e- 2  -0.0178 -0.000356
 #> 3 ELEV_DEM    -0.0269    0.00365     -7.38 1.60e-13  -0.0341 -0.0198

      -
      -

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      +
      +

      +

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      -
      -

      Site built with pkgdown 2.1.0.

      +
      +

      +

      Site built with pkgdown 2.0.9.

      -
      +
      + - + diff --git a/docs/news/index.html b/docs/news/index.html index d8bca9a..47b9e5c 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -1,43 +1,72 @@ -Changelog • SSN2 - Skip to contents - - -
      -
      -
      - -

      Changelog

      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Changelog

      Source: NEWS.md
      -

      SSN2 0.2.0

      CRAN release: 2024-07-26

      +

      SSN2 0.2.12024-08-28

      +
      +

      Minor Updates

      +
      • Enhanced numeric stability of deviance and pseudo R-squared for ssn_glm() models when family = "beta" (#23).
        • +
      • Updated reexport.Rd to reflect changes in spmodel v0.8.0’s handling of AIC() and AICc().
        • +
      +
      +
      +

      SSN2 0.2.02024-07-26

      Major Updates

      • Significant testing, documentation, and auxiliary (e.g., README.md) updates as part of a submission to Journal of Open Source Software. Relevant issues associated with the review are available at #11, #12, #13, #14, #15, #16, #17, #20, #21. The review is linked here.
        • @@ -60,7 +89,7 @@

        Bug Fixes -

        SSN2 0.1.1

        CRAN release: 2024-01-15

        +

        SSN2 0.1.12024-01-15

        Minor Updates

        • Changed network geometry name from netgeometry to netgeom to avoid exceeding the 10 character limit for column/field names while writing to shapefiles (#2).
          • @@ -81,26 +110,32 @@

          Bug Fixes -

          SSN2 0.1.0

          CRAN release: 2023-11-08

          +

          SSN2 0.1.02023-11-08

          • Initial CRAN submission.
        -
      +
      + -
      -

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      -
      -

      Site built with pkgdown 2.1.0.

      + +
      +

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      -
      +
      +

      Site built with pkgdown 2.0.9.

      +
      +
      + + diff --git a/docs/pkgdown.css b/docs/pkgdown.css new file mode 100644 index 0000000..80ea5b8 --- /dev/null +++ b/docs/pkgdown.css @@ -0,0 +1,384 @@ +/* Sticky footer */ + +/** + * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ + * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css + * + * .Site -> body > .container + * .Site-content -> body > .container .row + * .footer -> footer + * + * Key idea seems to be to ensure that .container and __all its parents__ + * have height set to 100% + * + */ + +html, body { + height: 100%; +} + +body { + position: relative; +} + +body > .container { + display: flex; + height: 100%; + flex-direction: column; +} + +body > .container .row { + flex: 1 0 auto; +} + +footer { + margin-top: 45px; + padding: 35px 0 36px; + border-top: 1px solid #e5e5e5; + color: #666; + display: flex; + flex-shrink: 0; +} +footer p { + margin-bottom: 0; +} +footer div { + flex: 1; +} +footer .pkgdown { + text-align: right; +} +footer p { + margin-bottom: 0; +} + +img.icon { + float: right; +} + +/* Ensure in-page images don't run outside their container */ +.contents img { + max-width: 100%; + height: auto; +} + +/* Fix bug in bootstrap (only seen in firefox) */ +summary { + display: list-item; +} + +/* Typographic tweaking ---------------------------------*/ + +.contents .page-header { + margin-top: calc(-60px + 1em); +} + +dd { + margin-left: 3em; +} + +/* Section anchors ---------------------------------*/ + +a.anchor { + display: none; + margin-left: 5px; + width: 20px; + height: 20px; + + background-image: url(./link.svg); + background-repeat: no-repeat; + background-size: 20px 20px; + background-position: center center; +} + +h1:hover .anchor, +h2:hover .anchor, +h3:hover .anchor, +h4:hover .anchor, +h5:hover .anchor, +h6:hover .anchor { + display: inline-block; +} + +/* Fixes for fixed navbar --------------------------*/ + +.contents h1, .contents h2, .contents h3, .contents h4 { + padding-top: 60px; + margin-top: -40px; +} + +/* Navbar submenu --------------------------*/ + +.dropdown-submenu { + position: relative; +} + +.dropdown-submenu>.dropdown-menu { + top: 0; + left: 100%; + margin-top: -6px; + margin-left: -1px; + border-radius: 0 6px 6px 6px; +} + +.dropdown-submenu:hover>.dropdown-menu { + display: block; +} + +.dropdown-submenu>a:after { + display: block; + content: " "; + float: right; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; + border-width: 5px 0 5px 5px; + border-left-color: #cccccc; + margin-top: 5px; + margin-right: -10px; +} + +.dropdown-submenu:hover>a:after { + border-left-color: #ffffff; +} + +.dropdown-submenu.pull-left { + float: none; +} + +.dropdown-submenu.pull-left>.dropdown-menu { + left: -100%; + margin-left: 10px; + border-radius: 6px 0 6px 6px; +} + +/* Sidebar --------------------------*/ + +#pkgdown-sidebar { + margin-top: 30px; + position: -webkit-sticky; + position: sticky; + top: 70px; +} + +#pkgdown-sidebar h2 { + font-size: 1.5em; 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+ font-size: 1.35rem; +} + +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 15px; +} + +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 15px; + font-weight: 500; + font-size: 1.35rem; +} + +/* orcid ------------------------------------------------------------------- */ + +.orcid { + font-size: 16px; + color: #A6CE39; + /* margins are required by official ORCID trademark and display guidelines */ + margin-left:4px; + margin-right:4px; + vertical-align: middle; +} + +/* Reference index & topics ----------------------------------------------- */ + +.ref-index th {font-weight: normal;} + +.ref-index td {vertical-align: top; min-width: 100px} +.ref-index .icon {width: 40px;} +.ref-index .alias {width: 40%;} +.ref-index-icons .alias {width: calc(40% - 40px);} +.ref-index .title {width: 60%;} + +.ref-arguments th {text-align: right; padding-right: 10px;} +.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px} +.ref-arguments .name {width: 20%;} +.ref-arguments .desc {width: 80%;} + +/* Nice scrolling for wide elements --------------------------------------- */ + +table { + display: block; + overflow: auto; +} + +/* Syntax highlighting ---------------------------------------------------- */ + +pre, code, pre code { + background-color: #f8f8f8; + color: #333; +} +pre, pre code { + white-space: pre-wrap; + word-break: break-all; + overflow-wrap: break-word; +} + +pre { + border: 1px solid #eee; +} + +pre .img, pre .r-plt { + margin: 5px 0; +} + +pre .img img, pre .r-plt img { + background-color: #fff; +} + +code a, pre a { + color: #375f84; +} + +a.sourceLine:hover { + text-decoration: none; +} + +.fl {color: #1514b5;} +.fu {color: #000000;} /* function */ +.ch,.st {color: #036a07;} /* string */ +.kw {color: #264D66;} /* keyword */ +.co {color: #888888;} /* comment */ + +.error {font-weight: bolder;} +.warning {font-weight: bolder;} + +/* Clipboard --------------------------*/ + +.hasCopyButton { + position: relative; +} + +.btn-copy-ex { + position: absolute; + right: 0; + top: 0; + visibility: hidden; +} + +.hasCopyButton:hover button.btn-copy-ex { + visibility: visible; +} + +/* headroom.js ------------------------ */ + +.headroom { + will-change: transform; + transition: transform 200ms linear; +} +.headroom--pinned { + transform: translateY(0%); +} +.headroom--unpinned { + transform: translateY(-100%); +} + +/* mark.js ----------------------------*/ + +mark { + background-color: rgba(255, 255, 51, 0.5); + border-bottom: 2px solid rgba(255, 153, 51, 0.3); + padding: 1px; +} + +/* vertical spacing after htmlwidgets */ +.html-widget { + margin-bottom: 10px; +} + +/* fontawesome ------------------------ */ + +.fab { + font-family: "Font Awesome 5 Brands" !important; +} + +/* don't display links in code chunks when printing */ +/* source: https://stackoverflow.com/a/10781533 */ +@media print { + code a:link:after, code a:visited:after { + content: ""; + } +} + +/* Section anchors --------------------------------- + Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 +*/ + +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 9757bf9..6f0eee4 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -2,43 +2,83 @@ (function($) { $(function() { - $('nav.navbar').headroom(); + $('.navbar-fixed-top').headroom(); - Toc.init({ - $nav: $("#toc"), - $scope: $("main h2, main h3, main h4, main h5, main h6") + $('body').css('padding-top', $('.navbar').height() + 10); + $(window).resize(function(){ + $('body').css('padding-top', $('.navbar').height() + 10); }); - if ($('#toc').length) { - $('body').scrollspy({ - target: '#toc', - offset: $("nav.navbar").outerHeight() + 1 - }); + $('[data-toggle="tooltip"]').tooltip(); + + var cur_path = paths(location.pathname); + var links = $("#navbar ul li a"); + var max_length = -1; + var pos = -1; + for (var i = 0; i < links.length; i++) { + if (links[i].getAttribute("href") === "#") + continue; + // Ignore external links + if (links[i].host !== location.host) + continue; + + var nav_path = paths(links[i].pathname); + + var length = prefix_length(nav_path, cur_path); + if (length > max_length) { + max_length = length; + pos = i; + } } - // Activate popovers - $('[data-bs-toggle="popover"]').popover({ - container: 'body', - html: true, - trigger: 'focus', - placement: "top", - sanitize: false, - }); + // Add class to parent
    • , and enclosing
    • if in dropdown + if (pos >= 0) { + var menu_anchor = $(links[pos]); + menu_anchor.parent().addClass("active"); + menu_anchor.closest("li.dropdown").addClass("active"); + } + }); - $('[data-bs-toggle="tooltip"]').tooltip(); + function paths(pathname) { + var pieces = pathname.split("/"); + pieces.shift(); // always starts with / + + var end = pieces[pieces.length - 1]; + if (end === "index.html" || end === "") + pieces.pop(); + return(pieces); + } + + // Returns -1 if not found + function prefix_length(needle, haystack) { + if (needle.length > haystack.length) + return(-1); + + // Special case for length-0 haystack, since for loop won't run + if (haystack.length === 0) { + return(needle.length === 0 ? 0 : -1); + } + + for (var i = 0; i < haystack.length; i++) { + if (needle[i] != haystack[i]) + return(i); + } + + return(haystack.length); + } /* Clipboard --------------------------*/ function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-bs-original-title'); - element.setAttribute('data-bs-original-title', msg); + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); $(element).tooltip('show'); - element.setAttribute('data-bs-original-title', tooltipOriginalTitle); + element.setAttribute('data-original-title', tooltipOriginalTitle); } if(ClipboardJS.isSupported()) { $(document).ready(function() { - var copyButton = ""; + var copyButton = ""; $("div.sourceCode").addClass("hasCopyButton"); @@ -49,106 +89,20 @@ $('.btn-copy-ex').tooltip({container: 'body'}); // Initialize clipboard: - var clipboard = new ClipboardJS('[data-clipboard-copy]', { + var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { text: function(trigger) { return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); } }); - clipboard.on('success', function(e) { + clipboardBtnCopies.on('success', function(e) { changeTooltipMessage(e.trigger, 'Copied!'); e.clearSelection(); }); - clipboard.on('error', function(e) { + clipboardBtnCopies.on('error', function() { changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); }); - }); } - - /* Search marking --------------------------*/ - var url = new URL(window.location.href); - var toMark = url.searchParams.get("q"); - var mark = new Mark("main#main"); - if (toMark) { - mark.mark(toMark, { - accuracy: { - value: "complementary", - limiters: [",", ".", ":", "/"], - } - }); - } - - /* Search --------------------------*/ - /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ - // Initialise search index on focus - var fuse; - $("#search-input").focus(async function(e) { - if (fuse) { - return; - } - - $(e.target).addClass("loading"); - var response = await fetch($("#search-input").data("search-index")); - var data = await response.json(); - - var options = { - keys: ["what", "text", "code"], - ignoreLocation: true, - threshold: 0.1, - includeMatches: true, - includeScore: true, - }; - fuse = new Fuse(data, options); - - $(e.target).removeClass("loading"); - }); - - // Use algolia autocomplete - var options = { - autoselect: true, - debug: true, - hint: false, - minLength: 2, - }; - var q; -async function searchFuse(query, callback) { - await fuse; - - var items; - if (!fuse) { - items = []; - } else { - q = query; - var results = fuse.search(query, { limit: 20 }); - items = results - .filter((x) => x.score <= 0.75) - .map((x) => x.item); - if (items.length === 0) { - items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}]; - } - } - callback(items); -} - $("#search-input").autocomplete(options, [ - { - name: "content", - source: searchFuse, - templates: { - suggestion: (s) => { - if (s.title == s.what) { - return `${s.dir} >
      ${s.title}
      `; - } else if (s.previous_headings == "") { - return `${s.dir} >
      ${s.title}
      > ${s.what}`; - } else { - return `${s.dir} >
      ${s.title}
      > ${s.previous_headings} > ${s.what}`; - } - }, - }, - }, - ]).on('autocomplete:selected', function(event, s) { - window.location.href = s.path + "?q=" + q + "#" + s.id; - }); - }); })(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 9143ebf..b054806 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,6 +1,10 @@ pandoc: 3.1.11 -pkgdown: 2.1.0 +pkgdown: 2.0.9 pkgdown_sha: ~ articles: introduction: introduction.html -last_built: 2024-07-26T23:43Z +last_built: 2024-08-28T17:48Z +urls: + reference: https://usepa.github.io/SSN2/reference + article: https://usepa.github.io/SSN2/articles + diff --git a/docs/reference/AIC.SSN2.html b/docs/reference/AIC.SSN2.html index 9cb3e27..5544e5d 100644 --- a/docs/reference/AIC.SSN2.html +++ b/docs/reference/AIC.SSN2.html @@ -1,98 +1,118 @@ -Compute AIC and AICc of fitted model objects — AIC.SSN2 • SSN2Compute AIC and AICc of fitted model objects — AIC.SSN2 • SSN2 - Skip to contents - - -
      -
      -
      - -

      Compute AIC and AICc of fitted model objects

      - Source: R/AIC.R -
      AIC.SSN2.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Compute AIC and AICc of fitted model objects

      + Source: R/AIC.R +
      -
      +

      Compute AIC and AICc for one or several fitted model objects for which a log-likelihood value can be obtained.

      -
      -

      Usage

      - 
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 AIC(object, ..., k = 2)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 AIC(object, ..., k = 2)
 
-# S3 method for class 'ssn_lm'
-AICc(object, ..., k = 2)
+# S3 method for ssn_lm
+AICc(object, ..., k = 2)
 
-# S3 method for class 'ssn_glm'
-AICc(object, ..., k = 2)
      
+# S3 method for ssn_glm
+AICc(object, ..., k = 2)
      -
      -

      Arguments

      - - -
      object
      +
      +

      Arguments

      +
      object

      A fitted model object from ssn_lm() or ssn_glm().

      -
      ...
      +
      ...

      Optionally more fitted model objects.

      -
      k
      +
      k

      The penalty parameter, taken to be 2. Currently not allowed to differ from 2 (needed for generic consistency).

      -
      -

      Value

      -

      If just one object is provided, a numeric value with the corresponding +

      +

      Value

      + + +

      If just one object is provided, a numeric value with the corresponding AIC or AICc.

      + +

      If multiple objects are provided, a data.frame with rows corresponding to the objects and columns representing the number of parameters estimated (df) and the AIC or AICc.

      -
      -

      Details

      +
      +

      Details

      When comparing models fit by maximum or restricted maximum likelihood, the smaller the AIC or AICc, the better the fit. The AICc contains a correction to AIC for small sample sizes. AIC and AICc comparisons between "ml" -and "reml" models are meaningless – comparisons should only be made +and "reml" models are meaningless -- comparisons should only be made within a set of models estimated using "ml" or a set of models estimated using "reml". AIC and AICc comparisons for "reml" must use the same fixed effects. To vary the covariance parameters and @@ -106,8 +126,8 @@

      Details

      -
      -

      Examples

      +
      +

      Examples

      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -123,27 +143,31 @@ 
      Examples)
 AIC(ssn_mod)
 #> [1] 82.8932
-AICc(ssn_mod)
+AICc(ssn_mod)
 #> [1] 83.47857
      -
      +
      + +
      -
      -

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      +
      +

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      -
      -

      Site built with pkgdown 2.1.0.

      +
      +

      Site built with pkgdown 2.0.9.

      -
      - +
      + + diff --git a/docs/reference/MiddleFork04.ssn.html b/docs/reference/MiddleFork04.ssn.html index d9d7c7d..031a0e6 100644 --- a/docs/reference/MiddleFork04.ssn.html +++ b/docs/reference/MiddleFork04.ssn.html @@ -1,62 +1,81 @@ -MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn • SSN2MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset — MiddleFork04.ssn • SSN2 - Skip to contents - - -
      -
      -
      - -

      MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset

      - Source: R/data.R -
      MiddleFork04.ssn.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset

      + Source: R/data.R +
      -
      +

      The MiddleFork04.ssn data folder contains the spatial, attribute, and topological information needed to construct an SSN object using the SSN2 package.

      -
      -

      Source

      +
      +

      Source

      edges are a modified version of the United States National Hydrography Dataset (http://nhd.usgs.gov/). sites, pred1km and CapeHorn are unpublished United States Forest Service data.

      -
      -

      Details

      +
      +

      Details

      The MiddleFork04.ssn folder contains five geopackages:

      -
      -

      See also

      +
      +

      See also

      mf04p for the Middle For 04 data as an SSN object.

      -
      +
      + +
      -
      -

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      +
      +

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      -
      -

      Site built with pkgdown 2.1.0.

      +
      +

      Site built with pkgdown 2.0.9.

      -
      - +
      + + diff --git a/docs/reference/SSN2-package.html b/docs/reference/SSN2-package.html index e7a1810..a561564 100644 --- a/docs/reference/SSN2-package.html +++ b/docs/reference/SSN2-package.html @@ -1,58 +1,78 @@ -SSN2: Spatial Modeling on Stream Networks — SSN2-package • SSN2 - Skip to contents - - -
      -
      -
      - -

      SSN2: Spatial Modeling on Stream Networks

      - Source: R/SSN2.R -
      SSN2-package.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      SSN2: Spatial Modeling on Stream Networks

      + Source: R/SSN2.R +
      -
      +

      Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) doi:10.1198/jasa.2009.ap08248 .) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.

      -
      -

      See also

      -

      Useful links:

      + +
      -
      -

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      +
      +

      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.

      -
      -

      Site built with pkgdown 2.1.0.

      +
      +

      Site built with pkgdown 2.0.9.

      -
      - +
      + + diff --git a/docs/reference/SSN_to_SSN2.html b/docs/reference/SSN_to_SSN2.html index 3266288..bcc0dc6 100644 --- a/docs/reference/SSN_to_SSN2.html +++ b/docs/reference/SSN_to_SSN2.html @@ -1,71 +1,89 @@ -Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2 • SSN2Convert object from SpatialStreamNetwork class to SSN class — SSN_to_SSN2 • SSN2 - Skip to contents - - -
      -
      -
      - -

      Convert object from SpatialStreamNetwork class to SSN class

      - Source: R/SSN_to_SSN2.R -
      SSN_to_SSN2.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Convert object from SpatialStreamNetwork class to SSN class

      + Source: R/SSN_to_SSN2.R +
      -
      +

      Convert an S4 SpatialStreamNetwork object created in the SSN package to an S3 SSN object used in the SSN2 package.

      -
      -

      Usage

      + 
      SSN_to_SSN2(object)
      -
      -

      Arguments

      - - -
      object
      +
      +

      Arguments

      +
      object

      A SpatialStreamNetwork object

      -
      -

      Value

      -

      An S3 SSN class object.

      +
      +

      Value

      + + +

      An S3 SSN class object.

      -
      -

      Details

      +
      +

      Details

      SSN_to_SSN2() has been made available to help users migrate from the SSN package to the updated SSN2 package. It is used to convert existing S4 SpatialStreamNetwork @@ -75,23 +93,27 @@

      Details

      -
      +
      + +
      -
      + + diff --git a/docs/reference/Torgegram.html b/docs/reference/Torgegram.html index 490b9a3..0ec2a7a 100644 --- a/docs/reference/Torgegram.html +++ b/docs/reference/Torgegram.html @@ -1,51 +1,70 @@ -Compute the empirical semivariogram — Torgegram • SSN2 - Skip to contents - - -
      -
      -
      - -

      Compute the empirical semivariogram

      - Source: R/Torgegram.R -
      Torgegram.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Compute the empirical semivariogram

      + Source: R/Torgegram.R +
      -
      +

      Compute the empirical semivariogram for varying bin sizes and cutoff values.

      -
      -

      Usage

      + 
      Torgegram(
   formula,
   ssn.object,
@@ -56,49 +75,49 @@ 
      Usage
 )
      -
      -

      Arguments

      - - -
      formula
      +
      +

      Arguments

      +
      formula

      A formula describing the fixed effect structure.

      -
      ssn.object
      +
      ssn.object

      A spatial stream network object with class SSN.

      -
      type
      +
      type

      The Torgegram type. A vector with possible values "flowcon" for flow-connected distances, "flowuncon" for flow-unconnected distances, and "euclid" for Euclidean distances. The default is to show both flow-connected and flow-unconnected distances.

      -
      bins
      +
      bins

      The number of equally spaced bins. The default is 15.

      -
      cutoff
      +
      cutoff

      The maximum distance considered. The default is half the diagonal of the bounding box from the coordinates.

      -
      partition_factor
      +
      partition_factor

      An optional formula specifying the partition factor. If specified, semivariances are only computed for observations sharing the same level of the partition factor.

      -
      -

      Value

      -

      A list with elements correspond to type. Each element +

      +

      Value

      + + +

      A list with elements correspond to type. Each element is data frame with distance bins (bins), the average distance (dist), the semivariance (gamma), and the number of (unique) pairs (np) for the respective type.

      -
      -

      Details

      +
      +

      Details

      The Torgegram is an empirical semivariogram is a tool used to visualize and model spatial dependence by estimating the semivariance of a process at varying distances separately for flow-connected, flow-unconnected, and Euclidean distances. @@ -119,20 +138,20 @@

      Detailscutoff).

      -
      -

      References

      +
      +

      References

      Zimmerman, D. L., & Ver Hoef, J. M. (2017). The Torgegram for fluvial variography: characterizing spatial dependence on stream networks. Journal of Computational and Graphical Statistics, -26(2), 253–264.

      +26(2), 253--264.

      -
      -

      See also

      +
      +

      See also

      plot.Torgegram()

      -
      -

      Examples

      +
      +

      Examples

      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -145,23 +164,27 @@ 
      Examples
      -
      +
      + +
      -
      + + diff --git a/docs/reference/anova.SSN2.html b/docs/reference/anova.SSN2.html index 19504a3..8bf8178 100644 --- a/docs/reference/anova.SSN2.html +++ b/docs/reference/anova.SSN2.html @@ -1,83 +1,100 @@ -Compute analysis of variance and likelihood ratio tests of fitted model objects — anova.SSN2 • SSN2 - Skip to contents - - -
      -
      -
      - -

      Compute analysis of variance and likelihood ratio tests of fitted model objects

      - Source: R/anova.R -
      anova.SSN2.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Compute analysis of variance and likelihood ratio tests of fitted model objects

      + Source: R/anova.R +
      -
      +

      Compute analysis of variance tables for a fitted model object or a likelihood ratio test for two fitted model objects.

      -
      -

      Usage

      - 
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 anova(object, ..., test = TRUE, Terms, L)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 anova(object, ..., test = TRUE, Terms, L)
 
-# S3 method for class 'anova.ssn_lm'
+# S3 method for anova.ssn_lm
 tidy(x, ...)
 
-# S3 method for class 'anova.ssn_glm'
+# S3 method for anova.ssn_glm
 tidy(x, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      An additional fitted model object from ssn_lm() or ssn_glm()
 (for anova()).
      
 
 
-
      test
      
+
      test
      
 
      A logical value indicating whether p-values from asymptotic Chi-squared
 hypothesis tests should be returned. Defaults to TRUE.
      
 
 
-
      Terms
      
+
      Terms
      
 
      An optional character or integer vector that specifies terms in the model
 used to jointly compute test statistics and p-values (if test = TRUE)
 against a null hypothesis of zero. Terms is only used when a single fitted model
@@ -89,7 +106,7 @@ 
      ArgumentsL
+
      L
      
 
      An optional numeric matrix or list specifying linear combinations
 of the coefficients in the model used to compute test statistics
 and p-values (if test = TRUE) for coefficient constraints corresponding to a null
@@ -103,27 +120,35 @@ 
      Argumentsx
+
      x
      
 
      An object from anova(object).
      
 
 
      
-    
      
-    
      Value
      
-    
      When one fitted model object is present, anova()
-returns a data frame with degrees of
+    
      
+    
      Value
      
+    
+
+
      When one fitted model object is present, anova()
      
+
+
+
      returns a data frame with degrees of
 freedom (Df), test statistics (Chi2), and p-values
 (Pr(>Chi2) if test = TRUE) corresponding
 to asymptotic Chi-squared hypothesis tests for each model term.
      
+
+
 
      When two fitted model objects are present, anova() returns a data frame
 with the difference in degrees of freedom between the full and reduced model (Df), a test
 statistic (Chi2), and a p-value corresponding to the likelihood ratio test
 (Pr(>Chi2) if test = TRUE).
      
+
+
 
      Whether one or two fitted model objects are provided,
 tidy() can be used
 to obtain tidy tibbles of the anova(object) output.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      When one fitted model object is present, anova()
 performs a general linear hypothesis test corresponding to some hypothesis
 specified by a matrix of constraints. If Terms and L are not specified,
@@ -136,8 +161,8 @@ 
      Detailsanova() performs a likelihood ratio test.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -168,23 +193,27 @@ 
      Examples#> 2 ELEV_DEM        1      60.8 6.32e-15
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/augment.SSN2.html b/docs/reference/augment.SSN2.html
index 1cf31a3..b42647a 100644
--- a/docs/reference/augment.SSN2.html
+++ b/docs/reference/augment.SSN2.html
@@ -1,5 +1,5 @@
 
-Augment data with information from fitted model objects — augment.SSN2 • SSN2Augment data with information from fitted model objects — augment.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Augment data with information from fitted model objects
      
-      Source: R/augment.R
-      
      augment.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Augment data with information from fitted model objects
      
+    Source: R/augment.R
+    
     
      
 
-    
      
+    
      
     
      Augment accepts a fitted model object and a data set and adds
 information about each observation in the data set. New columns always
 begin with a . prefix to avoid overwriting columns in the original
@@ -71,9 +82,8 @@ 
      Augment data with information from fitted model objects
      
 row in the new data set.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 augment(
   x,
   drop = TRUE,
@@ -84,7 +94,7 @@ 
      Usage
   ...
 )
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 augment(
   x,
   drop = TRUE,
@@ -99,21 +109,19 @@ 
      Usage
 )
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      drop
      
+
      drop
      
 
      A logical indicating whether to drop extra variables in the
 fitted model object x when augmenting. The default for drop is TRUE.
 drop is ignored if augmenting newdata.
      
 
 
-
      newdata
      
+
      newdata
      
 
      A vector that contains the names of the prediction sf
 objects from the original ssn.object requiring prediction.
 All of the original explanatory variables used to create the fitted model object x
@@ -124,45 +132,47 @@ 
      Argumentsse_fit
+
      se_fit
      
 
      Logical indicating whether or not a .se.fit column should
 be added to augmented output. Passed to predict() and
 defaults to FALSE.
      
 
 
-
      interval
      
+
      interval
      
 
      Character indicating the type of confidence interval columns to
 add to the augmented newdata output. Passed to predict() and defaults
 to "none".
      
 
 
-
      level
      
+
      level
      
 
      Tolerance/confidence level. The default is 0.95.
      
 
 
-
      ...
      
+
      ...
      
 
      Additional arguments to predict() when augmenting newdata.
      
 
 
-
      type
      
+
      type
      
 
      The scale (response or link) of predictions obtained
 using ssn_glm objects.
      
 
 
-
      newdata_size
      
+
      newdata_size
      
 
      The size value for each observation in newdata
 used when predicting for the binomial family.
      
 
 
-
      var_correct
      
+
      var_correct
      
 
      A logical indicating whether to return the corrected prediction
 variances when predicting via models fit using ssn_glm. The default is
 TRUE.
      
 
 
      
-    
      
-    
      Value
      
-    
      When augmenting the original data set, a tibble with additional columns
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/coef.SSN2.html b/docs/reference/coef.SSN2.html
index d731f45..03bc561 100644
--- a/docs/reference/coef.SSN2.html
+++ b/docs/reference/coef.SSN2.html
@@ -1,73 +1,90 @@
 
-Extract fitted model coefficients — coef.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Extract fitted model coefficients
      
-      Source: R/coef.R
-      
      coef.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Extract fitted model coefficients
      
+    Source: R/coef.R
+    
     
      
 
-    
      
+    
      
     
      coef extracts fitted model coefficients from fitted model objects.
 coefficients is an alias for it.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 coef(object, type = "fixed", ...)
 
-# S3 method for class 'ssn_lm'
+# S3 method for ssn_lm
 coefficients(object, type = "fixed", ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 coef(object, type = "fixed", ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 coefficients(object, type = "fixed", ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      type
      
+
      type
      
 
      "fixed" for fixed effect coefficients, "tailup" for
 tailup covariance parameter coefficients, "taildown" for
 taildown covariance parameter coefficients, "euclid" for
@@ -79,17 +96,19 @@ 
      Arguments.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A named vector of coefficients.
      
+    
      
+    
      Value
      
+    
+
+
      A named vector of coefficients.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -116,23 +135,27 @@ 
      Examples#>  80.8578372  -0.0341245 
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/confint.SSN2.html b/docs/reference/confint.SSN2.html
index f4c262b..9dfae09 100644
--- a/docs/reference/confint.SSN2.html
+++ b/docs/reference/confint.SSN2.html
@@ -1,88 +1,107 @@
 
-Confidence intervals for fitted model parameters — confint.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Confidence intervals for fitted model parameters
      
-      Source: R/confint.R
-      
      confint.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Confidence intervals for fitted model parameters
      
+    Source: R/confint.R
+    
     
      
 
-    
      
+    
      
     
      Computes confidence intervals for one or more parameters in a fitted
 model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 confint(object, parm, level = 0.95, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 confint(object, parm, level = 0.95, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      parm
      
+
      parm
      
 
      A specification of which parameters are to be given confidence
 intervals (a character vector of names). If missing, all parameters are considered.
      
 
 
-
      level
      
+
      level
      
 
      The confidence level required. The default is 0.95.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      Gaussian-based confidence intervals (two-sided and equal-tailed) for the
+    
      
+    
      Value
      
+    
+
+
      Gaussian-based confidence intervals (two-sided and equal-tailed) for the
 fixed effect coefficients based on the confidence level specified by level.
 For ssn_glm() objects, confidence intervals are on the link scale.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -106,23 +125,27 @@ 
      Examples#> ELEV_DEM    -0.04132307 -0.02692593
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/cooks.distance.SSN2.html b/docs/reference/cooks.distance.SSN2.html
index b149ec1..a8d9b49 100644
--- a/docs/reference/cooks.distance.SSN2.html
+++ b/docs/reference/cooks.distance.SSN2.html
@@ -1,89 +1,108 @@
 
-Compute Cook's distance — cooks.distance.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Compute Cook's distance
      
-      Source: R/cooks.distance.R
-      
      cooks.distance.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Compute Cook's distance
      
+    Source: R/cooks.distance.R
+    
     
      
 
-    
      
+    
      
     
      Compute the Cook's distance for each observation from a fitted
 model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 cooks.distance(model, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 cooks.distance(model, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      model
      
+    
      
+    
      Arguments
      
+    
      model
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A vector of Cook's distance values for each observation from the
+    
      
+    
      Value
      
+    
+
+
      A vector of Cook's distance values for each observation from the
 fitted model object.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      Cook's distance measures the influence of an observation on a fitted
 model object. If an observation is influential, its omission from the data
 noticeably impacts parameter estimates. The larger the Cook's distance, the
 larger the influence.
      
     
      
-    
      
-    
      See also
      
+    
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -116,23 +135,27 @@ 
      Examples#> 1.208746e-02 4.362963e-02 5.795615e-01 
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/copy_lsn_to_temp.html b/docs/reference/copy_lsn_to_temp.html
index a905072..e00afbe 100644
--- a/docs/reference/copy_lsn_to_temp.html
+++ b/docs/reference/copy_lsn_to_temp.html
@@ -1,68 +1,88 @@
 
-Copy LSN to temporary directory — copy_lsn_to_temp • SSN2Copy LSN to temporary directory — copy_lsn_to_temp • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Copy LSN to temporary directory
      
-      Source: R/copy_lsn_to_temp.R
-      
      copy_lsn_to_temp.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Copy LSN to temporary directory
      
+    Source: R/copy_lsn_to_temp.R
+    
     
      
 
-    
      
+    
      
     
      Copies the LSN directory MiddleFork04.ssn to R's temporary
 directory so the examples in SSN2 do not write to the local
 library or any other places.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      copy_lsn_to_temp()
      
     
      
 
-    
      
-    
      Value
      
-    
      A copy of MiddleFork04.ssn residing in R's temporary directory
      
+    
      
+    
      Value
      
+    
+
+
      A copy of MiddleFork04.ssn residing in R's temporary directory
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      Copies the LSN directory MiddleFork04.ssn to R's temporary directory
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      copy_lsn_to_temp()
 # getwd()
 # setwd(tempdir())
@@ -71,23 +91,27 @@ 
      Examples# if windows, list temporary directory contents using: shell('dir')
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/covmatrix.SSN2.html b/docs/reference/covmatrix.SSN2.html
index 34fe3a2..4eb55bd 100644
--- a/docs/reference/covmatrix.SSN2.html
+++ b/docs/reference/covmatrix.SSN2.html
@@ -1,64 +1,82 @@
 
-Create a covariance matrix — covmatrix.SSN2 • SSN2
-    Skip to contents
+Create a covariance matrix — covmatrix.SSN2 • SSN2
+    
 
+    
      
+      
      
-
      
-
      
-  
      
+      
 
-      
      Create a covariance matrix
      
-      Source: R/covmatrix.R
-      
      covmatrix.SSN2.Rd
      
+      
      
+  
      
+    
      
+    
      Create a covariance matrix
      
+    Source: R/covmatrix.R
+    
     
      
 
-    
      
+    
      
     
      Create a covariance matrix from a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 covmatrix(object, newdata, cov_type, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 covmatrix(object, newdata, cov_type, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object (e.g., ssn_lm() or ssn_glm()).
      
 
 
-
      newdata
      
+
      newdata
      
 
      If omitted, the covariance matrix of
 the observed data is returned. If provided, newdata is
 a data frame or sf object that contains coordinate information
@@ -69,7 +87,7 @@ 
      Argumentscov_type
+
      cov_type
      
 
      The type of covariance matrix returned. If newdata
 is omitted, the \(n \times n\) covariance matrix of the observed
 data is returned, where \(n\) is the sample size used to fit object.
@@ -82,17 +100,19 @@ 
      Arguments...
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A covariance matrix (see cov_type).
      
+    
      
+    
      Value
      
+    
+
+
      A covariance matrix (see cov_type).
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -3707,23 +3727,27 @@ 
      Examples#> [654,] 1.0542293     0
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/create_netgeom.html b/docs/reference/create_netgeom.html
index 8daf6ca..a310ac1 100644
--- a/docs/reference/create_netgeom.html
+++ b/docs/reference/create_netgeom.html
@@ -1,80 +1,99 @@
 
-Create netgeom column in SSN object — create_netgeom • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Create netgeom column in SSN object
      
-      Source: R/create_netgeom.R
-      
      create_netgeom.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Create netgeom column in SSN object
      
+    Source: R/create_netgeom.R
+    
     
      
 
-    
      
+    
      
     
      Create netgeom column for edges, observed sites,
 and/or prediction sites in a Landscape Network (LSN).
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      create_netgeom(sf_data, type = NULL, overwrite = FALSE)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      sf_data
      
+    
      
+    
      Arguments
      
+    
      sf_data
      
 
      An sf object with LINESTING or POINT geometry
 created using link{lsn_to_ssn} (see Details).
      
 
 
-
      type
      
+
      type
      
 
      Character string defining geometry type of
 sf_data. Default = NULL.
      
 
 
-
      overwrite
      
+
      overwrite
      
 
      Logical indicating whether existing data should be
 overwritten if present. Default = FALSE.
      
 
 
      
-    
      
-    
      Value
      
-    
      An sf object containing the original data from
+    
      
+    
      Value
      
+    
+
+
      An sf object containing the original data from
 sf_data and an additional column named netgeom.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      Most users will not need to run create_netgeom
 themselves because it is called internally when lsn_to_ssn
 is run or an SSN is imported using
@@ -95,8 +114,8 @@ 
      Detailssf_data, the data will be overwritten. Default = FALSE.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      ## Create local temporary copy of MiddleFork04.ssn found in
 ## the SSN2 package. Only necessary for this example.
 copy_lsn_to_temp()
@@ -123,23 +142,27 @@ 
      Examples)
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/deviance.SSN2.html b/docs/reference/deviance.SSN2.html
index 18fea64..f1516b1 100644
--- a/docs/reference/deviance.SSN2.html
+++ b/docs/reference/deviance.SSN2.html
@@ -1,79 +1,99 @@
 
-Fitted model deviance — deviance.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Fitted model deviance
      
-      Source: R/deviance.R
-      
      deviance.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Fitted model deviance
      
+    Source: R/deviance.R
+    
     
      
 
-    
      
+    
      
     
      Returns the deviance of a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 deviance(object, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 deviance(object, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      The deviance.
      
+    
      
+    
      Value
      
+    
+
+
      The deviance.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The deviance is twice the difference in log-likelihoods between the
 saturated (perfect-fit) model and the fitted model.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -92,23 +112,27 @@ 
      Examples#> [1] 9.832514e-05
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/fitted.SSN2.html b/docs/reference/fitted.SSN2.html
index 954a5c9..18fe26e 100644
--- a/docs/reference/fitted.SSN2.html
+++ b/docs/reference/fitted.SSN2.html
@@ -1,73 +1,90 @@
 
-Extract model fitted values — fitted.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Extract model fitted values
      
-      Source: R/fitted.R
-      
      fitted.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Extract model fitted values
      
+    Source: R/fitted.R
+    
     
      
 
-    
      
+    
      
     
      Extract fitted values from fitted model objects. fitted.values
 is an alias.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 fitted(object, type = "response", ...)
 
-# S3 method for class 'ssn_lm'
+# S3 method for ssn_lm
 fitted.values(object, type = "response", ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 fitted(object, type = "response", ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 fitted.values(object, type = "response", ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      type
      
+
      type
      
 
      "response" for fitted values of the response,
 "tailup" for fitted values of the tailup random errors,
 "taildown" for fitted values of the taildown random errors,
@@ -78,16 +95,18 @@ 
      Arguments.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      The fitted values according to type.
      
+    
      
+    
      Value
      
+    
+
+
      The fitted values according to type.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      When type is "response", the fitted values
 for each observation are the standard fitted values \(X \hat{\beta}\).
 When type is "tailup", "taildown", "euclid",
@@ -105,8 +124,8 @@ 
      Details
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -148,23 +167,27 @@ 
      Examples#> 10.186001  9.981254 12.062849 11.448608 11.175612 
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/formula.SSN2.html b/docs/reference/formula.SSN2.html
index 7514adb..a13b71b 100644
--- a/docs/reference/formula.SSN2.html
+++ b/docs/reference/formula.SSN2.html
@@ -1,74 +1,94 @@
 
-Model formulae — formula.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Model formulae
      
-      Source: R/formula.R
-      
      formula.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Model formulae
      
+    Source: R/formula.R
+    
     
      
 
-    
      
+    
      
     
      Return formula used by a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 formula(x, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 formula(x, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      The formula used by a fitted model object.
      
+    
      
+    
      Value
      
+    
+
+
      The formula used by a fitted model object.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -84,26 +104,30 @@ 
      Examples)
 formula(ssn_mod)
 #> Summer_mn ~ ELEV_DEM
-#> <environment: 0x0000024c3c78fe60>
+#> <environment: 0x0000028e1c9bdc80>
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
-  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
+      
      
+  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
 
      
 
-
      
-  
      Site built with pkgdown 2.1.0.
      
+
      
+  
      Site built with pkgdown 2.0.9.
      
 
      
 
-    
      
-
+      
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/glance.SSN2.html b/docs/reference/glance.SSN2.html
index e226027..3717f9e 100644
--- a/docs/reference/glance.SSN2.html
+++ b/docs/reference/glance.SSN2.html
@@ -1,76 +1,94 @@
 
-Glance at a fitted model object — glance.SSN2 • SSN2Glance at a fitted model object — glance.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Glance at a fitted model object
      
-      Source: R/glance.R
-      
      glance.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Glance at a fitted model object
      
+    Source: R/glance.R
+    
     
      
 
-    
      
+    
      
     
      Returns a row of model
 summaries from a fitted model object. Glance returns the same number of columns for all models
 and estimation methods.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 glance(x, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 glance(x, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A single-row tibble with columns
      • n The sample size.
        • 
+    
        
+    
        Value
        
+    
+
+
        A single-row tibble with columns
        • n The sample size.
          • 
 
        • p The number of fixed effects.
          • 
 
        • npar The number of estimated covariance parameters.
          • 
 
        • value The optimized value of the fitting function
          • 
@@ -81,8 +99,8 @@ 
          Value
          pseudo.r.squared The pseudo r-squared
          
 
        
 
-    
        
-    
        Examples
        
+    
        
+    
        Examples
        
     
        # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -103,23 +121,27 @@ 
        Examples#> 1    45     2     3  76.9  82.9  83.5  -38.4     43.0            0.585
 
        
     
        
-  
        
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/glances.SSN2.html b/docs/reference/glances.SSN2.html
index 435a7f7..0e0a78b 100644
--- a/docs/reference/glances.SSN2.html
+++ b/docs/reference/glances.SSN2.html
@@ -1,88 +1,107 @@
 
-Glance at many fitted model objects — glances.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Glance at many fitted model objects
      
-      Source: R/glances.R
-      
      glances.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Glance at many fitted model objects
      
+    Source: R/glances.R
+    
     
      
 
-    
      
+    
      
     
      glances() repeatedly calls glance() on several
 fitted model objects and binds the output together, sorted by a column of interest.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 glances(object, ..., sort_by = "AICc", decreasing = FALSE)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 glances(object, ..., sort_by = "AICc", decreasing = FALSE)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      Fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Additional fitted model objects from ssn_lm() or ssn_glm().
      
 
 
-
      sort_by
      
+
      sort_by
      
 
      Sort by a glance statistic (i.e., the name of a column
 output from glance() or the order of model input (sort_by = "order").
 The default is "AICc".
      
 
 
-
      decreasing
      
+
      decreasing
      
 
      Should sort_by be decreasing or not? The default is FALSE.
      
 
 
      
-    
      
-    
      Value
      
-    
      A tibble where each row represents the output of glance() for
+    
      
+    
      Value
      
+    
+
+
      A tibble where each row represents the output of glance() for
 each fitted model object.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -111,23 +130,27 @@ 
      Examples#> 2 ssn_mod2    45     2     3 123.  129.  129.   -61.4     43.4           0.0917
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/hatvalues.SSN2.html b/docs/reference/hatvalues.SSN2.html
index 6d6b72b..07c7e6c 100644
--- a/docs/reference/hatvalues.SSN2.html
+++ b/docs/reference/hatvalues.SSN2.html
@@ -1,90 +1,109 @@
 
-Compute leverage (hat) values — hatvalues.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Compute leverage (hat) values
      
-      Source: R/hatvalues.R
-      
      hatvalues.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Compute leverage (hat) values
      
+    Source: R/hatvalues.R
+    
     
      
 
-    
      
+    
      
     
      Compute the leverage (hat) value for each observation from a fitted
 model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 hatvalues(model, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 hatvalues(model, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      model
      
+    
      
+    
      Arguments
      
+    
      model
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A vector of leverage (hat) values for each observation from the
+    
      
+    
      Value
      
+    
+
+
      A vector of leverage (hat) values for each observation from the
 fitted model object.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      Leverage values measure how far an observation's explanatory variables
 are relative to the average of the explanatory variables. In other words, observations with high
 leverage are typically considered to have an extreme or unusual combination of explanatory
 variables. Leverage values are the diagonal of the hat (projection) matrix.
 The larger the hat value, the larger the leverage.
      
     
      
-    
      
-    
      See also
      
+    
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -117,23 +136,27 @@ 
      Examples#> 0.138148339 0.030372326 0.159209917 
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/index.html b/docs/reference/index.html
index c4c0eb8..59b59a0 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -1,392 +1,303 @@
 
-Package index • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Package index
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+      
      Reference
      
     
      
 
-    
      
-      
      All functions
      
-
-
-
-
-    
      
-
-
-
-
-      
      
-
-          AIC(<ssn_lm>) AIC(<ssn_glm>) AICc(<ssn_lm>) AICc(<ssn_glm>)
-
-        
      
-        
      Compute AIC and AICc of fitted model objects
      
-      
      
-
-          anova(<ssn_lm>) anova(<ssn_glm>) tidy(<anova.ssn_lm>) tidy(<anova.ssn_glm>)
-
-        
      
-        
      Compute analysis of variance and likelihood ratio tests of fitted model objects
      
-      
      
-
-          augment(<ssn_lm>) augment(<ssn_glm>)
-
-        
      
-        
      Augment data with information from fitted model objects
      
-      
      
-
-          coef(<ssn_lm>) coefficients(<ssn_lm>) coef(<ssn_glm>) coefficients(<ssn_glm>)
-
-        
      
-        
      Extract fitted model coefficients
      
-      
      
-
-          confint(<ssn_lm>) confint(<ssn_glm>)
-
-        
      
-        
      Confidence intervals for fitted model parameters
      
-      
      
-
-          cooks.distance(<ssn_lm>) cooks.distance(<ssn_glm>)
-
-        
      
-        
      Compute Cook's distance
      
-      
      
-
-          copy_lsn_to_temp()
-
-        
      
-        
      Copy LSN to temporary directory
      
-      
      
-
-          covmatrix(<ssn_lm>) covmatrix(<ssn_glm>)
-
-        
      
-        
      Create a covariance matrix
      
-      
      
-
-          create_netgeom()
-
-        
      
-        
      Create netgeom column in SSN object
      
-      
      
-
-          deviance(<ssn_lm>) deviance(<ssn_glm>)
-
-        
      
-        
      Fitted model deviance
      
-      
      
-
-          fitted(<ssn_lm>) fitted.values(<ssn_lm>) fitted(<ssn_glm>) fitted.values(<ssn_glm>)
-
-        
      
-        
      Extract model fitted values
      
-      
      
-
-          formula(<ssn_lm>) formula(<ssn_glm>)
-
-        
      
-        
      Model formulae
      
-      
      
-
-          glance(<ssn_lm>) glance(<ssn_glm>)
-
-        
      
-        
      Glance at a fitted model object
      
-      
      
-
-          glances(<ssn_lm>) glances(<ssn_glm>)
-
-        
      
-        
      Glance at many fitted model objects
      
-      
      
-
-          hatvalues(<ssn_lm>) hatvalues(<ssn_glm>)
-
-        
      
-        
      Compute leverage (hat) values
      
-      
      
-
-          influence(<ssn_lm>) influence(<ssn_glm>)
-
-        
      
-        
      Regression diagnostics
      
-      
      
-
-          labels(<ssn_lm>) labels(<ssn_glm>)
-
-        
      
-        
      Find labels from object
      
-      
      
-
-          logLik(<ssn_lm>) logLik(<ssn_glm>)
-
-        
      
-        
      Extract log-likelihood
      
-      
      
-
-          loocv(<ssn_lm>) loocv(<ssn_glm>)
-
-        
      
-        
      Perform leave-one-out cross validation
      
-      
      
-
-          mf04p
-
-        
      
-        
      Imported SSN object from the MiddleFork04.ssn data folder
      
-      
      
-
-          MiddleFork04.ssn
-
-        
      
-        
      MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset
      
-      
      
-
-          model.frame(<ssn_lm>) model.frame(<ssn_glm>)
-
-        
      
-        
      Extract the model frame from a fitted model object
      
-      
      
-
-          model.matrix(<ssn_lm>) model.matrix(<ssn_glm>)
-
-        
      
-        
      Extract the model matrix from a fitted model object
      
-      
      
-
-          plot(<ssn_lm>) plot(<ssn_glm>)
-
-        
      
-        
      Plot fitted model diagnostics
      
-      
      
-
-          plot(<Torgegram>)
-
-        
      
-        
      Plot Torgegram
      
-      
      
-
-          predict(<ssn_lm>) predict(<ssn_glm>)
-
-        
      
-        
      Model predictions (Kriging)
      
-      
      
-
-          print(<SSN>)
-
-        
      
-        
      Print SSN object
      
-      
      
-
-          print(<ssn_lm>) print(<ssn_glm>) print(<summary.ssn_lm>) print(<summary.ssn_glm>) print(<anova.ssn_lm>) print(<anova.ssn_glm>)
-
-        
      
-        
      Print values
      
-      
      
-
-          pseudoR2(<ssn_lm>) pseudoR2(<ssn_glm>)
-
-        
      
-        
      Compute a pseudo r-squared
      
-      
      
-
-          residuals(<ssn_lm>) resid(<ssn_lm>) rstandard(<ssn_lm>) residuals(<ssn_glm>) resid(<ssn_glm>) rstandard(<ssn_glm>)
-
-        
      
-        
      Extract fitted model residuals
      
-      
      
-
-          ssn_create_distmat()
-
-        
      
-        
      Calculate Hydrologic Distances for an SSN object
      
-      
      
-
-          ssn_get_data()
-
-        
      
-        
      Get a data.frame from an SSN, ssn_lm, or ssn_glm object
      
-      
      
-
-          ssn_get_netgeom()
-
-        
      
-        
      Extract netgeom column
      
-      
      
-
-          ssn_get_stream_distmat()
-
-        
      
-        
      Get stream distance matrices from an SSN object
      
-      
      
-
-          ssn_glm()
-
-        
      
-        
      Fitting Generalized Linear Models for Spatial Stream Networks
      
-      
      
-
-          ssn_import()
-
-        
      
-        
      Import SSN object
      
-      
      
-
-          ssn_import_predpts()
-
-        
      
-        
      Import prediction points into an SSN, ssn_lm, or ssn_glm object
      
-      
      
-
-          tailup_initial() taildown_initial() euclid_initial() nugget_initial()
-
-        
      
-        
      Create a covariance parameter initial object
      
-      
      
-
-          ssn_lm()
-
-        
      
-        
      Fitting Linear Models for Spatial Stream Networks
      
-      
      
-
-          ssn_names()
-
-        
      
-        
      Return names of data in an SSN object
      
-      
      
-
-          tailup_params() taildown_params() euclid_params() nugget_params()
-
-        
      
-        
      Create covariance parameter objects.
      
-      
      
-
-          ssn_put_data()
-
-        
      
-        
      Put an sf data.frame in an SSN object
      
-      
      
-
-          ssn_simulate() ssn_rbeta() ssn_rbinom() ssn_rgamma() ssn_rinvgauss() ssn_rnbinom() ssn_rnorm() ssn_rpois()
-
-        
      
-        
      Simulate random variables on a stream network
      
-      
      
-
-          ssn_split_predpts()
-
-        
      
-        
      Split a prediction dataset in an SSN object
      
-      
      
-
-          ssn_subset()
-
-        
      
-        
      Subset an SSN object
      
-      
      
-
-          SSN_to_SSN2()
-
-        
      
-        
      Convert object from SpatialStreamNetwork class to SSN class
      
-      
      
-
-          ssn_update_path()
-
-        
      
-        
      Update path in an SSN object
      
-      
      
-
-          ssn_write()
-
-        
      
-        
      write an SSN object
      
-      
      
-
-          summary(<SSN>)
-
-        
      
-        
      Summarize an SSN object
      
-      
      
-
-          summary(<ssn_lm>) summary(<ssn_glm>)
-
-        
      
-        
      Summarize a fitted model object
      
-      
      
-
-          tidy(<ssn_lm>) tidy(<ssn_glm>)
-
-        
      
-        
      Tidy a fitted model object
      
-      
      
-
-          Torgegram()
-
-        
      
-        
      Compute the empirical semivariogram
      
-      
      
-
-          varcomp(<ssn_lm>) varcomp(<ssn_glm>)
-
-        
      
-        
      Variability component comparison
      
-      
      
-
-          vcov(<ssn_lm>) vcov(<ssn_glm>)
-
-        
      
-        
      Calculate variance-covariance matrix for a fitted model object
      
-      
      
-  
      
+    
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
+        
+      
      
+          
      All functions 
      
+          
      
+        
      
+          
      AIC(<ssn_lm>) AIC(<ssn_glm>) AICc(<ssn_lm>) AICc(<ssn_glm>) 
      
+        
      Compute AIC and AICc of fitted model objects
      
+          
      anova(<ssn_lm>) anova(<ssn_glm>) tidy(<anova.ssn_lm>) tidy(<anova.ssn_glm>) 
      
+        
      Compute analysis of variance and likelihood ratio tests of fitted model objects
      
+          
      augment(<ssn_lm>) augment(<ssn_glm>) 
      
+        
      Augment data with information from fitted model objects
      
+          
      coef(<ssn_lm>) coefficients(<ssn_lm>) coef(<ssn_glm>) coefficients(<ssn_glm>) 
      
+        
      Extract fitted model coefficients
      
+          
      confint(<ssn_lm>) confint(<ssn_glm>) 
      
+        
      Confidence intervals for fitted model parameters
      
+          
      cooks.distance(<ssn_lm>) cooks.distance(<ssn_glm>) 
      
+        
      Compute Cook's distance
      
+          
      copy_lsn_to_temp() 
      
+        
      Copy LSN to temporary directory
      
+          
      covmatrix(<ssn_lm>) covmatrix(<ssn_glm>) 
      
+        
      Create a covariance matrix
      
+          
      create_netgeom() 
      
+        
      Create netgeom column in SSN object
      
+          
      deviance(<ssn_lm>) deviance(<ssn_glm>) 
      
+        
      Fitted model deviance
      
+          
      fitted(<ssn_lm>) fitted.values(<ssn_lm>) fitted(<ssn_glm>) fitted.values(<ssn_glm>) 
      
+        
      Extract model fitted values
      
+          
      formula(<ssn_lm>) formula(<ssn_glm>) 
      
+        
      Model formulae
      
+          
      glance(<ssn_lm>) glance(<ssn_glm>) 
      
+        
      Glance at a fitted model object
      
+          
      glances(<ssn_lm>) glances(<ssn_glm>) 
      
+        
      Glance at many fitted model objects
      
+          
      hatvalues(<ssn_lm>) hatvalues(<ssn_glm>) 
      
+        
      Compute leverage (hat) values
      
+          
      influence(<ssn_lm>) influence(<ssn_glm>) 
      
+        
      Regression diagnostics
      
+          
      labels(<ssn_lm>) labels(<ssn_glm>) 
      
+        
      Find labels from object
      
+          
      logLik(<ssn_lm>) logLik(<ssn_glm>) 
      
+        
      Extract log-likelihood
      
+          
      loocv(<ssn_lm>) loocv(<ssn_glm>) 
      
+        
      Perform leave-one-out cross validation
      
+          
      mf04p 
      
+        
      Imported SSN object from the MiddleFork04.ssn data folder
      
+          
      MiddleFork04.ssn 
      
+        
      MiddleFork04.ssn: Middle Fork 2004 stream temperature dataset
      
+          
      model.frame(<ssn_lm>) model.frame(<ssn_glm>) 
      
+        
      Extract the model frame from a fitted model object
      
+          
      model.matrix(<ssn_lm>) model.matrix(<ssn_glm>) 
      
+        
      Extract the model matrix from a fitted model object
      
+          
      plot(<ssn_lm>) plot(<ssn_glm>) 
      
+        
      Plot fitted model diagnostics
      
+          
      plot(<Torgegram>) 
      
+        
      Plot Torgegram
      
+          
      predict(<ssn_lm>) predict(<ssn_glm>) 
      
+        
      Model predictions (Kriging)
      
+          
      print(<SSN>) 
      
+        
      Print SSN object
      
+          
      print(<ssn_lm>) print(<ssn_glm>) print(<summary.ssn_lm>) print(<summary.ssn_glm>) print(<anova.ssn_lm>) print(<anova.ssn_glm>) 
      
+        
      Print values
      
+          
      pseudoR2(<ssn_lm>) pseudoR2(<ssn_glm>) 
      
+        
      Compute a pseudo r-squared
      
+          
      residuals(<ssn_lm>) resid(<ssn_lm>) rstandard(<ssn_lm>) residuals(<ssn_glm>) resid(<ssn_glm>) rstandard(<ssn_glm>) 
      
+        
      Extract fitted model residuals
      
+          
      ssn_create_distmat() 
      
+        
      Calculate Hydrologic Distances for an SSN object
      
+          
      ssn_get_data() 
      
+        
      Get a data.frame from an SSN, ssn_lm, or ssn_glm object
      
+          
      ssn_get_netgeom() 
      
+        
      Extract netgeom column
      
+          
      ssn_get_stream_distmat() 
      
+        
      Get stream distance matrices from an SSN object
      
+          
      ssn_glm() 
      
+        
      Fitting Generalized Linear Models for Spatial Stream Networks
      
+          
      ssn_import() 
      
+        
      Import SSN object
      
+          
      ssn_import_predpts() 
      
+        
      Import prediction points into an SSN, ssn_lm, or ssn_glm object
      
+          
      tailup_initial() taildown_initial() euclid_initial() nugget_initial() 
      
+        
      Create a covariance parameter initial object
      
+          
      ssn_lm() 
      
+        
      Fitting Linear Models for Spatial Stream Networks
      
+          
      ssn_names() 
      
+        
      Return names of data in an SSN object
      
+          
      tailup_params() taildown_params() euclid_params() nugget_params() 
      
+        
      Create covariance parameter objects.
      
+          
      ssn_put_data() 
      
+        
      Put an sf data.frame in an SSN object
      
+          
      ssn_simulate() ssn_rbeta() ssn_rbinom() ssn_rgamma() ssn_rinvgauss() ssn_rnbinom() ssn_rnorm() ssn_rpois() 
      
+        
      Simulate random variables on a stream network
      
+          
      ssn_split_predpts() 
      
+        
      Split a prediction dataset in an SSN object
      
+          
      ssn_subset() 
      
+        
      Subset an SSN object
      
+          
      SSN_to_SSN2() 
      
+        
      Convert object from SpatialStreamNetwork class to SSN class
      
+          
      ssn_update_path() 
      
+        
      Update path in an SSN object
      
+          
      ssn_write() 
      
+        
      write an SSN object
      
+          
      summary(<SSN>) 
      
+        
      Summarize an SSN object
      
+          
      summary(<ssn_lm>) summary(<ssn_glm>) 
      
+        
      Summarize a fitted model object
      
+          
      tidy(<ssn_lm>) tidy(<ssn_glm>) 
      
+        
      Tidy a fitted model object
      
+          
      Torgegram() 
      
+        
      Compute the empirical semivariogram
      
+          
      varcomp(<ssn_lm>) varcomp(<ssn_glm>) 
      
+        
      Variability component comparison
      
+          
      vcov(<ssn_lm>) vcov(<ssn_glm>) 
      
+        
      Calculate variance-covariance matrix for a fitted model object
      
+
+  
+
      
 
 
-    
      
-  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
+      
      
+  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
 
      
 
-
      
-  
      Site built with pkgdown 2.1.0.
      
+
      
+  
      Site built with pkgdown 2.0.9.
      
 
      
 
-    
      
-
+      
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/influence.SSN2.html b/docs/reference/influence.SSN2.html
index b30b17c..93e0ae4 100644
--- a/docs/reference/influence.SSN2.html
+++ b/docs/reference/influence.SSN2.html
@@ -1,88 +1,107 @@
 
-Regression diagnostics — influence.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Regression diagnostics
      
-      Source: R/influence.R
-      
      influence.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Regression diagnostics
      
+    Source: R/influence.R
+    
     
      
 
-    
      
+    
      
     
      Provides basic quantities which are used in forming
 a wide variety of diagnostics for checking the quality of fitted model objects.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 influence(model, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 influence(model, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      model
      
+    
      
+    
      Arguments
      
+    
      model
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A tibble with residuals (.resid), leverage values (.hat),
+    
      
+    
      Value
      
+    
+
+
      A tibble with residuals (.resid), leverage values (.hat),
 cook's distance (.cooksd), and standardized residuals (.std.resid).
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      This function calls residuals.SSN2(), hatvalues.SSN2(),
 and cooks.distance.SSN2() and puts the results into a tibble. It is
 primarily used when calling augment.SSN2().
      
     
      
-    
      
-    
      See also
      
+    
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -113,23 +132,27 @@ 
      Examples#> # ℹ 35 more rows
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/labels.SSN2.html b/docs/reference/labels.SSN2.html
index 46fcd1d..eb40267 100644
--- a/docs/reference/labels.SSN2.html
+++ b/docs/reference/labels.SSN2.html
@@ -1,75 +1,95 @@
 
-Find labels from object — labels.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Find labels from object
      
-      Source: R/labels.R
-      
      labels.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Find labels from object
      
+    Source: R/labels.R
+    
     
      
 
-    
      
+    
      
     
      Find a suitable set of labels from a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 labels(object, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 labels(object, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A character vector containing the terms used for the fixed effects
+    
      
+    
      Value
      
+    
+
+
      A character vector containing the terms used for the fixed effects
 from a fitted model object.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -87,23 +107,27 @@ 
      Examples#> [1] "ELEV_DEM"
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/logLik.SSN2.html b/docs/reference/logLik.SSN2.html
index 3636639..5cf247e 100644
--- a/docs/reference/logLik.SSN2.html
+++ b/docs/reference/logLik.SSN2.html
@@ -1,74 +1,94 @@
 
-Extract log-likelihood — logLik.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Extract log-likelihood
      
-      Source: R/logLik.R
-      
      logLik.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Extract log-likelihood
      
+    Source: R/logLik.R
+    
     
      
 
-    
      
+    
      
     
      Find the log-likelihood of a fitted model.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 logLik(object, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 logLik(object, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      The log-likelihood.
      
+    
      
+    
      Value
      
+    
+
+
      The log-likelihood.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -86,23 +106,27 @@ 
      Examples#> [1] -38.4466
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/loocv.SSN2.html b/docs/reference/loocv.SSN2.html
index 23dccff..729816f 100644
--- a/docs/reference/loocv.SSN2.html
+++ b/docs/reference/loocv.SSN2.html
@@ -1,83 +1,102 @@
 
-Perform leave-one-out cross validation — loocv.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Perform leave-one-out cross validation
      
-      Source: R/loocv.R, R/loocv_glm.R
-      
      loocv.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Perform leave-one-out cross validation
      
+    Source: R/loocv.R, R/loocv_glm.R
+    
     
      
 
-    
      
+    
      
     
      Perform leave-one-out cross validation with options for computationally
 efficient approximations for big data.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 loocv(object, cv_predict = FALSE, se.fit = FALSE, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 loocv(object, cv_predict = FALSE, se.fit = FALSE, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      cv_predict
      
+
      cv_predict
      
 
      A logical indicating whether the leave-one-out fitted values
 should be returned. Defaults to FALSE.
      
 
 
-
      se.fit
      
+
      se.fit
      
 
      A logical indicating whether the leave-one-out
 prediction standard errors should be returned. Defaults to FALSE.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      If cv_predict = FALSE and se.fit = FALSE,
+    
      
+    
      Value
      
+    
+
+
      If cv_predict = FALSE and se.fit = FALSE,
 a tibble indicating several
 leave-one-out cross validation error metrics. If cv_predict = TRUE or se.fit = TRUE,
 a list with elements: stats, a tibble indicating several
@@ -85,6 +104,8 @@ 
      Value
      cv_predict = TRUE      );
 and se.fit, a numeric vector with leave-one-out prediction standard
 errors for each observation (if se.fit = TRUE).
      
+
+
 
      If an ssn_lm object, the cross validation error metrics are:
      • bias: The average difference between the predicted value and true value
        • 
 
      • std.bias: The average standardized difference between the predicted value and true value
        • 
 
      • MSPE: The average squared difference between the predicted value and true value
        • 
@@ -100,16 +121,16 @@ 
        Value
        RMSPE: The root average squared difference between the predicted value and true value
        
 
      • RAV: The root of the average estimated variance of the predicted value (on the link scale)
        • 
 
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      Each observation is held-out from the data set and the remaining data
 are used to make a prediction for the held-out observation. This is compared
 to the true value of the observation and several model-fit statistics are computed
 across all observations.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -130,23 +151,27 @@ 
      Examples#> 1 0.0449   0.0456 0.273 0.522     1.23 0.418 0.930      0.8    0.889    0.911
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/mf04p.html b/docs/reference/mf04p.html
index 48b2d67..e6690fe 100644
--- a/docs/reference/mf04p.html
+++ b/docs/reference/mf04p.html
@@ -1,88 +1,109 @@
 
-Imported SSN object from the MiddleFork04.ssn data folder — mf04p • SSN2Imported SSN object from the MiddleFork04.ssn data folder — mf04p • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Imported SSN object from the MiddleFork04.ssn data folder
      
-      Source: R/data.R
-      
      mf04p.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Imported SSN object from the MiddleFork04.ssn data folder
      
+    Source: R/data.R
+    
     
      
 
-    
      
+    
      
     
      The MiddleFork04.ssn data folder contains the spatial, attribute,
 and topological information needed to construct a spatial stream
 network object using the SSN2 package. mf04p was created
 using ssn_import().
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      mf04p
      
     
      
 
-    
      
-    
      Format
      
+    
      
+    
      Format
      
     
      An object of class SSN of length 4.
      
     
      
-    
      
-    
      See also
      
+    
      
+    
      See also
      
     
      MiddleFork04.ssn for details about the contents of mf04p.
 ssn_import() to convert a .ssn object to an SSN object in R.
 ssn_create_distmat for details about the distance matrix file structure.
      
     
      
 
-  
      
+  
      
+  
+
      
 
 
-    
      
-  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
+      
      
+  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
 
      
 
-
      
-  
      Site built with pkgdown 2.1.0.
      
+
      
+  
      Site built with pkgdown 2.0.9.
      
 
      
 
-    
      
-
+      
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/model.frame.SSN2.html b/docs/reference/model.frame.SSN2.html
index cf1cd63..bc38a1c 100644
--- a/docs/reference/model.frame.SSN2.html
+++ b/docs/reference/model.frame.SSN2.html
@@ -1,79 +1,99 @@
 
-Extract the model frame from a fitted model object — model.frame.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Extract the model frame from a fitted model object
      
-      Source: R/model.frame.R
-      
      model.frame.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Extract the model frame from a fitted model object
      
+    Source: R/model.frame.R
+    
     
      
 
-    
      
+    
      
     
      Extract the model frame from a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 model.frame(formula, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 model.frame(formula, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      formula
      
+    
      
+    
      Arguments
      
+    
      formula
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A model frame that contains the variables used by the formula
+    
      
+    
      Value
      
+    
+
+
      A model frame that contains the variables used by the formula
 for the fitted model object.
      
     
      
-    
      
-    
      See also
      
+    
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -136,23 +156,27 @@ 
      Examples#> 45      9.02     2042
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/model.matrix.SSN2.html b/docs/reference/model.matrix.SSN2.html
index a88dcd1..66d58d4 100644
--- a/docs/reference/model.matrix.SSN2.html
+++ b/docs/reference/model.matrix.SSN2.html
@@ -1,80 +1,100 @@
 
-Extract the model matrix from a fitted model object — model.matrix.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Extract the model matrix from a fitted model object
      
-      Source: R/model.matrix.R
-      
      model.matrix.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Extract the model matrix from a fitted model object
      
+    Source: R/model.matrix.R
+    
     
      
 
-    
      
+    
      
     
      Extract the model matrix (X) from a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 model.matrix(object, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 model.matrix(object, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      The model matrix (of the fixed effects), whose rows represent
+    
      
+    
      Value
      
+    
+
+
      The model matrix (of the fixed effects), whose rows represent
 observations and whose columns represent explanatory variables corresponding
 to each fixed effect.
      
     
      
-    
      
-    
      See also
      
+    
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -139,23 +159,27 @@ 
      Examples#> [1] 0 1
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/plot.SSN2.html b/docs/reference/plot.SSN2.html
index e6730b8..8a79bb5 100644
--- a/docs/reference/plot.SSN2.html
+++ b/docs/reference/plot.SSN2.html
@@ -1,86 +1,104 @@
 
-Plot fitted model diagnostics — plot.SSN2 • SSN2Plot fitted model diagnostics — plot.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Plot fitted model diagnostics
      
-      Source: R/plot.R
-      
      plot.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Plot fitted model diagnostics
      
+    Source: R/plot.R
+    
     
      
 
-    
      
+    
      
     
      Plot fitted model diagnostics such as residuals vs fitted values,
 quantile-quantile, scale-location, Cook's distance, residuals vs leverage,
 and Cook's distance vs leverage.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 plot(x, which, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 plot(x, which, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      which
      
+
      which
      
 
      An integer vector taking on values between 1 and 6, which indicates
 the plots to return. Available plots are described in Details. If which
 has length greater than one, additional plots are stepped through in order
 using <Return>. The default is which = c(1, 2)
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments passed to other methods.
      
 
 
      
-    
      
-    
      Value
      
-    
      No return value. Function called for plotting side effects.
      
+    
      
+    
      Value
      
+    
+
+
      No return value. Function called for plotting side effects.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      For all fitted model objects,, the values of which make the
 corresponding plot:
      
-    
      
-    
      See also
      
+    
      
+    
      See also
      
     
      plot.Torgegram()
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -113,23 +131,27 @@ 
      Examples
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/plot.Torgegram.html b/docs/reference/plot.Torgegram.html
index 5fb227e..5a211a8 100644
--- a/docs/reference/plot.Torgegram.html
+++ b/docs/reference/plot.Torgegram.html
@@ -1,85 +1,105 @@
 
-Plot Torgegram — plot.Torgegram • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Plot Torgegram
      
-      Source: R/plot.R
-      
      plot.Torgegram.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Plot Torgegram
      
+    Source: R/plot.R
+    
     
      
 
-    
      
+    
      
     
      Plot Torgegram
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'Torgegram'
+    
      
+    
      # S3 method for Torgegram
 plot(x, type, separate = FALSE, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      A Torgegram object from Torgegram().
      
 
 
-
      type
      
+
      type
      
 
      The type of semivariogram. Can take character values that are a subset
 of objects in x. The default is names(x).
      
 
 
-
      separate
      
+
      separate
      
 
      When type is length greater than one, whether each
 type be placed in a separate plot. The default is FALSE.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments passed to other methods.
      
 
 
      
-    
      
-    
      Value
      
-    
      No return value. Function called for plotting side effects.
      
+    
      
+    
      Value
      
+    
+
+
      No return value. Function called for plotting side effects.
      
     
      
-    
      
-    
      See also
      
+    
      
+    
      See also
      
     
      plot.SSN2
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -92,23 +112,27 @@ 
      Examples
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/predict.SSN2.html b/docs/reference/predict.SSN2.html
index 7d49959..59bf7a2 100644
--- a/docs/reference/predict.SSN2.html
+++ b/docs/reference/predict.SSN2.html
@@ -1,49 +1,69 @@
 
-Model predictions (Kriging) — predict.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Model predictions (Kriging)
      
-      Source: R/predict.R, R/predict_glm.R
-      
      predict.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Model predictions (Kriging)
      
+    Source: R/predict.R, R/predict_glm.R
+    
     
      
 
-    
      
+    
      
     
      Predicted values and intervals based on a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 predict(
   object,
   newdata,
@@ -54,7 +74,7 @@ 
      Usage
   ...
 )
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 predict(
   object,
   newdata,
@@ -68,15 +88,13 @@ 
      Usage
 )
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      newdata
      
+
      newdata
      
 
      A character vector that indicates the name of the prediction data set
 for which predictions are desired (accessible via object$ssn.object$preds).
 Note that the prediction data must be in the original SSN object used to fit the model.
@@ -86,22 +104,22 @@ 
      Argumentsse.fit
+
      se.fit
      
 
      A logical indicating if standard errors are returned.
 The default is FALSE.
      
 
 
-
      interval
      
+
      interval
      
 
      Type of interval calculation. The default is "none".
 Other options are "confidence" (for confidence intervals) and
 "prediction" (for prediction intervals).
      
 
 
-
      level
      
+
      level
      
 
      Tolerance/confidence level. The default is 0.95.
      
 
 
-
      block
      
+
      block
      
 
      A logical indicating whether a block prediction over the entire
 region in newdata should be returned. The default is FALSE, which returns point
 predictions for each location in newdata. Currently only available for
@@ -109,36 +127,40 @@ 
      Arguments.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
-
      type
      
+
      type
      
 
      The scale (response or link) of predictions obtained
 using ssn_glm objects.
      
 
 
-
      newdata_size
      
+
      newdata_size
      
 
      The size value for each observation in newdata
 used when predicting for the binomial family.
      
 
 
-
      var_correct
      
+
      var_correct
      
 
      A logical indicating whether to return the corrected prediction
 variances when predicting via models fit using ssn_glm. The default is
 TRUE.
      
 
 
      
-    
      
-    
      Value
      
-    
      If se.fit is FALSE, predict.ssn() returns
+    
      
+    
      Value
      
+    
+
+
      If se.fit is FALSE, predict.ssn() returns
 a vector of predictions or a matrix of predictions with column names
-fit, lwr, and upr if interval is "confidence"
-or "prediction". If se.fit is TRUE, a list with the following components is returned:
      • fit: vector or matrix as above
        • 
+fit, lwr, and upr if interval is "confidence"
        
+
+
+
        or "prediction". If se.fit is TRUE, a list with the following components is returned:
        • fit: vector or matrix as above
          • 
 
        • se.fit: standard error of each fit
          • 
 
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The (empirical) best linear unbiased predictions (i.e., Kriging
 predictions) at each site are returned when interval is "none"
 or "prediction" alongside standard errors. Prediction intervals
@@ -147,8 +169,8 @@ 
      Details
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -215,23 +237,27 @@ 
      Examples#>  3.9415182 11.1746028  0.6856936  5.8826486  6.5147241 -0.1986592  5.0538075 
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/print.SSN.html b/docs/reference/print.SSN.html
index 1fe9d34..a27e6f8 100644
--- a/docs/reference/print.SSN.html
+++ b/docs/reference/print.SSN.html
@@ -1,86 +1,110 @@
 
-Print SSN object — print.SSN • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Print SSN object
      
-      Source: R/print.SSN.R
-      
      print.SSN.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Print SSN object
      
+    Source: R/print.SSN.R
+    
     
      
 
-    
      
+    
      
     
      Print information about the data found in an SSN object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'SSN'
+    
      
+    
      # S3 method for SSN
 print(x, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      An SSN object.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      Print summary to console
      
+    
      
+    
      Value
      
+    
+
+
      Print summary to console
      
     
      
 
-  
      
+  
      
+  
+
      
 
 
-    
      
-  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
+      
      
+  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
 
      
 
-
      
-  
      Site built with pkgdown 2.1.0.
      
+
      
+  
      Site built with pkgdown 2.0.9.
      
 
      
 
-    
      
-
+      
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/print.SSN2.html b/docs/reference/print.SSN2.html
index 9f3af53..dd6c691 100644
--- a/docs/reference/print.SSN2.html
+++ b/docs/reference/print.SSN2.html
@@ -1,55 +1,75 @@
 
-Print values — print.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Print values
      
-      Source: R/print.R
-      
      print.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Print values
      
+    Source: R/print.R
+    
     
      
 
-    
      
+    
      
     
      Print fitted model objects and summaries.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 print(x, digits = max(3L, getOption("digits") - 3L), ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 print(x, digits = max(3L, getOption("digits") - 3L), ...)
 
-# S3 method for class 'summary.ssn_lm'
+# S3 method for summary.ssn_lm
 print(
   x,
   digits = max(3L, getOption("digits") - 3L),
@@ -57,7 +77,7 @@ 
      Usage
   ...
 )
 
-# S3 method for class 'summary.ssn_glm'
+# S3 method for summary.ssn_glm
 print(
   x,
   digits = max(3L, getOption("digits") - 3L),
@@ -65,7 +85,7 @@ 
      Usage
   ...
 )
 
-# S3 method for class 'anova.ssn_lm'
+# S3 method for anova.ssn_lm
 print(
   x,
   digits = max(getOption("digits") - 2L, 3L),
@@ -73,7 +93,7 @@ 
      Usage
   ...
 )
 
-# S3 method for class 'anova.ssn_glm'
+# S3 method for anova.ssn_glm
 print(
   x,
   digits = max(getOption("digits") - 2L, 3L),
@@ -82,49 +102,53 @@ 
      Usage
 )
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      A fitted model object from ssn_lm(), a fitted model object from ssn_glm(),
 or output from summary(x) or or anova(x).
      
 
 
-
      digits
      
+
      digits
      
 
      The number of significant digits to use when printing.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments passed to or from other methods.
      
 
 
-
      signif.stars
      
+
      signif.stars
      
 
      Logical. If TRUE, significance stars are printed for each coefficient
      
 
 
      
-    
      
-    
      Value
      
-    
      Printed fitted model objects and summaries with formatting.
      
+    
      
+    
      Value
      
+    
+
+
      Printed fitted model objects and summaries with formatting.
      
     
      
 
-  
      
+  
      
+  
+
      
 
 
-    
      
-  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
+      
      
+  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
 
      
 
-
      
-  
      Site built with pkgdown 2.1.0.
      
+
      
+  
      Site built with pkgdown 2.0.9.
      
 
      
 
-    
      
-
+      
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/pseudoR2.SSN2.html b/docs/reference/pseudoR2.SSN2.html
index 5ec3c13..e7901bd 100644
--- a/docs/reference/pseudoR2.SSN2.html
+++ b/docs/reference/pseudoR2.SSN2.html
@@ -1,79 +1,99 @@
 
-Compute a pseudo r-squared — pseudoR2.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Compute a pseudo r-squared
      
-      Source: R/pseudoR2.R
-      
      pseudoR2.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Compute a pseudo r-squared
      
+    Source: R/pseudoR2.R
+    
     
      
 
-    
      
+    
      
     
      Compute a pseudo r-squared for a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 pseudoR2(object, adjust = FALSE, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 pseudoR2(object, adjust = FALSE, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      adjust
      
+
      adjust
      
 
      A logical indicating whether the pseudo r-squared
 should be adjusted to account for the number of explanatory variables. The
 default is FALSE.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      The pseudo r-squared as a numeric vector.
      
+    
      
+    
      Value
      
+    
+
+
      The pseudo r-squared as a numeric vector.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      Several pseudo r-squared statistics exist for in the literature.
 We define this pseudo r-squared as one minus the ratio of the deviance of a full model
 relative to the deviance of a null (intercept only) model. This pseudo r-squared
@@ -83,8 +103,8 @@ 
      Details
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -102,23 +122,27 @@ 
      Examples#> [1] 0.5854895
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html
index 1dd3aa6..aa9eb89 100644
--- a/docs/reference/reexports.html
+++ b/docs/reference/reexports.html
@@ -1,5 +1,5 @@
 
-Objects exported from other packages — reexports • SSN2Objects exported from other packages — reexports • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Objects exported from other packages
      
-      Source: R/utils.R
-      
      reexports.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Objects exported from other packages
      
+    Source: R/utils.R
+    
     
      
 
-    
      
+    
      
     
      These objects are imported from other packages. Follow the links
 below to see their documentation.
      
 
      generics
      
@@ -67,29 +77,34 @@ 
      Objects exported from other packages
      
 
 
   
      spmodel
      
-
      AICc, covmatrix, dispersion_initial, dispersion_params, glances, loocv, pseudoR2, randcov_initial, randcov_params, varcomp
      
+
      AICc, covmatrix, dispersion_initial, dispersion_params, glances, loocv, pseudoR2, randcov_initial, randcov_params, varcomp
      
 
 
 
      
 
 
 
-  
      
+  
      
+  
+
      
 
 
-    
      
-  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
+      
      
+  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
 
      
 
-
      
-  
      Site built with pkgdown 2.1.0.
      
+
      
+  
      Site built with pkgdown 2.0.9.
      
 
      
 
-    
      
-
+      
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/residuals.SSN2.html b/docs/reference/residuals.SSN2.html
index 6ac8ac4..55471f5 100644
--- a/docs/reference/residuals.SSN2.html
+++ b/docs/reference/residuals.SSN2.html
@@ -1,79 +1,96 @@
 
-Extract fitted model residuals — residuals.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Extract fitted model residuals
      
-      Source: R/residuals.R
-      
      residuals.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Extract fitted model residuals
      
+    Source: R/residuals.R
+    
     
      
 
-    
      
+    
      
     
      Extract residuals from a fitted model object.
 resid is an alias.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 residuals(object, type = "response", ...)
 
-# S3 method for class 'ssn_lm'
+# S3 method for ssn_lm
 resid(object, type = "response", ...)
 
-# S3 method for class 'ssn_lm'
+# S3 method for ssn_lm
 rstandard(model, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 residuals(object, type = "deviance", ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 resid(object, type = "deviance", ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 rstandard(model, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      type
      
+
      type
      
 
      "response" for response residuals, "pearson"
 for Pearson residuals, or "standardized" for standardized residuals.
 For ssn_lm() fitted model objects, the default is "response".
@@ -81,20 +98,22 @@ 
      Arguments) and are the default residual type.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
-
      model
      
+
      model
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
      
-    
      
-    
      Value
      
-    
      The residuals as a numeric vector.
      
+    
      
+    
      Value
      
+    
+
+
      The residuals as a numeric vector.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The response residuals are taken as the response minus the fitted values
 for the response: \(y - X \hat{\beta}\). The Pearson residuals are the
 response residuals pre-multiplied by their inverse square root.
@@ -104,8 +123,8 @@ 
      Detailsrstandard() is an alias for residuals(model, type = "standardized").
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -172,23 +191,27 @@ 
      Examples#>  0.41832097 -1.69498836 -2.69823469 
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_create_distmat.html b/docs/reference/ssn_create_distmat.html
index 8a533ac..7faff88 100644
--- a/docs/reference/ssn_create_distmat.html
+++ b/docs/reference/ssn_create_distmat.html
@@ -1,54 +1,72 @@
 
-Calculate Hydrologic Distances for an SSN object — ssn_create_distmat • SSN2Calculate Hydrologic Distances for an SSN object — ssn_create_distmat • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Calculate Hydrologic Distances for an SSN object
      
-      Source: R/ssn_create_distmat.R
-      
      ssn_create_distmat.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Calculate Hydrologic Distances for an SSN object
      
+    Source: R/ssn_create_distmat.R
+    
     
      
 
-    
      
+    
      
     
      Creates a collection of (non-symmetric) matrices
 containing pairwise downstream hydrologic distances between sites
 in an SSN object
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_create_distmat(
   ssn.object,
   predpts = NULL,
@@ -58,44 +76,44 @@ 
      Usage
 )
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      ssn.object
      
+    
      
+    
      Arguments
      
+    
      ssn.object
      
 
      An SSN object
      
 
 
-
      predpts
      
+
      predpts
      
 
      name of prediction points in an SSN object.
 When a vector with length greater than one, each name is iterated upon.
 Default is NULL.
      
 
 
-
      overwrite
      
+
      overwrite
      
 
      Logical. If TRUE, overwrite existing distance
 matrices. Defaults to FALSE.
      
 
 
-
      among_predpts
      
+
      among_predpts
      
 
      Logical. If TRUE, compute the pairwise distances
 between the prediction sites. Defaults to FALSE.
      
 
 
-
      only_predpts
      
+
      only_predpts
      
 
      Logical. If TRUE, only compute distances for
 prediction sites. Defaults to FALSE.
      
 
 
      
-    
      
-    
      Value
      
-    
      The ssn_create_distmat function creates a collection
+    
      
+    
      Value
      
+    
+
+
      The ssn_create_distmat function creates a collection
 of hierarchical directories in the ssn$path directory,
 which store the pairwise distances between sites associated with
 the SSN object. See details section for additional information.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      A distance matrix that contains the hydrologic distance
 between any two sites in SSN object is needed to fit a spatial
 statistical model using the tail-up and tail-down autocovariance
@@ -177,8 +195,8 @@ 
      Detailsamong_predpts = TRUE.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      ## Copy the MiddleForke04.ssn data to a local temporary directory.
 ## Only needed for this example.
 copy_lsn_to_temp()
@@ -204,23 +222,27 @@ 
      Examples)
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_get_data.html b/docs/reference/ssn_get_data.html
index 4fc7ff3..92eced2 100644
--- a/docs/reference/ssn_get_data.html
+++ b/docs/reference/ssn_get_data.html
@@ -1,77 +1,95 @@
 
-Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data • SSN2Get a data.frame from an SSN, ssn_lm, or ssn_glm object — ssn_get_data • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Get a data.frame from an SSN, ssn_lm, or ssn_glm object
      
-      Source: R/ssn_get_data.R
-      
      ssn_get_data.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Get a data.frame from an SSN, ssn_lm, or ssn_glm object
      
+    Source: R/ssn_get_data.R
+    
     
      
 
-    
      
+    
      
     
      The ssn_get_data function extracts an
 sf data.frame for the observation or prediction data from
 an SSN, ssn_lm, or ssn_glm object.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_get_data(x, name = "obs")
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      An object of class SSN, ssn_lm, or ssn_glm.
      
 
 
-
      name
      
+
      name
      
 
      the internal name of the dataset in the object
 x. For observed values, this will always be "obs", the
 default.
      
 
 
      
-    
      
-    
      Value
      
-    
      An sf data.frame
      
+    
      
+    
      Value
      
+    
+
+
      An sf data.frame
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The internal name for observed data in objects of
 class SSN is "obs" and it is the
 default. If another name is specified, it must represent a
@@ -81,13 +99,13 @@ 
      Detailsnames(x$ssn.object$preds).
      
     
      
-    
      
-    
      See also
      
+    
      
+    
      See also
      
     
      ssn_put_data()
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      ## Extract observed data from an SSN object
 # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
@@ -170,23 +188,27 @@ 
      Examples#>                    
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_get_netgeom.html b/docs/reference/ssn_get_netgeom.html
index dbcb5d5..528aafc 100644
--- a/docs/reference/ssn_get_netgeom.html
+++ b/docs/reference/ssn_get_netgeom.html
@@ -1,61 +1,79 @@
 
-Extract netgeom column — ssn_get_netgeom • SSN2
-    Skip to contents
+Extract netgeom column — ssn_get_netgeom • SSN2
+    
 
+    
      
+      
      
-
      
-
      
-  
      
+      
 
-      
      Extract netgeom column
      
-      Source: R/ssn_get_netgeom.R
-      
      ssn_get_netgeom.Rd
      
+      
      
+  
      
+    
      
+    
      Extract netgeom column
      
+    Source: R/ssn_get_netgeom.R
+    
     
      
 
-    
      
+    
      
     
      Extract topological information from netgeom column
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_get_netgeom(x, netvars = "all", reformat = FALSE)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      An sf data.frame found in an SSN object or the
 netgeom column as a vector
      
 
 
-
      netvars
      
+
      netvars
      
 
      Network coordinate variables to return. Default is
 "all". For edges, valid column names include: "NetworkID",
 "SegmentID", and "DistanceUpstream". For point datasets, valid column
@@ -63,18 +81,20 @@ 
      Arguments
      
 
 
-
      reformat
      
+
      reformat
      
 
      Convert network coordinate variables from character to numeric.
      
 
 
      
-    
      
-    
      Value
      
-    
      If more than one column is requested using netvars, the
+    
      
+    
      Value
      
+    
+
+
      If more than one column is requested using netvars, the
 function returns a data.frame (default). If only one column is
 requested, the result is a vector.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      When an SSN object is generated using the
 importSSN function, a text column named "netgeom" is added
 to the edges, observed sites, and prediction sites (if they
@@ -92,8 +112,8 @@ 
      Detailsnetvars.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -315,23 +335,27 @@ 
      Examples#> 163 7466.07885573737
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_get_stream_distmat.html b/docs/reference/ssn_get_stream_distmat.html
index 5714fde..57f70e9 100644
--- a/docs/reference/ssn_get_stream_distmat.html
+++ b/docs/reference/ssn_get_stream_distmat.html
@@ -1,63 +1,80 @@
 
-Get stream distance matrices from an SSN object — ssn_get_stream_distmat • SSN2
-    Skip to contents
+Get stream distance matrices from an SSN object — ssn_get_stream_distmat • SSN2
+    
 
+    
      
+      
      
-
      
-
      
-  
      
+      
 
-      
      Get stream distance matrices from an SSN object
      
-      Source: R/ssn_get_stream_distmat.R
-      
      ssn_get_stream_distmat.Rd
      
+      
      
+  
      
+    
      
+    
      Get stream distance matrices from an SSN object
      
+    Source: R/ssn_get_stream_distmat.R
+    
     
      
 
-    
      
+    
      
     
      Extracts the stream network distance matrices for the
 observation or prediction data from an SSN object.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_get_stream_distmat(x, name = "obs")
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      An SSN object
      
 
 
-
      name
      
+
      name
      
 
      Internal name of the dataset in the object
 x. For observed values, this will always be "obs", the
 default.  To get a stream network distance matrix for a
@@ -65,13 +82,15 @@ 
      Arguments
-    
      Value
      
-    
      A list of asymmetric downstream only
+    
      
+    
      Value
      
+    
+
+
      A list of asymmetric downstream only
 stream distance matrices, by network.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The internal name for observed data in objects of class
 SSN is "obs" and it is the default. If another name
 is specified, it must represent a prediction data set in the
@@ -127,20 +146,20 @@ 
      Details
     
      
-    
      
-    
      References
      
+    
      
+    
      References
      
     
      Ver Hoef, J.M. and Peterson, E.E. (2010) A moving
 average approach to spatial statistical models of stream
 networks. The Journal of the American Statistical Association,
-105(489), 22–24
      
+105(489), 22--24
      
     
      
-    
      
-    
      See also
      
+    
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      ## For this example only, copy MiddleFork04.ssn directory to R's
 ## temporary directory
 copy_lsn_to_temp()
@@ -151,12 +170,12 @@ 
      Examples
 ## Create distance matrices for obs x obs, obs x preds, and preds x
 ## preds
-if (FALSE) { # \dontrun{
+if (FALSE) {
 ssn_create_distmat(mf04p,
   predpts = "pred1km", among_predpts = TRUE,
   overwrite = TRUE
 )
-} # }
+}
 
 ## Check names of prediction datasets
 names(mf04p$preds)
@@ -276,23 +295,27 @@ 
      Examples#> 50    0    0  0 1000.000    0.000
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_glm.html b/docs/reference/ssn_glm.html
index e00c9d2..61556cb 100644
--- a/docs/reference/ssn_glm.html
+++ b/docs/reference/ssn_glm.html
@@ -1,51 +1,68 @@
 
-Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm • SSN2Fitting Generalized Linear Models for Spatial Stream Networks — ssn_glm • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Fitting Generalized Linear Models for Spatial Stream Networks
      
-      Source: R/ssn_glm.R
-      
      ssn_glm.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Fitting Generalized Linear Models for Spatial Stream Networks
      
+    Source: R/ssn_glm.R
+    
     
      
 
-    
      
+    
      
     
      This function works on spatial stream network objects to fit
 generalized linear models with spatially autocorrelated errors using likelihood methods, allowing for
 non-spatial random effects, anisotropy, partition factors, big data methods, and more.
@@ -53,8 +70,7 @@ 
      Fitting Generalized Linear Models for Spatial Stream Networks
      
 and Peterson and Ver Hoef (2010).
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_glm(
   formula,
   ssn.object,
@@ -78,21 +94,19 @@ 
      Usage
 )
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      formula
      
+    
      
+    
      Arguments
      
+    
      formula
      
 
      A two-sided linear formula describing the fixed effect structure
 of the model, with the response to the left of the ~ operator and
 the terms on the right, separated by + operators.
      
 
 
-
      ssn.object
      
+
      ssn.object
      
 
      A spatial stream network object with class SSN.
      
 
 
-
      family
      
+
      family
      
 
      The generalized linear model family for use with ssn_glm().
 Available options include "Gaussian", "poisson",
 "nbinomial" (negative binomial), "binomial", "beta",
@@ -103,21 +117,21 @@ 
      Argumentstailup_type
+
      tailup_type
      
 
      The tailup covariance function type. Available options
 include "linear", "spherical", "exponential",
 "mariah", "epa", and "none". Parameterizations are
 described in Details.
      
 
 
-
      taildown_type
      
+
      taildown_type
      
 
      The taildown covariance function type. Available options
 include "linear", "spherical", "exponential",
 "mariah", "epa", and "none". Parameterizations are
 described in Details.
      
 
 
-
      euclid_type
      
+
      euclid_type
      
 
      The euclidean covariance function type. Available options
 include "spherical", "exponential", "gaussian",
 "cosine", "cubic", "pentaspherical", "wave",
@@ -126,38 +140,38 @@ 
      Argumentsnugget_type
+
      nugget_type
      
 
      The nugget covariance function type. Available options
 include "nugget" or "none". Parameterizations are
 described in Details.
      
 
 
-
      tailup_initial
      
+
      tailup_initial
      
 
      An object from tailup_initial() specifying initial and/or
 known values for the tailup covariance parameters.
      
 
 
-
      taildown_initial
      
+
      taildown_initial
      
 
      An object from taildown_initial() specifying initial and/or
 known values for the taildown covariance parameters.
      
 
 
-
      euclid_initial
      
+
      euclid_initial
      
 
      An object from euclid_initial() specifying initial and/or
 known values for the euclidean covariance parameters.
      
 
 
-
      nugget_initial
      
+
      nugget_initial
      
 
      An object from nugget_initial() specifying initial and/or
 known values for the nugget covariance parameters.
      
 
 
-
      dispersion_initial
      
+
      dispersion_initial
      
 
      An object from dispersion_initial() specifying initial and/or
 known values for the tailup covariance parameters.
      
 
 
-
      additive
      
+
      additive
      
 
      The name of the variable in ssn.object that is used
 to define spatial weights. Can be quoted or unquoted. For the tailup covariance functions, these additive
 weights are used for branching. Technical details that describe the role
@@ -165,13 +179,13 @@ 
      Argumentsestmethod
+
      estmethod
      
 
      The estimation method. Available options include
 "reml" for restricted maximum likelihood and "ml" for maximum
 likelihood. The default is "reml".
      
 
 
-
      anisotropy
      
+
      anisotropy
      
 
      A logical indicating whether (geometric) anisotropy should
 be modeled. Not required if spcov_initial is provided with 1) rotate
 assumed unknown or assumed known and non-zero or 2) scale assumed unknown
@@ -179,7 +193,7 @@ 
      Argumentsrandom
+
      random
      
 
      A one-sided linear formula describing the random effect structure
 of the model. Terms are specified to the right of the ~ operator.
 Each term has the structure x1 + ... + xn | g1/.../gm, where x1 + ... + xn
@@ -195,24 +209,26 @@ 
      Argumentsrandcov_initial
+
      randcov_initial
      
 
      An optional object specifying initial and/or
 known values for the random effect variances. See spmodel::randcov_initial().
      
 
 
-
      partition_factor
      
+
      partition_factor
      
 
      A one-sided linear formula with a single term
 specifying the partition factor.  The partition factor assumes observations
 from different levels of the partition factor are uncorrelated.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments to stats::optim().
      
 
 
      
-    
      
-    
      Value
      
-    
      A list with many elements that store information about
+    
      
+    
      Value
      
+    
+
+
      A list with many elements that store information about
 the fitted model object and has class ssn_glm. Many generic functions that
 summarize model fit are available for ssn_glm objects, including
 AIC, AICc, anova, augment, coef,
@@ -221,6 +237,8 @@ 
      Value
      labels, logLik, loocv, model.frame, model.matrix,
 plot, predict, print, pseudoR2, summary,
 terms, tidy, update, varcomp, and vcov.
      
+
+
 
      This fitted model list contains the following elements:
      • additive: The name of the additive function value column.
        • 
 
      • anisotropy: Whether euclidean anisotropy was modeled.
        • 
 
      • call: The function call.
        • 
@@ -265,8 +283,8 @@ 
        Value
        object$residuals.
        
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The generalized linear model for spatial stream networks can be written as
 \(g(\mu) = \eta = X \beta + zu + zd + ze + n\), where \(\mu\) is the expectation
 of the response given the random errors, \(y\), \(g()\) is a function that links the mean
@@ -418,29 +436,29 @@ 
      Detailspredict(object).
      
     
      
-    
      
-    
      Note
      
+    
      
+    
      Note
      
     
      This function does not perform any internal scaling. If optimization is not
 stable due to large extremely large variances, scale relevant variables
 so they have variance 1 before optimization.
      
     
      
-    
      
-    
      References
      
+    
      
+    
      References
      
     
      Garreta, V., Monestiez, P. and Ver Hoef, J.M. (2010) Spatial modelling and
 prediction on river networks: up model, down model, or hybrid?
-Environmetrics 21(5), 439–456.
      
+Environmetrics 21(5), 439--456.
      
 
      McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.
      
 
      Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach
 to geostatistical modeling in stream networks. Ecology 91(3),
-644–651.
      
+644--651.
      
 
      Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-Journal of the American Statistical Association 105, 6–18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.
      
+Journal of the American Statistical Association 105, 6--18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -484,23 +502,27 @@ 
      Examples
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_import.html b/docs/reference/ssn_import.html
index 45629e2..6120ca8 100644
--- a/docs/reference/ssn_import.html
+++ b/docs/reference/ssn_import.html
@@ -1,82 +1,101 @@
 
-Import SSN object — ssn_import • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Import SSN object
      
-      Source: R/ssn_import.R
-      
      ssn_import.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Import SSN object
      
+    Source: R/ssn_import.R
+    
     
      
 
-    
      
+    
      
     
      This function reads spatial data from a .ssn folder
 and creates an SSN object.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_import(path, include_obs = TRUE, predpts = NULL, overwrite = FALSE)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      path
      
+    
      
+    
      Arguments
      
+    
      path
      
 
      Filepath to the .ssn directory. See details.
      
 
 
-
      include_obs
      
+
      include_obs
      
 
      default = TRUE. Logical indicating
 whether observed sites should be included in the SSN object.
      
 
 
-
      predpts
      
+
      predpts
      
 
      Vector of prediction site dataset names found within
 the .ssn folder. See details.
      
 
 
-
      overwrite
      
+
      overwrite
      
 
      default = FALSE. If TRUE, overwrite
 existing binaryID.db files and netgeom column(s) if it exists in the
 edges, observed sites (if include_obs = TRUE), and
 prediction site datasets (if they exist).
      
 
 
      
-    
      
-    
      Value
      
-    
      ssn_import returns an object of class SSN, which is a list
+    
      
+    
      Value
      
+    
+
+
      ssn_import returns an object of class SSN, which is a list
 with four elements containing:
      • edges: An sf data.frame containing the stream network,
 with an additional 'netgeom' column.
        • 
 
      • obs: An sf data.frame containing observed site locations,
@@ -87,8 +106,8 @@ 
        Value
        path: The local filepath for the .ssn directory associated with the SSN
 object.
        • 
 
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The ssn_import function imports spatial data (shapefile or GeoPackage format)
 from a .ssn folder generated using the
 SSNbler package function SSNbler::lsn_to_ssn. The .ssn folder contains all of the spatial, topological and
@@ -140,17 +159,17 @@ 
      DetailsSSN object.
      
     
      
-    
      
-    
      References
      
+    
      
+    
      References
      
     
      Peterson, E., and Ver Hoef, J.M. (2014) STARS: An
 ArcGIS toolset used to calculate the spatial information needed
 to fit spatial statistical stream network models to stream
 network data. Journal of Statistical Software
-56(2), 1–17.
      
+56(2), 1--17.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      ## Create local temporary copy of MiddleFork04.ssn found in
 # SSN2/lsndata folder. Only necessary for this example.
 copy_lsn_to_temp()
@@ -171,23 +190,27 @@ 
      Examples
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_import_predpts.html b/docs/reference/ssn_import_predpts.html
index 5c8403a..cbbbe8d 100644
--- a/docs/reference/ssn_import_predpts.html
+++ b/docs/reference/ssn_import_predpts.html
@@ -1,78 +1,96 @@
 
-Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts • SSN2Import prediction points into an SSN, ssn_lm, or ssn_glm object — ssn_import_predpts • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Import prediction points into an SSN, ssn_lm, or ssn_glm object
      
-      Source: R/ssn_import_predpts.R
-      
      ssn_import_predpts.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Import prediction points into an SSN, ssn_lm, or ssn_glm object
      
+    Source: R/ssn_import_predpts.R
+    
     
      
 
-    
      
+    
      
     
      A shapefile of prediction points found in the .ssn
 directory are imported into an existing object of class
 SSN, ssn_lm, or ssn_glm.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_import_predpts(x, predpts)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      An object of classSSN, ssn_lm, or
 ssn_glm.
      
 
 
-
      predpts
      
+
      predpts
      
 
      Name of the prediction point dataset to import in
 character format. See details.
      
 
 
      
-    
      
-    
      Value
      
-    
      an object of class SSN, ssn_lm, or
+    
      
+    
      Value
      
+    
+
+
      an object of class SSN, ssn_lm, or
 ssn_glm which contains the new prediction dataset.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      ssn_import_predpts imports one set of prediction
 points residing in the .ssn directory into an existing
 SSN, ssn_lm, or ssn_glm object. The
@@ -102,8 +120,8 @@ 
      Details
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      ## Create local temporary copy of MiddleFork04.ssn found in
 # SSN2/lsndata folder. Only necessary for this example.
 copy_lsn_to_temp()
@@ -133,23 +151,27 @@ 
      Examples
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_initial.html b/docs/reference/ssn_initial.html
index f772b8e..de56436 100644
--- a/docs/reference/ssn_initial.html
+++ b/docs/reference/ssn_initial.html
@@ -1,57 +1,74 @@
 
-Create a covariance parameter initial object — ssn_initial • SSN2Create a covariance parameter initial object — ssn_initial • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Create a covariance parameter initial object
      
-      Source: R/initial.R
-      
      ssn_initial.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Create a covariance parameter initial object
      
+    Source: R/initial.R
+    
     
      
 
-    
      
+    
      
     
      Create a covariance parameter initial object that specifies
 initial and/or known values to use while estimating specific covariance parameters
 with ssn_lm() or ssn_glm().  See spmodel::randcov_initial() for documentation regarding
 random effect covariance parameter initial objects.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      tailup_initial(tailup_type, de, range, known)
 
 taildown_initial(taildown_type, de, range, known)
@@ -61,38 +78,36 @@ 
      Usage
 nugget_initial(nugget_type, nugget, known)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      tailup_type
      
+    
      
+    
      Arguments
      
+    
      tailup_type
      
 
      The tailup covariance function type. Available options
 include "linear", "spherical", "exponential",
 "mariah", "epa", and "none".
      
 
 
-
      de
      
+
      de
      
 
      The spatially dependent (correlated) random error variance. Commonly referred to as
 a partial sill.
      
 
 
-
      range
      
+
      range
      
 
      The correlation parameter.
      
 
 
-
      known
      
+
      known
      
 
      A character vector indicating which covariance parameters are to be
 assumed known. The value "given" is shorthand for assuming all
 covariance parameters given to *_initial() are assumed known.
      
 
 
-
      taildown_type
      
+
      taildown_type
      
 
      The taildown covariance function type. Available options
 include "linear", "spherical", "exponential",
 "mariah", "epa", and "none".
      
 
 
-
      euclid_type
      
+
      euclid_type
      
 
      The euclidean covariance function type. Available options
 include "spherical", "exponential", "gaussian",
 "cosine", "cubic", "pentaspherical", "wave",
@@ -100,37 +115,39 @@ 
      Arguments.
      
 
 
-
      rotate
      
+
      rotate
      
 
      Anisotropy rotation parameter (from 0 to \(\pi\) radians) for
 the euclidean portion of the covariance. A value of 0 (the default) implies no rotation.
      
 
 
-
      scale
      
+
      scale
      
 
      Anisotropy scale parameter (from 0 to 1) for
 the euclidean portion of the covariance. A value of 1 (the default) implies no scaling.
      
 
 
-
      nugget_type
      
+
      nugget_type
      
 
      The nugget covariance function type. Available options
 include "nugget" or "none".
      
 
 
-
      nugget
      
+
      nugget
      
 
      The spatially independent (not correlated) random error variance. Commonly referred to as
 a nugget.
      
 
 
      
-    
      
-    
      Value
      
-    
      A list with two elements: initial and is_known.
+    
      
+    
      Value
      
+    
+
+
      A list with two elements: initial and is_known.
 initial is a named numeric vector indicating the spatial covariance parameters
 with specified initial and/or known values. is_known is a named
 numeric vector indicating whether the spatial covariance parameters in
 initial are known or not. The class of the list
 matches the the relevant spatial covariance type.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      Create an initial object for use with ssn_lm() or ssn_glm().
 NA values can be given for ie, rotate, and scale, which lets
 these functions find initial values for parameters that are sometimes
@@ -194,19 +211,19 @@ 
      DetailsDispersion and random effect initial objects are specified via
 spmodel::dispersion_initial() and spmodel::randcov_initial(), respectively.
      
     
      
-    
      
-    
      References
      
+    
      
+    
      References
      
     
      Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach
 to geostatistical modeling in stream networks. Ecology 91(3),
-644–651.
      
+644--651.
      
 
      Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-Journal of the American Statistical Association 105, 6–18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.
      
+Journal of the American Statistical Association 105, 6--18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      tailup_initial("exponential", de = 1, range = 20, known = "range")
 #> $initial
 #>    de range 
@@ -254,23 +271,27 @@ 
      Examples
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_lm.html b/docs/reference/ssn_lm.html
index 9f4cdd5..a0eede8 100644
--- a/docs/reference/ssn_lm.html
+++ b/docs/reference/ssn_lm.html
@@ -1,51 +1,68 @@
 
-Fitting Linear Models for Spatial Stream Networks — ssn_lm • SSN2Fitting Linear Models for Spatial Stream Networks — ssn_lm • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Fitting Linear Models for Spatial Stream Networks
      
-      Source: R/ssn_lm.R
-      
      ssn_lm.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Fitting Linear Models for Spatial Stream Networks
      
+    Source: R/ssn_lm.R
+    
     
      
 
-    
      
+    
      
     
      This function works on spatial stream network objects to fit
 linear models with spatially autocorrelated errors using likelihood methods, allowing for
 non-spatial random effects, anisotropy, partition factors, big data methods, and more.
@@ -53,8 +70,7 @@ 
      Fitting Linear Models for Spatial Stream Networks
      
 and Peterson and Ver Hoef (2010).
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_lm(
   formula,
   ssn.object,
@@ -76,35 +92,33 @@ 
      Usage
 )
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      formula
      
+    
      
+    
      Arguments
      
+    
      formula
      
 
      A two-sided linear formula describing the fixed effect structure
 of the model, with the response to the left of the ~ operator and
 the terms on the right, separated by + operators.
      
 
 
-
      ssn.object
      
+
      ssn.object
      
 
      A spatial stream network object with class SSN.
      
 
 
-
      tailup_type
      
+
      tailup_type
      
 
      The tailup covariance function type. Available options
 include "linear", "spherical", "exponential",
 "mariah", "epa", and "none". Parameterizations are
 described in Details.
      
 
 
-
      taildown_type
      
+
      taildown_type
      
 
      The taildown covariance function type. Available options
 include "linear", "spherical", "exponential",
 "mariah", "epa", and "none". Parameterizations are
 described in Details.
      
 
 
-
      euclid_type
      
+
      euclid_type
      
 
      The euclidean covariance function type. Available options
 include "spherical", "exponential", "gaussian",
 "cosine", "cubic", "pentaspherical", "wave",
@@ -113,33 +127,33 @@ 
      Argumentsnugget_type
+
      nugget_type
      
 
      The nugget covariance function type. Available options
 include "nugget" or "none". Parameterizations are
 described in Details.
      
 
 
-
      tailup_initial
      
+
      tailup_initial
      
 
      An object from tailup_initial() specifying initial and/or
 known values for the tailup covariance parameters.
      
 
 
-
      taildown_initial
      
+
      taildown_initial
      
 
      An object from taildown_initial() specifying initial and/or
 known values for the taildown covariance parameters.
      
 
 
-
      euclid_initial
      
+
      euclid_initial
      
 
      An object from euclid_initial() specifying initial and/or
 known values for the euclidean covariance parameters.
      
 
 
-
      nugget_initial
      
+
      nugget_initial
      
 
      An object from nugget_initial() specifying initial and/or
 known values for the nugget covariance parameters.
      
 
 
-
      additive
      
+
      additive
      
 
      The name of the variable in ssn.object that is used
 to define spatial weights. Can be quoted or unquoted. For the tailup covariance functions, these additive
 weights are used for branching. Technical details that describe the role
@@ -147,13 +161,13 @@ 
      Argumentsestmethod
+
      estmethod
      
 
      The estimation method. Available options include
 "reml" for restricted maximum likelihood and "ml" for maximum
 likelihood. The default is "reml".
      
 
 
-
      anisotropy
      
+
      anisotropy
      
 
      A logical indicating whether (geometric) anisotropy should
 be modeled. Not required if spcov_initial is provided with 1) rotate
 assumed unknown or assumed known and non-zero or 2) scale assumed unknown
@@ -161,7 +175,7 @@ 
      Argumentsrandom
+
      random
      
 
      A one-sided linear formula describing the random effect structure
 of the model. Terms are specified to the right of the ~ operator.
 Each term has the structure x1 + ... + xn | g1/.../gm, where x1 + ... + xn
@@ -177,24 +191,26 @@ 
      Argumentsrandcov_initial
+
      randcov_initial
      
 
      An optional object specifying initial and/or
 known values for the random effect variances. See spmodel::randcov_initial().
      
 
 
-
      partition_factor
      
+
      partition_factor
      
 
      A one-sided linear formula with a single term
 specifying the partition factor.  The partition factor assumes observations
 from different levels of the partition factor are uncorrelated.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments to stats::optim().
      
 
 
      
-    
      
-    
      Value
      
-    
      A list with many elements that store information about
+    
      
+    
      Value
      
+    
+
+
      A list with many elements that store information about
 the fitted model object and has class ssn_lm. Many generic functions that
 summarize model fit are available for ssn_lm objects, including
 AIC, AICc, anova, augment, coef,
@@ -203,6 +219,8 @@ 
      Value
      labels, logLik, loocv, model.frame, model.matrix,
 plot, predict, print, pseudoR2, summary,
 terms, tidy, update, varcomp, and vcov.
      
+
+
 
      This fitted model list contains the following elements:
      • additive: The name of the additive function value column.
        • 
 
      • anisotropy: Whether euclidean anisotropy was modeled.
        • 
 
      • call: The function call.
        • 
@@ -244,8 +262,8 @@ 
        Value
        object$residuals.
        
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The linear model for spatial stream networks can be written as
 \(y = X \beta + zu + zd + ze + n\), where \(X\) is the fixed effects design
 matrix, \(\beta\) are the fixed effects, \(zu\) is tailup random error,
@@ -345,28 +363,28 @@ 
      Detailspredict(object).
      
     
      
-    
      
-    
      Note
      
+    
      
+    
      Note
      
     
      This function does not perform any internal scaling. If optimization is not
 stable due to large extremely large variances, scale relevant variables
 so they have variance 1 before optimization.
      
     
      
-    
      
-    
      References
      
+    
      
+    
      References
      
     
      Garreta, V., Monestiez, P. and Ver Hoef, J.M. (2010) Spatial modelling and
 prediction on river networks: up model, down model, or hybrid?
-Environmetrics 21(5), 439–456.
      
+Environmetrics 21(5), 439--456.
      
 
      Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach
 to geostatistical modeling in stream networks. Ecology 91(3),
-644–651.
      
+644--651.
      
 
      Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-Journal of the American Statistical Association 105, 6–18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.
      
+Journal of the American Statistical Association 105, 6--18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -408,23 +426,27 @@ 
      Examples
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_names.html b/docs/reference/ssn_names.html
index 08c33a9..890294f 100644
--- a/docs/reference/ssn_names.html
+++ b/docs/reference/ssn_names.html
@@ -1,84 +1,107 @@
 
-Return names of data in an SSN object — ssn_names • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Return names of data in an SSN object
      
-      Source: R/ssn_names.R
-      
      ssn_names.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Return names of data in an SSN object
      
+    Source: R/ssn_names.R
+    
     
      
 
-    
      
+    
      
     
      Extract and print names from the edges, sites
 and preds elements of an SSN object.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_names(ssn.object)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      ssn.object
      
+    
      
+    
      Arguments
      
+    
      ssn.object
      
 
      An SSN object.
      
 
 
      
-    
      
-    
      Value
      
-    
      Print variable names to console
      
+    
      
+    
      Value
      
+    
+
+
      Print variable names to console
      
     
      
 
-  
      
+  
      
+  
+
      
 
 
-    
      
-  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
+      
      
+  
      Developed by Michael Dumelle, Jay M. Ver Hoef, Erin Peterson.
      
 
      
 
-
      
-  
      Site built with pkgdown 2.1.0.
      
+
      
+  
      Site built with pkgdown 2.0.9.
      
 
      
 
-    
      
-
+      
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_params.html b/docs/reference/ssn_params.html
index af40eda..32a3fd9 100644
--- a/docs/reference/ssn_params.html
+++ b/docs/reference/ssn_params.html
@@ -1,54 +1,72 @@
 
-Create covariance parameter objects. — ssn_params • SSN2Create covariance parameter objects. — ssn_params • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Create covariance parameter objects.
      
-      Source: R/params.R
-      
      ssn_params.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Create covariance parameter objects.
      
+    Source: R/params.R
+    
     
      
 
-    
      
+    
      
     
      Create a covariance parameter object for us with other functions.
 See spmodel::randcov_params() for documentation regarding
 random effect covariance parameter objects.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      tailup_params(tailup_type, de, range)
 
 taildown_params(taildown_type, de, range)
@@ -58,32 +76,30 @@ 
      Usage
 nugget_params(nugget_type, nugget)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      tailup_type
      
+    
      
+    
      Arguments
      
+    
      tailup_type
      
 
      The tailup covariance function type. Available options
 include "linear", "spherical", "exponential",
 "mariah", "epa", and "none".
      
 
 
-
      de
      
+
      de
      
 
      The spatially dependent (correlated) random error variance. Commonly referred to as
 a partial sill.
      
 
 
-
      range
      
+
      range
      
 
      The correlation parameter.
      
 
 
-
      taildown_type
      
+
      taildown_type
      
 
      The taildown covariance function type. Available options
 include "linear", "spherical", "exponential",
 "mariah", "epa", and "none".
      
 
 
-
      euclid_type
      
+
      euclid_type
      
 
      The euclidean covariance function type. Available options
 include "spherical", "exponential", "gaussian",
 "cosine", "cubic", "pentaspherical", "wave",
@@ -91,43 +107,45 @@ 
      Arguments.
      
 
 
-
      rotate
      
+
      rotate
      
 
      Anisotropy rotation parameter (from 0 to \(\pi\) radians) for
 the euclidean portion of the covariance. A value of 0 (the default) implies no rotation.
      
 
 
-
      scale
      
+
      scale
      
 
      Anisotropy scale parameter (from 0 to 1) for
 the euclidean portion of the covariance. A value of 1 (the default) implies no scaling.
      
 
 
-
      nugget_type
      
+
      nugget_type
      
 
      The nugget covariance function type. Available options
 include "nugget" or "none".
      
 
 
-
      nugget
      
+
      nugget
      
 
      The spatially independent (not correlated) random error variance. Commonly referred to as
 a nugget.
      
 
 
      
-    
      
-    
      Value
      
-    
      A parameter object with class that matches the relevant type argument.
      
+    
      
+    
      Value
      
+    
+
+
      A parameter object with class that matches the relevant type argument.
      
     
      
-    
      
-    
      References
      
+    
      
+    
      References
      
     
      Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach
 to geostatistical modeling in stream networks. Ecology 91(3),
-644–651.
      
+644--651.
      
 
      Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial
 statistical models of stream networks (with discussion).
-Journal of the American Statistical Association 105, 6–18.
-DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22–24.
      
+Journal of the American Statistical Association 105, 6--18.
+DOI: 10.1198/jasa.2009.ap08248.  Rejoinder pgs. 22--24.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      tailup_params("exponential", de = 1, range = 20)
 #>    de range 
 #>     1    20 
@@ -150,23 +168,27 @@ 
      Examples#> [1] "nugget_nugget"
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_put_data.html b/docs/reference/ssn_put_data.html
index 97ed0c7..e48369f 100644
--- a/docs/reference/ssn_put_data.html
+++ b/docs/reference/ssn_put_data.html
@@ -1,88 +1,106 @@
 
-Put an sf data.frame in an SSN object — ssn_put_data • SSN2Put an sf data.frame in an SSN object — ssn_put_data • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Put an sf data.frame in an SSN object
      
-      Source: R/ssn_put_data.R
-      
      ssn_put_data.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Put an sf data.frame in an SSN object
      
+    Source: R/ssn_put_data.R
+    
     
      
 
-    
      
+    
      
     
      The ssn_put_data function puts an sf
 data.frame representing observation or prediction
 data into an SSN, ssn_lm, or ssn_glm object.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_put_data(data, x, name = "obs", resize_data = FALSE)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      data
      
+    
      
+    
      Arguments
      
+    
      data
      
 
      sf data.frame with point geometry.
      
 
 
-
      x
      
+
      x
      
 
      An object of class SSN, ssn_lm, or ssn_glm.
      
 
 
-
      name
      
+
      name
      
 
      the internal name of the data set in the object
 x. For observed data, this will always be "obs", the
 default.
      
 
 
-
      resize_data
      
+
      resize_data
      
 
      Logical. Indicates whether sf_df can have a
 different number of features than the current data.frame in the
 object. Default is FALSE.
      
 
 
      
-    
      
-    
      Value
      
-    
      Returns an object of the same class as x, which contains
+    
      
+    
      Value
      
+    
+
+
      Returns an object of the same class as x, which contains
 the sf data.frame sf_data.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      The internal name for observed data in objects of
 class SSN, ssn_lm, and ssn_glm is "obs" and it is the
 default. If another name is specified, it must represent a
@@ -99,13 +117,13 @@ 
      Details
     
      
-    
      
-    
      See also
      
+    
      
+    
      See also
      
     
      ssn_get_data()
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      data(mf04p)
 ## Extract observation data.frame from SSN object
 obs.df <- ssn_get_data(mf04p)
@@ -130,23 +148,27 @@ 
      Examples#> 6     15.29 15.29 POINT (-1524599 930808.7)
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/ssn_simulate.html b/docs/reference/ssn_simulate.html
index f0b4cc6..85881fb 100644
--- a/docs/reference/ssn_simulate.html
+++ b/docs/reference/ssn_simulate.html
@@ -1,57 +1,74 @@
 
-Simulate random variables on a stream network — ssn_simulate • SSN2Simulate random variables on a stream network — ssn_simulate • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Simulate random variables on a stream network
      
-      Source: R/ssn_simulate.R, R/ssn_rbeta.R, R/ssn_rbinom.R, and 5 more
-      
      ssn_simulate.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Simulate random variables on a stream network
      
+    Source: R/ssn_simulate.R, R/ssn_rbeta.R, R/ssn_rbinom.R, and 5 more
+    
     
      
 
-    
      
+    
      
     
      Simulate random variables on a stream
 network with a specific mean and covariance structure. Designed to use
 ssn_simulate(), but individual simulation functions for each
 resposne distribution also exist.
      
     
      
 
-    
      
-    
      Usage
      
+    
      
     
      ssn_simulate(
   family = "Gaussian",
   ssn.object,
@@ -181,45 +198,43 @@ 
      Usage
 )
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      family
      
+    
      
+    
      Arguments
      
+    
      family
      
 
      The response distribution family. The default is "Gaussian".
      
 
 
-
      ssn.object
      
+
      ssn.object
      
 
      A spatial stream network object with class SSN. Random
 variables are simulated for each row of ssn.object$obs.
      
 
 
-
      network
      
+
      network
      
 
      The spatial stream network to simulate on. Currently only
 allowed to be "obs" for the ssn.object$obs object.
      
 
 
-
      tailup_params
      
+
      tailup_params
      
 
      An object from tailup_params() specifying
 the tailup covariance parameters.
      
 
 
-
      taildown_params
      
+
      taildown_params
      
 
      An object from taildown_params() specifying
 the taildown covariance parameters.
      
 
 
-
      euclid_params
      
+
      euclid_params
      
 
      An object from euclid_params() specifying
 the Euclidean covariance parameters.
      
 
 
-
      nugget_params
      
+
      nugget_params
      
 
      An object from nugget_params() specifying
 the nugget covariance parameters.
      
 
 
-
      additive
      
+
      additive
      
 
      The name of the variable in ssn.object that is used
 to define spatial weights. Can be quoted or unquoted. For the tailup covariance functions, these additive
 weights are used for branching. Technical details that describe the role
@@ -227,47 +242,51 @@ 
      Argumentsmean
+
      mean
      
 
      A numeric vector representing the mean. mean must have length 1
 (in which case it is recycled) or length equal
 to the number of rows in data. The default is 0.
      
 
 
-
      samples
      
+
      samples
      
 
      The number of independent samples to generate. The default
 is 1.
      
 
 
-
      dispersion
      
+
      dispersion
      
 
      The dispersion value (if relevant).
      
 
 
-
      size
      
+
      size
      
 
      A numeric vector representing the sample size for each binomial trial.
 The default is 1, which corresponds to a Bernoulli trial for each observation.
      
 
 
-
      randcov_params
      
+
      randcov_params
      
 
      A spmodel::randcov_params() object.
      
 
 
-
      partition_factor
      
+
      partition_factor
      
 
      A formula indicating the partition factor.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      If samples is 1, a vector of random variables for each row of ssn.object$obs
-is returned. If samples is greater than one, a matrix of random variables
+    
      
+    
      Value
      
+    
+
+
      If samples is 1, a vector of random variables for each row of ssn.object$obs
      
+
+
+
      is returned. If samples is greater than one, a matrix of random variables
 is returned, where the rows correspond to each row of ssn.object$obs and the columns
 correspond to independent samples.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      Random variables are simulated via the product of the covariance matrix's
 square (Cholesky) root and independent standard normal random variables
 on the link scale, which are then used to simulate a relevant variable on the response scale
@@ -287,16 +306,16 @@ 
      Details
      ssn_rgamma(): Simulate from a gamma distribution

    • ssn_rinvgauss(): Simulate from an inverse Gaussian distribution

      • -
      -

      References

      +
      +

      References

      Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial statistical models of stream networks (with discussion). -Journal of the American Statistical Association 105, 6–18. -DOI: 10.1198/jasa.2009.ap08248. Rejoinder pgs. 22–24.

      +Journal of the American Statistical Association 105, 6--18. +DOI: 10.1198/jasa.2009.ap08248. Rejoinder pgs. 22--24.

      -
      -

      Examples

      +
      +

      Examples

      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -320,23 +339,27 @@ 
      Examples
      -
      +
      + + - + + diff --git a/docs/reference/ssn_split_predpts.html b/docs/reference/ssn_split_predpts.html index ef006cb..4d1b812 100644 --- a/docs/reference/ssn_split_predpts.html +++ b/docs/reference/ssn_split_predpts.html @@ -1,53 +1,69 @@ -Split a prediction dataset in an SSN object — ssn_split_predpts • SSN2Split a prediction dataset in an SSN object — ssn_split_predpts • SSN2 - Skip to contents - - -
      -
      -
      - -

      Split a prediction dataset in an SSN object

      - Source: R/ssn_split_predpts.R -
      ssn_split_predpts.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Split a prediction dataset in an SSN object

      + Source: R/ssn_split_predpts.R +
      -
      +

      The splitPrediction function is used to split prediction sets in an SSN object into smaller prediction sets. It returns a SSN object with additional @@ -56,8 +72,7 @@

      Split a prediction dataset in an SSN object

      set, or a logical expression.

      -
      -

      Usage

      + 
      ssn_split_predpts(
   ssn,
   predpts,
@@ -71,68 +86,68 @@ 
      Usage
 )
      -
      -

      Arguments

      - - -
      ssn
      +
      +

      Arguments

      +
      ssn

      An SSN object.

      -
      predpts
      +
      predpts

      A character string representing the name of the prediction dataset.

      -
      size_predpts
      +
      size_predpts

      numeric value representing the size of the new prediction sets. The existing prediction set is split equally to produce multiple prediction sets of this size

      -
      by
      +
      by

      character string representing the column name of type factor, integer, character or logical that the split will be based on

      -
      subset
      +
      subset

      logical expression indicating which elements or rows to keep; missing values are taken as FALSE

      -
      id_predpts
      +
      id_predpts

      character string representing the new prediction dataset name. This value is only specified when the subset method is used

      -
      keep
      +
      keep

      logical value indicating whether the original prediction dataset should be retained in the SSN object. Default is TRUE

      -
      drop_levels
      +
      drop_levels

      logical value indicating whether empty factor levels should be dropped in the by column when the new prediction dataset(s) are created. Default is FALSE

      -
      overwrite
      +
      overwrite

      logical indicating whether the new prediction dataset geopackage should be deleted in the .ssn directory if it already exists. Default = FALSE

      -
      -

      Value

      -

      returns the SSN specified in ssn, with one or more new prediction +

      +

      Value

      + + +

      returns the SSN specified in ssn, with one or more new prediction sets. Geopackages of the new prediction sets are written to the .ssn directory designated in ssn$path.

      -
      -

      Details

      +
      +

      Details

      Three methods have been provided to split prediction sets: size, by, and subset. The size method is used to split the existing prediction @@ -160,8 +175,8 @@

      Details

      -
      -

      Examples

      +
      +

      Examples

      ## Import SSN object
 copy_lsn_to_temp() ## Only needed for this example
 ssn <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
@@ -198,23 +213,27 @@ 
      Examples#> [1] "pred1km"  "CapeHorn" "RATIO_05"
      -
      +
      + +
      -
      + + diff --git a/docs/reference/ssn_subset.html b/docs/reference/ssn_subset.html index 116c962..263d6a0 100644 --- a/docs/reference/ssn_subset.html +++ b/docs/reference/ssn_subset.html @@ -1,91 +1,110 @@ -Subset an SSN object — ssn_subset • SSN2 - Skip to contents - - -
      -
      -
      - -

      Subset an SSN object

      - Source: R/ssn_subset.R -
      ssn_subset.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Subset an SSN object

      + Source: R/ssn_subset.R +
      -
      +

      Returns an SSN object that has been subset based on a logical expression.

      -
      -

      Usage

      + 
      ssn_subset(ssn, path, subset, clip = FALSE, overwrite = FALSE)
      -
      -

      Arguments

      - - -
      ssn
      +
      +

      Arguments

      +
      ssn

      An SSN object.

      -
      path
      +
      path

      The filepath to the .ssn folder, in string format, where the subset SSN will be saved.

      -
      subset
      +
      subset

      A logical expression indicating which features to keep.

      -
      clip
      +
      clip

      If TRUE, create a subset of the edges and prediction sites, based on the same logical expression used to subset the observed sites. Default = FALSE.

      -
      overwrite
      +
      overwrite

      If TRUE, overwrite the folder specified in path if it exists. Default = FALSE.

      -
      -

      Value

      -

      an object of class SSN, which is stored locally in the .ssn +

      +

      Value

      + + +

      an object of class SSN, which is stored locally in the .ssn directory specified in path. It also creates and stores an SQLite database, binaryID.db, within the .ssn directory.

      -
      -

      Details

      +
      +

      Details

      This function creates a subset of the original SSN object based on a logical expression defined in the subset argument. The subset argument is treated as an expression @@ -108,8 +127,8 @@

      DetailsSSN.

      -
      -

      Examples

      +
      +

      Examples

      ## Import SSN object
 copy_lsn_to_temp() ## Only needed for this example
 mf04p <- ssn_import(paste0(tempdir(), "/MiddleFork04.ssn"),
@@ -132,23 +151,27 @@ 
      Examples)
      -
      +
      + +
      -
      + + diff --git a/docs/reference/ssn_update_path.html b/docs/reference/ssn_update_path.html index 348fb18..77814a2 100644 --- a/docs/reference/ssn_update_path.html +++ b/docs/reference/ssn_update_path.html @@ -1,78 +1,97 @@ -Update path in an SSN object — ssn_update_path • SSN2 - Skip to contents - - -
      -
      -
      - -

      Update path in an SSN object

      - Source: R/ssn_update_path.R -
      ssn_update_path.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Update path in an SSN object

      + Source: R/ssn_update_path.R +
      -
      +

      Update the local path in an existing SSN object based on an user defined file.

      -
      -

      Usage

      + 
      ssn_update_path(x, path, verbose = FALSE)
      -
      -

      Arguments

      - - -
      x
      +
      +

      Arguments

      +
      x

      An SSN, ssn_lm or ssn_glm object.

      -
      path
      +
      path

      Filepath to the .ssn folder associated with the SSN object.

      -
      verbose
      +
      verbose

      A logical that indicates if the new path should be printed to the console.

      -
      -

      Value

      -

      An SSN object with a new path list element.

      +
      +

      Value

      + + +

      An SSN object with a new path list element.

      -
      -

      Details

      +
      +

      Details

      At times, it may be necessary to move a .ssn directory, which is linked to an SSN object in an R workspace. If the .ssn directory is moved, the path must be updated before using the @@ -81,8 +100,8 @@

      Details

      -
      -

      Examples

      +
      +

      Examples

      ## Use mf04p SSN object provided in SSN2
 data(mf04p)
 
@@ -92,23 +111,27 @@ 
      Examplesmf04p <- ssn_update_path(mf04p, newpath)
      -
      +
      + +
      -
      + + diff --git a/docs/reference/ssn_write.html b/docs/reference/ssn_write.html index f027a48..c749d98 100644 --- a/docs/reference/ssn_write.html +++ b/docs/reference/ssn_write.html @@ -1,84 +1,103 @@ -write an SSN object — ssn_write • SSN2 - Skip to contents - - -
      -
      -
      - -

      write an SSN object

      - Source: R/ssn_write.R -
      ssn_write.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      write an SSN object

      + Source: R/ssn_write.R +
      -
      +

      This function writes an SSN object to a local .ssn directory

      -
      -

      Usage

      + 
      ssn_write(ssn, path, overwrite = FALSE, copy_dist = FALSE, import = FALSE)
      -
      -

      Arguments

      - - -
      ssn
      +
      +

      Arguments

      +
      ssn

      An SSN object.

      -
      path
      +
      path

      filepath to the local .ssn directory to write to.

      -
      overwrite
      +
      overwrite

      If TRUE, overwrite existing files in file (if it exists). Defaults to FALSE.

      -
      copy_dist
      +
      copy_dist

      If TRUE, copy distance matrices to file (if they exist). Defaults to FALSE.

      -
      import
      +
      import

      If TRUE, import and return the SSN object after writing to file. Defaults to FALSE.

      -
      -

      Value

      -

      ssn_write creates an .ssn directory that contains the +

      +

      Value

      + + +

      ssn_write creates an .ssn directory that contains the spatial, topological, and attribute information stored in the original SSN object. Spatial datasets found in the SSN object (e.g. edges, obs, and prediction sites) are @@ -86,8 +105,8 @@

      Value

      SSN object is imported and returned.

      -
      -

      Examples

      +
      +

      Examples

      ## For examples only, copy MiddleFork04.ssn directory to R's
 # temporary directory
 copy_lsn_to_temp()
@@ -110,23 +129,27 @@ 
      Examples), overwrite = TRUE, import = TRUE)
      -
      +
      + +
      -
      + + diff --git a/docs/reference/summary.SSN.html b/docs/reference/summary.SSN.html index ada97e9..1619d16 100644 --- a/docs/reference/summary.SSN.html +++ b/docs/reference/summary.SSN.html @@ -1,71 +1,91 @@ -Summarize an SSN object — summary.SSN • SSN2 - Skip to contents - - -
      -
      -
      - -

      Summarize an SSN object

      - Source: R/summary.SSN.R -
      summary.SSN.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Summarize an SSN object

      + Source: R/summary.SSN.R +
      -
      +

      Summarize data found in an SSN object.

      -
      -

      Usage

      - 
      # S3 method for class 'SSN'
+    
      
+    
      # S3 method for SSN
 summary(object, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      An SSN object.
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A list with several fitted model quantities used to create
+    
      
+    
      Value
      
+    
+
+
      A list with several fitted model quantities used to create
 informative summaries when printing.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      summary.SSN() creates a summary of a SSN object
 intended to be printed using print(). This summary
 contains information about the number of observed and prediction
@@ -73,23 +93,27 @@ 
      Details
     
      
 
-  
      
+  
      
+  
+
      
 
 
-
      + + diff --git a/docs/reference/summary.SSN2.html b/docs/reference/summary.SSN2.html index 5d958e1..31ed5b4 100644 --- a/docs/reference/summary.SSN2.html +++ b/docs/reference/summary.SSN2.html @@ -1,87 +1,107 @@ -Summarize a fitted model object — summary.SSN2 • SSN2 - Skip to contents - - -
      -
      -
      - -

      Summarize a fitted model object

      - Source: R/summary.R -
      summary.SSN2.Rd
      +
    • + Changelog +
      • +
      +
      +
      + + + +
      +
      +
      +

      Summarize a fitted model object

      + Source: R/summary.R +
      -
      +

      Summarize a fitted model object.

      -
      -

      Usage

      - 
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 summary(object, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 summary(object, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A list with several fitted model quantities used to create
+    
      
+    
      Value
      
+    
+
+
      A list with several fitted model quantities used to create
 informative summaries when printing.
      
     
      
-    
      
-    
      Details
      
+    
      
+    
      Details
      
     
      summary.ssn() creates a summary of a fitted model object
 intended to be printed using print(). This summary contains
 useful information like the original function call, residuals,
 a coefficients table, a pseudo r-squared, and estimated covariance
 parameters.
      
     
      
-    
      
-    
      See also
      
+    
      
+    
      See also
      
     
      print.SSN2
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -122,23 +142,27 @@ 
      Examples#> 
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/tidy.SSN2.html b/docs/reference/tidy.SSN2.html
index 4bdc9d3..c352bd5 100644
--- a/docs/reference/tidy.SSN2.html
+++ b/docs/reference/tidy.SSN2.html
@@ -1,75 +1,93 @@
 
-Tidy a fitted model object — tidy.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Tidy a fitted model object
      
-      Source: R/tidy.R
-      
      tidy.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Tidy a fitted model object
      
+    Source: R/tidy.R
+    
     
      
 
-    
      
+    
      
     
      Tidy a fitted model object into a summarized tibble.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 tidy(x, conf.int = FALSE, conf.level = 0.95, effects = "fixed", ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 tidy(x, conf.int = FALSE, conf.level = 0.95, effects = "fixed", ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      x
      
+    
      
+    
      Arguments
      
+    
      x
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      conf.int
      
+
      conf.int
      
 
      Logical indicating whether or not to include a confidence interval
 in the tidied output. The default is FALSE.
      
 
 
-
      conf.level
      
+
      conf.level
      
 
      The confidence level to use for the confidence interval if
 conf.int is TRUE. Must be strictly greater than 0 and less than 1.
 The default is 0.95, which corresponds to a 95 percent confidence interval.
      
 
 
-
      effects
      
+
      effects
      
 
      The type of effects to tidy. Available options are "fixed"
 (fixed effects), "tailup" (tailup covariance parameters),
 "taildown" (taildown covariance parameters), "euclid" (Euclidean
@@ -79,21 +97,23 @@ 
      Arguments, and "randcov" (random effect variances). The default is "fixed".
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A tidy tibble of summary information effects.
      
+    
      
+    
      Value
      
+    
+
+
      A tidy tibble of summary information effects.
      
     
      
-    
      
-    
      See also
      
+    
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -115,23 +135,27 @@ 
      Examples#> 2 ELEV_DEM     -0.0341   0.00438     -7.80 6.22e-15
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/varcomp.SSN2.html b/docs/reference/varcomp.SSN2.html
index 826bc13..29a12f0 100644
--- a/docs/reference/varcomp.SSN2.html
+++ b/docs/reference/varcomp.SSN2.html
@@ -1,73 +1,92 @@
 
-Variability component comparison — varcomp.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Variability component comparison
      
-      Source: R/varcomp.R
-      
      varcomp.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Variability component comparison
      
+    Source: R/varcomp.R
+    
     
      
 
-    
      
+    
      
     
      Compare the proportion of total variability explained by the fixed effects
 and each variance parameter.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 varcomp(object, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 varcomp(object, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
      
-    
      
-    
      Value
      
-    
      A tibble that partitions the the total variability by the fixed effects
+    
      
+    
      Value
      
+    
+
+
      A tibble that partitions the the total variability by the fixed effects
 and each variance parameter. The proportion of variability explained by the
 fixed effects is the pseudo R-squared obtained by psuedoR2(). The
 remaining proportion is spread accordingly among each variance parameter:
@@ -77,8 +96,8 @@ 
      Value
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -103,23 +122,27 @@ 
      Examples#> 5 nugget                 0.0155
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/reference/vcov.SSN2.html b/docs/reference/vcov.SSN2.html
index 663f26e..07772cc 100644
--- a/docs/reference/vcov.SSN2.html
+++ b/docs/reference/vcov.SSN2.html
@@ -1,81 +1,101 @@
 
-Calculate variance-covariance matrix for a fitted model object — vcov.SSN2 • SSN2
-    Skip to contents
-
-
-    
      
-
      
-  
      
-
-      
      Calculate variance-covariance matrix for a fitted model object
      
-      Source: R/vcov.R
-      
      vcov.SSN2.Rd
      
+
    • 
+  Changelog
+
      • 
+      
      
+  
      
+
      
+
+      
+
+      
      
+  
      
+    
      
+    
      Calculate variance-covariance matrix for a fitted model object
      
+    Source: R/vcov.R
+    
     
      
 
-    
      
+    
      
     
      Calculate variance-covariance matrix for a fitted model object.
      
     
      
 
-    
      
-    
      Usage
      
-    
      # S3 method for class 'ssn_lm'
+    
      
+    
      # S3 method for ssn_lm
 vcov(object, ...)
 
-# S3 method for class 'ssn_glm'
+# S3 method for ssn_glm
 vcov(object, var_correct = TRUE, ...)
      
     
      
 
-    
      
-    
      Arguments
      
-
-
-
      object
      
+    
      
+    
      Arguments
      
+    
      object
      
 
      A fitted model object from ssn_lm() or ssn_glm().
      
 
 
-
      ...
      
+
      ...
      
 
      Other arguments. Not used (needed for generic consistency).
      
 
 
-
      var_correct
      
+
      var_correct
      
 
      A logical indicating whether to return the corrected variance-covariance
 matrix for models fit using ssn_glm() (when family is different
 from "Gaussian"). The default is TRUE.
      
 
 
      
-    
      
-    
      Value
      
-    
      The variance-covariance matrix of coefficients obtained via coef().
+    
      
+    
      Value
      
+    
+
+
      The variance-covariance matrix of coefficients obtained via coef().
 Currently, only the variance-covariance matrix of the fixed effects is supported.
      
     
      
 
-    
      
-    
      Examples
      
+    
      
+    
      Examples
      
     
      # Copy the mf04p .ssn data to a local directory and read it into R
 # When modeling with your .ssn object, you will load it using the relevant
 # path to the .ssn data on your machine
@@ -95,23 +115,27 @@ 
      Examples#> ELEV_DEM    -0.03855169  1.915303e-05
 
      
     
      
-  
      
+  
      
+  
+
      
 
 
-    
      
 
+  
 
 
+  
 
   
 
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 90937ed..cd06bd3 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -1,70 +1,204 @@
-
-/404.html
-/articles/index.html
-/articles/introduction.html
-/authors.html
-/CONTRIBUTING.html
-/index.html
-/LICENSE.html
-/news/index.html
-/reference/AIC.SSN2.html
-/reference/anova.SSN2.html
-/reference/augment.SSN2.html
-/reference/coef.SSN2.html
-/reference/confint.SSN2.html
-/reference/cooks.distance.SSN2.html
-/reference/copy_lsn_to_temp.html
-/reference/covmatrix.SSN2.html
-/reference/create_netgeom.html
-/reference/deviance.SSN2.html
-/reference/fitted.SSN2.html
-/reference/formula.SSN2.html
-/reference/glance.SSN2.html
-/reference/glances.SSN2.html
-/reference/hatvalues.SSN2.html
-/reference/index.html
-/reference/influence.SSN2.html
-/reference/labels.SSN2.html
-/reference/logLik.SSN2.html
-/reference/loocv.SSN2.html
-/reference/mf04p.html
-/reference/MiddleFork04.ssn.html
-/reference/model.frame.SSN2.html
-/reference/model.matrix.SSN2.html
-/reference/names.SSN.html
-/reference/plot.SSN2.html
-/reference/plot.Torgegram.html
-/reference/predict.SSN2.html
-/reference/print.SSN.html
-/reference/print.SSN2.html
-/reference/pseudoR2.SSN2.html
-/reference/reexports.html
-/reference/residuals.SSN2.html
-/reference/SSN2-package.html
-/reference/ssn_create_distmat.html
-/reference/ssn_get_data.html
-/reference/ssn_get_netgeom.html
-/reference/ssn_get_netgeometry.html
-/reference/ssn_get_stream_distmat.html
-/reference/ssn_glm.html
-/reference/ssn_import.html
-/reference/ssn_import_predpts.html
-/reference/ssn_initial.html
-/reference/ssn_lm.html
-/reference/ssn_names.html
-/reference/ssn_params.html
-/reference/ssn_put_data.html
-/reference/ssn_simulate.html
-/reference/ssn_split_predpts.html
-/reference/ssn_subset.html
-/reference/SSN_to_SSN2.html
-/reference/ssn_update_path.html
-/reference/ssn_write.html
-/reference/summary.SSN.html
-/reference/summary.SSN2.html
-/reference/tidy.SSN2.html
-/reference/Torgegram.html
-/reference/varcomp.SSN2.html
-/reference/vcov.SSN2.html
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+
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+    https://usepa.github.io/SSN2/404.html
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+    https://usepa.github.io/SSN2/authors.html
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+    https://usepa.github.io/SSN2/CONTRIBUTING.html
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+    https://usepa.github.io/SSN2/index.html
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+  
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-