release

.Rbuildignore

@@ -3,3 +3,5 @@
 ^testing$
 ^data-raw$
 ^_archive$
+^cran-comments.md$
+^CRAN-SUBMISSION$

.gitignore

@@ -9,3 +9,4 @@ src/*.dll
 src/.n*
 .Rapp.history
 *.swp
+CRAN-SUBMISSION

DESCRIPTION

@@ -1,13 +1,12 @@
 Package: gmvjoint
 Type: Package
-Title: Joint models of survival and multivariate longitudinal data
-Version: 0.1
+Title: Joint Models of Survival and Multivariate Longitudinal Data
+Version: 0.1.0
 Date: 2022-12-05
-Author: James Murray
-Maintainer: James Murray <[email protected]>
+Authors@R: person("James", "Murray", email = "[email protected]", role = c("aut", "cre"))
 Description: Fit joint models of survival and multivariate longitudinal data. The longitudinal
-  data is specified by generalised linear mixed models. The joint models are fit via maximum
-  likelihood using an approximate EM algorithm.
+    data is specified by generalised linear mixed models. The joint models are fit via maximum
+    likelihood using an approximate EM algorithm.
 License: GPL-3
 Depends:
   R (>= 3.6.0),

R/EMUpdate.R

@@ -1,16 +1,16 @@
-#' EM update
+#' EM Update
 #' @keywords internal
 EMupdate <- function(Omega, family, X, Y, Z, b,                # Longit.
                      S, SS, Fi, Fu, l0i, l0u, Delta, l0, sv,   # Survival
                      w, v, n, m, hessian,                      # Quadrature + additional info.
                      beta.inds, b.inds, K, q, beta.quad){
 
-  #' Unpack Omega, the parameter vector
+  # Unpack Omega, the parameter vector
   D <- Omega$D; beta <- Omega$beta; sigma <- Omega$sigma; gamma <- Omega$gamma; zeta <- Omega$zeta
   beta.inds2 <- lapply(beta.inds, function(x) x - 1); b.inds2 <- lapply(b.inds, function(x) x - 1) # Indexed for C++ use.
   gamma.rep <- rep(gamma, sapply(b.inds, length))
 
-  #' Find b.hat and Sigma =====================
+  # Find b.hat and Sigma =====================
   if(hessian == 'auto') .hess <- T else .hess <- F
   b.update <- mapply(function(b, Y, X, Z, Delta, S, Fi, l0i, SS, Fu, l0u){
     optim(b, joint_density, joint_density_ddb,

R/PBC.R

@@ -1,6 +1,6 @@
-#' Primary billiary cirrhosis data
+#' Primary biliary cirrhosis data
 #' 
-#' Primary billiary cirrhosis (PBC) data. PBC is a chronic liver disease which affects the bile 
+#' Primary biliary cirrhosis (PBC) data. PBC is a chronic liver disease which affects the bile 
 #' ducts of the liver, complications of which can ultimately lead to death. The longitudinal 
 #' profile of numerous biomarkers were observed for 312 patients at the Mayo Clinic between 1974
 #' and 1984  with patients assigned to either the active (D-penicillamine, n=154 (50.6%)) or 

R/simData.R

@@ -1,9 +1,9 @@
 #' Simulate realisations from a generalised poisson distribution
 #' @param mu A numeric vector of rates \eqn{\exp{\eta}}, with \eqn{\eta} the linear predictor.
 #' @param phi A numeric specifying the dispersion \eqn{\varphi}. If \eqn{\varphi<0} the response 
-#' will be under-dispersed and overdispered if \eqn{\varphi>0}.
+#' will be under-dispersed and overdispersed if \eqn{\varphi>0}.
 #' 
-#' @details Follows the "GP-1" implementation of the generalised poissson distribution outlined 
+#' @details Follows the "GP-1" implementation of the generalised Poisson distribution outlined 
 #' in Zamani & Ismail (2012). The variance of produced \eqn{Y} is \eqn{(1+\varphi)^2\mu}.
 #' 
 #' @references 

README.md

@@ -12,14 +12,14 @@ longitudinal sub-models are specified by generalised linear mixed models (GLMMs)
 are fit via maximum likelihood using an approximate EM algorithm first proposed by Bernhardt *et
 al*. (2015). The GLMMs are specified using the same syntax as for package `glmmTMB` (Brooks *et
 al*., 2017). The joint models themselves are then the  flexible extensions to those in e.g.
-Wulfoshn and Tsiatis (1997). The user is able to simulate data under many different response
+Wulfsohn and Tsiatis (1997). The user is able to simulate data under many different response
 types.
 
 Currently, five families can be fit: Gaussian; Poisson; binomial; Gamma and generalised Poisson. 
 
 ## To-do list
 The package in current incantation is relatively skeletal, as such not a lot of post-hoc
-anaylses on fitted joint models is possible. As such, an immediate to-do list currently looks like
+analyses on fitted joint models is possible. As such, an immediate to-do list currently looks like
 
 * Dynamic predictions: Note that code to do this already exists at https://github.com/jamesmurray7/GLM/blob/main/Multi-test/DynamicPredictions.R, 
 which just needs to be ported over.
@@ -32,7 +32,7 @@ student and little cache is awarded for production or maintenance of R packages!
 To fit a joint model, we first need to specify the longitudinal and survival sub-models. 
 
 The longitudinal sub-model **must** be a list which contains the specification of the longitudinal process along with its random effects structure 
-in the same syntax as a [glmmTMB](https://cran.r-project.org/web/packages/glmmTMB/index.html) model (which itself is the same as the widely-used `lme4`). 
+in the same syntax as a [glmmTMB](https://cran.r-project.org/package=glmmTMB) model (which itself is the same as the widely-used `lme4`). 
 As an example, suppose we want to fit a trivariate model on the oft-used PBC data, with a linear time-drug interaction term on albumin, a spline term on
 (logged) serum bilirubin and a linear fit on spiders, we specify
 ```r
@@ -45,7 +45,7 @@ long.formulas <- list(
 ```
 where we note interactions and spline-time fits are possible. Currently, transformations on variables (e.g. `log(Y)`) must be done *before* this setup of formulae. 
 
-The survival sub-model must be set-up using `Surv()` from the [survival](https://cran.r-project.org/web/packages/survival/) package e.g.
+The survival sub-model must be set-up using `Surv()` from the [survival](https://cran.r-project.org/package=survival) package e.g.
 ```r
 surv.formula <- Surv(survtime, status) ~ drug
 ```
@@ -57,7 +57,7 @@ list. We call our `fit` via
 fit <- joint(long.formulas = long.formulas, surv.formula = surv.formula, data = PBC, 
              family = list("gaussian", "gaussian", "binomial"))
 ```
-where extra control arguments are documented in `?joint`.
+where extra control arguments are documented in `?joint`. Numerous S3 methods exist for the class of object `joint` creates, for example `summary()`, `logLik()`, and `fixef()`.
 
 ## References
 

cran-comments.md

@@ -0,0 +1,17 @@
+## R CMD check results
+0 Errors | 0 Warnings | 1 Note.
+
+Note: 
+   installed size is  5.9Mb
+   sub-directories of 1Mb or more:
+     libs   5.6Mb
+
+i.e. gmvjoint.so has size 5.6Mb.
+
+## Test environments 
+
+* Local Ubuntu (R version 3.6.3).
+* MacOS (Release, via mac.r-project.org).
+* Windows (Release and devel, via win-builde.r-project.org).
+
+R-hub build OK.

man/gmvjoint-package.Rd

@@ -11,7 +11,7 @@ longitudinal data is specified by generalised linear mixed models (GLMMs). The j
 are fit via maximum likelihood using an approximate EM algorithm first proposed by Bernhardt et
 al. (2015). The GLMMs are specified using the same syntax as for package \code{glmmTMB} Brooks et
 al. (2017). The joint models themselves are then the  flexible extensions to those in e.g.
-Wulfoshn and Tsiatis (1997). The user is able to simulate data under many different response
+Wulfsohn and Tsiatis (1997). The user is able to simulate data under many different response
 types.
 }