From e66b4db2ef80fa39cd2a53d588f916901e0662e0 Mon Sep 17 00:00:00 2001 From: Rob Hyndman Date: Fri, 1 Dec 2023 12:17:48 +1100 Subject: [PATCH] Moved repo to tidyverts --- DESCRIPTION | 4 ++-- README.Rmd | 6 ++--- README.md | 46 ++++++++++++++++++------------------- _pkgdown.yml | 4 ++-- man/fable.binary-package.Rd | 4 ++-- 5 files changed, 32 insertions(+), 32 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 79ed022..8ab678f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -12,8 +12,8 @@ Authors@R: email = "mail@mitchelloharawild.com", role = "aut")) License: GPL-3 -URL: https://github.com/robjhyndman/fable.binary -BugReports: https://github.com/robjhyndman/fable.binary/issues +URL: https://github.com/tidyverts/fable.binary +BugReports: https://github.com/tidyverts/fable.binary/issues Depends: R (>= 3.4.0), fabletools (>= 0.3.0) diff --git a/README.Rmd b/README.Rmd index fd436a4..b344ab0 100644 --- a/README.Rmd +++ b/README.Rmd @@ -21,11 +21,11 @@ The R package *fable.binary* provides a collection of time series forecasting mo ## Installation You can install the **development** version from -[GitHub](https://github.com/robjhyndman/fable.binary) +[GitHub](https://github.com/tidyverts/fable.binary) ```{r gh-installation, eval = FALSE} # install.packages("remotes") -remotes::install_github("robjhyndman/fable.binary") +remotes::install_github("tidyverts/fable.binary") ``` ## Examples @@ -50,7 +50,7 @@ fit |> select(nn) |> glance() fit |> select(nn) |> report() augment(fit) -# Produce forecasts. For neural network, use +# Produce forecasts. For neural network, use fc <- forecast(fit, h = "2 years") as_tibble(fc) |> ggplot(aes(x = Date, y = .mean, col = .model)) + diff --git a/README.md b/README.md index f2dc421..297c4d2 100644 --- a/README.md +++ b/README.md @@ -14,11 +14,11 @@ tidyverse. ## Installation You can install the **development** version from -[GitHub](https://github.com/robjhyndman/fable.binary) +[GitHub](https://github.com/tidyverts/fable.binary) ``` r # install.packages("remotes") -remotes::install_github("robjhyndman/fable.binary") +remotes::install_github("tidyverts/fable.binary") ``` ## Examples @@ -28,13 +28,13 @@ library(fable.binary) #> Loading required package: fabletools library(ggplot2) library(dplyr) -#> +#> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': -#> +#> #> filter, lag #> The following objects are masked from 'package:base': -#> +#> #> intersect, setdiff, setequal, union # Fit models @@ -67,25 +67,25 @@ fit |> select(logistic) |> glance() #> 1 logistic 11 -2787. 5596. 5596. 5666. 5574. 4311 11 #> # ℹ 3 more variables: null_deviance , df_null , nobs fit |> select(logistic) |> report() -#> Series: Wet -#> Model: LOGISTIC -#> +#> Series: Wet +#> Model: LOGISTIC +#> #> Coefficients: -#> Estimate Std. Error t value Pr(>|t|) +#> Estimate Std. Error t value Pr(>|t|) #> (Intercept) -0.573059 0.032114 -17.845 < 2e-16 *** #> fourier(K = 5, period = "year")C1_365 -0.324774 0.045754 -7.098 1.26e-12 *** #> fourier(K = 5, period = "year")S1_365 -0.278737 0.045075 -6.184 6.25e-10 *** -#> fourier(K = 5, period = "year")C2_365 -0.020283 0.045519 -0.446 0.656 -#> fourier(K = 5, period = "year")S2_365 -0.031195 0.045310 -0.688 0.491 -#> fourier(K = 5, period = "year")C3_365 -0.069556 0.045449 -1.530 0.126 -#> fourier(K = 5, period = "year")S3_365 -0.020730 0.045374 -0.457 0.648 -#> fourier(K = 5, period = "year")C4_365 -0.034241 0.045438 -0.754 0.451 -#> fourier(K = 5, period = "year")S4_365 0.022435 0.045375 0.494 0.621 -#> fourier(K = 5, period = "year")C5_365 -0.018772 0.045275 -0.415 0.678 -#> fourier(K = 5, period = "year")S5_365 0.008153 0.045337 0.180 0.857 +#> fourier(K = 5, period = "year")C2_365 -0.020283 0.045519 -0.446 0.656 +#> fourier(K = 5, period = "year")S2_365 -0.031195 0.045310 -0.688 0.491 +#> fourier(K = 5, period = "year")C3_365 -0.069556 0.045449 -1.530 0.126 +#> fourier(K = 5, period = "year")S3_365 -0.020730 0.045374 -0.457 0.648 +#> fourier(K = 5, period = "year")C4_365 -0.034241 0.045438 -0.754 0.451 +#> fourier(K = 5, period = "year")S4_365 0.022435 0.045375 0.494 0.621 +#> fourier(K = 5, period = "year")C5_365 -0.018772 0.045275 -0.415 0.678 +#> fourier(K = 5, period = "year")S5_365 0.008153 0.045337 0.180 0.857 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -#> +#> #> # A tibble: 1 × 11 #> df log_lik AIC AICc BIC deviance df.residual rank null_deviance #> @@ -97,13 +97,13 @@ fit |> select(nn) |> glance() #> #> 1 nn 2 2 9 20 0.227 fit |> select(nn) |> report() -#> Series: Wet -#> Model: BINNET: 2 -#> +#> Series: Wet +#> Model: BINNET: 2 +#> #> Average of 20 networks, each of which is #> a 2-2-1 network with 9 weights #> options were - -#> +#> #> sigma^2 estimated as 0.2269 augment(fit) #> # A tsibble: 8,644 x 6 [1D] @@ -122,7 +122,7 @@ augment(fit) #> 10 nn 2000-01-10 TRUE 0.247 0.753 0.753 #> # ℹ 8,634 more rows -# Produce forecasts. For neural network, use +# Produce forecasts. For neural network, use fc <- forecast(fit, h = "2 years") as_tibble(fc) |> ggplot(aes(x = Date, y = .mean, col = .model)) + diff --git a/_pkgdown.yml b/_pkgdown.yml index 9dd815b..4d86599 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,4 +1,4 @@ -url: https://pkg.robjhyndman.com/fable.binary/ +url: http://tidyverts.github.io/fable.binary/ template: bootstrap: 5 theme: tango @@ -25,7 +25,7 @@ navbar: href: news/index.html right: - icon: fa-github fa-lg - href: https://github.com/robjhyndman/fable.binary + href: https://github.com/tidyverts/fable.binary reference: - title: "Functions" diff --git a/man/fable.binary-package.Rd b/man/fable.binary-package.Rd index fead2d7..8e05780 100644 --- a/man/fable.binary-package.Rd +++ b/man/fable.binary-package.Rd @@ -11,8 +11,8 @@ Provides a collection of time series forecasting models suitable for binary time \seealso{ Useful links: \itemize{ - \item \url{https://github.com/robjhyndman/fable.binary} - \item Report bugs at \url{https://github.com/robjhyndman/fable.binary/issues} + \item \url{https://github.com/tidyverts/fable.binary} + \item Report bugs at \url{https://github.com/tidyverts/fable.binary/issues} } }