Move ggplot2 to Suggests.

DESCRIPTION

@@ -33,7 +33,6 @@ Imports:
     assertthat,
     butcher,
     cli,
-    ggplot2,
     methods,
     nnet,
     pROC,
@@ -50,6 +49,7 @@ Suggests:
     data.table,
     foreach,
     geodist,
+    ggplot2,
     knitr,
     rmarkdown,
     testthat (>= 3.0.0),

NAMESPACE

@@ -6,8 +6,6 @@ S3method(as_covlmc,tune_covlmc)
 S3method(as_vlmc,ctx_tree)
 S3method(as_vlmc,ctx_tree_cpp)
 S3method(as_vlmc,tune_vlmc)
-S3method(autoplot,tune_covlmc)
-S3method(autoplot,tune_vlmc)
 S3method(children,ctx_node)
 S3method(children,ctx_node_cpp)
 S3method(coef,constant_model)
@@ -36,6 +34,8 @@ S3method(draw,vlmc_cpp)
 S3method(find_sequence,covlmc)
 S3method(find_sequence,ctx_tree)
 S3method(find_sequence,ctx_tree_cpp)
+S3method(ggplot2::autoplot,tune_covlmc)
+S3method(ggplot2::autoplot,tune_vlmc)
 S3method(glm_coef,constant_model)
 S3method(glm_coef,default)
 S3method(glm_coef,glm)
@@ -191,7 +191,6 @@ export(tune_vlmc)
 export(vlmc)
 import(Rcpp)
 importFrom(Rcpp,evalCpp)
-importFrom(ggplot2,autoplot)
 importFrom(rlang,.data)
 importFrom(stats,predict)
 importFrom(stats,simulate)

R/autoplot_tune.R

@@ -24,7 +24,7 @@
 #' print(vlmc_plot)
 #' ## simple post customisation
 #' print(vlmc_plot + ggplot2::geom_point())
-#' @export
+#' @exportS3Method ggplot2::autoplot
 autoplot.tune_vlmc <- function(object, cutoff = c("quantile", "native"), ...) {
   cutoff <- match.arg(cutoff)
   if (cutoff == "quantile") {
@@ -75,7 +75,7 @@ autoplot.tune_vlmc <- function(object, cutoff = c("quantile", "native"), ...) {
 #' @param object a `tune_covlmc` object
 #' @param ... additional parameters (not used currently)
 #' @returns a ggplot object
-#' @export
+#' @exportS3Method ggplot2::autoplot
 #' @examples
 #' pc <- powerconsumption[powerconsumption$week %in% 10:12, ]
 #' rdts <- cut(pc$active_power, breaks = c(0, quantile(pc$active_power, probs = c(0.5, 1))))

R/mixvlmc-package.R

@@ -39,7 +39,6 @@
 #' @importFrom Rcpp evalCpp
 #' @importFrom stats simulate
 #' @importFrom stats predict
-#' @importFrom ggplot2 autoplot
 #' @importFrom rlang .data
 #' @useDynLib mixvlmc
 ## usethis namespace: end

R/zzz.R

@@ -1,5 +1,7 @@
 Rcpp::loadModule("suffixtree", TRUE)
 .onLoad <- function(libname, pkgname) {
+  s3_register("ggplot2::autoplot", "tune_vlmc")
+  s3_register("ggplot2::autoplot", "tune_covlmc")
   current_op <- options()
   ## default options
   mixvlmc_options <- list(

README.Rmd

@@ -61,7 +61,7 @@ The package is loaded in a standard way.
 
 ```{r}
 library(mixvlmc)
-library(ggplot2)
+library(ggplot2) ## we load ggplot2 for the autoplot examples
 ```
 
 The main function of VLMC is `vlmc()` which can be called on a time series represented by a numerical vector or a factor, for instance.

README.md

@@ -71,7 +71,7 @@ The package is loaded in a standard way.
 
 ``` r
 library(mixvlmc)
-library(ggplot2)
+library(ggplot2) ## we load ggplot2 for the autoplot examples
 ```
 
 The main function of VLMC is `vlmc()` which can be called on a time