CRAN updates (#26)

* remove imports handled by BayesTools and suppress start-up message

* update unit tests

* update ggplot2 aes_string

Author: FBartos

.gitignore

@@ -17,3 +17,4 @@ RoBMA.Rcheck
 .Rprofile
 /doc/
 /Meta/
+models/MetaRegression/*

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: RoBMA
 Title: Robust Bayesian Meta-Analyses
-Version: 2.3.1
+Version: 2.3.2
 Maintainer: František Bartoš <f.bartos96@gmail.com>
 Authors@R: c( 
     person("František", "Bartoš",     role = c("aut", "cre"),
@@ -34,24 +34,22 @@ License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.0
+RoxygenNote: 7.2.3
 SystemRequirements: JAGS >= 4.3.1 (https://mcmc-jags.sourceforge.io/)
 NeedsCompilation: yes
 Depends:
     R (>= 4.0.0)
 Imports:
     BayesTools (>= 0.2.0),
     runjags,
-    bridgesampling,
     rjags,
     coda,
-    psych,
     stats,
     graphics,
-    extraDistr,
     mvtnorm,
     scales,
     Rdpack,
+    rlang,
     ggplot2
 Suggests:
     parallel,

NAMESPACE

@@ -73,3 +73,5 @@ importFrom(BayesTools,is.prior.none)
 importFrom(BayesTools,is.prior.point)
 importFrom(BayesTools,is.prior.simple)
 importFrom(BayesTools,is.prior.weightfunction)
+importFrom(Rdpack,reprompt)
+importFrom(rlang,.data)

NEWS.md

@@ -1,3 +1,8 @@
+## version 2.3.2
+### Fixes
+- suppressing start-up message 
+- cleaning up imports
+
 ## version 2.3.1
 ### Fixes
 - fixing weighted meta-analysis parameterization 

R/RoBMA-package.R

@@ -25,5 +25,7 @@
 ##'
 ##' @references \insertAllCited{}
 ##' @importFrom BayesTools is.prior is.prior.none is.prior.point is.prior.simple is.prior.PET is.prior.PEESE is.prior.weightfunction
+##' @importFrom Rdpack reprompt
+##' @importFrom rlang .data
 "_PACKAGE"
 

R/diagnostics.R

@@ -199,9 +199,17 @@ diagnostics <- function(fit, parameter, type, plot_type = "base", show_models =
 
   }else if(plot_type == "ggplot"){
 
-    graph <- ggplot2::ggplot(plot_data$samp, ggplot2::aes_string(x = "iteration", y = "value", color = "chain")) +
+    graph <- ggplot2::ggplot(
+      data    = data.frame(
+        x     = plot_data$samp$iteration,
+        y     = plot_data$samp$value,
+        color = plot_data$samp$chain),
+      mapping = ggplot2::aes(
+        x     = .data[["x"]],
+        y     = .data[["y"]],
+        color = .data[["color"]])) +
       ggplot2::geom_path() +
-      ggplot2::scale_color_manual(values = .diagnostics_color(plot_data$nchains))
+      ggplot2::scale_color_manual(name = "chain", values = .diagnostics_color(plot_data$nchains))
     temp_x_range <- range(plot_data$samp$iteration)
     temp_y_range <- range(plot_data$samp$value)
     graph <- graph + ggplot2::scale_x_continuous(
@@ -283,9 +291,15 @@ diagnostics <- function(fit, parameter, type, plot_type = "base", show_models =
 
   }else if(plot_type == "ggplot"){
 
-    graph <-  ggplot2::ggplot(plot_data$samp, ggplot2::aes_string(x = "value")) +
-      ggplot2::geom_density(mapping = ggplot2::aes_string(fill = "chain"), color = "black", alpha = .5) +
-      ggplot2::scale_fill_manual(values = .diagnostics_color(plot_data$nchains))
+    graph <-  ggplot2::ggplot(
+      data    = data.frame(
+        x    = plot_data$samp$value,
+        fill = plot_data$samp$chain),
+      mapping = ggplot2::aes(
+        x    = .data[["x"]],
+        fill = .data[["fill"]])) +
+      ggplot2::geom_density(color = "black", alpha = .5) +
+      ggplot2::scale_fill_manual(name = "chain", values = .diagnostics_color(plot_data$nchains))
     temp_y_max   <- max(ggplot2::ggplot_build(graph)$data[[1]]$density)
     temp_x_range <- if(par == "omega") c(0, 1) else range(plot_data$samp$value)
     graph <- graph +  ggplot2::scale_y_continuous(
@@ -339,8 +353,14 @@ diagnostics <- function(fit, parameter, type, plot_type = "base", show_models =
     graph <- NULL
 
   }else if(plot_type == "ggplot"){
-    graph     <- ggplot2::ggplot(ac_dat, ggplot2::aes_string(x = "lag", y = "ac")) +
-      ggplot2::geom_bar(size = .5, color = "black", fill = "#B2001D", position = "dodge", stat = "summary", fun = "mean") +
+    graph     <- ggplot2::ggplot(
+      data    = data.frame(
+        x     = ac_dat$lag,
+        y     = ac_dat$ac),
+      mapping = ggplot2::aes(
+        x     = .data[["x"]],
+        y     = .data[["y"]])) +
+      ggplot2::geom_bar(linewidth = .5, color = "black", fill = "#B2001D", position = "dodge", stat = "summary", fun = "mean") +
       ggplot2::scale_y_continuous(breaks = seq(0, 1, 0.25)) +
       ggplot2::labs(x = "Lag", y = "Avg. autocorrelation")
     if(!is.null(title)){

R/utilities.R

@@ -47,13 +47,14 @@ RoBMA.get_option <- function(name){
 	return(eval(RoBMA.private[[name]]))
 }
 
-
+# export the function directly to suppress import warnings
+.runjags__findjags <- function() runjags::findjags()
 
 # adapted from the runjags package version 2.2.0
 RoBMA.private <- new.env()
 # Use 'expression' for functions to avoid having to evaluate before the package is fully loaded:
 assign("defaultoptions",  list(
-  jagspath = expression(runjags::findjags()),
+  jagspath = expression(.runjags__findjags()),
   envir    = RoBMA.private))
 
 assign("options",         RoBMA.private$defaultoptions,   envir = RoBMA.private)
@@ -86,6 +87,7 @@ assign("max_cores",       parallel::detectCores(logical = TRUE) - 1,  envir = Ro
     "2.2.3" = c("0.2.3", "999.999.999"),
     "2.3.0" = c("0.2.3", "999.999.999"),
     "2.3.1" = c("0.2.3", "999.999.999"),
+    "2.3.2" = c("0.2.3", "999.999.999"),
     stop("New RoBMA version needs to be defined in '.check_BayesTools' function!")
   )
 

R/zzz.R

@@ -12,7 +12,10 @@
     module_location <- NULL
     warning('The RoBMA module could not be loaded.', call. = FALSE)
   }else{
-    rjags::load.module("RoBMA", path = module_location)
+    rjags::load.module("RoBMA", path = module_location, quiet = TRUE)
+    if(!"RoBMA" %in% rjags::list.modules()){
+      warning('The RoBMA module could not be loaded.', call. = FALSE)
+    }
   }
 
   RoBMA.private$module_location <- module_location