more cran fixes and jamovi 2.5 fix in plot_scatter and pkgdown update

.Rbuildignore

@@ -2,6 +2,8 @@
 ^\.Rproj\.user$
 ^.*\.jmo
 ^.*\.pdf
+build/js
+build/R4.1.3-win64
 ^data-raw$
 esci_logo.svg
 ^.*\.csv

DESCRIPTION

@@ -2,39 +2,39 @@ Package: esci
 Type: Package
 Title: Estimation Statistics with Confidence Intervals
 Version: 1.0.2
-Author: Robert J Calin-Jageman
-Maintainer: Robert J Calin-Jageman <rcalinjageman@dom.edu>
+Authors@R: person("Robert", "Calin-Jageman", email = "rcalinjageman@dom.edu",
+  role = c("aut", "cre", "cph"))
 Description: An r package and 'jamovi' module for the estimation approach 
     to inferential statistics, the approach which emphasizes effect sizes, 
     interval estimates, and meta-analysis. Nearly all functions
     are based on 'statpsych' or 'metafor'.  This package is still under active
     development, and breaking changes are likely, especially with the
     plot and hypothesis test functions.  If you need a stable and
     production-ready approach for confidence intervals, use
-    'statpsych'.  Please Post issues, bugs, and feature requests 
-    to <https://github.com/rcalinjageman/esci>.
+    'statpsych'.
+BugReports: https://github.com/rcalinjageman/esci
 License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Imports: 
-  jmvcore (>= 0.8.5), 
-  R6,
-  rlang,
-  methods,
-  stats,
-  multcomp,
-  sadists,
-  statpsych,
-  metafor,
-  ggplot2,
-  ggdist,
-  ggh4x,
-  ggtext,
-  ggbeeswarm, 
-  glue,
-  Rdpack,
-  mathjaxr,
-  stringr
+    jmvcore (>= 0.8.5),
+    R6,
+    rlang,
+    methods,
+    stats,
+    multcomp,
+    sadists,
+    statpsych,
+    metafor,
+    ggplot2,
+    ggdist,
+    ggh4x,
+    ggtext,
+    ggbeeswarm,
+    glue,
+    Rdpack,
+    stringr,
+    mathjaxr
 RoxygenNote: 7.3.1
 Roxygen: list(markdown = TRUE)
 RdMacros: Rdpack, mathjaxr

R/estimate_mdiff_2x2_between.R

@@ -1,7 +1,7 @@
 #' Estimates for a 2x2 between-subjects design with a continuous outcome
 #' variable
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_mdiff_2x2_between` is suitable for a 2x2 between-subjects design
 #' with a continuous outcome variable.  It estimates each main effect, the
 #' simple effects for the first factor, and the interaction.  It can express

R/estimate_mdiff_2x2_mixed.R

@@ -1,7 +1,7 @@
 #' Estimates for a 2x2 mixed factorial design with a continuous outcome
 #' variable
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_mdiff_2x2_mixed` is suitable for a 2x2 mixed-factorial design
 #' with a continuous outcome variable.  It estimates each main effect, the
 #' simple effects for the repeated-measures factor, and the interaction.

R/estimate_mdiff_ind_contrast.R

@@ -1,7 +1,7 @@
 #' Estimates for a multi-group design with a continuous outcome
 #' variable
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_mdiff_ind_contrast` is suitable for a multi-group design
 #' (between subjects) with a continuous outcome variable.  It accepts
 #' a user-defined set of contrast weights that allows estimation of any

R/estimate_mdiff_one.R

@@ -1,7 +1,7 @@
 #' Estimates for a single-group design with a continuous outcome
 #' variable compared to a reference or population value
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_mdiff_one` is suitable for a single-group design
 #' with a continuous outcome variable that is compared to a reference
 #' or population value.  It can express estimates as mean differences,

R/estimate_mdiff_paired.R

@@ -2,7 +2,7 @@
 #' variable
 #'
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_mdiff_paired` is suitable for a simple paired design
 #' with a continuous outcome variable.  It provides estimates and CIs for the
 #' population mean difference between the repeated measures, the standardized

R/estimate_mdiff_two.R

@@ -1,7 +1,7 @@
 #' Estimates for a two-group study with a continuous outcome variable
 #'
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_mdiff_two` is suitable for a simple two-group design
 #' with a continuous outcome variable.  It provides estimates and CIs for the
 #' population mean difference between the repeated measures, the standardized

R/estimate_pdiff_ind_contrast.R

@@ -1,7 +1,7 @@
 #' Estimates for a multi-group study with a categorical outcome
 #' variable
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_pdiff_ind_contrast` is suitable for a multi-group design
 #' (between subjects) with a categorical outcome variable.  It accepts
 #' a user-defined set of contrast weights that allows estimation of any

R/estimate_pdiff_one.R

@@ -1,7 +1,7 @@
 #' Estimates for a single-group design with a categorical outcome
 #' variable compared to a reference or population value.
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_pdiff_one` is suitable for a single-group design
 #' (between subjects) with a categorical outcome variable.  It calculates
 #' the effect sizes with respect to a reference or population proportion

R/estimate_pdiff_paired.R

@@ -1,7 +1,7 @@
 #' Estimates for a repeated-measures study with two measures of a categorical
 #' variable
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_pdiff_paired` is suitable for a simple paired design
 #' with a categorical outcome variable.  It provides estimates and CIs for the
 #' population proportion difference between the repeated measures.  You can

R/estimate_pdiff_two.R

@@ -1,7 +1,7 @@
 #' Estimates for a two-group study with a categorical outcome variable
 #'
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_pdiff_two` is suitable for a simple two-group design
 #' with a categorical outcome variable.  It provides estimates and CIs for the
 #' difference in proportions between the two groups, the odds ratio, and phi.

R/estimate_rdiff_two.R

@@ -2,7 +2,7 @@
 #' two continuous outcome variables
 #'
 #'
-#' @description \loadmathjax
+#' @description Returns object
 #' `estimate_rdiff_two` is suitable for a simple two-group design
 #' with two continuous outcome variables where you want to estimate the
 #' difference in the strength of the relationship between the two groups.

R/geom_meta_diamond.R

@@ -106,6 +106,32 @@ draw_panel_meta_diamond_h <- function(self, data, panel_params, coord, height =
 #' - **`xmax`** - The right-side start of the diamond
 #' - **`height`** - The vertical span of the diamond
 #'
+#' @examples
+#' # example code
+#' library(ggplot2)
+#'
+#' my_effects <- data.frame(
+#'   effect_size = c(1, 2, 1, 0),
+#'   UL = c(2, 3, 2, 1),
+#'   LL = c(0, 1, 0, -1),
+#'   y = c(1, 2, 3, 4)
+#' )
+#'
+#'
+#' myplot <- ggplot2::ggplot()
+#' myplot <- myplot + geom_meta_diamond_h(
+#'   data = my_effects,
+#'   ggplot2::aes(
+#'     x = effect_size,
+#'     xmin = LL,
+#'     xmax = UL,
+#'     y = y
+#'   ),
+#'   height = 0.25,
+#'   color = "black",
+#'   fill = "red",
+#' )
+#' myplot
 #'
 #' @export
 geom_meta_diamond_h <- function(mapping = NULL, data = NULL,

R/plot_scatter.R

@@ -434,7 +434,7 @@ plot_scatter <- function(
       xlab <- paste(
         xlab,
         "<br>",
-        format(estimate$properties$conf_level*100, digits=0),
+        format(estimate$properties$conf_level*100, digits=1),
         "% CI[", format(ci[1, "lwr"], digits=3),
         ",", format(ci[1, "upr"], digits=3),
         "]",
@@ -451,7 +451,7 @@ plot_scatter <- function(
         pi[1, "lwr"] <- (pi[1, "lwr"] - y_mean) / y_sd
         pi[1, "upr"] <- (pi[1, "upr"] - y_mean) / y_sd
       }
-      xlab <- paste(xlab, "<br>", format(estimate$properties$conf_level*100, digits=0), "% PI[", format(pi[1, "lwr"], digits=3), ",", format(pi[1, "upr"], digits=3), "]", sep = "")
+      xlab <- paste(xlab, "<br>", format(estimate$properties$conf_level*100, digits=1), "% PI[", format(pi[1, "lwr"], digits=3), ",", format(pi[1, "upr"], digits=3), "]", sep = "")
       myplot <- myplot + geom_segment(alpha = 0.1, size = 2, color = "red", aes(x = predict_from_x, xend = predict_from_x, y=pi[1, "lwr"], yend = pi[1, "upr"]))
       myplot <- esci_plot_layers(myplot, "prediction_prediction_interval")
     }