tweaks towards 1.0.3

updated docs site, updated ci_smd_ind_contrast with math support, tweaks to work with statpsych 1.5 and 1.6

DESCRIPTION

@@ -28,7 +28,7 @@ Imports:
     Rdpack,
     stringr,
     mathjaxr
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Roxygen: list(markdown = TRUE)
 RdMacros: Rdpack, mathjaxr
 Suggests: 

NEWS.md

@@ -3,7 +3,7 @@ esci version 1.0.3 (Release data: July 2024)
 
 Changes:
 
-* Tweaks to deal with changes in statpsych 1.6.  No substantive updates.  
+* Tweaks to deal with changes in statpsych 1.6. No substantive updates.  
 
 
 

R/CI_smd_ind_contrast.R

@@ -2,11 +2,11 @@
 #' contrast
 #'
 #'
-#' @description `CI_smd_ind_contrast` returns the point estimate
+#' @description \loadmathjax  `CI_smd_ind_contrast` returns the point estimate
 #'   and confidence interval for a standardized mean difference (smd aka Cohen's
 #' *d* aka Hedges *g*). A standardized mean difference is a difference in means standardized
 #'   to a standard deviation:
-#'   d = psi/s
+#'   \mjdeqn{d = \frac{ \psi }{s}}{d = psi/s}
 #'
 #'
 #' @param means A vector of 2 or more means
@@ -88,16 +88,16 @@
 #' When equal variance is assumed, the standardized mean difference is
 #' d_s, defined in Kline, p. 196:
 #'
-#'  d_s = psi / sd_s
+#'  \mjdeqn{ d_s = \frac{ \psi }{ sd_{pooled} } }{ d_s = psi / sd_s}
 #'
 #'
 #' where psi is defined in Kline, equation 7.8:
 #'
-#' psi = sum(contrasts*means)
+#' \mjdeqn{ \psi =\sum_{i=1}^{a}c_iM_i }{psi = sum(contrasts*means)}
 #'
 #'
 #' and where sd_pooled is defined in Kline, equation 3.11
-#' sqrt(sum(variances*dfs) / sum(dfs))
+#' \mjdeqn{sd_{pooled} = { \frac{ \sum_{i=1}^{a} (n_i -1) s_i^2 } { \sum_{i=1}^{a} (n_i-1) } }}{sqrt(sum(variances*dfs) / sum(dfs))}
 #'
 #'
 #' The CI for d_s is derived from lambda-prime transformation from Lecoutre,
@@ -115,12 +115,12 @@
 #' assumed, the standardized mean difference is d_avg, defined in Bonett,
 #' equation 6:
 #'
-#' d_avg = psi / sd_avg
+#' \mjdeqn{ d_{avg} = \frac{ \psi }{ sd_{avg} }}{ d_avg = psi / sd_avg}
 #'
 #' Where sd_avg is the square root of the average of the group variances, as
 #' given in Bonett, explanation of equation 6:
 #'
-#' sqrt(mean(variances))
+#' \mjdeqn{sd_{avg} = \sqrt{ \frac{ \sum_{i=1}^{a} s_i^2 }{ a } }}{sqrt(mean(variances))}
 #'
 #'
 #' #### If only 2 groups ####

R/estimate_magnitude.R

@@ -16,9 +16,11 @@
 #' If you want to compare your sample to a known value or reference, then
 #' use [esci::estimate_mdiff_one()].
 #'
-#' The estimated mean is from [statpsych::ci.mean()].
+#' The estimated mean is from [statpsych::ci.mean1()] (renamed ci.mean as of
+#' statpsych 1.6).
 #'
-#' The estimated median is from [statpsych::ci.median()]
+#' The estimated median is from [statpsych::ci.median1()] (renamed ci.median
+#' as of statpsych 1.6)
 #'
 #'
 #' @param data For raw data - A data frame or tibble

R/estimate_mdiff_one.R

@@ -16,11 +16,13 @@
 #' it with [esci::plot_mdiff()] and you can test hypotheses with
 #' [esci::test_mdiff()].
 #'
-#' The estimated mean differences are from [statpsych::ci.mean()].
+#' The estimated mean differences are from [statpsych::ci.mean1()] (renamed
+#' ci.mean as of statpsych 1.6).
 #'
 #' The estimated SMDs are from [esci::CI_smd_one()].
 #'
-#' The estimated median differences are from [statpsych::ci.median()]
+#' The estimated median differences are from [statpsych::ci.median1()] (renamed
+#' ci.median as of statpsych 1.6)
 #'
 #'
 #' @param data For raw data - a data frame or tibble

R/estimate_pdiff_one.R

@@ -15,7 +15,8 @@
 #' it with [esci::plot_pdiff()] and you can test hypotheses with
 #' [esci::test_pdiff()].
 #'
-#' The estimated proportion differences are from [statpsych::ci.prop()].
+#' The estimated proportion differences are from [statpsych::ci.prop1()] (renamed
+#' ci.prop as of statpsych 1.6).
 #'
 #'
 #' @param data For raw data - a dataframe or tibble

R/estimate_proportion.R

@@ -14,7 +14,8 @@
 #' If you want to compare your estimate to a known value or reference, then
 #' use [esci::estimate_pdiff_one()].
 #'
-#' The estimated proportions are from [statpsych::ci.prop()].
+#' The estimated proportions are from [statpsych::ci.prop1()] (renamed
+#' ci.prop as of statpsych 1.6).
 #'
 #'
 #' @param data For raw data - a data frame or tibble

R/overview.R

@@ -10,8 +10,8 @@
 #'
 #' @details
 #' If equal variance is not assumed, each group is treated independently.  In
-#' that case, the estimated mean, CI, and SE is from [statpsych::ci.mean()],
-#' and the estimated median, CI, and SE is from [statpsych::ci.median()].  If
+#' that case, the estimated mean, CI, and SE is from [statpsych::ci.mean1()],
+#' and the estimated median, CI, and SE is from [statpsych::ci.median1()].  If
 #' equal variance is assumed, each group CI is calculated as with respect to all
 #' group data, using [statpsych::ci.lc.mean.bs()] and
 #' [statpsych::ci.lc.median.bs()]

README.md

@@ -1,4 +1,4 @@
-# esci <img src="man/figures/esci_logo.svg" align="right" />
+# esci <img src="man/figures/esci_logo.svg" align="right" alt = "esci logo"/>
 
 esci provides student-friendly tools for estimation statistics:
 
@@ -90,4 +90,4 @@ plot_mdiff(estimate)
 
 ```
 
-<img src="man/figures/plot_mdiff.png" align="right" />
+<img src="man/figures/plot_mdiff.png" align="right" alt="Example difference plot"/>

_pkgdown.yml

@@ -2,6 +2,7 @@ url: https://rcalinjageman.github.io/esci
 
 template:
   bootstrap: 5
+  math-rendering: mathjax
 
 development:
   mode: auto