Adding a manual

R/fitMetaDprime.R

@@ -43,18 +43,18 @@
 #' hypothetical signal detection model assumed by Maniscalco and Lau (2012, 2014)
 #' or the one assumed by Fleming (2014).
 #'
-#' The conceptual idea of meta-d′ is to quantify metacognition in terms of sensitivity
+#' The conceptual idea of meta-d' is to quantify metacognition in terms of sensitivity
 #' in a hypothetical signal detection rating model describing the primary task,
 #' under the assumption that participants had perfect access to the sensory evidence
 #' and were perfectly consistent in placing their confidence criteria (Maniscalco & Lau, 2012, 2014).
 #' Using a signal detection model describing the primary task to quantify metacognition allows
 #'  a direct comparison between metacognitive accuracy and discrimination performance
-#'  because both are measured on the same scale. Meta-d′ can be compared against
+#'  because both are measured on the same scale. Meta-d' can be compared against
 #'   the estimate of the distance between the two stimulus distributions
-#'    estimated from discrimination responses, which is referred to as d′:
-#'    If meta-d′ equals d′, it means that metacognitive accuracy is exactly
+#'    estimated from discrimination responses, which is referred to as d':
+#'    If meta-d' equals d', it means that metacognitive accuracy is exactly
 #'     as good as expected from discrimination performance.
-#'     Ifmeta-d′ is lower than d′, it means that metacognitive accuracy is suboptimal.
+#'     Ifmeta-d' is lower than d', it means that metacognitive accuracy is suboptimal.
 #'     It can be shown that the implicit model of confidence underlying the meta-d'/d'
 #'      method is identical to the independent truncated Gaussian model.
 #'

paper.md

@@ -58,9 +58,11 @@ kinds of measures of metacognition:
 - meta-d$^\prime$/d$^\prime$, the most widely-used measure of metacognitive efficiency, allowing both @Maniscalco2012's and @Fleming2017a's model specification, 
 - Information-theoretic measures [@dayan_metacognitive_2023], including
   - meta-I, an information-theoretic measures of metacognitive sensitivity, 
-  - $meta-I_{1}^{r}$ and $meta-I_{2}^{r}$, two measures of metacognitive efficiency. 
+  - $meta-I_{1}^{r}$ and $meta-I_{2}^{r}$, two measures of metacognitive efficiency proposed by @dayan_metacognitive_2023, 
+  - RMI, a novel measure of metacognitive accuracy. 
 Finally, the `statConfR` package includes an example data set previously published in @hellmann_simultaneous_2023, with which the functions can be  tested. 
 The `statConfR` reference manual provides documentation of each function of the latest release (https://cran.r-project.org/web/packages/statConfR/statConfR.pdf). 
+A seperate mannual is avialble for the development version (https://cran.r-project.org/web/packages/statConfR/statConfR.pdf). 
 
 # Statement of need
 

usefultools.R

@@ -29,3 +29,6 @@ rhub::check....
 # NOTES und ERRORs aus.
 cran_checks <- rhub::check_for_cran()
 cran_checks$cran_summary()
+
+
+system("R CMD Rd2pdf C:/Users/PPA714/KU2/Projekte/StatConfR")