diff --git a/R/fitMetaDprime.R b/R/fitMetaDprime.R
index b691d9a..4bb7230 100644
--- a/R/fitMetaDprime.R
+++ b/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.
 #'
diff --git a/StatConfR.pdf b/StatConfR.pdf
new file mode 100644
index 0000000..ce8e387
Binary files /dev/null and b/StatConfR.pdf differ
diff --git a/man/fitMetaDprime.Rd b/man/fitMetaDprime.Rd
index 0e8caaa..9b47c50 100644
--- a/man/fitMetaDprime.Rd
+++ b/man/fitMetaDprime.Rd
@@ -62,18 +62,18 @@ The function computes meta-d' and meta-d'/d' either using the
 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.
 
diff --git a/paper.md b/paper.md
index 31ea32d..e6b3f9c 100644
--- a/paper.md
+++ b/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
 
diff --git a/usefultools.R b/usefultools.R
index 0edb505..90c2f83 100644
--- a/usefultools.R
+++ b/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")