Item Analysis for Multiple Choice Tests (itan)
Functions for analyzing multiple choice items. These analyses include the convertion of student response into binaty data (correct/incorrect), the computation of the number of corrected responses and grade for each subject, the calculation of item difficulty and discrimination, the computation of the frecuency and point-biserial correlation for each distractor and the graphical analysis of each item.

Precision of Discrete Parameters in Transdimensional MCMC (MCMCprecision)
Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2017) <https://…/1703.10364> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.

Quantification of Color Pattern Variation (patternize)
Quantification of variation in organismal color patterns as obtained from image data. Patternize defines homology between pattern positions across images either through fixed landmarks or image registration. Pattern identification is performed by categorizing the distribution of colors using either an RGB threshold or unsupervised image segmentation.

Compile and Preview Snippets of ‘LaTeX’ in ‘RStudio’ (texPreview)
Compile and preview snippets of ‘LaTeX’. Can be used directly from the R console, from ‘RStudio’, in Shiny apps and R Markdown documents. Must have ‘pdflatex’ or ‘xelatex’ or ‘lualatex’ in ‘PATH’.

Formalized Plots for Self-Describing Data (metaplot)
Creates fully-annotated plots with minimum guidance. Since the data is self-describing, less effort is needed for creating the plot. Generally expects data of class folded (see fold package). If attributes GUIDE and LABEL are present, they will be used to create formal axis labels. Several aesthetics are supported, such as reference lines, unity lines, smooths, and log transformations.