Support for Compiling Examination Tasks using the 'exams' Package

The main aim is to further facilitate the creation of exercises based on the package 'exams' by Grün, B., and Zeileis, A. (2009) . Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'.

Drug Demand Forecasting

Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) ). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) ). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)).

Easy Visualization of Conditional Effects from Regression Models

Offers a flexible and user-friendly interface for visualizing conditional effects from a broad range of regression models, including mixed-effects and generalized additive (mixed) models. Compatible model types include lm(), rlm(), glm(), glm.nb(), and gam() (from 'mgcv'); nonlinear models via nls(); and generalized least squares via gls(). Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), glmer(), glmer.nb(), glmmTMB(), or gam() (from 'mgcv', via smooth terms). Plots are rendered using base R graphics with extensive customization options. Approximate confidence intervals for nls() models are computed using the delta method. Robust standard errors for rlm() are computed using the sandwich estimator (Zeileis 2004) . Methods for generalized additive models follow Wood (2017) . For linear mixed-effects models with 'lme4', see Bates et al. (2015) . For mixed models using 'glmmTMB', see Brooks et al. (2017) .