Lightweight Extension of the Base R Graphics System

Lightweight extension of the base R graphics system, with support for automatic legends, facets, themes, and various other enhancements.

Nested Dichotomy Logistic Regression Models

Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663.

Psychometric Mixture Models

Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models with different parameterizations of the score distribution (saturated vs. mean/variance specification), Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated with or without concomitant variables. See Frick et al. (2012) and Frick et al. (2015) for details on the Rasch mixture models.

Stability Assessment of Statistical Learning Methods

Graphical and computational methods that can be used to assess the stability of results from supervised statistical learning.

Discrete Choice (Binary, Poisson and Ordered) Models with Random Parameters

An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) .

Stratified and Personalised Models Based on Model-Based Trees and Forests

Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) ; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) .

Embedding 'exams' Exercises as Forms in 'rmarkdown' or 'quarto' Documents

Automatic generation of quizzes or individual questions as (interactive) forms within 'rmarkdown' or 'quarto' documents based on 'R/exams' exercises.

Estimate Structured Additive Regression Models with 'BayesX'

An R interface to estimate structured additive regression (STAR) models with 'BayesX'.

Probability Distributions as S3 Objects

Tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.

Recursive Partitioning of Network Models

Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) .