Predictive Probability for a Continuous Response with an ANOVA Structure

A Bayesian approach to using predictive probability in an ANOVA construct with a continuous normal response, when threshold values must be obtained for the question of interest to be evaluated as successful (Sieck and Christensen (2021) ). The Bayesian Mission Mean (BMM) is used to evaluate a question of interest (that is, a mean that randomly selects combination of factor levels based on their probability of occurring instead of averaging over the factor levels, as in the grand mean). Under this construct, in contrast to a Gibbs sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is required. The nested sampler determines the conditional posterior distribution of the model parameters, given Y, and the outside sampler determines the marginal posterior distribution of Y (also commonly called the predictive distribution for Y). This approach provides a sample from the joint posterior distribution of Y and the model parameters, while also accounting for the threshold value that must be obtained in order for the question of interest to be evaluated as successful.

Publication-Ready Tables for Descriptive Statistics and Regression Models

Provides publication-ready tables for descriptive statistics and regression models: frequency tables and cross-tabulations with association measures (Cramér's V, Kendall's Tau-b, and others), categorical and continuous summary tables, by group or from a complex survey design, and regression tables for one or more models side by side, across more than thirty model classes from mixed-effects to survival and Bayesian, with robust standard errors, average marginal effects, and univariable screening. Tables follow APA conventions by default, can switch to named journal styles such as JAMA, NEJM, or The Lancet, and render identically in the console and in 'gt', 'tinytable', 'flextable', 'Word', 'Excel', or the clipboard. Declared missing values in labelled data are honored and disclosed throughout the descriptive tables. Helpers cover codebooks, variable inspection, and row-wise summaries.

Geographic Data Analysis and Modeling

Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package < https://CRAN.R-project.org/package=terra>.

Survival Analysis

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

Bayesian Causal Inference for Periodontal Diseases in Longitudinal Studies

Implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian framework for estimating causal effects of clinical interventions on bounded continuous outcomes in longitudinal observational studies with irregular visits. The methodology is specifically designed for periodontal disease research, where discrete treatments and continuous disease states (e.g., proportion of periodontal pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. The package integrates a double-censored Tobit likelihood to handle boundary mass at zero and one, subject-specific random effects to capture within-subject correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) to model complex nonlinear interactions without parametric restrictions. Causal identification is established under the potential outcomes framework via the G-computation formula, with key estimands including the Mixed Average Potential Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The package provides functions for model fitting, posterior inference, and causal estimand estimation.

Testing Linear Regression Models

A collection of tests, data sets, and examples for diagnostic checking in linear regression models. Furthermore, some generic tools for inference in parametric models are provided.

Feed-Forward Neural Networks and Multinomial Log-Linear Models

Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.

Bayesian Graphical Models using MCMC

Interface to the JAGS MCMC library.

Statistical and Machine Learning Engine for Long-Term Natural Resource Management Data

A comprehensive toolkit for statistical and machine learning-based analysis of long-term Natural Resource Management (NRM) datasets. Integrates formula-driven approaches, statistical inference, and machine learning (ML) models for advanced analytics. Modules cover trend and structural analysis (Mann-Kendall test, slope estimation, Chow test, structural break detection), multivariate system modelling (Partial Least Squares (PLS), Structural Equation Modelling (SEM)), response curve optimisation, time-series forecasting (Autoregressive Integrated Moving Average (ARIMA), hybrid models), panel data and treatment effects (Difference-in-Differences (DiD), causal machine learning), uncertainty and sensitivity analysis (bootstrap, Monte Carlo, Bayesian), and automated model selection and performance comparison. Designed for long-term datasets covering soil, water, crop, and climate domains. Key references: Mann and Kendall (1945) ; Sen (1968) ; Bai and Perron (2003) ; Rosseel (2012) ; Croissant and Millo (2008) .

Understand and Describe Bayesian Models and Posterior Distributions

Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 ) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) .