Time-Varying Effect Models

Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time. These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) . We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Gradient-Based Coenospace Vegetation Simulator

Simulates the composition of samples of vegetation according to gradient-based vegetation theory. Features a flexible algorithm incorporating competition and complex multi-gradient interaction.

A Multi-Process 'dplyr' Backend

Partition a data frame across multiple worker processes to provide simple multicore parallelism.

Generalised Additive Extreme Value Models

Methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. For details of distributions see Coles, S.G. (2001) , GAMs see Wood, S.N. (2017) , and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) . Details of how evgam works and various examples are given in Youngman, B.D. (2022) .

Bayesian Cost Effectiveness Analysis

Produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) .

Generate Samples from Multivariate Truncated Normal Distributions

Efficient sampling from high-dimensional truncated Gaussian distributions, or multivariate truncated normal (MTN). Techniques include zigzag Hamiltonian Monte Carlo as in Akihiko Nishimura, Zhenyu Zhang and Marc A. Suchard (2024) , and harmonic Monte Carlo in Ari Pakman and Liam Paninski (2014) .

Estimate Structured Additive Regression Models with 'BayesX'

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

Iterative Steps for Postprocessing Model Predictions

Postprocessors refine predictions outputted from machine learning models to improve predictive performance or better satisfy distributional limitations. This package introduces 'tailor' objects, which compose iterative adjustments to model predictions. A number of pre-written adjustments are provided with the package, such as calibration. See Lichtenstein, Fischhoff, and Phillips (1977) . Other methods and utilities to compose new adjustments are also included. Tailors are tightly integrated with the 'tidymodels' framework.

Data for 'GAMs: An Introduction with R'

Data sets and scripts used in the book 'Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.

Create Contour Plots from Data or a Function

Provides functions for making contour plots. The contour plot can be created from grid data, a function, or a data set. If non-grid data is given, then a Gaussian process is fit to the data and used to create the contour plot.