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) .

Tools for Post-Processing Predicted Values

Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations as well as calibration tools and conformal inference techniques for regression models.

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.

Estimate Structured Additive Regression Models with 'BayesX'

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

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.

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.

Nonparametric Preprocessing for Parametric Causal Inference

Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) . (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at < https://www.gurobi.com>.)

Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) and the R package in Umlauf, Klein, Simon, Zeileis (2021) .

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) .