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.

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>.)

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) .

Nonlinear Time Series Models with Regime Switching

Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).

Estimate Structured Additive Regression Models with 'BayesX'

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

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.

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) .

Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here ). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.

Statistical Analysis in Epidemiology

Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data. Has functions for extracting and manipulating parameter estimates and predicted values (ci.lin and its cousins), as well as a number of epidemiological data sets.