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.

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'.

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.

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.

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>.)

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) .

Novel Methods for Parallel Coordinates

New approaches to parallel coordinates plots for multivariate data visualization, including applications to clustering, outlier hunting and regression diagnostics. Includes general functions for multivariate nonparametric density and regression estimation, using parallel computation.

Modeling Animal Movement with Continuous-Time Discrete-Space Markov Chains

Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.