Raster Randomization for Null Hypothesis Testing

Randomization of presence/absence species distribution raster data with or without including spatial structure for calculating standardized effect sizes and testing null hypothesis. The randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, ) implemented for raster data.

Multivariate and Univariate Meta-Analysis and Meta-Regression

Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.

Adversarial Random Forests

Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2023) < https://proceedings.mlr.press/v206/watson23a.html>.

Mendelian Randomization Package

Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.

Multiple Imputation by Chained Equations with Random Forests

Multiple Imputation has been shown to be a flexible method to impute missing values by Van Buuren (2007) . Expanding on this, random forests have been shown to be an accurate model by Stekhoven and Buhlmann to impute missing values in datasets. They have the added benefits of returning out of bag error and variable importance estimates, as well as being simple to run in parallel.

Random Ferns Classifier

Provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) , modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) .

Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) .

Random Fields on Metric Graphs

Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) , Bolin, Kovacs, Kumar and Simas (2023) and Bolin, Simas and Wallin (2023) and .

Random Wishart Matrix Generation

An expansion of R's 'stats' random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) , and pseudo wishart, Diaz-Garcia, et al.(1997) , matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) , commonly used in volatility modeling. Users can also use this package to create random covariance matrices.

Vectors Representing Random Variables

Random vectors, called rvecs. An rvec holds multiple draws, but tries to behave like a standard R vector, including working well in data frames. Rvecs are useful for analysing output from a simulation or a Bayesian analysis.