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Truncated Multivariate Normal and Student t Distribution
Random number generation for the truncated multivariate normal and Student t distribution. Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
Randomization for Clinical Trials
This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, 'randomizeR' provides a function for the comparison of randomization procedures.
Combinatorics Utilities
Provides routines for combinatorial enumeration including generation of all combinations, permutations, and lattice points on hypercuboids and simplex lattices. Includes utilities for multinomial distributions: the multinomial probability mass function, random sampling with varying parameters, and encoding conversions between simplex representations. Also supplies exact and log-scale factorial computation and the generalized binomial coefficient for real-valued n. Package functions include procedures described in Reingold, Nievergelt and Deo (1977) Combinatorial Algorithms: Theory and Practice (dl.acm.org/citation.cfm?id=1096489), Feller volume 1, and Nijenhuis and Wilf (1978) Combinatorial Algorithms for Computers and Calculators (ISBN 0125192606 / 9780125192606).
Create Random ADaM Datasets
A set of functions to create random Analysis Data Model (ADaM) datasets and cached dataset. ADaM dataset specifications are described by the Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model Team.
Random Variate Generator for the GIG Distribution
Generator and density function for the Generalized Inverse Gaussian (GIG) distribution.
Regression Models for Ordinal Data
Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.
Simulation of Discrete Random Variables with Marginal Distributions and Correlation Matrix and via a Gaussian or Student's t Copula
A Gaussian or Student's t copula-based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.
Fit, Simulate and Diagnose Exponential-Family Models for Networks
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008)
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>.
Generate Random Given and Surnames
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.