Draw Samples of Truncated Multivariate Normal Distributions

Draw samples from truncated multivariate normal distribution using the sequential nearest neighbor (SNN) method introduced in "Scalable Sampling of Truncated Multivariate Normals Using Sequential Nearest-Neighbor Approximation" .

Closed Testing Procedure (CTP)

This is a package for constructing hypothesis trees for treatment comparisons based on the closure principle and analysing the corresponding Closed Testing Procedures (CTP) using adjusted p-values. For reference, see Marcus, R., Peritz, E, and Gabriel, K.R. (1976) and Bauer, P (1991) .

Reference Interval Estimation using Real-World Data

Indirect method for the estimation of reference intervals (RIs) using Real-World Data ('RWD') and methods for comparing and verifying RIs. Estimates RIs by applying advanced statistical methods to routine diagnostic test measurements, which include both pathological and non-pathological samples, to model the distribution of non-pathological samples. This distribution is then used to derive reference intervals and support RI verification, i.e., deciding if a specific RI is suitable for the local population. The package also provides functions for printing and plotting algorithm results. See ?refineR for a detailed description of features. Version 1.0 of the algorithm is described in 'Ammer et al. (2021)' . Additional guidance is in 'Ammer et al. (2023)' . The verification method is described in 'Beck et al. (2025)' .

'Google Ads API' Interface

Interface for the 'Google Ads API'. 'Google Ads' is an online advertising service that enables advertisers to display advertising to web users (see < https://developers.google.com/google-ads/> for more information).

Linear and Smooth Predictor Modelling with Penalisation and Variable Selection

Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model).

Boosting Methods for 'GAMLSS'

Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.

Intensity Analysis of Spatial Point Patterns on Complex Networks

Tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks. This package is based on the following research: Eckardt and Mateu (2018) . Eckardt and Mateu (2021) .

Aggregate Numeric, Date and Categorical Variables

Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. There is also support for dummy variables in predictive contexts. Code has been completely re-written in data.table for computational speed.

Scalable Gaussian-Process Approximations

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) . Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) and MaxMin ordering proposed in Guinness (2018) .

Multiple Primary Endpoints

Functions for calculating sample size and power for clinical trials with multiple (co-)primary endpoints.