Statistical Mediation Analysis for SEMs

Conducts mediation analysis for structural equation models (SEM) estimated with 'lavaan', 'blavaan', 'cSEM', or 'modsem'. Implements the Baron and Kenny (1986) and Zhao, Lynch & Chen (2010) approaches to determine the presence and type of mediation. Supports covariance-based SEM, partial least squares SEM, Bayesian SEM, and moderated mediation and mediated moderation models. Tests indirect effects with the Sobel, Delta, Monte-Carlo, and bootstrap methods or, for Bayesian models, with posterior summaries and equal-tailed or highest density credible intervals. Reports the effect size measures RIT, RID, and Upsilon of Lachowicz, Preacher and Kelley (2018) . Results can be summarized, extracted with standard methods such as summary(), coef() and confint(), and plotted.

Fit and Deploy DECORATE Trees

DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples) builds an ensemble of J48 trees by recursively adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005) ").

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)' .

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) .

Boosting Methods for 'GAMLSS'

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

'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).

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.