2025-12-22
Qianxiang Zang, Chen Xu, Kelly Burkett
Sparsity-restricted maximum likelihood estimation (SMLE) has received considerable attention for feature screening in ultrahigh-dimensional regression. SMLE is a computationally convenient method that naturally incorporates the joint effects among features in the screening process. We develop a publicly available R package SMLE, which provides a user-friendly environment to carry out the SMLE method in generalized linear models. In particular, the package includes functions to conduct SMLE-screening and the related post-screening selection with popular selection criteria such as AIC and (extended) BIC. The package gives users the flexibility in controlling a series of screening parameters and accommodates both numerical and categorical feature input. The usage of SMLE is illustrated on extensive numerical examples, where the promising performance of the package is well observed.
2025-12-19
Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio
Counterfactual explanation methods provide information on how feature values of individual observations must be changed to obtain a desired prediction. Despite the increasing amount of proposed methods in research, only a few implementations exist, whose interfaces and requirements vary widely. In this work, we introduce the counterfactuals R package, which provides a modular and unified R6-based interface for counterfactual explanation methods. We implemented three existing counterfactual explanation methods and propose some optional methodological extensions to generalize these methods to different scenarios and to make them more comparable. We explain the structure and workflow of the package using real use cases and show how to integrate additional counterfactual explanation methods into the package. In addition, we compared the implemented methods for a variety of models and datasets with regard to the quality of their counterfactual explanations and their runtime behavior.
2025-12-19
Michela Battauz
Item response theory (IRT) provides a framework for modeling the responses given to a test or questionnaire, which are assumed to depend on an underlying latent variable and on some item parameters. Due to identifiability issues, when the parameters are estimated separately on different datasets, the estimates of the item parameters and the predicted values of the latent variable are not directly comparable. Equating is a statistical procedure that can be used to convert these values to a common metric and to obtain comparable test scores. The R package equateMultiple implements methods to link the parameters estimated on many different datasets. After briefly reviewing the IRT models and the equating methods, this article illustrates the use of the package.
2025-12-19
Lukas Sablica, Kurt Hornik, Josef Leydold
In this paper we present and showcase the R package watson which provides a computational framework for fitting and random sampling of the Watson distribution on a p-dimensional sphere. We first introduce the random sampling scheme of the package, which offers two sampling algorithms that are based of the results of Sablica, Hornik, and Leydold (2025). What is more, the package offers a smart tool to combine these two methods, and based on the selected parameters, it approximates the relative sampling speed for both methods and picks the faster one. In addition, we describe the main fitting function for the mixtures of Watson distribution which uses the expectation-maximization (EM) algorithm. Special features are the possibility to use multiple variants of the E-step and M-step, sparse matrices for the data representation and a control parameter which will dynamically eliminate small clusters with overall contribution smaller than this parameter. Moreover, we discuss the numerical issues of the whole fitting procedure and describe how this is handled and solved in the package. Finally, we demonstrate the package on multiple examples involving misspecified simulation study, estimation of the New Zealand earthquake data and depth image clustering.