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The purpose of this paper is to list the recent updates of the \proglang{R} package \pkg{catR}. This package allows for generating response patterns under a computerized adaptive testing (CAT) framework with underlying item response theory (IRT) models. Among the most important updates, well-known polytomous IRT models are now supported by \pkg{catR}; several item selection rules have been added; and it is now possible to perform post-hoc simulations. Some functions were also rewritten or withdr...

We provide a package called plssem that fits partial least squares structural equation models, which is often considered an alternative to the commonly known covariance-based structural equation modeling. plssem is developed in line with the algorithm provided by Wold (1975) and Lohmoller (1989). To demonstrate its features, we present an empirical application on the relationship between perception of self-attractiveness and two specific types of motivations for working out using a real-life dat...

The simmer package brings discrete-event simulation to R. It is designed as a generic yet powerful process-oriented framework. The architecture encloses a robust and fast simulation core written in C++ with automatic monitoring capabilities. It provides a rich and flexible R API (application programming interface) that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type.

Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital, and widely used for data analysis, dimensionality reduction, and data compression. Massive datasets, however, pose a computational challenge for traditional algorithms, placing significant constraints on both memory and processing power. Recently, the powerful concept of randomness has been introduced as a strategy t...

We describe efficient algorithms and open-source code for the second-order statistical analysis of point events on a linear network. Typical summary statistics are adaptations of Ripley's K-function and the pair correlation function to the case of a linear network, with distance measured by the shortest path in the network. Simple implementations consume substantial time and memory. For an efficient implementation, the data structure representing the network must be economical in its use of memo...

The detection of regions with unusually high risk plays an important role in disease mapping and the analysis of public health data. In particular, the detection of groups of areas (i.e., clusters) where the risk is significantly high is often conducted by public health authorities. Many methods have been proposed for the detection of these disease clusters, most of them based on moving windows, such as Kulldorff's spatial scan statistic. Here we describe a model-based approach for the detection...

This article describes the R package BinaryEPPM and its use in determining maximum likelihood estimates of the parameters of extended Poisson process models for grouped binary data. These provide a Poisson process family of flexible models that can handle unlimited under-dispersion but limited over-dispersion in such data, with the binomial distribution being a special case. Within BinaryEPPM, models with the mean and variance related to covariates are constructed to match a generalized linear m...

A new alternative to the standard Poisson regression model for count data is suggested. This new family of models is based on discrete distributions derived from renewal processes, i.e., distributions of the number of events by some time t. Unlike the Poisson model, these models have, in general, time-dependent hazard functions. Any survival distribution can be used to describe the inter-arrival times between events, which gives a rich class of count processes with great flexibility for modellin...

The CDF-quantile family of two-parameter distributions with support (0, 1) described in Smithson and Merkle (2014) and recently elaborated by Smithson and Shou (2017), considerably expands the variety of distributions available for modeling random variables on the unit interval. This family is especially useful for modeling quantiles, and also sometimes out-performs the other distributions. The distributions are very tractable, with a location and dispersion parameter, explicit probability distr...

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