Extend the functionality of the mclust package for Gaussian finite mixture modeling by including: density estimation for data with bounded support (Scrucca, 2019 <doi:10.1002/bimj.201800174>); modal clustering using MEM (Modal EM) algorithm for Gaussian mixtures (Scrucca, 2021 <doi:10.1002/sam.11527>); entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023 <doi:10.1016/j.csda.2022.107582>); Gaussian mixtures modeling of financial log-returns (Scrucca, 2024 <doi:10.3390/e26110907>).

Create native charts for Microsoft PowerPoint and Microsoft Word documents. These can then be edited and annotated. Functions are provided to let users create charts, modify and format their content. The chart's underlying data is automatically saved within the Word document or PowerPoint presentation. It extends package officer that does not contain any feature for Microsoft native charts production.

This package provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.

Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the DescTools package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research.

Automatic marking of R assignments for students and teachers based on testthat test suites.

Visualize confounder control in meta-analysis. metaconfoundr is an approach to evaluating bias in studies used in meta-analyses based on the causal inference framework. Study groups create a causal diagram displaying their assumptions about the scientific question. From this, they develop a list of important confounders'. Then, they evaluate whether studies controlled for these variables well. metaconfoundr is a toolkit to facilitate this process and visualize the results as heat maps, traffic light plots, and more.

This package provides functions to calculate the minimum and maximum possible values of Cronbach's alpha when item-level missing data are present. Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used measures of internal consistency in the social, behavioral, and medical sciences (Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011 <doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume complete data, and listwise deletion is often applied when missingness occurs, which can lead to biased or overly optimistic reliability estimates (Enders, 2003 <doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies including enumeration, Monte Carlo sampling, and optimization algorithms (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under arbitrary missing data patterns. The approach is motivated by Manski's partial identification framework and pessimistic bounding ideas from optimization literature.

Model evaluation based on a modified version of the recursive feature elimination algorithm. This package is designed to determine the optimal model(s) by leveraging all available features.

Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous. Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile. You can find more information and validation in the Open Access paper. Laszlo Baranyai (2020) <doi:10.1016/j.mex.2020.100958>.

Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.

Implementation of Multidimensional Top Scoring method for creativity assessment proposed in Boris Forthmann, Maciej Karwowski, Roger E. Beaty (2023) <doi:10.1037/aca0000571>.

Simple tools to perform mixture optimization based on the desirability package by Max Kuhn. It also provides a plot routine using ggplot2 and patchwork'.

This package contains the data sets for the first and second editions of the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The first edition of the book was published by Oxford University Press in 2018 with ISBN-13: 978-019882472. The second edition is expected to be published in January 2027.

This package provides a tool for optimizing scales of effect when modeling ecological processes in space. Specifically, the scale parameter of a distance-weighted kernel distribution is identified for all environmental layers included in the model. Includes functions to assist in model selection, model evaluation, efficient transformation of raster surfaces using fast Fourier transformation, and projecting models. For more details see Peterman (2025) <doi:10.21203/rs.3.rs-7246115/v1>.

Allows users familiar with MATLAB to use MATLAB-named functions in R. Several basic MATLAB functions are written in this package to mimic the behavior of their original counterparts, with more to come as this package grows.

This package provides a basic interface for accessing annotation data from the Multi-CAST collection, a database of spoken natural language texts edited by Geoffrey Haig and Stefan Schnell. The collection draws from a diverse set of languages and has been annotated across multiple levels. Annotation data is downloaded on request from the servers of the University of Bamberg. See the Multi-CAST website <https://multicast.aspra.uni-bamberg.de/> for more information and a list of related publications.

Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.

Various functions for random number generation, density estimation, classification, curve fitting, and spatial data analysis.

Given a set of data points, a clustering is defined as a disjoint partition where each pair of sets in a partition has no overlapping elements. This package provides 25 methods that play a role somewhat similar to distance or metric that measures similarity of two clusterings - or partitions. For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.

Various kinds of plots (observations, variables, correlations, weights, regression coefficients and Variable Importance in the Projection) and aids to interpretation (coefficients, Q2, correlations, redundancies) for partial least squares regressions computed with the pls package, following Tenenhaus (1998, ISBN:2-7108-0735-1).

Dichotomous responses having two categories can be analyzed with stats::glm() or lme4::glmer() using the family=binomial option. Unfortunately, polytomous responses with three or more unordered categories cannot be analyzed similarly because there is no analogous family=multinomial option. For between-subjects data, nnet::multinom() can address this need, but it cannot handle random factors and therefore cannot handle repeated measures. To address this gap, we transform nominal response data into counts for each categorical alternative. These counts are then analyzed using (mixed) Poisson regression as per Baker (1994) <doi:10.2307/2348134>. Omnibus analyses of variance can be run along with post hoc pairwise comparisons. For users wishing to analyze nominal responses from surveys or experiments, the functions in this package essentially act as though stats::glm() or lme4::glmer() provide a family=multinomial option.

Detect outlying observations in functional data sets based on the minimum regularized covariance trace (MRCT) estimator. Includes implementation of Oguamalam et al. (2023) <arXiv:2307.13509>.

This package performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.

Mouse-tracking, the analysis of mouse movements in computerized experiments, is a method that is becoming increasingly popular in the cognitive sciences. The mousetrap package offers functions for importing, preprocessing, analyzing, aggregating, and visualizing mouse-tracking data. An introduction into mouse-tracking analyses using mousetrap can be found in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2023) <doi:10.31234/osf.io/v685r> (preprint: <https://osf.io/preprints/psyarxiv/v685r>).