To calculate the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.

This package provides functions to collapse a tidy data frame into matrices in a data frame and expand a data frame of matrices into a tidy data frame.

Computes the degrees of freedom of the lasso, elastic net, generalized elastic net and adaptive lasso based on the generalized path seeking algorithm. The optimal model can be selected by model selection criteria including Mallows Cp, bias-corrected AIC (AICc), generalized cross validation (GCV) and BIC.

The method m:Explorer associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.

Mixture of logistic regressions parameters (H)estimation with (U)spectral methods. The main methods take d-dimensional inputs and a vector of binary outputs, and return parameters according to the GLMs mixture model (General Linear Model). For more details see chapter 3 in the PhD thesis of Mor-Absa Loum: <https://theses.fr/s156435>, available here <https://theses.hal.science/tel-01877796/document>.

This package provides a fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) <doi:10.21105/joss.05026>.

Computes dielectric response and optical cross-sections of metallic nanoparticles using Drude dielectric model and Rayleigh approximation.

This package provides density, distribution, quantile, and random generation functions for the Modified Half-Normal (MHN) distribution, along with moments and mode; the Fox-Wright Psi normalizing constant is evaluated internally. The MHN distribution arises as a conditional posterior in Bayesian Markov chain Monte Carlo and generalizes the half-normal, truncated normal, and square-root gamma distributions. Implements efficient sampling via the Sun, Kong & Pal (2023) <doi:10.1080/03610926.2021.1934700> algorithms and the Gao & Wang (2025) <doi:10.1080/03610918.2025.2524551> relaxed transformed density rejection method.

To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) <doi:10.48550/arXiv.2406.00128>. Estimation of traditional matrix factor models is also supported. Supplementary functions for testing MEFM over factor models are included.

The Mutual Information Index (M) introduced to social science literature by Theil and Finizza (1971) <doi:10.1080/0022250X.1971.9989795> is a multigroup segregation measure that is highly decomposable and that according to Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008> and Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> satisfies the Strong Unit Decomposability and Strong Group Decomposability properties. This package allows computing and decomposing the total index value into its "between" and "within" terms. These last terms can also be decomposed into their contributions, either by group or unit characteristics. The factors that produce each "within" term can also be displayed at the user's request. The results can be computed considering a variable or sets of variables that define separate clusters.

For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.

This package provides generalized functions to compute Maximum Likelihood Estimation (MLE) for any univariate distribution under various censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, support bounds, and initial parameter values; the package constructs and maximizes the appropriate log-likelihood automatically. Supported schemes include right and left truncation, random, right, left, interval, and middle censoring, block random censoring, balanced joint progressive Type-II (BJPT-II), progressive first failure, joint Type-I, Type-I, Type-II, progressive Type-II, Type-II progressively hybrid, joint Type-II, hybrid, hybrid Type-I, doubly Type-II, Type-I hybrid, and hybrid Type-II censoring. Optimization methods include Newton-Raphson (NR), Broyden-Fletcher-Goldfarb-Shanno (BFGS), the BFGS algorithm implemented in R (BFGSR), Berndt-Hall-Hall-Hausman (BHHH), Simulated Annealing (SANN), Conjugate Gradients (CG), and Nelder-Mead (NM). Inference summaries provide the Akaike Information Criterion (AIC), estimated coefficients, log-likelihood, iteration count, standard errors, z-values, p-values, and the variance-covariance matrix. Methods are described in Nagar, Kumar, and Krishna (2026) <doi:10.59467/IJASS.2026.22.1>, Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"), Wu and Kus (2009) <doi:10.1016/j.csda.2009.03.010>, Goel and Krishna (2026) <doi:10.1007/s13198-026-03208-w>, Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5), Mondal and Kundu (2020) <doi:10.1080/03610926.2018.1554128>, Ding and Gui (2023) <doi:10.3390/math11092003>, Prajapati, Mitra, and Kundu (2019) <doi:10.1007/s13571-018-0167-0>, Yadav, Jaiswal, and Yadav (2026) <doi:10.1007/s11135-026-02647-8>, Iyer, Jammalamadaka, and Kundu (2008) <doi:10.1016/j.jspi.2007.03.062>, Banerjee and Kundu (2008) <doi:10.1109/TR.2008.916890>, Kundu and Joarder (2006) <doi:10.1016/j.csda.2005.05.002>, Berndt, Hall, Hall, and Hausman (1974) "Estimation and Inference in Nonlinear Structural Models" <doi:10.3386/t0003>, Fletcher (1987, "Practical Methods of Optimization", ISBN:978-0-471-91547-8), Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, McKinnon (1999) "Convergence of the Nelder-Mead simplex method to a non-stationary point" <doi:10.1137/S1052623496303482>, Kirkpatrick, Gelatt, and Vecchi (1983) <doi:10.1126/science.220.4598.671>, Fletcher and Reeves (1964) <doi:10.1093/comjnl/7.2.149>, and Nocedal and Wright (2006, "Numerical Optimization", ISBN:978-0-387-30303-1).

This package provides tools for estimation and prediction using Maximum Agreement Linear Predictors (MALPs). MALPs provide an alternative to least squares linear predictors when agreement between predicted and observed values, as measured by Lin's Concordance Correlation Coefficient (CCC), is of primary interest. Applications include missing value imputation and calibration studies. The package includes functions for model estimation, prediction, statistical inference, cross-validation, and model diagnostics. The implemented methodology is described in Kim et al. (2026) <doi:10.1214/26-EJS2550>.

This package provides a rmarkdown template that supports company logo, contact info, watermarks and more. Currently restricted to Latex'/'Markdown'; a similar HTML theme will be added in the future.

The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and MioÄ eviÄ (2024) <doi:10.3758/s13428-023-02079-4>. Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented.

Computationally efficient functions to provide direct likelihood-based inference for partially-observed multivariate birth-death processes. Such processes range from a simple Yule model to the complex susceptible-infectious-removed model in disease dynamics. Efficient likelihood evaluation facilitates maximum likelihood estimation and Bayesian inference.

Solves the Multi-Criteria Minimum Spanning Tree (mc-MST) problem on complete weighted graphs by combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with optional Pareto local search operators. Chromosomes are represented as Prufer sequences so that every random individual decodes to a valid spanning tree (Cayley's theorem), avoiding repair operators. Four solver variants are provided: pure NSGA-II ("base"), Path Relinking ("PR"), Pareto Local Search ("PLS"), and Tabu Search ("TS"). The package supports 2 and 3 objective formulations and provides convenience functions to plot Pareto fronts and best-compromise spanning trees. This package is the reference implementation of the method described in Parraga-Alava, Inostroza-Ponta and Dorn (2017) <doi:10.1109/CEC.2017.7969432>.

Estimation methods for phase-type distribution (PH) and Markovian arrival process (MAP) from empirical data (point and grouped data) and density function. The tool is based on the following researches: Okamura et al. (2009) <doi:10.1109/TNET.2008.2008750>, Okamura and Dohi (2009) <doi:10.1109/QEST.2009.28>, Okamura et al. (2011) <doi:10.1016/j.peva.2011.04.001>, Okamura et al. (2013) <doi:10.1002/asmb.1919>, Horvath and Okamura (2013) <doi:10.1007/978-3-642-40725-3_10>, Okamura and Dohi (2016) <doi:10.15807/jorsj.59.72>.

This package provides a framework for deconvolution, alignment and postprocessing of 1-dimensional (1d) nuclear magnetic resonance (NMR) spectra, resulting in a data matrix of aligned signal integrals. The deconvolution part uses the algorithm described in Koh et al. (2009) <doi:10.1016/j.jmr.2009.09.003>. The alignment part is based on functions from the speaq package, described in Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018> and Vu et al. (2011) <doi:10.1186/1471-2105-12-405>. A detailed description and evaluation of an early version of the package, MetaboDecon1D v0.2.2', can be found in Haeckl et al. (2021) <doi:10.3390/metabo11070452>.

Offers automation tools to parallelize Mplus operations when using R for data generation. It facilitates streamlined integration between Mplus and R', allowing users to run and manage multiple Mplus models simultaneously and efficiently in R'.

Flexible automated machine learning (AutoML) system for the mlr3 ecosystem. Automatically selects a suitable machine learning algorithm and tunes its hyperparameters for a given task. Constructs preprocessing pipelines with multiple parallel branches using mlr3pipelines and jointly optimizes them together with the learners using mlr3tuning'. The optimization is driven by asynchronous decentralized Bayesian optimization by Egele et al. (2023) <doi:10.1109/e-Science58273.2023.10254839>.

This package provides functions to classify mass spectra in known categories and to determine discriminant mass-to-charge values (m/z). Includes easy-to-use preprocessing pipelines for Matrix Assisted Laser Desorption Ionisation - Time Of Flight Mass Spectrometry (MALDI-TOF) mass spectra, methods to select discriminant m/z from labelled libraries, and tools to predict categories (species, phenotypes, etc.) from selected features. Also provides utilities to build design matrices from peak intensities and labels. While this package was developed with the aim of identifying very similar species or phenotypes of bacteria from MALDI-TOF MS, the functions of this package can also be used to classify other categories associated to mass spectra; or from mass spectra obtained with other mass spectrometry techniques. Parallelized processing and optional C++-accelerated functions are available (notably to deal with large datasets) from version 0.5.0. If you use this package in your research, please cite the associated publication (<doi:10.1016/j.eswa.2025.128796>). For a comprehensive guide, additional applications, and detailed examples, see <https://github.com/agodmer/MSclassifR_examples>.

Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.

MatLab'-Style Modeling of Optimization Problems with R'. This package provides a set of convenience functions to transform a MatLab'-style optimization modeling structure to its ROI equivalent.