Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.

Extends the base classes and methods of caret package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.

The need for anonymization of individual survey responses often leads to many suppressed grid cells in a regular grid. Here we provide functionality for creating multi-resolution gridded data, respecting the confidentiality rules, such as a minimum number of units and dominance by one or more units for each grid cell. The functions also include the possibility for contextual suppression of data. For more details see Skoien et al. (2025) <doi:10.48550/arXiv.2410.17601>.

This package implements the multivariate autoregressive distributed lag (ARDL) unit root test proposed by Sam, McNown, Goh, and Goh (2024) <doi:10.1080/03796205.2024.2439101>. The test augments the standard ADF regression with lagged levels of a covariate to improve power when cointegration exists. Bootstrap critical values ensure correct size regardless of nuisance parameters. Provides automatic lag selection via AIC/BIC, diagnostic tests, and comprehensive inference tables following the four-case framework.

Estimates the sample size needed to detect microbial contamination in a lot with a user-specified detection probability and user-specified analytical sensitivity. Various patterns of microbial contamination are accounted for: homogeneous (Poisson), heterogeneous (Poisson-Gamma) or localized(Zero-inflated Poisson). Ida Jongenburger et al. (2010) <doi:10.1016/j.foodcont.2012.02.004> "Impact of microbial distributions on food safety". Leroy Simon (1963) <doi:10.1017/S0515036100001975> "Casualty Actuarial Society - The Negative Binomial and Poisson Distributions Compared".

Multi-criteria design of experiments algorithm that simultaneously optimizes up to six different criteria ('I', Id', D', Ds', A and As'). The algorithm finds the optimal Pareto front and, if requested, selects a possible symmetrical design on it. The symmetrical design is selected based on two techniques: minimum distance with the Utopia point or the TOPSIS approach.

Estimates Shannon entropy, per gene and per genomic position, associated with non-synonymous mutation frequencies in viral populations, such as wastewater samples. The package uses codon translations for functional insights. Each amino acid can be treated as an individual state, resulting in a 20-state entropy computation, or grouped into one of six physicochemical classes, adding further functional context. Provides normalized values (0-1 scale) to facilitate the direct comparison of different genomic positions or total functional entropy across multiple metagenomes. Designed to analyze mutational data using tabular Single Nucleotide Variant (SNV) frequency tables generated by variant callers (e.g., iVar or LoFreq'), operating independently of consensus sequence estimation and multiple sequence alignment.

Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>. Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.

Fits fixed-, random-, or mixed-effects multivariate meta-analysis models using dynamic model estimates from each individual building on and extending Lee and Gates (2023) <doi:10.1080/00273171.2023.2229310>.

This package implements a regularized Bayesian estimator that optimizes the estimation of between-group coefficients for multilevel latent variable models by minimizing mean squared error (MSE) and balancing variance and bias. The package provides more reliable estimates in scenarios with limited data, offering a robust solution for accurate parameter estimation in two-level latent variable models. It is designed for researchers in psychology, education, and related fields who face challenges in estimating between-group effects under small sample sizes and low intraclass correlation coefficients. The package includes comprehensive S3 methods for result objects: print(), summary(), coef(), se(), vcov(), confint(), as.data.frame(), dim(), length(), names(), and update() for enhanced usability and integration with standard R workflows. Dashuk et al. (2025a) <doi:10.1017/psy.2025.10045> derived the optimal regularized Bayesian estimator; Dashuk et al. (2025b) <doi:10.1007/s41237-025-00264-7> extended it to the multivariate case; and Luedtke et al. (2008) <doi:10.1037/a0012869> formalized the two-level latent variable framework.

This package provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.

This package provides a hybrid modeling framework combining Support Vector Regression (SVR) with metaheuristic optimization algorithms, including the Archimedes Optimization Algorithm (AO) (Hashim et al. (2021) <doi:10.1007/s10489-020-01893-z>), Coot Bird Optimization (CBO) (Naruei & Keynia (2021) <doi:10.1016/j.eswa.2021.115352>), and their hybrid (AOCBO), as well as several others such as Harris Hawks Optimization (HHO) (Heidari et al. (2019) <doi:10.1016/j.future.2019.02.028>), Gray Wolf Optimizer (GWO) (Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>), Ant Lion Optimization (ALO) (Mirjalili (2015) <doi:10.1016/j.advengsoft.2015.01.010>), and Enhanced Harris Hawk Optimization with Coot Bird Optimization (EHHOCBO) (Cui et al. (2023) <doi:10.32604/cmes.2023.026019>). The package enables automatic tuning of SVR hyperparameters (cost, gamma, and epsilon) to enhance prediction performance. Suitable for regression tasks in domains such as renewable energy forecasting and hourly data prediction. For more details about implementation and parameter bounds see: Setiawan et al. (2021) <doi:10.1016/j.procs.2020.12.003> and Liu et al. (2018) <doi:10.1155/2018/6076475>.

This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include: (i) Normalization and/or variance stabilization of the data, (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow), (iii) Generation of diverse diagnostic plots, (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.

Run flexible mediation analyses using natural effect models as described in Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.

This package provides functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses. The response dimension reduction is theoretically founded in Yoo and Cook (2008) <doi:10.1016/j.csda.2008.07.029>. Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) <doi:10.1080/02331888.2017.1410152> and Yoo (2019) <doi:10.1016/j.jkss.2019.02.001>. The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace.

This package provides a flexible computational framework for mixture distributions with the focus on the composite models.

Exploratory and predictive methods for the analysis of several blocks of variables measured on the same individuals.

Uses memory-mapping to enable the random access of elements of a text file of characters separated by characters as if it were a simple R(cpp) matrix.

Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. <doi:10.1016/j.jmp.2019.102297>.

Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615> for VLMC with covariates.

Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).

The MetAlyzer S4 object provides methods to read and reformat metabolomics data for convenient data handling, statistics and downstream analysis. The resulting format corresponds to input data of the Shiny app MetaboExtract (<https://www.metaboextract.shiny.dkfz.de/MetaboExtract/>).

This package provides tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>, Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>, Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>, Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>, Bladt, M. (2022) <doi:10.1017/asb.2021.40>, Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>, Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>, Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.