Fit multivariate mixture of normal distribution using covariance structure.

This package provides a set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: SMS-EMOA <doi:10.1016/j.ejor.2006.08.008> NSGA-III <doi:10.1109/TEVC.2013.2281535> MO-CMA-ES <doi:10.1145/1830483.1830573> The following many-objective benchmark problems are also provided: DTLZ1'-'DTLZ4 from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and WFG4'-'WFG9 from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.

This package provides a set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi:10.1111/1365-2656.12674>. Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movement. Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.

Framework for building modular Monte Carlo risk analysis models. It extends the capabilities of mc2d to facilitate working with multiple risk pathways, variates and scenarios. It provides tools to organize risk analysis in independent flexible modules, align multivariate mcnodes, automate the creation of mcnodes, visualise model structure, assess convergence, and perform sensitivity analysis. For more details see Ciria (2026) <https://nataliaciria.com/mcmodule/>.

This package provides a comprehensive R interface to the MultiChain blockchain JSON-RPC API <https://www.multichain.com/developers/json-rpc-api/>. Allows users to manage blockchain nodes, create and subscribe to data streams, issue assets, and manage network permissions directly from the R console. Supports both local node management and remote server interaction.

Morphometric data collected on animal populations can be subject to measurement error, which leads to biased estimators using line-fitting techniques such as linear regression and reduced major axis. The models implemented in this package were described by Stevenson, Smit, and Setyawan (2026) <DOI:10.1214/26-AOAS2164>. They explicitly accommodate measurement error, allow for multivariate data, estimate relationships between dimensions, allow missing data, and provide tests for isometric relationships between dimensions. Morphometric data of the reef manta ray, collected in Raja Ampat, Indonesia, are included.

Computing the Mann-Whitney effect based on copula models. Estimation of the association parameter in survival copula models. A description of the underlying methods is described in Nakazono et al. (2024) <doi:10.3390/math12101453> and Nakazono et al. (accepted for publication in Statistical Papers).

Flexible and informed regression with Multiple Change Points. mcp can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. mcp supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. mcp is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.

Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.

This package provides tools for calculating I-Scores, a simple way to measure how successful minor political parties are at influencing the major parties in their environment. I-Scores are designed to be a more comprehensive measurement of minor party success than vote share and legislative seats won, the current standard measurements, which do not reflect the strategies that most minor parties employ. The procedure leverages the Manifesto Project's NLP model to identify the issue areas that sentences discuss, see Burst et al. (2024) <doi:10.25522/manifesto.manifestoberta.56topics.context.2024.1.1>, and the Wordfish algorithm to estimate the relative positions that platforms take on those issue areas, see Slapin and Proksch (2008) <doi:10.1111/j.1540-5907.2008.00338.x>.

Integrating morphological modeling with machine learning to support structured decision-making (e.g., in management and consulting). The package enumerates a morphospace of feasible configurations and uses random forests to estimate class probabilities over that space, bridging deductive model exploration with empirical validation. It includes utilities for factorizing inputs, model training, morphospace construction, and an interactive shiny app for scenario exploration.

Two pipelines are provided to study microbial turnover along a gradient, including the beta diversity and microbial abundance change. The betaturn class consists of the steps of community dissimilarity matrix generation, matrix conversion, differential test and visualization. The workflow of taxaturn class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization. Multiple statistical approaches can contribute to the analysis of microbial turnover.

This package provides a sample size calculator for micro-randomized trials (MRTs) with binary outcomes based on Cohn et al. (2023) <doi:10.1002/sim.9748>. Also provides a power calculator when the sample size is input by the user.

This package provides a causal mediation approach under the counterfactual framework to test the significance of total, direct and indirect effects. In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information.

This package provides a framework to perform soft clustering using simplex-structured matrix factorisation (SSMF). The package contains a set of functions for determining the optimal number of prototypes, the optimal algorithmic parameters, the estimation confidence intervals and the diversity of clusters. Abdolali, Maryam & Gillis, Nicolas (2020) <doi:10.1137/20M1354982>.

Supports matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry workflows from raw Bruker spectra to cohort-level peak matrices. Provides spectrum loading, Savitzky-Golay smoothing, baseline correction (SNIP and TopHat), Gaussian kernel-regression-based peak detection including shoulder peaks, peak-quality assessment, filtering, and cohort feature analysis. Computationally intensive routines are implemented in C++ using Rcpp'. The implemented signal-processing methods include those described by Savitzky and Golay (1964) <doi:10.1021/ac60214a047>, Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, Stanford, Bagley and Solomon (2016) <doi:10.1186/s12953-016-0107-8>, and Nadaraya-Watson kernel regression (Nadaraya (1964) <doi:10.1137/1109020>; Watson (1964) <https://www.jstor.org/stable/25049340>).

Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 <doi:10.1111/2041-210X.12472>.

This package implements the multivariate autoregressive distributed lag (ARDL) unit root test of Sam, McNown, Goh and Goh (2025) <doi:10.1080/03796205.2024.2439101>. The test augments the ADF regression with the lagged level, the current difference and lagged differences of one or more covariates so that cointegration between the series under test and the covariates is taken into account. The t statistic on the lagged level of the series and the joint F statistic on the lagged levels of the covariates are bootstrapped with the respective null imposed (residual bootstrap), giving critical values and p-values. Provides automatic lag selection via AIC or BIC, diagnostic plots, and the four-case classification of the order of integration of the series.

Provide a sample size calculator for micro-randomized trials (MRTs) based on methodology developed in Sample Size Calculations for Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.

Algorithms for multivariate outlier detection when missing values occur. Algorithms are based on Mahalanobis distance or data depth. Imputation is based on the multivariate normal model or uses nearest neighbour donors. The algorithms take sample designs, in particular weighting, into account. The methods are described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.

The Mapper algorithm from Topological Data Analysis, the steps are as follows 1. Define a filter (lens) function on the data. 2. Perform clustering within each level set. 3. Generate a complex from the clustering results.

Implementation of a framework for cluster analysis with selection of the final number of clusters and an optional variable selection procedure. The package is designed to integrate the results of multiple imputed datasets while accounting for the uncertainty that the imputations introduce in the final results. In addition, the package can also be used for a cluster analysis of the complete cases of a single dataset. The package also includes specific methods to summarize and plot the results. The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.

This package provides a new way to predict time series using the marginal distribution table in the absence of the significance of traditional models.

This package implements shared frailty regression models for survival data under eight censoring mechanisms: exact, right censoring (Kalbfleisch and Prentice, 2002), left censoring, interval censoring (Sun, 2006), progressive Type I censoring, and progressive Type II censoring (Balakrishnan and Aggarwala, 2000 <doi:10.1007/978-1-4612-1334-5>). Combines four frailty distributions -- Gamma (Clayton, 1978), Inverse Gaussian (Hougaard, 1984), and two variants of the Generalized Lindley (GL) distribution: GL Type 1, a two-component gamma mixture with distribution-specific scale/shape linkage (Pandey, Hanagal, and Tyagi, 2022), and GL Type 2, a two-component gamma mixture with a common rate parameter (Pandey and Tyagi, 2021 <doi:10.1134/S1995080222010140>) -- with two baseline hazard distributions: the two-parameter Weibull distribution (Weibull, 1951) and the three-parameter Generalized (Exponentiated) Weibull distribution (Mudholkar and Srivastava, 1993 <doi:10.1109/24.229504>). A no-frailty baseline-only model is also supported for nested model comparison. Maximum likelihood estimation is conducted using Newton-Raphson and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms via the maxLik package (Henningsen and Toomet, 2011 <doi:10.1007/s00180-010-0217-1>). Provides standard errors, confidence intervals, hypothesis tests, Akaike Information Criterion (AIC, Akaike, 1974 <doi:10.1109/TAC.1974.1100705>), Bayesian Information Criterion (BIC, Schwarz, 1978 <doi:10.1214/aos/1176344136>), corrected Akaike Information Criterion (AICc, Hurvich and Tsai, 1989), Hannan-Quinn Information Criterion (HQIC, Hannan and Quinn, 1979), a bootstrap approximation of the Widely Applicable Information Criterion (WAIC, Watanabe, 2010), k-fold cross-validation, frailty variance estimation, survival, hazard, median, risk, and marginal predictions, Cox-Snell (Cox and Snell, 1968), martingale (Barlow and Prentice, 1988), and deviance residuals with a Kolmogorov-Smirnov goodness-of-fit test, influence diagnostics (leverage, Cook's distance, difference in fits (DFFITS), difference in betas (DFBETAS); Belsley, Kuh, and Welsch, 1980), random data generation under all eight censoring mechanisms, a Monte Carlo simulation-study function, and a diagnostic and survival plotting suite.