Builds matched nulls for cluster-count claims: synthetic twins of a dataset that preserve every marginal distribution and the full correlation matrix while containing no cluster structure by construction. A reported number of clusters or "types" can then be tested against what the data's own margins and covariance already produce, using any clustering pipeline. A t-copula option adds tail dependence to the null, so that an apparent excess of clusters can be checked against a heavier-tailed alternative before it is read as evidence of types. Implements the matched-null procedure of Meng (2026) "Types Without Taxa" <doi:10.17605/OSF.IO/2EKCG>.

This package implements three bias-correction techniques from Battaglia et al. (2025 <doi:10.48550/arXiv.2402.15585>) to improve inference in regression models with covariates generated by AI or machine learning.

This package implements a semi-parametric estimation framework combined with a boosting algorithm to marginally estimate the conditional cumulative distribution function of survival times given informative covariates. It then utilizes the graphical lasso method to reconstruct network structures among multivariate time-to-event variables, accommodating both multivariate outcomes measured within a single dataset and survival times integrated from heterogeneous (multi-source) datasets..

We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.

Gene Expression datasets for the MM2S package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.

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.

Determines single or multiple modes (most frequent values). Checks if missing values make this impossible, and returns NA in this case. Dependency-free source code. See Franzese and Iuliano (2019) <doi:10.1016/B978-0-12-809633-8.20354-3>.

Fits latent Dirichlet allocation (LDA), supervised topic models, and multilevel supervised topic models for text data with multiple outcome variables. Core estimation routines are implemented in C++ using the Rcpp ecosystem. For topic models, see Blei et al. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>. For supervised topic models, see Blei and McAuliffe (2007) <https://papers.nips.cc/paper_files/paper/2007/hash/d56b9fc4b0f1be8871f5e1c40c0067e7-Abstract.html>.

Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, MR.RGM extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. MR.RGM facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, MR.RGM provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0).

Perform a mail merge (mass email) using the message defined in markdown, the recipients in a csv file, and gmail as the mailing engine. With this package you can parse markdown documents as the body of email, and the yaml header to specify the subject line of the email. Any braces in the email will be encoded with glue::glue()'. You can preview the email in the RStudio viewer pane, and send (draft) email using gmailr'.

Three generalizations of the synthetic control method (which has already an implementation in package Synth') are implemented: first, MSCMT allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klöà ner (2018) <doi:10.1016/j.ecosta.2017.08.002>.

The Multivariate Asymptotic Non-parametric Test of Association (MANTA) enables non-parametric, asymptotic P-value computation for multivariate linear models. MANTA relies on the asymptotic null distribution of the PERMANOVA test statistic. P-values are computed using a highly accurate approximation of the corresponding cumulative distribution function. Garrido-Martà n et al. (2022) <doi:10.1101/2022.06.06.493041>.

Computational functions for player metrics in major league baseball including batting, pitching, fielding, base-running, and overall player statistics. This package is actively maintained with new metrics being added as they are developed.

This package provides an extension of the shadow-test approach to computerized adaptive testing (CAT) implemented in the TestDesign package for the assessment framework involving multiple tests administered periodically throughout the year. This framework is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports multiple item pools vertically scaled and multiple phases (stages) of CAT within each test. Between phases and tests, transitioning from one item pool (and associated constraints) to another is allowed as deemed necessary to enhance the quality of measurement.

The target of margaret is help to extract data from Minciencias to analyze scientific production in Colombia.

Map image classification efficacy (MICE) adjusts the accuracy rate relative to a random classification baseline (Shao et al. (2021)<doi:10.1109/ACCESS.2021.3116526> and Tang et al. (2024)<doi:10.1109/TGRS.2024.3446950>). Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results.

Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.

This package provides the ability to perform "Marginal Mediation"--mediation wherein the indirect and direct effects are in terms of the average marginal effects (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package. This framework allows the use of categorical mediators and outcomes with little change in interpretation from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details on the method.

This package provides functions for multivariate analysis and visualization of neuroimaging data. The package contains the functions and example data used in the book Multivariate Analysis for Neuroimaging Data by Kawaguchi (2021, ISBN: 978-0367255329). It includes utilities for image visualization, image data matrix construction, basis reconstruction, multicomponent visualization, predictive modeling, simulation, and multiblock analysis. Version 3.0 preserves the public interfaces used in the accompanying package vignettes.

This package provides a GUI with which users can construct and interact with Multibiplot Analysis.

Statistical Analyses and Pooling after Multiple Imputation. A large variety of repeated statistical analysis can be performed and finally pooled. Statistical analysis that are available are, among others, Levene's test, Odds and Risk Ratios, One sample proportions, difference between proportions and linear and logistic regression models. Functions can also be used in combination with the Pipe operator. More and more statistical analyses and pooling functions will be added over time. Heymans (2007) <doi:10.1186/1471-2288-7-33>. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>. Sidi (2021) <doi:10.1080/00031305.2021.1898468>. Lott (2018) <doi:10.1080/00031305.2018.1473796>. Grund (2021) <doi:10.31234/osf.io/d459g>.

This package provides a set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space. Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition. Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix. These models have significant differences in terms of the analytical approach. Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes). Research project No. 1.1.1.2/VIAA/1/16/075.

This package provides an extensible framework for conducting simulations to compare data generating processes, item selection algorithms, parameter update algorithms, and stopping rules in computer adaptive testing (CAT) applications. Bundled algorithms include the Elo-based update rules of Klinkenberg, Straatemeier and van der Maas (2011) <doi:10.1016/j.compedu.2011.02.003> and Vermeiren, Kruis, Bolsinova, van der Maas and Hofman (2025) <doi:10.1016/j.caeai.2025.100376>.

This package provides tools to calculate quantitative scores for the United Nations Sustainable Development Goals (SDGs) for the mining, minerals and metals sector. Retrieves official indicator data from the United Nations SDG API', runs trend, stability, benchmarking and convergence diagnostics, maps indicators to mining-sector materiality domains via a bundled ontology, computes site-level Key Performance Indicators (KPIs) aligned with Global Reporting Initiative (GRI) 11, International Council on Mining and Metals (ICMM) Mining Principles and Sustainability Accounting Standards Board (SASB) EM-MM conventions, scores sites on a 0-100 SDG scorecard, and ships an interactive shiny dashboard with demonstration datasets. An Environmental, Social and Governance (ESG) reporting layer generates Global Reporting Initiative (GRI), International Council on Mining and Metals (ICMM) and Business Responsibility and Sustainability Reporting (BRSR) reports from a disclosure bundle interface, with framework mappings shipped as data and rendering to HTML', PDF', Word and Excel via Quarto and openxlsx2'. Official Sustainable Development Goals information and indicator methodology are available from the United Nations Sustainable Development Goals website <https://sdgs.un.org/goals>.