Two novel matching-based methods for estimating group average treatment effects (GATEs). The match_y1y0() and match_y1y0_bc() functions are used for imputing the potential outcomes based on matching and bias-corrected matching techniques, respectively. The EstGATE() function is employed to estimate the GATE after imputing the potential outcomes.

This package provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, <doi:10.18637/jss.v042.i07>).

An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.

This package provides functions to facilitate model-based clustering of nodes in a network in a mixture of experts setting, which incorporates covariate information on the nodes in the modelling process. Isobel Claire Gormley and Thomas Brendan Murphy (2010) <doi:10.1016/j.stamet.2010.01.002>.

Spatio-temporal multivariate occupancy models can handle multiple species in occupancy models. This method for fitting such models is described in Hepler and Erhardt (2021) "A spatiotemporal model for multivariate occupancy data".

Facilitate tasks typically encountered during metabolomics data analysis including data import, filtering, missing value imputation (Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>, Stekhoven et al. (2012) <doi:10.1093/bioinformatics/btr597>, Tibshirani et al. (2017) <doi:10.18129/B9.BIOC.IMPUTE>, Troyanskaya et al. (2001) <doi:10.1093/bioinformatics/17.6.520>), normalization (Bolstad et al. (2003) <doi:10.1093/bioinformatics/19.2.185>, Dieterle et al. (2006) <doi:10.1021/ac051632c>, Zhao et al. (2020) <doi:10.1038/s41598-020-72664-6>) transformation, centering and scaling (Van Den Berg et al. (2006) <doi:10.1186/1471-2164-7-142>) as well as statistical tests and plotting. metamorphr introduces a tidy (Wickham et al. (2019) <doi:10.21105/joss.01686>) format for metabolomics data and is designed to make it easier to build elaborate analysis workflows and to integrate them with tidyverse packages including dplyr and ggplot2'.

Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Ilmarinen et al. (2023) <doi:10.1177/08902070221088155>. Deconstructing difference score correlations (e.g., gender-equality paradox), see Ilmarinen & Lönnqvist (2024) <doi:10.1037/pspp0000508>. Includes also tools that help in understanding difference score reliability, conditional intra-class correlations, tail-dependency, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891.

Gradient boosting is a powerful statistical learning method known for its ability to model complex relationships between predictors and outcomes while performing inherent variable selection. However, traditional gradient boosting methods lack flexibility in handling longitudinal data where within-subject correlations play a critical role. In this package, we propose a novel approach Mixed Effect Gradient Boosting ('MEGB'), designed specifically for high-dimensional longitudinal data. MEGB incorporates a flexible semi-parametric model that embeds random effects within the gradient boosting framework, allowing it to account for within-individual covariance over time. Additionally, the method efficiently handles scenarios where the number of predictors greatly exceeds the number of observations (p>>n) making it particularly suitable for genomics data and other large-scale biomedical studies.

This package performs mean shift classification using linear and k-d tree based nearest neighbor implementations for the Gaussian, Epanechnikov, and biweight product kernels.

Generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011). Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs, Journal of Statistical Theory and Practice, 5:4, 627-648, <doi:10.1080/15598608.2011.10483735>.

Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package GGIR is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations. To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through GGIR as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running GGIR for accelerometer data. In module 1, all csv files in the GGIR output directory were read, transformed and then merged. In module 2, the GGIR output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44.

We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables 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. More details can be referred to Liu et al. (2025) <doi:10.48550/arXiv.2507.09889>.

This package implements the Mittag-Leffler function, distribution, random variate generation, and estimation. Based on the Laplace-Inversion algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.

This package provides a standalone package combining several stop-word lists for 65 languages with a median of 329 stop words for language and over 1,000 entries for English, Breton, Latin, Slovenian, and Ancient Greek! The user automatically gets access to all the unique stop words contained in: the StopwordISO repository; the Natural Language Toolkit for python'; the Snowball stop-word list; the R package quanteda'; the marimo repository; the Perseus project; and A. Berra's list of stop words for Ancient Greek and Latin.

This package provides a mechanism to plot an interactive map using Mapbox GL (<https://docs.mapbox.com/mapbox-gl-js/api/>), a javascript library for interactive maps, and Deck.gl (<https://deck.gl/>), a javascript library which uses WebGL for visualising large data sets.

Multiple imputation using XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.

Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.

Alternative implementation of the beautiful MissForest algorithm used to impute mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the lightning fast random forest package ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow, e.g., to do multiple imputation when repeating the call to missRanger(). Out-of-sample application is supported as well.

The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.

DNA methylation is an epigenetic modification involved in genomic stability, gene regulation, development and disease. DNA methylation occurs mainly through the addition of a methyl group to cytosines, for example to cytosines in a CpG dinucleotide context (CpG stands for a cytosine followed by a guanine). Tissue-specific methylation patterns lead to genomic regions with different characteristic methylation levels. E.g. in vertebrates CpG islands (regions with high CpG content) that are associated to promoter regions of expressed genes tend to be unmethylated. MethEvolSIM is a model-based simulation software for the generation and modification of cytosine methylation patterns along a given tree, which can be a genealogy of cells within an organism, a coalescent tree of DNA sequences sampled from a population, or a species tree. The simulations are based on an extension of the model of Grosser & Metzler (2020) <doi:10.1186/s12859-020-3438-5> and allows for changes of the methylation states at single cytosine positions as well as simultaneous changes of methylation frequencies in genomic structures like CpG islands.

This package provides a variable selection tool for multivariate normal variables with missing-at-random values using Bayesian Hierarchical Model. Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). Users can also visualize the heatmap of the posterior mean of covariance matrix. Kim, T. Nicolae, D. (2019) <https://github.com/tk382/MMVBVS/blob/master/workingpaper.pdf>. Guan, Y. Stephens, M. (2011) <doi:10.1214/11-AOAS455>.

Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold. The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator. Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.

Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating parameters of the symmetric stable distribution within the Bayesian framework.

The mFilter package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package.