Generalized Propensity Score Estimation and Matching for Multiple Groups

Implements the Vector Matching algorithm to match multiple treatment groups based on previously estimated generalized propensity scores. The package includes tools for visualizing initial confounder imbalances, estimating treatment assignment probabilities using various methods, defining the common support region, performing matching across multiple groups, and evaluating matching quality. For more details, see Lopez and Gutman (2017) .

Make Multiple 'leaflet' Maps in 'Shiny'

Simplify creating multiple, related 'leaflet' maps across tabs for a 'shiny' application. Users build lists of any polygons, points, and polylines needed for the project, use the map_server() function to assign built lists and other chosen aesthetics into each tab, and the package leverages modules to generate all map tabs.

Multivariate Normal and t Distributions

Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package.

Generating Funky Heatmaps for Data Frames

Allows generating heatmap-like visualisations for data frames. Funky heatmaps can be fine-tuned by providing annotations of the columns and rows, which allows assigning multiple palettes or geometries or grouping rows and columns together in categories. Saelens et al. (2019) .

Simultaneous Inference in General Parametric Models

Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book "Multiple Comparisons Using R" (Bretz, Hothorn, Westfall, 2010, CRC Press).

Analysis of Alternative Polyadenylation Using 3' End-Linked Reads

A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) .

Prototype of Multiple Latent Dirichlet Allocation Runs

Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.

Simultaneous Analysis of Multiplexed Metabarcodes

A comprehensive set of wrapper functions for the analysis of multiplex metabarcode data. It includes robust wrappers for 'Cutadapt' and 'DADA2' to trim primers, filter reads, perform amplicon sequence variant (ASV) inference, and assign taxonomy. The package can handle single metabarcode datasets, datasets with two pooled metabarcodes, or multiple datasets simultaneously. The final output is a matrix per metabarcode, containing both ASV abundance data and associated taxonomic assignments. An optional function converts these matrices into 'phyloseq' and 'taxmap' objects. For more information on 'DADA2', including information on how DADA2 infers samples sequences, see Callahan et al. (2016) . For more details on the demulticoder R package see Sudermann et al. (2025) .

Multiple Precision Arithmetic

Multiple Precision Arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the C library GMP (GNU Multiple Precision Arithmetic).

Stratified Randomized Experiments

Estimate average treatment effects (ATEs) in stratified randomized experiments. 'sreg' is designed to accommodate scenarios with multiple treatments and cluster-level treatment assignments, and accommodates optimal linear covariate adjustment based on baseline observable characteristics. 'sreg' computes estimators and standard errors based on Bugni, Canay, Shaikh (2018) ; Bugni, Canay, Shaikh, Tabord-Meehan (2024+) ; and Jiang, Linton, Tang, Zhang (2023+) .