It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The MultiGroupSequential package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) <doi:10.1080/19466315.2023.2191989> and the graphical procedure proposed by Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>. Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013).

This package provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model <doi:10.5281/zenodo.16529986> and <doi:10.5281/zenodo.16529016>. It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. mlspatial enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions.

This package provides a set of tools to facilitate data sonification and handle the musicXML format <https://usermanuals.musicxml.com/MusicXML/Content/XS-MusicXML.htm>. Several classes are defined for basic musical objects such as note pitch, note duration, note, measure and score. Moreover, sonification utilities functions are provided, e.g. to map data into musical attributes such as pitch, loudness or duration. A typical sonification workflow hence looks like: get data; map them to musical attributes; create and write the musicXML score, which can then be further processed using specialized music software (e.g. MuseScore', GuitarPro', etc.). Examples can be found in the blog <https://globxblog.github.io/>, the presentation by Renard and Le Bescond (2022, <https://hal.science/hal-03710340v1>) or the poster by Renard et al. (2023, <https://hal.inrae.fr/hal-04388845v1>).

This package implements functional principal component analysis (FPCA) for univariate and multivariate sparse functional data. The package estimates eigenfunctions, eigenvalues, and error variance simultaneously via maximum likelihood estimation (MLE), using a spline basis representation of the eigenfunctions. Orthonormality of the estimated eigenfunctions is enforced through a modified Gram-Schmidt (MGS) orthogonalization procedure applied iteratively during estimation, avoiding direct optimization over the Stiefel manifold and improving numerical stability. The optimal number of basis functions and principal components is selected via an Akaike Information Criterion (AIC)-type criterion, supporting both a full grid-search strategy and a computationally efficient sequential selection approach. Principal component scores are estimated by conditional expectation, enabling reconstruction of individual trajectories over the entire domain from sparse observations. Pointwise confidence intervals for reconstructed trajectories are also provided. Methods are described in Mbaka, Cao and Carey (2026) <doi:10.48550/arXiv.2603.18833> and Mbaka and Carey (2026) <doi:10.48550/arXiv.2603.19799>.

This package provides accessible, interactive visualizations through the MAIDR (Multimodal Access and Interactive Data Representation) system. Converts ggplot2 and Base R plots into accessible HTML/SVG formats with keyboard navigation, screen reader support, and sonification capabilities. Supports bar charts (simple, grouped, stacked), histograms, line plots, scatter plots, box plots, violin plots, candlestick (OHLC) charts, heat maps, density/smooth curves, faceted plots, multi-panel layouts (including patchwork), and multi-layered plot combinations. Enables data exploration for users with visual impairments through multiple sensory modalities. For more details see the MAIDR project <https://maidr.ai/>.

Software to support the introductory *MOSAIC Calculus* textbook <https://www.mosaic-web.org/MOSAIC-Calculus/>), one of many data- and modeling-oriented educational resources developed by Project MOSAIC (<https://www.mosaic-web.org/>). Provides symbolic and numerical differentiation and integration, as well as support for applied linear algebra (for data science), and differential equations/dynamics. Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc. The software is suitable for general use, but intended mainly for teaching calculus.

Test the marginal correlation between a scalar response variable with a vector of explanatory variables using the max-type test with bootstrap. The test is based on the max-type statistic and its asymptotic distribution under the null hypothesis of no marginal correlation. The bootstrap procedure is used to approximate the null distribution of the test statistic. The package provides a function for performing the test. For more technical details, refer to Zhang and Laber (2014) <doi:10.1080/01621459.2015.1106403>.

Search and access transit feed data from the Mobility Database <https://mobilitydatabase.org>. The package wraps the Mobility Database API, allowing users to discover GTFS (General Transit Feed Specification) and GBFS (General Bikeshare Feed Specification) feeds from agencies worldwide. Functions are designed to integrate seamlessly with packages like tidytransit and gtfstools for subsequent feed analysis.

There are an increasing number of mega-phylogenies available nowadays, with many of them being sets of thousands of posterior distribution phylogenies. For ecological studies, we may need to randomly select many such posterior phylogenies to conduct analyses. This data package serves this purpose by providing a small number (100 or 50) of randomly selected posterior phylogenies (if available) so that we can readily use them for our downstream analyses without repeating the downloading and selecting processes.

This package provides an R wrapper for the MD4C (Markdown for C') library. Functions exist for parsing markdown ('CommonMark compliant) along with support for other common markdown extensions (e.g. GitHub flavored markdown, LaTeX equation support, etc.). The package also provides a number of higher level functions for exploring and manipulating markdown abstract syntax trees as well as translating and displaying the documents.

Mass measurement corrections and uncertainties using calibration data, as recommended by EURAMET's guideline No. 18 (2015) ISBN:978-3-942992-40-4 . The package provides classes, functions, and methods for storing information contained in calibration certificates and converting balance readings to both conventional mass and real mass. For the latter, the Magnitude of the Air Buoyancy Correction factor employs models (such as the CIMP-2007 formula revised by Picard, Davis, Gläser, and Fujii (2008) <doi:10.1088/0026-1394/45/2/004>) to estimate the local air density using measured environmental conditions.

Builds and interprets multi-response machine learning models using tidymodels syntax. Users can supply a tidy model, and mrIML automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) <doi:10.1111/1755-0998.13495> and Fountain-Jones et al. (2024) <doi:10.22541/au.172676147.77148600/v1>.

This package provides a set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication <doi:10.1007/978-1-4939-7819-9_20>.

Efficient finite difference method for valuing European and American multi-asset options.

Given independent and identically distributed observations X(1), ..., X(n) from a density f, provides five methods to perform a multiscale analysis about f as well as the necessary critical values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive correction term Gamma in either method offers another two approaches which are more powerful on small scales and less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically equivalent to the first and was introduced in Rufibach and Walther (2010).

This package provides functions and tools for analysing consumer demand with the Almost Ideal Demand System (AIDS) suggested by Deaton and Muellbauer (1980).

Missing data imputation based on the missForest algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>.

This package provides an interface to OpenML.org to list and download machine learning data, tasks and experiments. The OpenML objects can be automatically converted to mlr3 objects. For a more sophisticated interface with more upload options, see the OpenML package.

Package for estimating, analyzing, and forecasting multi-country macro-finance affine term structure models (ATSMs). All setups build on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) <doi:10.1111/jofi.12131>. Multicountry extensions by Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>, Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon and Moura (2024, JFEC) <doi:10.1093/jjfinec/nbae008> are also available. The package also provides tools for bias correction as in Bauer Rudebusch and Wu (2012, JBES) <doi:10.1080/07350015.2012.693855>, bootstrap analysis, and several graphical/numerical outputs.

This package provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from glmnet', lightgbm', ranger', e1071', and caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility.

An S4 update of the mefa package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.

Diagnostic tools as residual analysis, global, local and total-local influence for the multivariate model from the random intercept Poisson generalized log gamma model are available in this package. Including also, the estimation process by maximum likelihood method, for details see Fabio, L. C; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2023) <doi:10.1080/03610926.2021.1939380> and Fábio, L. C.; Villegas, C.; Mamun, A. S. M. A. and Carrasco, J. M. F. (2025) <doi:10.28951/bjb.v43i1.728>.

Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response data. Predicting mixture toxicity based on reference models such as concentration addition', independent action', and generalized concentration addition'.

The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week.