Bayesian multivariate age-period-cohort (MAPC) models for analyzing health data, with support for model fitting, visualization, stratification, and model comparison. Inference focuses on identifiable cross-strata differences, as described by Riebler and Held (2010) <doi:10.1093/biostatistics/kxp037>. Methods for handling complex survey data via the survey package are included, as described in Mercer et al. (2014) <doi:10.1016/j.spasta.2013.12.001>.

Multivariate generalized Gaussian distribution, Multivariate Cauchy distribution, Multivariate t distribution. Distance between two distributions (see N. Bouhlel and A. Dziri (2019): <doi:10.1109/LSP.2019.2915000>, N. Bouhlel and D. Rousseau (2022): <doi:10.3390/e24060838>, N. Bouhlel and D. Rousseau (2023): <doi:10.1109/LSP.2023.3324594>). Manipulation of these multivariate probability distributions. This package replaces mggd', mcauchyd and mstudentd'.

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).

Generate a monochrome palette from a starting colour for a specified number of colours. The package can also be used to display colour palettes in the plot window, with or without hex codes and colour labels.

The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. In this package, we consider tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history in Cox models for the transition intensities; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markov Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where patients are grouped by the state occupied of the process at a particular time point (see Soutinho G, Meira-Machado L (2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H (2020) <doi:10.1093/biostatistics/kxaa030>).

Estimate Multidimensional Poverty Index (MPI) measures from household survey microdata using the Alkire-Foster dual-cutoff counting method (Alkire and Foster, 2011). Load indicator specifications from CSV, Excel, JSON, or plain-text files; compute the headcount ratio (H), intensity (A), and MPI = H x A across any subgroup; and export results to formatted Excel reports. Supports complex survey designs â stratification, clustering, and probability weights â and optionally appends design-based standard errors and confidence intervals.

Supports the generation of parallelogram, equilateral triangle, regular hexagon, isosceles trapezoid, Koch snowflake, hexaflake', Sierpinski triangle, Sierpinski carpet and Sierpinski trapezoid mazes via TurtleGraphics'. Mazes are generated by the recursive method: the domain is divided into sub-domains in which mazes are generated, then dividing lines with holes are drawn between them, see J. Buck, Recursive Division, <http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.

This function allows to generate two biological conditions synthetic microarray dataset which has similar behavior to those currently observed with common platforms. User provides a subset of parameters. Available default parameters settings can be modified.

Estimation of models with dependent variable left-censored at zero. Null values may be caused by a selection process Cragg (1971) <doi:10.2307/1909582>, insufficient resources Tobin (1958) <doi:10.2307/1907382>, or infrequency of purchase Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.

It contains the function to apply MARMoT balancing technique discussed in: Silan, Boccuzzo, Arpino (2021) <DOI:10.1002/sim.9192>, Silan, Belloni, Boccuzzo, (2023) <DOI:10.1007/s10260-023-00695-0>; furthermore it contains a function for computing the Deloof's approximation of the average rank (and also a parallelized version) and a function to compute the Absolute Standardized Bias.

Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.

An implementation of the Super Learner prediction algorithm from van der Laan, Polley, and Hubbard (2007) <doi:10.2202/1544-6115.1309 using the mlr3 framework.

Calculate morphine milligram equivalents (MME) for opioid dose comparison using standardized methods. Can directly call the NIH HEAL MME Online Calculator <https://research-mme.wakehealth.edu/api> API or replicate API calculations on the user's local machine from the comfort of R'. Creation of the NIH HEAL MME Online Calculator and the MME calculations implemented in this package are described in Adams MCB, Sward KA, Perkins ML, Hurley RW (2025) <doi:10.1097/j.pain.0000000000003529>.

An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.

This package provides tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from movebank <https://www.movebank.org/cms/movebank-main> and files is facilitated.

Fits probabilistic principal components analysis, probabilistic principal components and covariates analysis and mixtures of probabilistic principal components models to metabolomic spectral data.

Normalize data to minimize the difference between sample plates (batch effects). For given data in a matrix and grouping variable (or plate), the function normn_MA normalizes the data on MA coordinates. More details are in the citation. The primary method is Multi-MA'. Other fitting functions on MA coordinates can also be employed e.g. loess.

Many useful functions and extensions for dealing with meteorological data in the tidy data framework. Extends ggplot2 for better plotting of scalar and vector fields and provides commonly used analysis methods in the atmospheric sciences.

Fit, plot, and predict a multivariate response, using an arbitrary univariate Bayesian regression model to independently fit basis components (e.g., principal components) of the response (Francom et al., 2025 <DOI:10.1137/24M1644092>).

This package provides new data-structure support for multi-precision computing for R users. The package supports 16-bit, 32-bit, and 64-bit operations. To the best of our knowledge, MPCR differs from the currently available packages in the following: MPCR introduces a new data structure that supports three different precisions (16-bit, 32-bit, and 64-bit), allowing for optimized memory allocation based on the desired precision. This feature offers significant advantages in memory optimization. MPCR extends support to all basic linear algebra methods across different precisions. Optional GPU acceleration via CUDA is available for 32-bit and 64-bit operations when CUDA Toolkit is detected during installation, while 16-bit operations are GPU-only and limited to matrix-matrix multiplication. MPCR maintains a consistent interface with normal R functions, allowing for seamless code integration and a user-friendly experience.

This package provides the mean to parse and render markdown text with grid along with facilities to define the styling of the text.

Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. The naive analysis which is solely based on the observed data without adjustment may lead to bias. The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given validation set. This package is an implementation of Chen (2013) <doi:10.1002/bimj.201200195>.

Analysis and visualisation of synchrony, interaction, and joint movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and Tarsitani (2021) <doi:10.17605/OSF.IO/KS325>, while example analyses can be found in Clayton, Jakubowski, and Eerola (2019) <doi:10.1177/1029864919844809>. Additionally, wavelet analysis techniques have been applied to examine movement-related musical interactions, as shown in Eerola et al. (2018) <doi:10.1098/rsos.171520>.

Function ModEstM() is the only one of this package, it estimates the modes of an empirical univariate distribution. It relies on the stats::density() function, even for input control. Due to very good performance of the density estimation, computation time is not an issue. The multiple modes are handled using dplyr::group_by(). For conditions and rates of convergences, see Eddy (1980) <doi:10.1214/aos/1176345080>.