This package provides tools for fitting spatial Mixed Data Sampling (MIDAS) regression models using Integrated Nested Laplace Approximation (INLA). The package is designed for settings where responses and explanatory variables are observed at different temporal frequencies and supports both constant and spatially varying regression coefficients.

This package provides a higher-level interface to continuous-time Markov multi-state models for panel (interval-censored) data. Seven canonical clinical process structures are supplied with structurally valid generator matrices, so that transition matrices and starting values need not be constructed by hand. Panel data can be simulated from exact trajectories under regular or irregular observation schedules, with optional exactly observed absorption times and optional Weibull holding times for assessing the Markov assumption. A single fitting call validates the input against the assumed structure and returns the estimated generator with confidence intervals, mean sojourn times, transition probability matrices and observed transition counts, together with the optimiser's convergence code. A Monte Carlo driver reports Monte Carlo standard errors alongside bias, root mean squared error and interval coverage. Likelihood evaluation is delegated to msm (Jackson, 2011, <doi:10.18637/jss.v038.i08>); the panel-data likelihood is that of Kalbfleisch and Lawless (1985) <doi:10.1080/01621459.1985.10478195>.

This package provides a set of functions to obtain modified score test for generalized linear models.

Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. (2017). Objective Bayesian analysis for the multivariate skew-t model. Statistical Methods & Applications <doi: 10.1007/s10260-017-0404-0>.

This package provides a single function plotting Marradi's trees: a graphical representation of a numerical variable for comparing the variable mean and standard deviation across subgroups. See A. Marradi "L'analisi monovariata" (1993, ISBN: 9788820496876).

The current version (0.3.0) streamlines the underlying code, adds a feasibility check to maicWt and maxessWt', extends exmWt.2ipd with options target.ipd and method for one-sided MAIC weighting between two IPDs, and adds wtTrtDiff for the weighted treatment-effect difference with a Wald confidence interval based on Section 5 of Glimm & Yau (2026). The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2026) <doi:10.1080/19466315.2025.2507378>). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2022) <doi:10.1002/pst.2210>.).

MTrackJ is an ImageJ plugin for motion tracking and analysis (see <https://imagescience.org/meijering/software/mtrackj/>). This package reads and writes MTrackJ Data Files ('.mdf', see <https://imagescience.org/meijering/software/mtrackj/format/>). It supports 2D data and read/writes cluster, point, and channel information. If desired, generates track identifiers that are unique over the clusters. See the project page for more information and examples.

Utility functions for mutational signature analysis as described in Alexandrov, L. B. (2020) <doi:10.1038/s41586-020-1943-3>. This package provides two groups of functions. One is for dealing with mutational signature "exposures" (i.e. the counts of mutations in a sample that are due to each mutational signature). The other group of functions is for matching or comparing sets of mutational signatures. mSigTools stands for mutational Signature analysis Tools.

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

MUI X Charts React chart components for data visualization for building shiny applications and quarto documents. Bundles the MIT-licensed community edition of the @mui/x-charts JavaScript library (the commercial Pro tier is not included).

This package provides a collection of functions to download and process weather data from the Oklahoma Mesonet <https://mesonet.org>. Functions are available for downloading station metadata, downloading Mesonet time series (MTS) files, importing MTS files into R, and converting soil temperature change measurements into soil matric potential and volumetric soil moisture.

Morphometric data collected on animal populations can be subject to measurement error, which leads to biased estimators using line-fitting techniques such as linear regression and reduced major axis. The models implemented in this package were described by Stevenson, Smit, and Setyawan (2026) <DOI:10.1214/26-AOAS2164>. They explicitly accommodate measurement error, allow for multivariate data, estimate relationships between dimensions, allow missing data, and provide tests for isometric relationships between dimensions. Morphometric data of the reef manta ray, collected in Raja Ampat, Indonesia, are included.

Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper423/>.).

Data sets and scripts for Modeling Psychophysical Data in R (Springer).

Analysis of musical scales (& modes, grooves, etc.) in the vein of Sherrill 2025 <doi:10.1215/00222909-11595194>. The initials MCT in the package title refer to the article's title: "Modal Color Theory." Offers support for conventional musical pitch class set theory as developed by Forte (1973, ISBN: 9780300016109) and David Lewin (1987, ISBN: 9780300034936), as well as for the continuous geometries of Callender, Quinn, & Tymoczko (2008) <doi:10.1126/science.1153021>. Identifies structural properties of scales and calculates derived values (sign vector, color number, brightness ratio, etc.). Creates plots such as "brightness graphs" which visualize these properties.

This package provides tools for econometric production analysis with the Symmetric Normalized Quadratic (SNQ) profit function, e.g. estimation, imposing convexity in prices, and calculating elasticities and shadow prices.

This package implements area level of multivariate small area estimation using Hierarchical Bayesian method under Normal and T distribution. The rjags package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

This package provides pipe-style interface for data.table'. Package preserves all data.table features without significant impact on performance. let and take functions are simplified interfaces for most common data manipulation tasks. For example, you can write take(mtcars, mean(mpg), by = am) for aggregation or let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg) for modification. Use take_if/let_if for conditional aggregation/modification. Additionally there are some conveniences such as automatic data.frame conversion to data.table'.

Includes functions for calculating basic indices of macrozoobenthos for water quality and is designed to provide researchers and environmental professionals with a comprehensive tool for evaluating the ecological health of aquatic ecosystems.The package is based on the following references: Paisley, M. F., Trigg, D. J. and Walley, W. J. (2014)<doi:10.1002/rra.2686>. Arslan, N., Salur, A., Kalyoncu, H. et al.(2016) <doi:10.1515/biolog-2016-0005>. Hilsenhoff W.L. (1987). Hilsenhoff. W.L. (1988) Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B. (1999).

Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.

The OpenStreetMap database provides a wide range of highly detailed geographic layers on a global scale. To obtain synthetic information for cartographic purposes, layers must be selected, simplified, merged, or modified. maposm downloads OpenStreetMap extracts and performs these operations to create a set of composite layers of urban areas, buildings, green spaces, main roads, secondary roads, railways, and water bodies.

Simulation results detailed in Esarey and Menger (2019) <doi:10.1017/psrm.2017.42> demonstrate that cluster adjusted t statistics (CATs) are an effective method for correcting standard errors in scenarios with a small number of clusters. The mmiCATs package offers a suite of tools for working with CATs. The mmiCATs() function initiates a shiny web application, facilitating the analysis of data utilizing CATs, as implemented in the cluster.im.glm() function from the clusterSEs package. Additionally, the pwr_func_lmer() function is designed to simplify the process of conducting simulations to compare mixed effects models with CATs models. For educational purposes, the CloseCATs() function launches a shiny application card game, aimed at enhancing users understanding of the conditions under which CATs should be preferred over random intercept models.

Computation of the multivariate marine recovery index, including functions for data visualization and ecological diagnostics of marine ecosystems. The computational details are described in the original publication. Reference: Chauvel, N., Grall, J., Thiébaut, E., Houbin, C., Pezy, J.-P., 2026. A general-purpose multivariate marine recovery index (MUMARINEX) for quantifying the influence of human activities on benthic habitat ecological status. Ecological Indicators 188, 115002.

Implementation of commonly used p-value-based and parametric multiple testing procedures (computation of adjusted p-values and simultaneous confidence intervals) and parallel gatekeeping procedures based on the methodology presented in the book "Multiple Testing Problems in Pharmaceutical Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and Frank Bretz) published by Chapman and Hall/CRC Press 2009.