This package implements differential methylation region (DMR) detection using a multistage Markov chain Monte Carlo (MCMC) algorithm based on the alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes the Anderson-Darling test stage, improves computational efficiency of the core ASGN and multistage MCMC routines, and adds convenience functions for summarizing and visualizing detected DMRs. The methodology is based on Yang (2025) <https://www.proquest.com/docview/3218878972>.

The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and TMB (using the marssTMB companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>.

Leverages the R language to automate latent variable model estimation and interpretation using Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics.

Biodiversity areas, especially primary forest, serve a multitude of functions for local economy, regional functionality of the ecosystems as well as the global health of our planet. Recently, adverse changes in human land use practices and climatic responses to increased greenhouse gas emissions, put these biodiversity areas under a variety of different threats. The present package helps to analyse a number of biodiversity indicators based on freely available geographical datasets. It supports computational efficient routines that allow the analysis of potentially global biodiversity portfolios. The primary use case of the package is to support evidence based reporting of an organization's effort to protect biodiversity areas under threat and to identify regions were intervention is most duly needed.

This package provides methods for maximum likelihood and Bayesian estimation for the Wishart mixture model and the mixture-of-experts Wishart (MoE-Wishart) model. The package provides four inference algorithms for these models, each implemented using the expectationâ maximization (EM) algorithm for maximum likelihood estimation and a fully Bayesian approach via Gibbs-within-Metropolisâ Hastings sampling.

Incorporates a Bayesian monotonic single-index mixed-effect model with a multivariate skew-t likelihood, specifically designed to handle survey weights adjustments. Features include a simulation program and an associated Gibbs sampler for model estimation. The single-index function is constrained to be monotonic increasing, utilizing a customized Gaussian process prior for precise estimation. The model assumes random effects follow a canonical skew-t distribution, while residuals are represented by a multivariate Student-t distribution. Offers robust Bayesian adjustments to integrate survey weight information effectively.

This package provides functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.

This package provides a collection of methods for large scale single mediator hypothesis testing. The six included methods for testing the mediation effect are Sobel's test, Max P test, joint significance test under the composite null hypothesis, high dimensional mediation testing, divide-aggregate composite null test, and Sobel's test under the composite null hypothesis. Du et al (2023) <doi:10.1002/gepi.22510>.

This package provides a tool for optimizing scales of effect when modeling ecological processes in space. Specifically, the scale parameter of a distance-weighted kernel distribution is identified for all environmental layers included in the model. Includes functions to assist in model selection, model evaluation, efficient transformation of raster surfaces using fast Fourier transformation, and projecting models. For more details see Peterman (2025) <doi:10.21203/rs.3.rs-7246115/v1>.

This package provides an implementation of methods for multivariate multiple regression with adaptive shrinkage priors as described in F. Morgante et al (2023) <doi:10.1371/journal.pgen.1010539>.

Visualise admixture as pie charts on a projected map, admixture as traditional structure barplots or facet barplots, and scatter plots from genotype principal components analysis. A shiny app allows users to create admixture maps interactively. Jenkins TL (2024) <doi:10.1111/1755-0998.13943>.

This package provides methods to estimate serial intervals and time-varying case reproduction numbers from infectious disease outbreak data. Serial intervals measure the time between symptom onset in linked transmission pairs, while case reproduction numbers quantify how many secondary cases each infected individual generates over time. These parameters are essential for understanding transmission dynamics, evaluating control measures, and informing public health responses. The package implements the maximum likelihood framework from Vink et al. (2014) <doi:10.1093/aje/kwu209> for serial interval estimation and the retrospective method from Wallinga & Lipsitch (2007) <doi:10.1098/rspb.2006.3754> for reproduction number estimation. Originally developed for scabies transmission analysis but applicable to other infectious diseases including influenza, COVID-19, and emerging pathogens. Designed for epidemiologists, public health researchers, and infectious disease modelers working with outbreak surveillance data.

Procedures to fit species distributions models from occurrence records and environmental variables, using glmnet for model fitting. Model structure is the same as for the Maxent Java package, version 3.4.0, with the same feature types and regularization options. See the Maxent website <http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.

This package provides a collection of functions for the analysis of archaeological mortality data (on the topic see e.g. Chamberlain 2006 <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). It takes demographic data in different formats and displays the result in a standard life table as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population). It also checks for possible biases in the age structure and applies corrections to life tables.

This package provides methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" <doi:10.1002/9780470683019> for more information.

Import bathymetric and hypsometric data from the NOAA (National Oceanic and Atmospheric Administration, <https://www.ncei.noaa.gov/products/etopo-global-relief-model>), GEBCO (General Bathymetric Chart of the Oceans, <https://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.

Create variable width bar charts i.e. "bar mekko" charts to include important quantitative context. Closely related to mosaic, spine (or spinogram), matrix, submarine, olympic, Mondrian or product plots and tree maps.

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.

This package provides a comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. Multidendrograms can group more than two clusters when ties in proximity data occur, and therefore they do not depend on the order of the input data. Descriptive measures to analyze the resulting dendrogram are additionally provided. <doi:10.18637/jss.v114.i02>.

This package creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). MixSIAR is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. MixSIAR incorporates several years of advances since MixSIR and SIAR'.

This package performs multivariate meta-analysis for high-dimensional data to integrate and collectively analyse individual-level data from multiple studies, as well as to combine summary estimates. This approach accounts for correlation between outcomes, incorporates withinâ and betweenâ study variability, handles missing values, and uses shrinkage estimation to accommodate high dimensionality. The MetaHD R package provides access to our multivariate meta-analysis approach, along with a comprehensive suite of existing meta-analysis methods, including fixed-effects and random-effects models, Fisherâ s method, Stoufferâ s method, the weighted Z method, Lancasterâ s method, the weighted Fisherâ s method, and vote-counting approach. A detailed vignette with example datasets and code for data preparation and analysis is available at <https://alyshadelivera.github.io/MetaHD_vignette/>.

This package provides a new method to implement clustering from multiple modality data of certain samples, the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data.

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using emmeans'.

66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().