These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories.

It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).

This package performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) <doi:10.1101/2020.02.16.20023465>.

This package provides ggplot2 equivalents of fixest::coefplot() and fixest::iplot(), for producing nice coefficient plots and interaction plots. Enables some additional functionality and convenience features, including grouped multi-'fixest object faceting and programmatic updates to existing plots (e.g., themes and aesthetics).

Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.

Penalised likelihood estimation of a covariance matrix via the ridge-regularised covglasso estimator described in Cibinel et al. (2024) <doi:10.48550/arXiv.2410.02403>. Based on the C++ code of the R package covglasso (by Michael Fop, <https://orcid.org/0000-0003-3936-2757>) and the R code of icf (by Mathias Drton, <https://orcid.org/0000-0001-5614-3025>) within the R package ggm'.

Can be used for optimal transport between two-dimensional grids with respect to separable cost functions of l^p form. It utilizes the Frank-Wolfe algorithm to approximate so-called pivot measures: One-dimensional transport plans that fully describe the full transport, see G. Auricchio (2023) <doi:10.4171/RLM/1026>. For these, it offers methods for visualization and to extract the corresponding transport plans and costs. Additionally, related functions for one-dimensional optimal transport are available.

This package provides a tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from GENEActiv <https://activinsights.com/>, binary (.gt3x) and .csv-export data from Actigraph <https://theactigraph.com> devices, and binary (.cwa) and .csv-export data from Axivity <https://axivity.com>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format. Also the package allows for external function embedding.

This package provides convenient access to the official spatial datasets of Peru as sf objects in R. This package includes a wide range of geospatial data covering various aspects of Peruvian geography, such as: administrative divisions (Source: INEI <https://ide.inei.gob.pe/>), protected natural areas (Source: GEO ANP - SERNANP <https://geo.sernanp.gob.pe/visorsernanp/>). All datasets are harmonized in terms of attributes, projection, and topology, ensuring consistency and ease of use for spatial analysis and visualization.

Implemented are the Wald-type statistic, a permuted version thereof as well as the ANOVA-type statistic for general factorial designs, even with non-normal error terms and/or heteroscedastic variances, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.

This package provides functions and analytics for GENEA-compatible accelerometer data into R objects. See topic GENEAread for an introduction to the package. See <https://activinsights.com/technology/geneactiv/> for more details on the GENEActiv device.

Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.

Google offers public access to global search volumes from its search engine through the Google Trends portal. The package downloads these search volumes provided by Google Trends and uses them to measure and analyze the distribution of search scores across countries or within countries. The package allows researchers and analysts to use these search scores to investigate global trends based on patterns within these scores. This offers insights such as degree of internationalization of firms and organizations or dissemination of political, social, or technological trends across the globe or within single countries. An outline of the package's methodological foundations and potential applications is available as a working paper: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3969013>.

This package provides a user-friendly shiny application for Bayesian machine learning analysis of marine species distributions. GLOSSA (Global Ocean Species Spatio-temporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales. Optionally, pseudo-absences can be generated within the environmental space using the external package flexsdm (not on CRAN), which can be downloaded from <https://github.com/sjevelazco/flexsdm>; this functionality is used conditionally when available and all core features work without it.

Access and analyze multi-band greenspace seasonality data cubes (available for 1,028 major global cities), global Normalized Difference Vegetation Index / land cover data from the European Space Agency WorldCover 10m Dataset, and Sentinel-2-l2a images. Users can download data using bounding boxes, city names, and filter by year or seasonal time window. The package also supports calculating human exposure to greenspace using a population-weighted greenspace exposure model introduced by Chen et al. (2022) <doi:10.1038/s41467-022-32258-4> based on Global Human Settlement Layer population data, and calculating a set of greenspace morphology metrics at patch and landscape levels.

An intuitive interface to simulate (1) superimposed (marked) point patterns with vectorized parameterization of random point pattern and distribution of marks; and (2) grouped hyper data frame based on population parameters and subject-specific random effects.

This package provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots as described by Archer and Williams (2012) <doi:10.1002/sim.4484>.

This package provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day ('GSOD') weather data from the from the USA National Centers for Environmental Information ('NCEI'). Units are converted from from United States Customary System ('USCS') units to International System of Units ('SI'). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative humidity ('RH') are calculated from the original data using the improved August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags. For information on the GSOD data from NCEI', please see the GSOD readme.txt file available from, <https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.

This is an add on package to GAMLSS. The purpose of this package is to allow users to defined truncated distributions in GAMLSS models. The main function gen.trun() generates truncated version of an existing GAMLSS family distribution.

Introduces a Copilot'-like completion experience, but it knows how to talk to the objects in your R environment. ellmer chats are integrated directly into your RStudio and Positron sessions, automatically incorporating relevant context from surrounding lines of code and your global environment (like data frame columns and types). Open the package dialog box with a keyboard shortcut, type your request, and the assistant will stream its response directly into your documents.

Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (in press) <doi:10.1016/j.ecosta.2025.09.003>.

Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.

Add vector field layers to ggplots. Ideal for visualising wind speeds, water currents, electric/magnetic fields, etc. Accepts data.frames, simple features (sf), and spatiotemporal arrays (stars) objects as input. Vector fields are depicted as arrows starting at specified locations, and with specified angles and radii.

This package provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) <arXiv:1407.7819>. Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) <https://jmlr.org/papers/v20/17-501.html>). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) <doi:10.2307/2528966>). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes.