Easily load and install multiple packages from different sources, including CRAN and GitHub. The libraries function allows you to load or attach multiple packages in the same function call. The packages function will load one or more packages, and install any packages that are not installed on your system (after prompting you). Also included is a from_import function that allows you to import specific functions from a package into the global environment.

Extend the functionality of the mclust package for Gaussian finite mixture modeling by including: density estimation for data with bounded support (Scrucca, 2019 <doi:10.1002/bimj.201800174>); modal clustering using MEM (Modal EM) algorithm for Gaussian mixtures (Scrucca, 2021 <doi:10.1002/sam.11527>); entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023 <doi:10.1016/j.csda.2022.107582>); Gaussian mixtures modeling of financial log-returns (Scrucca, 2024 <doi:10.3390/e26110907>).

This package provides a function that wraps mcparallel() and mccollect() from parallel with temporary variables and a task handler. Wrapped in this way the results of an mcparallel() call can be returned to the R session when the fork is complete without explicitly issuing a specific mccollect() to retrieve the value. Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with a single call to mcparallelDoCheck().

This package provides methods to calculate and present PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA (<https://www.fva-bw.de/en/homepage/>) Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria.

This package provides tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>, extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.

This package provides a wrapper to access data from the SeeClickFix web API for R. SeeClickFix is a central platform employed by many cities that allows citizens to request their city's services. This package creates several functions to work with all the built-in calls to the SeeClickFix API. Allows users to download service request data from numerous locations in easy-to-use dataframe format manipulable in standard R functions.

This package provides a collection of data sets relating to ADHD (Attention Deficit Hyperactivity Disorder) which have been sourced from other packages on CRAN or from publications on other websites such as Kaggle <http://www.kaggle.com/>.The package also includes some simple functions for analysing data sets. The data sets and descriptions of the data sets may differ from what is on CRAN or other source websites. The aim of this package is to bring together data sets from a variety of ADHD research publications. This package would be useful for those interested in finding out what research has been done on the topic of ADHD, or those interested in comparing the results from different existing works. I started this project because I wanted to put together a collection of the data sets relevant to ADHD research, which I have a personal interest in. This work was conducted with the support of my mentor within the Global Talent Mentoring platform. <https://globaltalentmentoring.org/>.

This package provides a macro package for use with epsf.tex which allows PostScript labels in an Encapsulated PostScript file to be replaced by TeX labels. The package provides commands \relabel (simply replace a PostScript string), \adjustrelabel (replace a PostScript string, with position adjustment), and \extralabel (add a label at given coordinates). You can, if you so choose, use the facilities of the labelfig package in place of using \extralabel.

The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data.

Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

This package provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. BSSoverSpace is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.

Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, and were set as the default palette for the Python Matplotlib library <https://matplotlib.org/>. Other palettes available in this library have been derived from RColorBrewer <https://CRAN.R-project.org/package=RColorBrewer> and colorspace <https://CRAN.R-project.org/package=colorspace> packages.

Calculates confidence intervals after variable selection using repeated data splits. The package offers methods to address the challenges of post-selection inference, ensuring more accurate confidence intervals in models involving variable selection. The two main functions are lmps', which records the different models selected across multiple data splits as well as the corresponding coefficient estimates, and cips', which takes the lmps object as input to select variables and perform inferences using two types of voting.

Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.

This package provides functions for determining and evaluating high-risk zones and simulating and thinning point process data, as described in Determining high risk zones using point process methodology - Realization by building an R package Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> and Determining high-risk zones for unexploded World War II bombs by using point process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.

This package provides a shiny application to automate forward and back survey translation with optional reconciliation using large language models (LLMs). Supports OpenAI (GPT), Google Gemini, and Anthropic Claude models. It follows the TRAPD (Translation, Review, Adjudication, Pretesting, Documentation) framework and ISPOR (International Society for Pharmacoeconomics and Outcomes Research) recommendations. See Harkness et al. (2010) <doi:10.1002/9780470609927.ch7> and Wild et al. (2005) <doi:10.1111/j.1524-4733.2005.04054.x>.

This package provides functions to compute Euclidean minimum spanning trees using single-, sesqui-, and dual-tree Boruvka algorithms. Thanks to K-d trees, they are fast in spaces of low intrinsic dimensionality. Mutual reachability distances (used in the definition of the HDBSCAN* algorithm) are also supported. The package also features relatively fast fallback minimum spanning tree and nearest-neighbours algorithms for spaces of higher dimensionality. The Python version of quitefastmst is available via PyPI'.

Automates documentation of test_that() calls within R test files. The package scans test sources, extracts human-readable test titles (even when composed with functions like paste() or glue::glue(), ... etc.), and generates reproducible roxygen2-style listings that can be inserted both globally and per-section. It ensures idempotent updates and supports customizable numbering templates with hierarchical indices. Designed for developers, QA teams, and package maintainers seeking consistent, self-documenting test inventories.

Framework for visualising tables of counts, proportions and probabilities. The framework is called product plots, alluding to the computation of area as a product of height and width, and the statistical concept of generating a joint distribution from the product of conditional and marginal distributions. The framework, with extensions, is sufficient to encompass over 20 visualisations previously described in fields of statistical graphics and infovis, including bar charts, mosaic plots, treemaps, equal area plots and fluctuation diagrams.

peakCombiner, a fully R based, user-friendly, transparent, and customizable tool that allows even novice R users to create a high-quality consensus peak list. The modularity of its functions allows an easy way to optimize input and output data. A broad range of accepted input data formats can be used to create a consensus peak set that can be exported to a file or used as the starting point for most downstream peak analyses.

This package implements a new method ClussCluster descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, ClussCluster incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells.

Interfaces R with LSD simulation models. Reads object-oriented data in results files (.res[.gz]) produced by LSD and creates appropriate multi-dimensional arrays in R. Supports multiple core parallel threads of multi-file data reading for increased performance. Also provides functions to extract basic information and statistics from data files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).

Bootstrap routines for nested linear mixed effects models fit using either lme4 or nlme'. The provided bootstrap() function implements the parametric, residual, cases, random effect block (REB), and wild bootstrap procedures. An overview of these procedures can be found in Van der Leeden et al. (2008) <doi: 10.1007/978-0-387-73186-5_11>, Carpenter, Goldstein & Rasbash (2003) <doi: 10.1111/1467-9876.00415>, and Chambers & Chandra (2013) <doi: 10.1080/10618600.2012.681216>.

Returns nonparametric aligned rank tests for the interaction in two-way factorial designs, on R data sets with repeated measures in wide format. Five ANOVAs tables are reported. A PARAMETRIC one on the original data, one for a CHECK upon the interaction alignments, and three aligned rank tests: one on the aligned REGULAR, one on the FRIEDMAN, and one on the KOCH ranks. In these rank tests, only the resulting values for the interaction are relevant.