This package provides functions for computing a standardized moderation effect in moderated regression and forming its confidence interval by nonparametric bootstrapping as proposed in Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Also includes simple-to-use functions for computing conditional effects (unstandardized or standardized) and plotting moderation effects.

This package provides efficient R and C++ routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.

Build custom Europe SpatialPolygonsDataFrame, if you don't know what is a SpatialPolygonsDataFrame see SpatialPolygons() in sp', by example for mapLayout() in antaresViz'. Antares is a powerful software developed by RTE to simulate and study electric power systems (more information about Antares here: <https://antares-simulator.org/>).

Allows the user to connect with IBGE's (Instituto Brasileiro de Geografia e Estatistica, see <https://www.ibge.gov.br/> for more information) SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de Recuperacao Automatica" and is the system where IBGE turns available aggregate data from their researches.

Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates. The submax or subgroup-maximum method of Lee et al. (2018) <doi:10.1111/biom.12884> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.

Generate LaTeX tables directly from R. It builds LaTeX tables in blocks in the spirit of ggplot2 using the + and / operators for concatenation in the vertical and horizontal dimensions, respectively. It exports tables in the LaTeX tabular environment using .tex code. It can compile .tex code to PDF automatically.

The Ultimate Microrray Prediction, Reality and Inference Engine (UMPIRE) is a package to facilitate the simulation of realistic microarray data sets with links to associated outcomes. See Zhang and Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the ability to simulate realistic mixed-typed clinical data.

This package provides a YAML-based mechanism for working with table metadata. Supports compact syntax for creating, modifying, viewing, exporting, importing, displaying, and plotting metadata coded as column attributes. The yamlet dialect is valid YAML with defaults and conventions chosen to improve readability. See ?yamlet, ?decorate, ?modify, ?io_csv, and ?ggplot.decorated.

Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.

The package is an R wrapper for Progenetix REST API built upon the Beacon v2 protocol. Its purpose is to provide a seamless way for retrieving genomic data from Progenetix database—an open resource dedicated to curated oncogenomic profiles. Empowered by this package, users can effortlessly access and visualize data from Progenetix.

This package provides tools to calculate functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships, and so on.

This package provides functions for Maximum Likelihood (ML) estimation, non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models.

Pure OCaml regular expressions with:

1. Perl-style regular expressions (module Re_perl)

2. Posix extended regular expressions (module Re_posix)

3. Emacs-style regular expressions (module Re_emacs)

4. Shell-style file globbing (module Re_glob)

5. Compatibility layer for OCaml's built-in Str module (module Re_str)

This package performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.

This package contains a shiny application called AdEPro (Animation of Adverse Event Profiles) which (audio-)visualizes adverse events occurring in clinical trials. As this data is usually considered sensitive, this tool is provided as a stand-alone application that can be launched from any local machine on which the data is stored.

This package provides easy installation and loading of core ArcGIS location services packages arcgislayers', arcgisutils', arcgisgeocode', and arcgisplaces'. Enabling developers to interact with spatial data and services from ArcGIS Online', ArcGIS Enterprise', and ArcGIS Platform'. Learn more about the arcgis meta-package at <https://developers.arcgis.com/r-bridge/>.

Allows the user to connect with the World Spider Catalogue (WSC; <https://wsc.nmbe.ch/>) and the World Spider Trait (WST; <https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/>), and mapping.

This package provides a cross-platform representation of models as sets of equations that facilitates modularity in model building and allows users to harness modern techniques for numerical integration and data visualization. Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.

Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.

This package provides functions for performing experimental comparisons of algorithms using adequate sample sizes for power and accuracy. Implements the methodology originally presented in Campelo and Takahashi (2019) <doi:10.1007/s10732-018-9396-7> for the comparison of two algorithms, and later generalised in Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.

This package implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) <doi:10.2307/2579183>. Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a tidyselect based workflow.

Companion package to the paper: An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.

This package provides functions for creating flashcard decks of terms and definitions. This package creates HTML slides using revealjs that can be viewed in the RStudio viewer or a web browser. Users can create flashcards from either existing built-in decks or create their own from CSV files or vectors of function names.

This package provides a program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.