This package provides an R interface to the RCSB Protein Data Bank ('PDB') Search and Data APIs (<https://www.rcsb.org/>). Supports full-text, attribute, sequence, motif, structure, and chemical searches; retrieval of entry-, assembly-, polymer-entity-, and chemical-component-level metadata; and conversion of API responses into analysis-ready tables and typed R objects for reproducible structural bioinformatics workflows.

Client for the web service methods provided by DataCite (<https://www.datacite.org/>), including functions to interface with their RESTful search API. The API is backed by Elasticsearch', allowing expressive queries, including faceting.

This package provides functions to conduct hypothesis tests and derive confidence intervals for quantiles, linear combinations of quantiles, ratios of dependent linear combinations and differences and ratios of all of the above for comparisons between independent samples. Additionally, quantile-based measures of inequality are also considered.

Wrapper for the RSpace Electronic Lab Notebook (<https://www.researchspace.com/>) API. This packages provides convenience functions to browse, search, create, and edit your RSpace documents. In addition, it enables filling RSpace templates from R Markdown/Quarto templates or tabular data (e.g., Excel files). This R package is not developed or endorsed by Research Space'.

Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.

An implementation of Bayesian model-averaged t-tests that allows users to draw inferences about the presence versus absence of an effect, variance heterogeneity, and potential outliers. The RoBTT package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, <doi:10.3758/s13423-024-02590-5>). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, <doi:10.31234/osf.io/j9f3s>). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics.

This is a port of Jonathan Shewchuk's Triangle library to R. From his description: "Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis.".

This package provides different methods of multi-criteria decision analysis.

This package provides tools for large, sparse optimal matching of treated units and control units in observational studies. Provisions are made for refined covariate balance constraints, which include fine and near-fine balance as special cases. Matches are optimal in the sense that they are computed as solutions to network optimization problems rather than greedy algorithms. See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data in a crossed design with an arbitrary number of factors. Brunner, E., Bathke, A. and Konietschke, F. (2018) <doi:10.1007/978-3-030-02914-2>.

Transform coordinates from a specified source to a specified target map projection. This uses the PROJ library directly, by wrapping the PROJ package which leverages libproj', otherwise the proj4 package. The reproj() function is generic, methods may be added to remove the need for an explicit source definition. If proj4 is in use reproj() handles the requirement for conversion of angular units where necessary. This is for use primarily to transform generic data formats and direct leverage of the underlying PROJ library. (There are transformations that aren't possible with PROJ and that are provided by the GDAL library, a limitation which users of this package should be aware of.) The PROJ library is available at <https://proj.org/>.

R Markdown format for reveal.js presentations, a framework for easily creating beautiful presentations using HTML.

This package provides an interface between R and PostGIS'-enabled PostgreSQL databases to transparently transfer spatial data. Both vector (points, lines, polygons) and raster data are supported in read and write modes. Also provides convenience functions to execute common procedures in PostgreSQL/PostGIS'.

This package produces Shiny applications for different types of popular functional data analyses. The functional data analyses are implemented in the refund package, then refund.shiny reads in the refund object and implements an object-specific set of plots based on the object class using S3.

This package provides a checkbox group input for usage in a Shiny application. The checkbox group has a head checkbox allowing to check or uncheck all the checkboxes in the group. The checkboxes are customizable.

This package implements robust median-based Bayesian growth curve models that handle Missing Completely at Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) missing-data mechanisms, and allow auxiliary variables. Models are fitted via rjags (interface to JAGS') and summarized with coda'.

S3 implementation of the Random Forest MErging Procedure (RF-MEP), which combines two or more satellite-based datasets (e.g., precipitation products, topography) with ground observations to produce a new dataset with improved spatio-temporal distribution of the target field. In particular, this package was developed to merge different Satellite-based Rainfall Estimates (SREs) with measurements from rain gauges, in order to obtain a new precipitation dataset where the time series in the rain gauges are used to correct different types of errors present in the SREs. However, this package might be used to merge other hydrological/environmental gridded datasets with point observations. For details, see Baez-Villanueva et al. (2020) <doi:10.1016/j.rse.2019.111606>. Bugs / comments / questions / collaboration of any kind are very welcomed.

An R Interface to Bloomberg is provided via the Blp API'.

An implementation of a method based on information theory devised for the identification of genes showing a significant variation of expression across multiple conditions. Given expression estimates from any number of RNA-Seq samples and conditions it identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. Zambelli et al. (2018) <doi:10.1093/nar/gky055>.

The rank distance correlation <doi:10.1080/01621459.2020.1782223> is computed. Included also is a function to perform permutation based testing.

This package provides a function for multivariate outlier detection named Modified Stahel-Donoho (MSD) estimators is contained. The function is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. The function is called the single core version in Wada & Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> and evaluated with other methods in Wada, Kawano & Tsubaki (2020) <doi:10.17713/ajs.v49i2.872>.

This package provides bioaccumulation factors from a toxicokinetic model fitted to accumulation-depuration data. It is designed to fulfil the requirements of regulators when examining applications for market authorization of active substances.

Build regular expressions piece by piece using human readable code. This package contains Unicode functionality, and is primarily intended to be used by package developers.

Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, <doi:10.1093/aje/kwi188>), and others.