This package provides a method to download Department of Education College Scorecard data using the public API <https://collegescorecard.ed.gov/data/data-documentation/>. It is based on the dplyr model of piped commands to select and filter data in a single chained function call. An API key from the U.S. Department of Education is required.

This package provides tools for response surface analysis, using a comparative framework that identifies best-fitting solutions across 37 families of polynomials. Many of these tools are based upon and extend the RSA package, by testing a larger scope of polynomials (+27 families), more diverse response surface probing techniques (+acceleration points), more plots (+line of congruence, +line of incongruence, both with extrema), and other useful functions for exporting results.

This package contains three functions that access environmental data from any ERDDAPâ ¢ data web service. The rxtracto() function extracts data along a trajectory for a given "radius" around the point. The rxtracto_3D() function extracts data in a box. The rxtractogon() function extracts data in a polygon. All of those three function use the rerddap package to extract the data, and should work with any ERDDAPâ ¢ server. There are also two functions, plotBBox() and plotTrack() that use the plotdap package to simplify the creation of maps of the data.

Rcmdr menu support for many of the functions in the HH package. The focus is on menu items for functions we use in our introductory courses.

This package implements the t-walk algorithm, a general-purpose, self-adjusting Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as described by Christen & Fox (2010) <doi:10.1214/10-BA603>. The t-walk requires no tuning and is robust for a wide range of target distributions, including high-dimensional and multimodal problems. This implementation includes an option for running multiple chains in parallel to accelerate sampling and facilitate convergence diagnostics.

Utility functions to download data from the RESOURCECODE hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See <https://resourcecode.ifremer.fr> for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density.

This package provides methods for calculating diversity indices on numerical matrices, based on information theory, following Rocchini, Marcantonio and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039> and Rocchini et al. (2021) <doi:10.1101/2021.01.23.427872>.

This package provides classes and functions for modelling health care interventions using decision trees and semi-Markov models. Mechanisms are provided for associating an uncertainty distribution with each source variable and for ensuring transparency of the mathematical relationships between variables. The package terminology follows Briggs "Decision Modelling for Health Economic Evaluation" (2006, ISBN:978-0-19-852662-9).

Enhanced functionality for reactable in shiny applications, offering interactive and dynamic data table capabilities with ease. With reactable.extras', easily integrate a range of functions and components to enrich your shiny apps and facilitate user-friendly data exploration.

Jalali calendar, or solar Hijri, is calendar of Iran and Afghanistan (<https://en.wikipedia.org/wiki/Solar_Hijri_calendar>). This package is designed to working with Jalali date. For this purpose, It defines JalaliDate class that is similar to Date class.

Helps to fit thermal performance curves (TPCs). rTPC contains 26 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.

Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) <doi:10.1080/01621459.2013.766613>. Users can enjoy the near optimal, consistent, and oracle properties of the procedures.

Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id.

The rmoo package is a framework for multi- and many-objective optimization, which allows researchers and users versatility in parameter configuration, as well as tools for analysis, replication and visualization of results. The rmoo package was built as a fork of the GA package by Luca Scrucca(2017) <DOI:10.32614/RJ-2017-008> and implementing the Non-Dominated Sorting Genetic Algorithms proposed by K. Deb's.

The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. RGAP facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. RGAP features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the AMECO <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.

QuantLib bindings are provided for R using Rcpp via an updated variant of the header-only Quantuccia project (put together initially by Peter Caspers) offering an essential subset of QuantLib (and now maintained separately for the calendaring subset). See the included file AUTHORS for a full list of contributors to both QuantLib and Quantuccia'. Note that this package provided an initial viability proof, current work is done (via approximately quarterly releases tracking QuantLib') in the smaller package qlcal which is generally preferred.

Read raw and processed data from acoustic ejection mass spectrometry (AEMS) files produced by the Sciex EchoMS instrument. Includes functions to create interactive reader objects, extract raw intensity measurements, mass spectra, and fully-processed mass-transition intensity areas. Methods for data processing and analysis are described in Rimmer et al. (2025) <doi:10.1021/acs.analchem.5c03730>. Supports both multiple reaction monitoring (MRM) and full-scan (neutral loss and precursor ion) data formats.

This package provides functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005) <https://epub.uni-bayreuth.de/839/2/DissMKohl.pdf>).

Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.

Studies of resilience in older adults employ a single-arm design where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to regression-to-the-mean. This package provides a method to correct the bias. It also allows covariates to be included. The method implemented in the package is described in Varadhan, R., Zhu, J., and Bandeen-Roche, K (2024), Biostatistics 25(4): 1094-1111.

This package contains functions to create regulatory-style statistical reports. Originally designed to create tables, listings, and figures for the pharmaceutical, biotechnology, and medical device industries, these reports are generalized enough that they could be used in any industry. Generates text, rich-text, PDF, HTML, and Microsoft Word file formats. The package specializes in printing wide and long tables with automatic page wrapping and splitting. Reports can be produced with a minimum of function calls, and without relying on other table packages. The package supports titles, footnotes, page header, page footers, spanning headers, page by variables, and automatic page numbering.

Cross-Linguistic Data Format (CLDF) is a framework for storing cross-linguistic data, ensuring compatibility and ease of data exchange between different linguistic datasets see Forkel et al. (2018) <doi:10.1038/sdata.2018.205>. The rcldf package is designed to facilitate the manipulation and analysis of these datasets by simplifying the loading, querying, and visualisation of CLDF datasets making it easier to conduct comparative linguistic analyses, manage language data, and apply statistical methods directly within R.

This package implements two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, <doi:10.1093/biostatistics/kxx039>) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.

Generation of univariate and multivariate data that follow the generalized Poisson distribution. The details of the univariate part are explained in Demirtas (2017) <doi: 10.1080/03610918.2014.968725>, and the multivariate part is an extension of the correlated Poisson data generation routine that was introduced in Yahav and Shmueli (2012) <doi: 10.1002/asmb.901>.