Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 <doi:10.1016/j.patcog.2009.03.027>, allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.

This package provides functions to convert Rd to roxygen documentation. It can parse an Rd file to a list, create the roxygen documentation and update the original R script (e.g. the one containing the definition of the function) accordingly. This package also provides utilities that can help developers build packages using roxygen more easily. The formatR package can be used to reformat the R code in the examples sections so that the code will be more readable.

R interface to the LTP'-Cloud service for Natural Language Processing in Chinese (http://www.ltp-cloud.com/).

This package provides the Jester Dataset for package recommenderlab.

The JSON format is ubiquitous for data interchange, and the simdjson library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel SIMD instruction manages to parse these files as faster than disk speed. See the <doi:10.48550/arXiv.1902.08318> paper for more details about simdjson'. This package parses JSON from string, file, or remote URLs under a variety of settings.

This package provides a tool to exchange data between R and Raven sound analysis software (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package warbleR'.

Calculation of ratios between two data sets containing environmental data like element concentrations by different methods. Additionally plant element concentrations can be corrected for adhering particles (soil, airborne dust).

Builds the TinyCC (Tiny C Compiler) command-line interface and library for package use in R'. The package compiles TinyCC from source and provides R functions to interact with the compiler. TinyCC can be used for header preprocessing, just-in-time compilation of C code in R', and lightweight C scripting workflows.

Streamline the calculation of conditional probabilities for various numeric ranges in an R DataFrame. It automates the need to convert numerical data into categorical data for conditional probability calculation, making it ideal for quick and preliminary data analysis.

This package provides functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) <doi:10.1007/978-1-4615-0079-7_1>, (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) <doi:10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3>, and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables.

Wrapper for Datamuse API to find rhyming and other associated words. This includes words of similar meaning, spelling, or other related words. Learn more about the Datamuse API here <https://www.datamuse.com/api/>.

Configuration management using files (YAML, JSON, INI, TXT), JSON strings, and command line arguments. Command line arguments can be used to override configuration. Period-separated command line flags are parsed as hierarchical lists. Environment variables, R global variables, and configuration values can be substituted.

In order to facilitate parsing of http requests and creating appropriate responses this package provides two classes to handle a lot of the housekeeping involved in working with http exchanges. The infrastructure builds upon the rook specification and is thus well suited to be combined with httpuv based web servers.

An EM algorithm to fit Mallows Models to full or partial rankings, with or without ties. Based on Adkins and Flinger (1998) <doi:10.1080/03610929808832223>.

Automatically creates separate regression models for different spatial regions. The prediction surface is smoothed using a regional border smoothing method. If regional models are continuous, the resulting prediction surface is continuous across the spatial dimensions, even at region borders. Methodology is described in Wagstaff and Bean (2023) <doi:10.32614/RJ-2023-004>.

Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>, the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>, the Merge-split/Recombination algorithms of Carter et al. (2019) <doi:10.48550/arXiv.1911.01503> and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst optimization algorithm of Cannon et al. (2020) <doi:10.48550/arXiv.2011.02288>.

An implementation of a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multi-core processing to enable optimization of time-consuming functions.

Downloads and imports agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) <https://www.agriculture.gov.au/abares> and the Australian Bureau of Statistics (ABS) <https://www.abs.gov.au>. Supports multiple data formats including spreadsheets, commaâ separated value (CSV) files, and geospatial data such as shapefiles and GeoTIFFs. Covers topics such as broadacre crops, livestock, soils, commodities and related agricultural information. The package standardises field names and data formats to improve interoperability and simplify analysis. It also streamlines the import of geospatial data and corrects common issues found in these data sources upon loading.

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.

Using this package, it is possible to call a BUGS model, summarize inferences and convergence in a table and graph, and save the simulations in arrays for easy access in R.

Includes algorithms to facilitate the assessment of extinction risk of species according to the IUCN (International Union for Conservation of Nature, see <https://iucn.org/> for more information) red list criteria.

This package implements random variables by means of S4 classes and methods.

Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>. Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) <doi:10.1002/wics.70051>.