Matrix reconstruction, also known as matrix completion, is the task of inferring missing entries of a partially observed matrix. This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.

Random number generation library implemented in C with multiple engines and distribution functions, providing an R interface focused on correctness, speed, and reproducibility. Supports various PRNGs including xoshiro256++/**, PCG64, Philox, and ChaCha20, with methods for continuous, discrete, and multivariate distributions.

This package implements the Representation-Level Control Surfaces (RLCS) paradigm for ensuring the reliability of autonomous systems and AI models. It provides three deterministic sensors: Residual Likelihood (ResLik) for population-level anomaly detection, Temporal Consistency Sensor (TCS) for drift and shock detection, and Agreement Sensor for multi-modal redundancy checks. These sensors feed into a standardized control surface that issues PROCEED', DEFER', or ABSTAIN signals based on strict safety invariants, allowing systems to detect and react to out-of-distribution states, sensor failures, and environmental shifts before they propagate to decision-making layers.

This package provides a task-oriented R interface to the RDKit <https://www.rdkit.org> library through its Python API via reticulate'. The package offers high-level cheminformatics functionality, including molecule parsing, descriptor calculation, and fingerprint generation without replicating the native structure of RDKit'.

These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses.

Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.

The ropenblas package (<https://prdm0.github.io/ropenblas/>) is useful for users of any GNU/Linux distribution. It will be possible to download, compile and link the OpenBLAS library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the GNU/Linux distribution used. With the ropenblas package it is possible to download, compile and link the latest version of the OpenBLAS library even the repositories of the GNU/Linux distribution used do not include the latest versions of OpenBLAS'. If of interest, older versions of the OpenBLAS library may be considered. Linking R with an optimized version of BLAS (<https://netlib.org/blas/>) may improve the computational performance of R code. The OpenBLAS library is an optimized implementation of BLAS that can be easily linked to R with the ropenblas package.

Utilities for accessing RePEc (Research Papers in Economics) through a RESTful API. You can request a code and get detailed information at the following page: <https://ideas.repec.org/api.html>.

Generate, simulate and visualise ODE models of consumer-resource interactions. At its core, rescomp provides a resource competition modelling focused interface to deSolve', alongside flexible functions for visualising model properties and dynamics. More information, documentation and examples can be found on the package website.

Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).

Random forest with a variety of additional features for regression, classification and survival analysis. The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable, based on Zhu, Zeng & Kosorok (2015) <doi:10.1080/01621459.2015.1036994>, subject weight, variable weight, tracking subjects used in each tree, etc.

Download and handle spatial and temporal data from the CAMELS-CL dataset (Catchment Attributes and Meteorology for Large Sample Studies, Chile) <https://camels.cr2.cl/>, developed by Alvarez-Garreton et al. (2018) <doi:10.5194/hess-22-5817-2018>. The package does not generate new data, it only facilitates direct access to the original dataset for hydrological analyses.

R wrapper of the libmf library <https://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc. High performance multi-core parallel computing is supported in this package.

The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. This package is focused on determining whether or not the degree of approximate multicollinearity in a multiple linear regression model is of concern, meaning that it affects the statistical analysis (i.e. individual significance tests) of the model. This objective is achieved by using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., Garcà a C.B. and Garcà a J. (2018) <doi:10.1080/00949655.2018.1463376>, Salmerón, R., Rodrà guez, A. and Garcà a C. (2020) <doi:10.1007/s00180-019-00922-x>, Salmerón, R., Garcà a, C.B, Rodrà guez, A. and Garcà a, C. (2022) <doi:10.32614/RJ-2023-010>, Salmerón, R., Garcà a, C.B. and Garcà a, J. (2025) <doi:10.1007/s10614-024-10575-8> and Salmerón, R., Garcà a, C.B, Garcà a J. (2023, working paper) <doi:10.48550/arXiv.2005.02245>. You can also view the package vignette using browseVignettes("rvif")', the package website (<https://www.ugr.es/local/romansg/rvif/index.html>) using browseURL(system.file("docs/index.html", package = "rvif")) or version control on GitHub (<https://github.com/rnoremlas/rvif_package>).

The main purpose of this package is to streamline the generation of exams that include random elements in exercises. Exercises can be defined in a table, based on text and figures, and may contain gaps to be filled with provided options. Exam documents can be generated in various formats. It allows us to generate a version for conducting the assessment and another version that facilitates correction, linked through a code.

It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.

Modeling and plotting functions for Reliability Growth Analysis (RGA) and Non-Homogeneous Poisson Process (NHPP) models for repairable systems. RGA models include the Duane (1962) <doi:10.1109/TA.1964.4319640>, NHPP by Crow (1975) (No. AMSAATR138), Piecewise Weibull NHPP by Guo et al. (2010) <doi:10.1109/RAMS.2010.5448029>, and Piecewise Weibull NHPP with Change Point Detection based on the segmented package by Muggeo (2024) <https://cran.r-project.org/package=segmented>. Repairable systems functions include the Mean Cumulative Function (MCF) using the Nelson-Aalen estimator, parametric Power Law and Log-Linear NHPP models, and forecasting.

Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>. This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.

Import SGF (Smart Game File) into R.

This package implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.

This package provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <DOI:10.48550/arXiv.1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <DOI:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <DOI:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <DOI:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <DOI:10.1017/S026646661600044X>, and Wied et al. (2014) <DOI:10.1016/j.csda.2013.03.005>.

This package provides a flexible alternative to the built-in rank() function called smartrank(). Optionally rank categorical variables by frequency (instead of in alphabetical order), and control whether ranking is based on descending/ascending order. smartrank() is suitable for both numerical and categorical data.

This package provides a Java implementation of the RAKE algorithm ('Rose', S., Engel', D., Cramer', N. and Cowley', W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.

Set of functions for Regression Discontinuity Design ('RDD'), for data visualisation, estimation and testing.