This package provides functions to handle command-line arguments for R scripting. It enables building stand-alone R programs that accept and parse command-line options in BIOS style. Zhang (2025) <https://github.com/bedapub/ribiosArg>.

The LabKey client library for R makes it easy for R users to load live data from a LabKey Server, <https://www.labkey.com/>, into the R environment for analysis, provided users have permissions to read the data. It also enables R users to insert, update, and delete records stored on a LabKey Server, provided they have appropriate permissions to do so.

Suite of tools for using D3', a library for producing dynamic, interactive data visualizations. Supports translating objects into D3 friendly data structures, rendering D3 scripts, publishing D3 visualizations, incorporating D3 in R Markdown, creating interactive D3 applications with Shiny, and distributing D3 based htmlwidgets in R packages.

This package provides a programmatic interface to openfisheries.org'. This package is part of the rOpenSci suite (http://ropensci.org).

This companion package extends the package robmed (Alfons, Ates & Groenen, 2022b; <doi:10.18637/jss.v103.i13>) in various ways. Most notably, it provides a graphical user interface for the robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>) to make the method more accessible to less proficient R users, as well as functions to export the results as a table in a Microsoft Word or Microsoft Powerpoint document, or as a LaTeX table. Furthermore, the package contains a shiny app to compare various bootstrap procedures for mediation analysis on simulated data.

Reconstructs retinae by morphing a flat surface with cuts (a dissected flat-mount retina) onto a curvilinear surface (the standard retinal shape). It can estimate the position of a point on the intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates. For more details see Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D. (2013) <doi:10.1371/journal.pcbi.1002921>.

This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: <https://www.monash.edu/business/ebs/research/publications/ebs/wp16-2019.pdf>.

Spatial Dispersion Index (SDI) is a generalized measurement index, or rather a family of indices to evaluate spatial dispersion of movements/flows in a network in a problem neutral way as described in: Gencer (2023) <doi:10.1007/s12061-023-09545-8>. This package computes and optionally visualizes this index with minimal hassle.

This package provides estimation and inference procedures for boundary regression discontinuity (RD) designs using local polynomial methods, based on either bivariate coordinates or distance-based approaches. Methods are developed in Cattaneo, Titiunik, and Yu (2025) <https://mdcattaneo.github.io/papers/Cattaneo-Titiunik-Yu_2025_BoundaryRD.pdf>.

This package provides a direct interface to the underlying XML representation of DDI Codebook 2.5 with flexible API creation.

Download the lyrics of your favorite songs in text and table formats. Also search for related songs or song information. More information: <https://docs.genius.com/> .

Algorithms to price American and European equity options, convertible bonds and a variety of other financial derivatives. It uses an extension of the usual Black-Scholes model in which jump to default may occur at a probability specified by a power-law link between stock price and hazard rate as found in the paper by Takahashi, Kobayashi, and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>. We use ideas and techniques from Andersen and Buffum (2002) <doi:10.2139/ssrn.355308> and Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.

This package provides functions to query (filter or transform), pivot (convert from array-of-objects to object-of-arrays, for easy import as R data frame), search, patch (edit), and validate (against JSON Schema') JSON and NDJSON strings, files, or URLs. Query and pivot support JSONpointer', JSONpath or JMESpath expressions. The implementation uses the jsoncons <https://danielaparker.github.io/jsoncons/> header-only library; the library is easily linked to other packages for direct access to C++ functionality not implemented here.

Root Expected Proportion Squared Difference (REPSD) is a nonparametric differential item functioning (DIF) method that (a) allows practitioners to explore for DIF related to small, fine-grained focal groups of examinees, and (b) compares the focal group directly to the composite group that will be used to develop the reported test score scale. Using your provided response matrix with a column that identifies focal group membership, this package provides the REPSD values, a simulated null distribution of possible REPSD values, and the simulated p-values identifying items possibly displaying DIF without requiring enormous sample sizes.

R interface to access prices and market data with the Bloomberg Data License service from <https://www.bloomberg.com/professional/product/data-license/>. As a prerequisite, a valid Data License from Bloomberg is needed together with the corresponding SFTP credentials and whitelisting of the IP from which accessing the service. This software and its author are in no way affiliated, endorsed, or approved by Bloomberg or any of its affiliates. Bloomberg is a registered trademark.

This package provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) <doi:10.1007/978-94-011-2546-8_20>, Sen, P. K. (1968) <doi:10.1080/01621459.1968.10480934>.

Empirical best linear unbiased prediction (EBLUP) and robust prediction of the area-level means under the basic unit-level model. The model can be fitted by maximum likelihood or a (robust) M-estimator. Mean square prediction error is computed by a parametric bootstrap.

We provide several avenues to predict and account for user-based mortality and tag loss during mark-recapture studies. When planning a study on a target species, the retentionmort_generation() function can be used to produce multiple synthetic mark-recapture datasets to anticipate the error associated with a planned field study to guide method development to reduce error. Similarly, if field data was already collected, the retentionmort() function can be used to predict the error from already generated data to adjust for user-based mortality and tag loss. The test_dataset_retentionmort() function will provide an example dataset of how data should be inputted into the function to run properly. Lastly, the retentionmort_figure() function can be used on any dataset generated from either model function to produce an rmarkdown printout of preliminary analysis associated with the model, including summary statistics and figures. Methods and results pertaining to the formation of this package can be found in McCutcheon et al. (in review, "Predicting tagging-related mortality and tag loss during mark-recapture studies").

Enables binary package installations on Linux distributions. Provides access to RStudio public repositories at <https://packagemanager.posit.co>, and transparent management of system requirements without administrative privileges. Currently supported distributions are CentOS / RHEL', and several RHEL derivatives ('Rocky Linux', AlmaLinux', Oracle Linux', and Amazon Linux'), openSUSE / SLES', Debian', and Ubuntu LTS.

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.

Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the whole world thanks to the Iowa Environment Mesonet website.

Detecting outliers using robust methods, i.e. the Median Absolute Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, & Licata (2013) <doi:10.1016/j.jesp.2013.03.013> and the Mahalanobis-Minimum Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein, O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>. There is also the more known but less robust Mahalanobis distance method, only for comparison purposes.

This package provides a machine learning package for automatic text classification that makes it simple for novice users to get started with machine learning, while allowing experienced users to easily experiment with different settings and algorithm combinations. The package includes eight algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks), comprehensive analytics, and thorough documentation.

Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the Hit Rate is calculated), RR@K (reciprocal rank at k, from which the MRR or mean reciprocal rank is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation.