This package implements safe policy learning under regression discontinuity designs with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>. The learned cutoffs are guaranteed to perform no worse than the existing cutoffs in terms of overall outcomes. The rdlearn package also includes features for visualizing the learned cutoffs relative to the baseline and conducting sensitivity analyses.

This package provides a strong type system for R which supports symbol declaration and assignment with type checking and condition checking.

The aim of the report package is to bridge the gap between Râ s output and the formatted results contained in your manuscript. This package converts statistical models and data frames into textual reports suited for publication, ensuring standardization and quality in results reporting.

This package provides functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.

This package provides several non parametric randomness tests for numeric sequences.

This package provides a method to decompose a univariate time series into meaningful subcomponents for analysis and denoising.

This package provides methods for calculation and visualization of the Repertoire Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) <doi:10.1186/s12859-017-1556-5>.

Helper function to install packages for R using an external requirements.txt or a string containing diverse packages from several resources like Github or CRAN.

Create doxygen documentation for source code in R packages. Includes a RStudio Addin, that allows to trigger the doxygenize process.

This project is a tool for words edit similarity joins (a.k.a. all-pairs similarity search) under small (< 3) edit distance constraints. It works for Levenshtein/Hamming distances and words from any alphabet. The software was originally developed for joining amino-acid/nucleotide sequences from Adaptive Immune Repertoires, where the number of words is relatively large (10^5-10^6) and the average length of words is relatively small (10-100).

An R interface to estimate structured additive regression (STAR) models with BayesX'.

Make it easy to use React in R with htmlwidget scaffolds, helper dependency functions, an embedded Babel transpiler', and examples.

Interface to access data via the United States Department of Agriculture's National Agricultural Statistical Service (NASS) Quick Stats web API <https://quickstats.nass.usda.gov/api/>. Convenience functions facilitate building queries based on available parameters and valid parameter values. This product uses the NASS API but is not endorsed or certified by NASS.

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

Download and access datasets from the Rdatasets archive (<https://vincentarelbundock.github.io/Rdatasets/>). The package provides functions to search, download, and view documentation for thousands of datasets from various R packages, available in both CSV and Parquet formats for efficient access.

This package contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.

An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.

This package provides R and JavaScript functions to allow WebGL'-based 3D plotting using the three.js JavaScript library. Interactivity through roll-over highlighting and toggle buttons is also supported.

Client for accessing data journalism APIs from ProPublica <http://www.propublica.org/>.

Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators.

Data objects in R can be rendered as HTML tables using the JavaScript library ag-grid (typically via R Markdown or Shiny'). The ag-grid library has been included in this R package. The package name RagGrid is an abbreviation of R agGrid'.

This package provides a structural, reproducible workflow for the processing and analysis of respirometry data. It contains analytical functions and utilities for working with oxygen time-series to determine respiration or oxygen production rates, and to make it easier to report and share analyses. See Harianto et al. 2019 <doi:10.1111/2041-210X.13162>.

ROSE (RObust Semiparametric Efficient) random forests for robust semiparametric efficient estimation in partially parametric models (containing generalised partially linear models). Details can be found in the paper by Young and Shah (2024) <doi:10.48550/arXiv.2410.03471>.

The traditional linear regression trend, Modified Mann-Kendall (MK) non-parameter trend and bootstrap trend are included in this package. Linear regression trend is rewritten by .lm.fit'. MK trend is rewritten by Rcpp'. Finally, those functions are about 10 times faster than previous version in R. Reference: Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. <doi:10.1016/S0022-1694(97)00125-X>.