Analyzes revisions in real-time time series vintages. The package converts between wide revision triangles and tidy long vintages, extracts selected releases, computes revision series, visualizes vintage paths, and summarizes revision properties such as bias, dispersion, autocorrelation, and news-noise diagnostics. It also identifies efficient releases and estimates state-space models for revision nowcasting. Methods are based on Howrey (1978) <doi:10.2307/1924972>, Jacobs and Van Norden (2011) <doi:10.1016/j.jeconom.2010.04.010>, and Kishor and Koenig (2012) <doi:10.1198/jbes.2010.08169>.

Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 <doi:10.1371/journal.pgen.1009923>). Ravages also enables to perform genetic simulations (Bocher et al. 2023 <doi:10.1002/gepi.22529>).

Offers functions for fetching JSON data from the US EPA Air Quality System (AQS) API with options to comply with the API rate limits. See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for details of the AQS API.

Build regular expressions piece by piece using human readable code. This package contains number-related functionality, and is primarily intended to be used by package developers.

Residual balancing is a robust method of constructing weights for marginal structural models, which can be used to estimate (a) the average treatment effect in a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020 <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(), that produce residual balancing weights for estimating (a), (b), (c), respectively.

Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.

This package performs all steps in the credit scoring process. This package allows the user to follow all the necessary steps for building an effective scorecard. It provides the user functions for coarse binning of variables, Weights of Evidence (WOE) transformation, variable clustering, custom binning, visualization, and scaling of logistic regression coefficients. The results will generate a scorecard that can be used as an effective credit scoring tool to evaluate risk. For complete details on the credit scoring process, see Siddiqi (2005, ISBN:047175451X).

This package provides methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages rCDK and fingerprint for cheminformatics functionality. Methods for reaction similarity and sub-structure masking are as described in: Giri et al. (2015) <doi:10.1093/bioinformatics/btv416>.

Set of classes and methods to read data and metadata documents exchanged through the Statistical Data and Metadata Exchange (SDMX) framework, currently focusing on the SDMX XML standard format (SDMX-ML).

CausalEGM is a general causal inference framework for estimating causal effects by encoding generative modeling, which can be applied in both discrete and continuous treatment settings. A description of the methods is given in Liu (2022) <arXiv:2212.05925>.

Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.

Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The RavenR package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>. The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.

Earth Engine <https://earthengine.google.com/> client library for R. All of the Earth Engine API classes, modules, and functions are made available. Additional functions implemented include importing (exporting) of Earth Engine spatial objects, extraction of time series, interactive map display, assets management interface, and metadata display. See <https://r-spatial.github.io/rgee/> for further details.

Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.

This package provides programmatic access to Glottography, an online repository of geospatial speaker-area polygons for the world's languages. The package allows users to list available datasets, download and install them, and load speaker-area polygons as standard spatial sf objects in R. Data are sourced from either the Glottography organization on GitHub <https://github.com/Glottography> or the Glottography community on Zenodo <https://zenodo.org/communities/glottography>. Based on Ranacher et al. (2026) <doi:10.5334/johd.459>.

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'.

This package provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. The GeoDa software and its documentation are available at <https://geodacenter.github.io>.

Exports an Rcpp interface for the Bessel functions in the Bessel package, which can then be called from the C++ code of other packages. For the original Fortran implementation of these functions see Amos (1995) <doi:10.1145/212066.212078>.

Native R only allows PDF exports of reference manuals. The Rd2md package converts the package documentation files into markdown files and combines them into a markdown version of the package reference manual.

This package provides plotting helpers, table-formatting utilities, and report templates for econometrics, model development, and applied data analysis. Includes University of Cincinnati branded themes for ggplot2', modelsummary', flextable', rmarkdown', bookdown', and quarto'.

Applies quality control to daily precipitation observations; reconstructs the original series by estimating precipitation in missing values; and creates gridded datasets of daily precipitation.

This package provides a set of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging; TLS). Its creation is based on key aspects of the TLS application in forestry and ecology. Currently, the main routines are based on filtering, neighboring features of points, voxelization, canopy structure, and the creation of artificial stands. It is written using data.table and C++ language and in most of the functions it is possible to use parallel processing to speed-up the routines.

Upload R data.frame to Arm Treasure Data, see <https://www.treasuredata.com/>. You can execute database or table handling for resources on Arm Treasure Data.

Generate a Core Collection with Principal Component Scoring Strategy (PCSS) using qualitative and/or quantitative trait data according to Hamon and Noirot (1990) <https://www.documentation.ird.fr/hor/fdi:36506>, Noirot et al. (1996) <doi:10.2307/2527837> and Noirot et al. (2003) <https://www.documentation.ird.fr/hor/fdi:010031886>.