The IRLS (Iteratively Reweighted Least Squares) and GMM (Generalized Method of Moments) methods are applied to estimate mixed correlation coefficient matrix (Pearson, Polyseries, Polychoric), which can be estimated in pairs or simultaneously. For more information see Peng Zhang and Ben Liu (2024) <doi:10.1080/10618600.2023.2257251>; Ben Liu and Peng Zhang (2024) <doi:10.48550/arXiv.2404.06781>.

This package provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.

The algorithm first identifies a population of individuals from Danish register data with any type of diabetes as individuals with two or more inclusion events. Then, it splits this population into individuals with either type 1 diabetes or type 2 diabetes by identifying individuals with type 1 diabetes and classifying the remainder of the diabetes population as having type 2 diabetes.

An algorithm for fitting interpretable additive neural networks for identifiable and visualizable feature effects using post hoc orthogonalization. Fit custom neural networks intuitively using established R formula notation, including interaction effects of arbitrary order while preserving identifiability to enable a functional decomposition of the prediction function. For more details see Koehler et al. (2025) <doi:10.1038/s44387-025-00033-7>.

This comprehensive toolkit for skewed regression is designated as "SLIC" (The LIC for Distributed Skewed Regression Analysis). It is predicated on the assumption that the error term follows a skewed distribution, such as the Skew-Normal, Skew-t, or Skew-Laplace. The methodology and theoretical foundation of the package are described in Guo G.(2020) <doi:10.1080/02664763.2022.2053949>.

Connecting to databases requires boilerplate code to specify connection parameters and to set up sessions properly with the DBMS. This package provides a simple tool to fill two purposes: abstracting connection details, including secret credentials, out of your source code and managing configuration for frequently-used database connections in a persistent and flexible way, while minimizing requirements on the runtime environment.

Useful functions to connect to TM1 <https://www.ibm.com/uk-en/products/planning-and-analytics> instance from R via REST API. With the functions in the package, data can be imported from TM1 via mdx view or native view, data can be sent to TM1', processes and chores can be executed, and cube and dimension metadata information can be taken.

Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of variable annuities, which reflect realistic features of common annuity contracts in practice. It aims to facilitate the development and dissemination of research related to the efficient valuation of a portfolio of large variable annuities. The main valuation methodology was proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.

This package provides a simple git client for R based on libgit2 with support for SSH and HTTPS remotes. All functions in gert use basic R data types (such as vectors and data-frames) for their arguments and return values. User credentials are shared with command line git through the git-credential store and SSH keys stored on disk or ssh-agent.

redsea is a lightweight command-line FM Radio Data System (FM-RDS) decoder. Redsea can be used with any RTL-SDR USB radio stick with the rtl_fm tool, or any other software-defined radio (SDR) via csdr, for example. It can also decode raw ASCII bitstream, the hex format used by RDS Spy, and audio files containing multiplex signals (MPX).

This is a package for Non-Negative Linear Models (NNLM). It implements fast sequential coordinate descent algorithms for non-negative linear regression and non-negative matrix factorization (NMF). It supports mean square error and Kullback-Leibler divergence loss. Many other features are also implemented, including missing value imputation, domain knowledge integration, designable W and H matrices and multiple forms of regularizations.

Compute bounds for the treatment effect after adjusting for the presence of omitted variables in linear econometric models, according to the method of Basu (2022) <arXiv:2203.12431>. You supply the data, identify the outcome and treatment variables and additional regressors. The main functions will compute bounds for the bias-adjusted treatment effect. Many plot functions allow easy visualization of results.

Data frame, tibble, or tbl objects are converted to data package objects using specific metadata labels (name, version, title, homepage, description). A data package object ('dpkg') can be written to disk as a parquet file or released to a GitHub repository. Data package objects can be read into R from online repositories and downloaded files are cached locally across R sessions.

Data and analysis of ecological multilayer networks, including standardization of data structures and functions to convert between them. Includes an interactive multilayer network visualizer (beta, paper forthcoming), and a collection of 78 empirical ecological multilayer network datasets. This work was supported by research grant ISF (Israel Science Foundation) 1281/20 to Shai Pilosof. Noa Frydman (2023) <doi:10.1111/2041-210X.14225>.

This package provides tools for importing, analyzing and visualizing ego-centered network data. Supports several data formats, including the export formats of EgoNet', EgoWeb 2.0 and openeddi'. An interactive (shiny) app for the intuitive visualization of ego-centered networks is provided. Also included are procedures for creating and visualizing Clustered Graphs (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).

This package provides a highly configurable jQuery plugin offering a simple interface to create complex queries/filters in Shiny'. The outputted rules can easily be parsed into a set of R and/or SQL queries and used to filter data. Custom parsing of the rules is also supported. For more information about jQuery QueryBuilder see <https://querybuilder.js.org/>.

This package provides functions for vectorised conditional recoding of variables. case_when() enables you to vectorise multiple if and else statements (like CASE WHEN in SQL'). if_else() is a stricter and more predictable version of ifelse() in base that preserves attributes. These functions are forked from dplyr with all package dependencies removed and behave identically to the originals.

Simulates the Multi-Attribute Search and Choice (MASC) model of Gluth, Deakin and Rieskamp (2026) <doi:10.1037/rev0000614> for multi-attribute decision-making, including sequential information search, Bayesian belief updating, and choice. Beliefs may be treated as univariate (independent attributes), or multivariate over correlated attributes ('MASC-C'), in which observing one attribute updates beliefs about correlated attributes via a Kalman filter.

Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.

The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). PPS can be useful for data exploration purposes, in the same way correlation analysis is. For more information on PPS, see <https://github.com/paulvanderlaken/ppsr>.

This package provides a secure and user-friendly interface to interact with the Plug <https://plugbytpf.com.br> API'. It enables developers to store and manage tokens securely using the keyring package, retrieve data from API endpoints with the httr2 package, and handle large datasets with chunked data fetching. Designed for simplicity and security, the package facilitates seamless integration with Plug ecosystem.

This package provides a tidyverse'-style interface to the Brazilian Central Bank (<https://www.bcb.gov.br>) PIX Open Data API <https://olinda.bcb.gov.br/olinda/servico/Pix_DadosAbertos/versao/v1/aplicacao#!/recursos>. Retrieve statistics on PIX keys, transactions by municipality, and monthly transaction summaries. All functions return tibbles and support OData query parameters for filtering, selecting, and ordering data.

This package provides a comprehensive set of string manipulation functions based on those found in Python without relying on reticulate'. It provides functions that intend to (1) make it easier for users familiar with Python to work with strings, (2) reduce the complexity often associated with string operations, (3) and enable users to write more readable and maintainable code that manipulates strings.

Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".