Data sets and sample analyses from Jay L. Devore (2008), "Probability and Statistics for Engineering and the Sciences (7th ed)", Thomson.

Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings.

Shows you which rows have changed between two data frames with the same column structure. Useful for diffing slowly mutating data.

Implementation of a modular framework for ecosystem risk assessments, combining existing risk assessment approaches tailored to semi-quantitative and quantitative analyses.

This package provides a collection of utility functions to download and manage data sets from the Internet or from other sources.

The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.

It provides materials (i.e. serial axes objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.

Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model.

This package provides a collection of popular/useful JavaScript utilities, including the terser minifier, sass compiler, typescript transpiler, and more.

Interface to NatureServe (<https://www.natureserve.org/>). Includes methods to get data, image metadata, search taxonomic names, and make maps.

Th-U-Pb electron microprobe age dating of monazite, as originally described in <doi:10.1016/0009-2541(96)00024-1>.

Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arXiv:1712.02412>.

Routines for flexible functional form estimation via basis regression, with model selection via the adaptive LASSO or SCAD to prevent overfitting.

Estimates corrected Procrustean correlation between matrices for removing overfitting effect. Coissac Eric and Gonindard-Melodelima Christelle (2019) <doi:10.1101/842070>.

This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.

Various data sets (stocks, stock indices, constituent data, FX, zero-coupon bond yield curves, volatility, commodities) for Quantitative Risk Management practice.

Computes the entire regularization path for the two-class svm classifier with essentially the same cost as a single SVM fit.

This package provides most of the data files used in the textbook "Scientific Research and Methodology" by Dunn (2025, ISBN: 9781032496726).

Format text (bold, italic, ...) and numbers using UTF-8. Offers functions to search for emojis and include them in your text.

Udev is a daemon which dynamically creates and removes device nodes from /dev/, handles hotplug events and loads drivers at boot time.

Recipes is an extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.

Download up-to-date data from the Reserve Bank of Australia in a tidy data frame. Package includes functions to download current and historical statistical tables (<https://www.rba.gov.au/statistics/tables/>) and forecasts (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data includes a broad range of Australian macroeconomic and financial time series.

This package creates interactive analytic graphs with R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as D3.js graphs embedded in a web page ready for its interactive analysis and exploration.

This package performs goodness of fits tests for both high and low-dimensional linear models. It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity. In addition one can test the significance of potentially large groups of variables, and also produce p-values for the significance of individual variables in high-dimensional linear regression.