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r-corrr
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A tool for exploring correlations. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualising the matrix in terms of the strength of the correlations.
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2025-03-25 |
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r-collapsibletree
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Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it. Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame. See 'collapsibleTree' website for more information and examples.
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2025-03-25 |
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r-aer
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Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)
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2025-03-25 |
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r-vim
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New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
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2025-03-25 |
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r-smooth
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Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes Exponential Smoothing, SARIMA, Complex Exponential Smoothing, Simple Moving Average, Vector Exponential Smoothing in state space forms, several simulation functions and intermittent demand state space models.
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2025-03-25 |
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r-widyr
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Encapsulates the pattern of untidying data into a wide matrix, performing some processing, then turning it back into a tidy form. This is useful for several operations such as co-occurrence counts, correlations, or clustering that are mathematically convenient on wide matrices.
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2025-03-25 |
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r-usingr
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A collection of data sets to accompany the textbook "Using R for Introductory Statistics," second edition.
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2025-03-25 |
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r-tsoutliers
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Detection of outliers in time series following the Chen and Liu (1993) <DOI:10.2307/2290724> procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.
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2025-03-25 |
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r-tables
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Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.
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2025-03-25 |
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r-statsr
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Provides functions and datasets to support inference with the open access book "An Introduction to Bayesian Thinking", available online <https://statswithr.github.io/book> and online videos for the "Statistics with R Specialization" <https://www.coursera.org/specializations/statistics>. which includes an introduction to Bayesian inference and decision making for one and two sample credible intervals and hypothesis testing for Gaussian and Binomial data, in addition to frequentist inference using model-based and randomization-based methods. To help with understanding concepts, 'shiny' applications are used to aide visualization of sampling distributions, credible intervals, hypothesis testing, Lindley's and Bartlett's paradoxes. For development versions or to report issues, please visit <https://github.com/StatsWithR/statsr>.
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2025-03-25 |
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r-sjmisc
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Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data, and all integrate seamlessly into a 'tidyverse'-workflow.
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2025-03-25 |
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r-questionr
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Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions.
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2025-03-25 |
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r-naniar
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Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data.
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2025-03-25 |
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r-modelr
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Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.
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2025-03-25 |
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r-mcomp
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The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.
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2025-03-25 |
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r-lexicon
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A collection of lexical hash tables, dictionaries, and word lists.
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2025-03-25 |
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r-fma
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All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
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2025-03-25 |
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r-expsmooth
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Data sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008).
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2025-03-25 |
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r-editdata
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An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame' without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications.
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2025-03-25 |
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r-data.tree
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Create tree structures from hierarchical data, and traverse the tree in various orders. Aggregate, cumulate, print, plot, convert to and from data.frame and more. Useful for decision trees, machine learning, finance, conversion from and to JSON, and many other applications.
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2025-03-25 |
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r-car
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Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, in press.
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2025-03-25 |
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r-seriation
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Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT).
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2025-03-25 |
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r-mice
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Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
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2025-03-25 |
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r-greybox
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Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.
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2025-03-25 |
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r-caret
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Misc functions for training and plotting classification and regression models.
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2025-03-25 |
