|
r-corrr
|
public |
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.
|
2025-03-25 |
|
r-collapsibletree
|
public |
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.
|
2025-03-25 |
|
r-aer
|
public |
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.)
|
2025-03-25 |
|
r-vim
|
public |
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.
|
2025-03-25 |
|
r-smooth
|
public |
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.
|
2025-03-25 |
|
r-widyr
|
public |
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.
|
2025-03-25 |
|
r-usingr
|
public |
A collection of data sets to accompany the textbook "Using R for Introductory Statistics," second edition.
|
2025-03-25 |
|
r-tsoutliers
|
public |
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.
|
2025-03-25 |
|
r-tables
|
public |
Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.
|
2025-03-25 |
|
r-statsr
|
public |
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>.
|
2025-03-25 |
|
r-sjmisc
|
public |
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.
|
2025-03-25 |
|
r-questionr
|
public |
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.
|
2025-03-25 |
|
r-naniar
|
public |
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.
|
2025-03-25 |
|
r-modelr
|
public |
Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.
|
2025-03-25 |
|
r-mcomp
|
public |
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>.
|
2025-03-25 |
|
r-lexicon
|
public |
A collection of lexical hash tables, dictionaries, and word lists.
|
2025-03-25 |
|
r-fma
|
public |
All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
|
2025-03-25 |
|
r-expsmooth
|
public |
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008).
|
2025-03-25 |
|
r-editdata
|
public |
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.
|
2025-03-25 |
|
r-data.tree
|
public |
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.
|
2025-03-25 |
|
r-car
|
public |
Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, in press.
|
2025-03-25 |
|
r-seriation
|
public |
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).
|
2025-03-25 |
|
r-mice
|
public |
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.
|
2025-03-25 |
|
r-greybox
|
public |
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.
|
2025-03-25 |
|
r-caret
|
public |
Misc functions for training and plotting classification and regression models.
|
2025-03-25 |
|
r-visdat
|
public |
Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.
|
2025-03-25 |
|
r-unpivotr
|
public |
Tools for converting data from complex or irregular layouts to a columnar structure. For example, tables with multilevel column or row headers, or spreadsheets. Header and data cells are selected by their contents and position, as well as formatting and comments where available, and are associated with one other by their proximity in given directions. Functions for data frames and HTML tables are provided.
|
2025-03-25 |
|
r-tsna
|
public |
Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
|
2025-03-25 |
|
r-threejs
|
public |
Create interactive 3D scatter plots, network plots, and globes using the 'three.js' visualization library (<https://threejs.org>).
|
2025-03-25 |
|
r-syuzhet
|
public |
Extracts sentiment and sentiment-derived plot arcs from text using a variety of sentiment dictionaries conveniently packaged for consumption by R users. Implemented dictionaries include "syuzhet" (default) developed in the Nebraska Literary Lab "afinn" developed by Finn {\AA}rup Nielsen, "bing" developed by Minqing Hu and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the "get_sentiment" function. The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization.
|
2025-03-25 |
|
r-skimr
|
public |
A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the "Using skimr" vignette and the README.
|
2025-03-25 |
|
r-sjlabelled
|
public |
Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.
|
2025-03-25 |
|
r-rio
|
public |
Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data structure from the file extension, reasonable defaults are used for data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly without explicit decompression, and fast import packages are used where appropriate. An additional convenience function, 'convert()', provides a simple method for converting between file types.
|
2025-03-25 |
|
r-recipes
|
public |
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.
|
2025-03-25 |
|
r-rattle
|
public |
The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. A Gnome (RGtk2) based graphical interface is included with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file (or via ODBC), transform and explore the data, build and evaluate models, and export models as PMML (predictive modelling markup language) or as scores. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself.
|
2025-03-25 |
|
r-networkdynamicdata
|
public |
A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
|
2025-03-25 |
|
r-mitml
|
public |
Provides tools for multiple imputation of missing data in multilevel modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo', and several functions for visualization, data management and the analysis of multiply imputed data sets.
|
2025-03-25 |
|
r-manipulatewidget
|
public |
Like package 'manipulate' does for static graphics, this package helps to easily add controls like sliders, pickers, checkboxes, etc. that can be used to modify the input data or the parameters of an interactive chart created with package 'htmlwidgets'.
|
2025-03-25 |
|
r-leaflet
|
public |
Create and customize interactive maps using the 'Leaflet' JavaScript library and the 'htmlwidgets' package. These maps can be used directly from the R console, from 'RStudio', in Shiny applications and R Markdown documents.
|
2025-03-25 |
|
r-labelled
|
public |
Work with labelled data imported from 'SPSS' or 'Stata' with 'haven' or 'foreign'. This package provides useful functions to deal with "haven_labelled" and "haven_labelled_spss" classes introduced by 'haven' package.
|
2025-03-25 |
|
r-janitor
|
public |
The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and isolate duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Microsoft Excel. This package follows the principles of the "tidyverse" and works well with the pipe function %>%. janitor was built with beginning-to-intermediate R users in mind and is optimized for user-friendliness. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff.
|
2025-03-25 |
|
r-ggpubr
|
public |
The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.
|
2025-03-25 |
|
r-ggfortify
|
public |
Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using 'ggplot2'.
|
2025-03-25 |
|
r-fuzzyjoin
|
public |
Join tables together based not on whether columns match exactly, but whether they are similar by some comparison. Implementations include string distance and regular expression matching.
|
2025-03-25 |
|
r-dt
|
public |
Data objects in R can be rendered as HTML tables using the JavaScript library 'DataTables' (typically via R Markdown or Shiny). The 'DataTables' library has been included in this R package. The package name 'DT' is an abbreviation of 'DataTables'.
|
2025-03-25 |
|
r-diagrammer
|
public |
Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.
|
2025-03-25 |
|
r-dendextend
|
public |
Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.
|
2025-03-25 |
|
r-broom
|
public |
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
|
2025-03-25 |
|
r-bayesplot
|
public |
Plotting functions for posterior analysis, prior and posterior predictive checks, and MCMC diagnostics. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.
|
2025-03-25 |
|
r-tsibble
|
public |
Provides a 'tbl_ts' class (the 'tsibble') to store and manage temporal data in a data-centric format, which is built on top of the 'tibble'. The 'tsibble' aims at easily manipulating and analysing temporal data, including counting and filling in time gaps, aggregate over calendar periods, performing rolling window calculations, and etc.
|
2025-03-25 |
