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r-essentials
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Some essential packages for working with R
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2025-04-22 |
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r-tidyverse
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The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step. Learn more about the 'tidyverse' at <https://tidyverse.org>.
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2025-04-22 |
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r-anomalydetection
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Implements procedures developed by Gutierrez et al. (2017, <https://journal.r-project.org/archive/2017/RJ-2017-039/index.html>) to aid in detecting network log anomalies. By combining various multivariate analytic approaches relevant to network anomaly detection, it provides cyber analysts efficient means to detect suspected anomalies requiring further evaluation.
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2025-04-22 |
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r-tidytext
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Text mining for word processing and sentiment analysis using 'dplyr', 'ggplot2', and other tidy tools.
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2025-04-22 |
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r-caret
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Misc functions for training and plotting classification and regression models.
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2025-04-22 |
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r-tkrgl
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Provides 'TK' widget tools for the 'rgl' package.
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2025-04-22 |
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r-tensorflow
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Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
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2025-04-22 |
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r-sparklyr
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R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
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2025-04-22 |
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r-recipes
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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.
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2025-04-22 |
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r-pool
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Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are 'DBI' connections.
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2025-04-22 |
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r-plotly
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Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics.
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2025-04-22 |
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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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2025-04-22 |
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r-lahman
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Provides the tables from the 'Sean Lahman Baseball Database' as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2015, as recorded in the 2016 version of the database.
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2025-04-22 |
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r-highcharter
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A wrapper for the 'Highcharts' library including shortcut functions to plot R objects. 'Highcharts' <http://www.highcharts.com/> is a charting library offering numerous chart types with a simple configuration syntax.
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2025-04-22 |
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r-rglwidget
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The contents of this package have been merged into rgl, so it is no longer needed.
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2025-04-22 |
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r-keras
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Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
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2025-04-22 |
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r-implyr
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'SQL' back-end to 'dplyr' for Apache Impala, the massively parallel processing query engine for Apache 'Hadoop'. Impala enables low-latency 'SQL' queries on data stored in the 'Hadoop' Distributed File System '(HDFS)', Apache 'HBase', Apache 'Kudu', Amazon Simple Storage Service '(S3)', Microsoft Azure Data Lake Store '(ADLS)', and Dell 'EMC' 'Isilon'. See <https://impala.apache.org> for more information about Impala.
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2025-04-22 |
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r-diagrammer
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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.
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2025-04-22 |
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r-broom
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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.
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2025-04-22 |
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r-afex
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Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).
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2025-04-22 |
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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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2025-04-22 |
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r-rms
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Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. 'rms' works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.
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2025-04-22 |
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r-rgl
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Provides medium to high level functions for 3D interactive graphics, including functions modelled on base graphics (plot3d(), etc.) as well as functions for constructing representations of geometric objects (cube3d(), etc.). Output may be on screen using OpenGL, or to various standard 3D file formats including WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
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2025-04-22 |
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rpy2
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Python interface to the R language (embedded R)
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2025-04-22 |
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r-gistr
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Work with 'GitHub' 'gists' from 'R' (e.g., <http://en.wikipedia.org/wiki/GitHub#Gist>, <https://help.github.com/articles/about-gists/>). A 'gist' is simply one or more files with code/text/images/etc. This package allows the user to create new 'gists', update 'gists' with new files, rename files, delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists', open a 'gist' in your default browser, get embed code for a 'gist', list 'gist' 'commits', and get rate limit information when 'authenticated'. Some requests require authentication and some do not. 'Gists' website: <https://gist.github.com/>.
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2025-04-22 |
