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CORElearn
View on CRAN: Click
here
Download and install CORElearn package within the R console
Install from CRAN:
install.packages("CORElearn")
Install from Github:
library("remotes")
install_github("cran/CORElearn")
Install by package version:
library("remotes")
install_version("CORElearn", "1.57.3")
Attach the package and use:
library("CORElearn")
Maintained by
"Marko Robnik-Sikonja"
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2009-09-20
Latest Update: 2022-11-18
Description:
A suite of machine learning algorithms written in C++ with the R
interface contains several learning techniques for classification and regression.
Predictive models include e.g., classification and regression trees with
optional constructive induction and models in the leaves, random forests, kNN,
naive Bayes, and locally weighted regression. All predictions obtained with these
models can be explained and visualized with the 'ExplainPrediction' package.
This package is especially strong in feature evaluation where it contains several variants of
Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini,
information gain, MDL, and DKM. These methods can be used for feature selection
or discretization of numeric attributes.
The OrdEval algorithm and its visualization is used for evaluation
of data sets with ordinal features and class, enabling analysis according to the
Kano model of customer satisfaction.
Several algorithms support parallel multithreaded execution via OpenMP.
The top-level documentation is reachable through ?CORElearn.
How to cite:
"Marko Robnik-Sikonja" (2009). CORElearn: Classification, Regression and Feature Evaluation. R package version 1.57.3, https://cran.r-project.org/web/packages/CORElearn
Previous versions and publish date:
0.9.22 (2009-09-20 22:07), 0.9.24 (2009-12-06 11:29), 0.9.25 (2010-01-08 09:08), 0.9.26 (2010-01-11 08:42), 0.9.28 (2010-09-03 09:31), 0.9.29 (2010-09-08 08:44), 0.9.30 (2010-09-14 09:36), 0.9.32 (2010-12-01 15:29), 0.9.33 (2011-03-26 16:43), 0.9.34 (2011-04-04 09:36), 0.9.35 (2011-08-19 09:02), 0.9.36 (2012-01-03 17:20), 0.9.37 (2012-01-17 15:11), 0.9.39 (2012-01-28 06:02), 0.9.40 (2012-07-10 07:46), 0.9.41 (2013-01-04 12:07), 0.9.42 (2013-10-18 10:24), 0.9.43 (2014-05-12 07:51), 0.9.44 (2014-12-24 06:22), 0.9.45 (2015-01-27 10:08), 0.9.46 (2015-06-03 19:06), 1.47.1 (2015-09-04 07:35), 1.48.0 (2016-07-25 20:39), 1.50.1 (2017-03-26 23:02), 1.50.2 (2017-03-28 10:56), 1.50.3 (2017-03-28 17:27), 1.51.2 (2017-08-08 16:00), 1.52.0 (2018-01-04 16:46), 1.52.1 (2018-04-02 16:31), 1.53.1 (2018-09-29 12:30), 1.54.2 (2020-02-08 11:20), 1.56.0 (2021-03-23 08:50), 1.57.1 (2022-11-06 16:10), 1.57.2 (2022-11-16 13:11)
Other packages that cited CORElearn R package
View CORElearn citation profile
Other R packages that CORElearn depends,
imports, suggests or enhances
Functions, R codes and Examples using
the CORElearn R package
Some associated functions: CORElearn-internal . CORElearn-package . CoreModel . attrEval . auxTest . calibrate . classDataGen . classPrototypes . cvGen . destroyModels . discretize . display.CoreModel . getCoreModel . getRFsizes . getRpartModel . helpCore . infoCore . modelEval . noEqualRows . ordDataGen . ordEval . paramCoreIO . plot.CoreModel . plot.ordEval . predict.CoreModel . preparePlot . regDataGen . reliabiltyPlot . rfAttrEval . rfClustering . rfOOB . rfOutliers . rfProximity . saveRF . testCore . versionCore .
Some associated R codes: Rinterface.R . dataGenerator.R . init.R . ordEval.R . rfVisualize.R . testCore.R . util.R . Full CORElearn package functions and examples
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