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mlpack  

'Rcpp' Integration for the 'mlpack' Library
View on CRAN: Click here


Download and install mlpack package within the R console
Install from CRAN:
install.packages("mlpack")

Install from Github:
library("remotes")
install_github("cran/mlpack")

Install by package version:
library("remotes")
install_version("mlpack", "4.6.3")



Attach the package and use:
library("mlpack")
Maintained by
Ryan Curtin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-12-08
Latest Update: 2025-05-22
Description:
A fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. See also Curtin et al. (2023) .
How to cite:
Ryan Curtin (2020). mlpack: 'Rcpp' Integration for the 'mlpack' Library. R package version 4.6.3, https://cran.r-project.org/web/packages/mlpack. Accessed 14 Jul. 2026.
Previous versions and publish date:
(2026-07-09 06:31), 3.4.2.1 (2020-12-19 00:20), 3.4.2 (2020-12-08 18:40), 4.0.0 (2022-10-28 08:52), 4.0.1 (2023-01-13 11:10), 4.1.0 (2023-05-01 18:30), 4.2.0 (2023-06-25 17:40), 4.2.1 (2023-09-26 19:10), 4.3.0.1 (2024-02-24 06:00), 4.3.0 (2023-11-29 07:40), 4.4.0 (2024-06-01 00:10), 4.5.0 (2024-10-05 06:20), 4.5.1 (2024-12-05 19:20), 4.6.0 (2025-04-07 16:50), 4.6.1 (2025-05-16 11:40), 4.6.2 (2025-05-23 01:10), 4.6.3 (2025-09-01 21:10)
Other packages that cited mlpack R package
View mlpack citation profile
Other R packages that mlpack depends, imports, suggests or enhances
Complete documentation for mlpack
Functions, R codes and Examples using the mlpack R package
Some associated functions: adaboost . approx_kfn . bayesian_linear_regression . cf . dbscan . decision_tree . det . emst . fastmks . gmm_generate . gmm_probability . gmm_train . hmm_generate . hmm_loglik . hmm_train . hmm_viterbi . hoeffding_tree . image_converter . kde . kernel_pca . kfn . kmeans . knn . krann . lars . linear_regression . linear_svm . lmnn . local_coordinate_coding . logistic_regression . lsh . mean_shift . mlpack-serialization . mlpack . nbc . nca . nmf . pca . perceptron . preprocess_binarize . preprocess_describe . preprocess_one_hot_encoding . preprocess_scale . preprocess_split . radical . random_forest . softmax_regression . sparse_coding . test_r_binding . 
Some associated R codes: RcppExports.R . adaboost.R . approx_kfn.R . bayesian_linear_regression.R . cf.R . dbscan.R . decision_tree.R . det.R . emst.R . fastmks.R . gmm_generate.R . gmm_probability.R . gmm_train.R . hmm_generate.R . hmm_loglik.R . hmm_train.R . hmm_viterbi.R . hoeffding_tree.R . image_converter.R . kde.R . kernel_pca.R . kfn.R . kmeans.R . knn.R . krann.R . lars.R . linear_regression.R . linear_svm.R . lmnn.R . local_coordinate_coding.R . logistic_regression.R . lsh.R . matrix_utils.R . mean_shift.R . nbc.R . nca.R . nmf.R . package.R . pca.R . perceptron.R . preprocess_binarize.R . preprocess_describe.R . preprocess_one_hot_encoding.R . preprocess_scale.R . preprocess_split.R . radical.R . random_forest.R . serialization.R . softmax_regression.R . sparse_coding.R . test_r_binding.R .  Full mlpack package functions and examples
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