Package: mlpack 4.8.0

Ryan Curtin

mlpack: 'Rcpp' Integration for the 'mlpack' Library

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) <doi:10.21105/joss.05026>.

Authors:Yashwant Singh Parihar [aut, ctb, cph], Ryan Curtin [aut, ctb, cph, cre], Dirk Eddelbuettel [aut, ctb, cph], James Balamuta [aut, ctb, cph], Bill March [ctb, cph], Dongryeol Lee [ctb, cph], Nishant Mehta [ctb, cph], Parikshit Ram [ctb, cph], James Cline [ctb, cph], Sterling Peet [ctb, cph], Matthew Amidon [ctb, cph], Neil Slagle [ctb, cph], Ajinkya Kale [ctb, cph], Vlad Grantcharov [ctb, cph], Noah Kauffman [ctb, cph], Rajendran Mohan [ctb, cph], Trironk Kiatkungwanglai [ctb, cph], Patrick Mason [ctb, cph], Marcus Edel [ctb, cph], Mudit Raj Gupta [ctb, cph], Sumedh Ghaisas [ctb, cph], Michael Fox [ctb, cph], Siddharth Agrawal [ctb, cph], Saheb Motiani [ctb, cph], Yash Vadalia [ctb, cph], Abhishek Laddha [ctb, cph], Vahab Akbarzadeh [ctb, cph], Andrew Wells [ctb, cph], Zhihao Lou [ctb, cph], Udit Saxena [ctb, cph], Stephen Tu [ctb, cph], Jaskaran Singh [ctb, cph], Hritik Jain [ctb, cph], Vladimir Glazachev [ctb, cph], QiaoAn Chen [ctb, cph], Janzen Brewer [ctb, cph], Trung Dinh [ctb, cph], Tham Ngap Wei [ctb, cph], Grzegorz Krajewski [ctb, cph], Joseph Mariadassou [ctb, cph], Pavel Zhigulin [ctb, cph], Andy Fang [ctb, cph], Barak Pearlmutter [ctb, cph], Ivari Horm [ctb, cph], Dhawal Arora [ctb, cph], Alexander Leinoff [ctb, cph], Palash Ahuja [ctb, cph], Yannis Mentekidis [ctb, cph], Ranjan Mondal [ctb, cph], Mikhail Lozhnikov [ctb, cph], Marcos Pividori [ctb, cph], Keon Kim [ctb, cph], Nilay Jain [ctb, cph], Peter Lehner [ctb, cph], Anuraj Kanodia [ctb, cph], Ivan Georgiev [ctb, cph], Shikhar Bhardwaj [ctb, cph], Yashu Seth [ctb, cph], Mike Izbicki [ctb, cph], Sudhanshu Ranjan [ctb, cph], Piyush Jaiswal [ctb, cph], Dinesh Raj [ctb, cph], Vivek Pal [ctb, cph], Prasanna Patil [ctb, cph], Lakshya Agrawal [ctb, cph], Praveen Ch [ctb, cph], Kirill Mishchenko [ctb, cph], Abhinav Moudgil [ctb, cph], Thyrix Yang [ctb, cph], Sagar B Hathwar [ctb, cph], Nishanth Hegde [ctb, cph], Parminder Singh [ctb, cph], CodeAi [ctb, cph], Franciszek Stokowacki [ctb, cph], Samikshya Chand [ctb, cph], N Rajiv Vaidyanathan [ctb, cph], Kartik Nighania [ctb, cph], Eugene Freyman [ctb, cph], Manish Kumar [ctb, cph], Haritha Sreedharan Nair [ctb, cph], Sourabh Varshney [ctb, cph], Projyal Dev [ctb, cph], Nikhil Goel [ctb, cph], Shikhar Jaiswal [ctb, cph], B Kartheek Reddy [ctb, cph], Atharva Khandait [ctb, cph], Wenhao Huang [ctb, cph], Roberto Hueso [ctb, cph], Prabhat Sharma [ctb, cph], Tan Jun An [ctb, cph], Moksh Jain [ctb, cph], Manthan-R-Sheth [ctb, cph], Namrata Mukhija [ctb, cph], Conrad Sanderson [ctb, cph], Thanasis Mattas [ctb, cph], Shashank Shekhar [ctb, cph], Yasmine Dumouchel [ctb, cph], German Lancioni [ctb, cph], Arash Abghari [ctb, cph], Ayush Chamoli [ctb, cph], Tommi Laivamaa [ctb, cph], Kim SangYeon [ctb, cph], Niteya Shah [ctb, cph], Toshal Agrawal [ctb, cph], Dan Timson [ctb, cph], Miguel Canteras [ctb, cph], Bishwa Karki [ctb, cph], Mehul Kumar Nirala [ctb, cph], Heet Sankesara [ctb, cph], Jeffin Sam [ctb, cph], Vikas S Shetty [ctb, cph], Khizir Siddiqui [ctb, cph], Tejasvi Tomar [ctb, cph], Jai Agarwal [ctb, cph], Ziyang Jiang [ctb, cph], Rohit Kartik [ctb, cph], Aditya Viki [ctb, cph], Kartik Dutt [ctb, cph], Suryoday Basak [ctb, cph], Sriram S K [ctb, cph], Manoranjan Kumar Bharti [ctb, cph], Saraansh Tandon [ctb, cph], Gaurav Singh [ctb, cph], Lakshya Ojha [ctb, cph], Bisakh Mondal [ctb, cph], Benson Muite [ctb, cph], Sarthak Bhardwaj [ctb, cph], Aakash Kaushik [ctb, cph], Anush Kini [ctb, cph], Nippun Sharma [ctb, cph], Rishabh Garg [ctb, cph], Sudhakar Brar [ctb, cph], Alex Nguyen [ctb, cph], Gaurav Ghati [ctb, cph], Anmolpreet Singh [ctb, cph], Anjishnu Mukherjee [ctb, cph], Omar Shrit [ctb, cph], Tru Hoang [ctb, cph], Mark Fischinger [ctb, cph], Muhammad Fawwaz Mayda [ctb, cph], Roshan Nrusing Swain [ctb, cph], Suvarsha Chennareddy [ctb, cph], Shubham Agrawal [ctb, cph], Sri Madhan M [ctb, cph], Zhuojin Liu [ctb, cph], Richèl Bilderbeek [ctb, cph], Chetan Pandey [ctb, cph], Nikolay Apanasov [ctb, cph], Martin Lambertsen [ctb, cph], Andrea Novellini [ctb, cph], Felix Patschkowski [ctb, cph], Benjamin A. Beasley [ctb, cph], Maksym Prots [ctb, cph], Zachary Ng [ctb, cph], Ranjodh Singh [ctb, cph], Mohammad Mundiwala [ctb, cph]

mlpack_4.8.0.tar.gz
mlpack_4.8.0.zip(r-4.7)mlpack_4.8.0.zip(r-4.6)mlpack_4.8.0.zip(r-4.5)
mlpack_4.8.0.tgz(r-4.6-x86_64)mlpack_4.8.0.tgz(r-4.6-arm64)mlpack_4.8.0.tgz(r-4.5-x86_64)mlpack_4.8.0.tgz(r-4.5-arm64)
mlpack_4.8.0.tar.gz(r-4.7-arm64)mlpack_4.8.0.tar.gz(r-4.7-x86_64)mlpack_4.8.0.tar.gz(r-4.6-arm64)mlpack_4.8.0.tar.gz(r-4.6-x86_64)
mlpack_4.8.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mlpack/json (API)

# Install 'mlpack' in R:
install.packages('mlpack', repos = c('https://eddelbuettel.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mlpack/mlpack/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.75 score 11 packages 57 scripts 825 downloads 3 mentions 67 exports 3 dependencies

Last updated from:4cf12a50c7. Checks:9 OK, 4 WARNING. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK1257
linux-devel-x86_64OK1262
source / vignettesOK1285
linux-release-arm64OK1258
linux-release-x86_64OK1231
macos-release-arm64WARNING910
macos-release-x86_64WARNING2511
macos-oldrel-arm64WARNING943
macos-oldrel-x86_64WARNING1455
windows-develOK1942
windows-releaseOK1899
windows-oldrelOK1725
wasm-releaseOK779

Exports:adaboostadaboost_classifyadaboost_probabilitiesadaboost_trainapprox_kfnbayesian_linear_regressionbayesian_linear_regression_predictbayesian_linear_regression_traincfdbscandecision_treedecision_tree_classifydecision_tree_probabilitiesdecision_tree_traindetemstfastmksgmm_generategmm_probabilitygmm_trainhmm_generatehmm_loglikhmm_trainhmm_viterbihoeffding_treeimage_converterkdekernel_pcakfnkmeansknnkrannlarslars_predictlars_trainlinear_regressionlinear_regression_predictlinear_regression_trainlinear_svmlmnnlocal_coordinate_codinglogistic_regressionlogistic_regression_classifylogistic_regression_probabilitieslogistic_regression_trainlshmean_shiftnbcncanmfpcaperceptronpreprocess_binarizepreprocess_describepreprocess_one_hot_encodingpreprocess_scalepreprocess_splitradicalrandom_forestrandom_forest_classifyrandom_forest_probabilitiesrandom_forest_trainSerializesoftmax_regressionsparse_codingtest_r_bindingUnserialize

Dependencies:RcppRcppArmadilloRcppEnsmallen

Readme and manuals

Help Manual

Help pageTopics
AdaBoostadaboost
AdaBoost Predictionadaboost_classify predict.mlpack_adaboost
AdaBoost Probability Predictionadaboost_probabilities
AdaBoostadaboost_train
Approximate furthest neighbor searchapprox_kfn
BayesianLinearRegressionbayesian_linear_regression predict.mlpack_bayesian_linear_regression
BayesianLinearRegression Predictionbayesian_linear_regression_predict
BayesianLinearRegression Trainingbayesian_linear_regression_train
Collaborative Filteringcf
DBSCAN clusteringdbscan
Decision treedecision_tree
Decision tree Predictiondecision_tree_classify
Decision tree Predictiondecision_tree_probabilities
Decision tree trainingdecision_tree_train
Density Estimation With Density Estimation Treesdet
Fast Euclidean Minimum Spanning Treeemst
FastMKS (Fast Max-Kernel Search)fastmks
GMM Sample Generatorgmm_generate
GMM Probability Calculatorgmm_probability
Gaussian Mixture Model (GMM) Traininggmm_train
Hidden Markov Model (HMM) Sequence Generatorhmm_generate
Hidden Markov Model (HMM) Sequence Log-Likelihoodhmm_loglik
Hidden Markov Model (HMM) Traininghmm_train
Hidden Markov Model (HMM) Viterbi State Predictionhmm_viterbi
Hoeffding treeshoeffding_tree
Image Converterimage_converter
Kernel Density Estimationkde
Kernel Principal Components Analysiskernel_pca
k-Furthest-Neighbors Searchkfn
K-Means Clusteringkmeans
k-Nearest-Neighbors Searchknn
K-Rank-Approximate-Nearest-Neighbors (kRANN)krann
LARSlars
LARS Traininglars_train
Simple Linear Regression and Predictionlinear_regression
Simple Linear Regressionlinear_regression_train
Linear SVM is an L2-regularized support vector machine.linear_svm
Large Margin Nearest Neighbors (LMNN)lmnn
Local Coordinate Codinglocal_coordinate_coding
L2-regularized Logistic Regression and Predictionlogistic_regression
L2-regularized Logistic Regression Probabilitieslogistic_regression_probabilities
L2-regularized Logistic Regression Training and Predictionlogistic_regression_train
K-Approximate-Nearest-Neighbor Search with LSHlsh
Mean Shift Clusteringmean_shift
mlpackmlpack-package mlpack
Parametric Naive Bayes Classifiernbc
Neighborhood Components Analysis (NCA)nca
Non-negative Matrix Factorizationnmf
Principal Components Analysispca
Perceptronperceptron
LARS Predictionlars_predict predict.mlpack_lars
Linear Regression Predictionlinear_regression_predict predict.mlpack_linear_regression
L2-regularized Logistic Regression Classificationlogistic_regression_classify predict.mlpack_logistic_regression
Random Forestspredict.mlpack_random_forest random_forest
Binarize Datapreprocess_binarize
Descriptive Statisticspreprocess_describe
One Hot Encodingpreprocess_one_hot_encoding
Scale Datapreprocess_scale
Split Datapreprocess_split
RADICALradical
Random Forests Predictionrandom_forest_classify
Random Forests Probabilitiesrandom_forest_probabilities
Random Forests trainrandom_forest_train
Serialize/Unserialize an mlpack model.Serialize Unserialize
Softmax Regressionsoftmax_regression
Sparse Codingsparse_coding
R binding testtest_r_binding