Amazing ML libraries to use in R

Zoshua Colah

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Data Science Library

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7 min read

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Nov 8, 2018

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The no-nonsense guide to Machine Learning libraries to use in R

sourced from: https://github.com/qinwf/awesome-R

• AnomalyDetection - AnomalyDetection R package from Twitter.
• ahaz — Regularization for semiparametric additive hazards regression.
• arules — Mining Association Rules and Frequent Itemsets
• bigrf — Big Random Forests: Classification and Regression Forests for Large Data Sets
• bigRR — Generalized Ridge Regression (with special advantage for p >> n cases)
• bmrm — Bundle Methods for Regularized Risk Minimization Package
• Boruta — A wrapper algorithm for all-relevant feature selection
• BreakoutDetection- Breakout Detection via Robust E-Statistics from Twitter.
• bst — Gradient Boosting
• CausalImpact- Causal inference using Bayesian structural time-series models.
• C50 — C5.0 Decision Trees and Rule-Based Models
• caret - Classification and Regression Training
• Clever Algorithms For Machine Learning
• CORElearn — Classification, regression, feature evaluation and ordinal evaluation
• CoxBoost — Cox models by likelihood based boosting for a single survival endpoint or competing risks
• Cubist — Rule- and Instance-Based Regression Modeling
• e1071 — Misc Functions of the Department of Statistics (e1071), TU Wien
• earth — Multivariate Adaptive Regression Spline Models
• elasticnet — Elastic-Net for Sparse Estimation and Sparse PCA
• ElemStatLearn — Data sets, functions and examples from the book: “The Elements of Statistical Learning, Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani and Jerome Friedman
• evtree — Evolutionary Learning of Globally Optimal Trees
• forecast — Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models
• forecastHybrid — Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the “forecast” package
• prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
• FSelector — A feature selection framework, based on subset-search or feature ranking approches.
• frbs — Fuzzy Rule-based Systems for Classification and Regression Tasks
• GAMBoost — Generalized linear and additive models by likelihood based boosting
• gamboostLSS — Boosting Methods for GAMLSS
• gbm — Generalized Boosted Regression Models
• glmnet - Lasso and elastic-net regularized generalized linear models
• glmpath — L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model
• GMMBoost — Likelihood-based Boosting for Generalized mixed models
• grplasso — Fitting user specified models with Group Lasso penalty
• grpreg — Regularization paths for regression models with grouped covariates
• h2o - Deeplearning, Random forests, GBM, KMeans, PCA, GLM
• hda — Heteroscedastic Discriminant Analysis
• ipred — Improved Predictors
• kernlab — kernlab: Kernel-based Machine Learning Lab
• klaR — Classification and visualization
• kohonen — Supervised and Unsupervised Self-Organising Maps.
• lars — Least Angle Regression, Lasso and Forward Stagewise
• lasso2 — L1 constrained estimation aka ‘lasso’
• LiblineaR — Linear Predictive Models Based On The Liblinear C/C++ Library
• lme4 - Mixed-effects models
• LogicReg — Logic Regression
• maptree — Mapping, pruning, and graphing tree models
• mboost — Model-Based Boosting
• Machine Learning For Hackers
• mlr - Extensible framework for classification, regression, survival analysis and clustering
• mvpart — Multivariate partitioning
• MXNet - MXNet brings flexible and efficient GPU computing and state-of-art deep learning to R.
• ncvreg — Regularization paths for SCAD- and MCP-penalized regression models
• nnet — eed-forward Neural Networks and Multinomial Log-Linear Models
• oblique.tree — Oblique Trees for Classification Data
• pamr — Pam: prediction analysis for microarrays
• party — A Laboratory for Recursive Partytioning
• partykit — A Toolkit for Recursive Partytioning
• penalized — L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model
• penalizedLDA — Penalized classification using Fisher’s linear discriminant
• penalizedSVM — Feature Selection SVM using penalty functions
• quantregForest — quantregForest: Quantile Regression Forests
• randomForest — randomForest: Breiman and Cutler’s random forests for classification and regression.
• randomForestSRC — randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).
• ranger — A Fast Implementation of Random Forests.
• rattle — Graphical user interface for data mining in R.
• rda — Shrunken Centroids Regularized Discriminant Analysis
• rdetools — Relevant Dimension Estimation (RDE) in Feature Spaces
• REEMtree — Regression Trees with Random Effects for Longitudinal (Panel) Data
• relaxo — Relaxed Lasso
• rgenoud — R version of GENetic Optimization Using Derivatives
• rgp — R genetic programming framework
• Rmalschains — Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R
• rminer — Simpler use of data mining methods (e.g. NN and SVM) in classification and regression
• ROCR — Visualizing the performance of scoring classifiers
• RoughSets — Data Analysis Using Rough Set and Fuzzy Rough Set Theories
• rpart — Recursive Partitioning and Regression Trees
• RPMM — Recursively Partitioned Mixture Model
• RSNNS — Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)
• Rsomoclu — Parallel implementation of self-organizing maps.
• RWeka — R/Weka interface
• RXshrink — RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression
• sda — Shrinkage Discriminant Analysis and CAT Score Variable Selection
• SDDA — Stepwise Diagonal Discriminant Analysis
• SuperLearner and subsemble — Multi-algorithm ensemble learning packages.
• svmpath — svmpath: the SVM Path algorithm
• tgp — Bayesian treed Gaussian process models
• tree — Classification and regression trees
• varSelRF — Variable selection using random forests
• xgboost- eXtreme Gradient Boosting Tree model, well known for its speed and performance.
• AnomalyDetection - AnomalyDetection R package from Twitter.
• ahaz — Regularization for semiparametric additive hazards regression.
• arules — Mining Association Rules and Frequent Itemsets
• bigrf — Big Random Forests: Classification and Regression Forests for Large Data Sets
• bigRR — Generalized Ridge Regression (with special advantage for p >> n cases)
• bmrm — Bundle Methods for Regularized Risk Minimization Package
• Boruta — A wrapper algorithm for all-relevant feature selection
• BreakoutDetection - Breakout Detection via Robust E-Statistics from Twitter.
• bst — Gradient Boosting
• CausalImpact - Causal inference using Bayesian structural time-series models.
• C50 — C5.0 Decision Trees and Rule-Based Models
• caret - Classification and Regression Training
• Clever Algorithms For Machine Learning
• CORElearn — Classification, regression, feature evaluation and ordinal evaluation
• CoxBoost — Cox models by likelihood based boosting for a single survival endpoint or competing risks
• Cubist — Rule- and Instance-Based Regression Modeling
• e1071 — Misc Functions of the Department of Statistics (e1071), TU Wien
• earth — Multivariate Adaptive Regression Spline Models
• elasticnet — Elastic-Net for Sparse Estimation and Sparse PCA
• ElemStatLearn — Data sets, functions and examples from the book: “The Elements of Statistical Learning, Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani and Jerome Friedman
• evtree — Evolutionary Learning of Globally Optimal Trees
• forecast — Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models
• forecastHybrid — Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the “forecast” package
• prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
• FSelector — A feature selection framework, based on subset-search or feature ranking approches.
• frbs — Fuzzy Rule-based Systems for Classification and Regression Tasks
• GAMBoost — Generalized linear and additive models by likelihood based boosting
• gamboostLSS — Boosting Methods for GAMLSS
• gbm — Generalized Boosted Regression Models
• glmnet - Lasso and elastic-net regularized generalized linear models
• glmpath — L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model
• GMMBoost — Likelihood-based Boosting for Generalized mixed models
• grplasso — Fitting user specified models with Group Lasso penalty
• grpreg — Regularization paths for regression models with grouped covariates
• h2o - Deeplearning, Random forests, GBM, KMeans, PCA, GLM
• hda — Heteroscedastic Discriminant Analysis
• ipred — Improved Predictors
• kernlab — kernlab: Kernel-based Machine Learning Lab
• klaR — Classification and visualization
• kohonen — Supervised and Unsupervised Self-Organising Maps.
• lars — Least Angle Regression, Lasso and Forward Stagewise
• lasso2 — L1 constrained estimation aka ‘lasso’
• LiblineaR — Linear Predictive Models Based On The Liblinear C/C++ Library
• lme4 - Mixed-effects models
• LogicReg — Logic Regression
• maptree — Mapping, pruning, and graphing tree models
• mboost — Model-Based Boosting
• Machine Learning For Hackers
• mlr - Extensible framework for classification, regression, survival analysis and clustering
• mvpart — Multivariate partitioning
• MXNet - MXNet brings flexible and efficient GPU computing and state-of-art deep learning to R.
• ncvreg — Regularization paths for SCAD- and MCP-penalized regression models
• nnet — eed-forward Neural Networks and Multinomial Log-Linear Models
• oblique.tree — Oblique Trees for Classification Data
• pamr — Pam: prediction analysis for microarrays
• party — A Laboratory for Recursive Partytioning
• partykit — A Toolkit for Recursive Partytioning
• penalized — L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model
• penalizedLDA — Penalized classification using Fisher’s linear discriminant
• penalizedSVM — Feature Selection SVM using penalty functions
• quantregForest — quantregForest: Quantile Regression Forests
• randomForest — randomForest: Breiman and Cutler’s random forests for classification and regression.
• randomForestSRC — randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).
• ranger — A Fast Implementation of Random Forests.
• rattle — Graphical user interface for data mining in R.
• rda — Shrunken Centroids Regularized Discriminant Analysis
• rdetools — Relevant Dimension Estimation (RDE) in Feature Spaces
• REEMtree — Regression Trees with Random Effects for Longitudinal (Panel) Data
• relaxo — Relaxed Lasso
• rgenoud — R version of GENetic Optimization Using Derivatives
• rgp — R genetic programming framework
• Rmalschains — Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R
• rminer — Simpler use of data mining methods (e.g. NN and SVM) in classification and regression
• ROCR — Visualizing the performance of scoring classifiers
• RoughSets — Data Analysis Using Rough Set and Fuzzy Rough Set Theories
• rpart — Recursive Partitioning and Regression Trees
• RPMM — Recursively Partitioned Mixture Model
• RSNNS — Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)
• Rsomoclu — Parallel implementation of self-organizing maps.
• RWeka — R/Weka interface
• RXshrink — RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression
• sda — Shrinkage Discriminant Analysis and CAT Score Variable Selection
• SDDA — Stepwise Diagonal Discriminant Analysis
• SuperLearner and subsemble — Multi-algorithm ensemble learning packages.
• svmpath — svmpath: the SVM Path algorithm
• tgp — Bayesian treed Gaussian process models
• tree — Classification and regression trees
• varSelRF — Variable selection using random forests
• xgboost - eXtreme Gradient Boosting Tree model, well known for its speed and performance.

Thank you for reading. A big thank you to https://github.com/qinwf/awesome-R#2018