# Machine Learning with R

## Amazing ML libraries to use in R

Nov 8, 2018 · 7 min read

**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