Machine-Learning R General-Purpose Machine Learning
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arules: Mining Association Rules and Frequent Itemsetsbiglasso: Extending Lasso Model Fitting to Big Data in R.bmrm: Bundle Methods for Regularized Risk Minimization Package.Boruta: A wrapper algorithm for all-relevant feature selection.bst: Gradient Boosting.C50: C5.0 Decision Trees and Rule-Based Models.Classification and Regression Training: Unified interface to ~150 ML algorithms in R.General purpose gradient boosting on decision trees library with categorical features support out of the box for R.CORElearn: Classification, regression, feature evaluation and ordinal evaluation.Cubist: Rule- and Instance-Based Regression Modeling.e1071: Misc Functions of the Department of Statistics (e1071), TU Wienearth: Multivariate Adaptive Regression Spline Modelselasticnet: 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 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.fpc: Flexible procedures for clustering.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.grplasso: Fitting user specified models with Group Lasso penalty.grpreg: Regularization paths for regression models with grouped covariates.A framework for fast, parallel, and distributed machine learning algorithms at scale -- Deeplearning, Random forests, GBM, KMeans, PCA, GLM.ipred: Improved Predictors.kernlab: Kernel-based Machine Learning Lab.klaR: Classification and visualization.L0Learn: Fast algorithms for best subset selection.lasso2: L1 constrained estimation aka ‘lasso’.LiblineaR: Linear Predictive Models Based On The Liblinear C/C++ Library.LogicReg: Logic Regression.mboost: Model-Based Boosting.medley: Blending regression models, using a greedy stepwise approach.mlr: Machine Learning in R.ncvreg: Regularization paths for SCAD- and MCP-penalized regression models.party: A Laboratory for Recursive Partitioningpartykit: A Toolkit for Recursive Partitioning.penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model.penalizedSVM: Feature Selection SVM using penalty functions.quantregForest: Quantile Regression Forests.randomForest: Breiman and Cutler's random forests for classification and regression.randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC).rattle: Graphical user interface for data mining in R.rda: Shrunken Centroids Regularized Discriminant Analysis.rgenoud: R version of GENetic Optimization Using DerivativesRmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R.rpart: Recursive Partitioning and Regression Trees.RPMM: Recursively Partitioned Mixture Model.RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS).RWeka: R/Weka interface.RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression.spectralGraphTopology: Learning Graphs from Data via Spectral Constraints.Multi-algorithm ensemble learning packages.tree: Classification and regression trees.varSelRF: Variable selection using random forests.R binding for eXtreme Gradient Boosting (Tree) Library.A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. Optunity is written in Python but interfaces seamlessly to R.General purpose graph library.Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.Two data science utilities in R from Microsoft: 1) Interactive Data Exploration, Analysis, and Reporting (IDEAR) ; 2) Automated Modeling and Reporting (AMR).