# MachineLearning

More info on CRANName | Description | Percentile | Stars |

ahaz | Regularization for semiparametric additive hazards regression | 0th | |

arules | Mining Association Rules and Frequent Itemsets | 0th | |

BayesTree | Bayesian Additive Regression Trees | 0th | |

bigRR | Generalized Ridge Regression (with special advantage for p >> n cases) | 0th | |

bmrm | Bundle Methods for Regularized Risk Minimization Package | 0th | |

Boruta | Wrapper Algorithm for All Relevant Feature Selection | 0th | |

bst | Gradient Boosting | 0th | |

C50 | C5.0 Decision Trees and Rule-Based Models | 0th | |

caret | Classification and Regression Training | 0th | |

CORElearn | Classification, Regression and Feature Evaluation | 0th | |

CoxBoost | Cox models by likelihood based boosting for a single survival endpoint or competing risks | 0th | |

Cubist | Rule- And Instance-Based Regression Modeling | 0th | |

e1071 | Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien | 0th | |

earth | Multivariate Adaptive Regression Splines | 0th | |

elasticnet | Elastic-Net for Sparse Estimation and Sparse PCA | 0th | |

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 | 0th | |

evtree | Evolutionary Learning of Globally Optimal Trees | 0th | |

FCNN4R | Fast Compressed Neural Networks for R | 0th | |

frbs | Fuzzy Rule-Based Systems for Classification and Regression Tasks | 0th | |

GAMBoost | Generalized linear and additive models by likelihood based boosting | 0th | |

gamboostLSS | Boosting Methods for 'GAMLSS' | 0th | |

gbm | Generalized Boosted Regression Models | 0th | |

glmnet | Lasso and Elastic-Net Regularized Generalized Linear Models | 0th | |

glmpath | L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model | 0th | |

GMMBoost | Likelihood-Based Boosting for Generalized Mixed Models | 0th | |

grplasso | Fitting User-Specified Models with Group Lasso Penalty | 0th | |

grpreg | Regularization Paths for Regression Models with Grouped Covariates | 0th | |

h2o | R Interface for the 'H2O' Scalable Machine Learning Platform | 0th | |

hda | Heteroscedastic Discriminant Analysis | 0th | |

hdi | High-Dimensional Inference | 0th | |

hdm | High-Dimensional Metrics | 0th | |

ipred | Improved Predictors | 0th | |

kernlab | null | 0th | |

klaR | Classification and Visualization | 0th | |

lars | Least Angle Regression, Lasso and Forward Stagewise | 0th | |

lasso2 | L1 Constrained Estimation aka `lasso' | 0th | |

LiblineaR | Linear Predictive Models Based on the 'LIBLINEAR' C/C++ Library | 0th | |

LogicForest | Logic Forest | 0th | |

LogicReg | Logic Regression | 0th | |

maptree | Mapping, pruning, and graphing tree models | 0th | |

mboost | Model-Based Boosting | 0th | |

mlr | Machine Learning in R | 0th | |

ncvreg | Regularization Paths for SCAD and MCP Penalized Regression Models | 0th | |

nnet | Feed-Forward Neural Networks and Multinomial Log-Linear Models | 0th | |

oblique.tree | Oblique Trees for Classification Data | 0th | |

pamr | Pam: Prediction Analysis for Microarrays | 0th | |

party | A Laboratory for Recursive Partytioning | 0th | |

partykit | A Toolkit for Recursive Partytioning | 0th | |

penalized | L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model | 0th | |

penalizedLDA | Penalized Classification using Fisher's Linear Discriminant | 0th | |

penalizedSVM | Feature Selection SVM using Penalty Functions | 0th | |

quantregForest | Quantile Regression Forests | 0th | |

randomForest | Breiman and Cutler's Random Forests for Classification and Regression | 0th | |

randomForestSRC | Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) | 0th | |

ranger | A Fast Implementation of Random Forests | 0th | |

rattle | Graphical User Interface for Data Science in R | 0th | |

Rborist | Extensible, Parallelizable Implementation of the Random Forest Algorithm | 0th | |

rda | Shrunken Centroids Regularized Discriminant Analysis | 0th | |

rdetools | Relevant Dimension Estimation (RDE) in Feature Spaces | 0th | |

REEMtree | Regression Trees with Random Effects for Longitudinal (Panel) Data | 0th | |

relaxo | Relaxed Lasso | 0th | |

rgenoud | R Version of GENetic Optimization Using Derivatives | 0th | |

rgp | R genetic programming framework | 0th | |

Rmalschains | Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R | 0th | |

rminer | Data Mining Classification and Regression Methods | 0th | |

ROCR | Visualizing the Performance of Scoring Classifiers | 0th | |

RoughSets | Data Analysis Using Rough Set and Fuzzy Rough Set Theories | 0th | |

rpart | Recursive Partitioning and Regression Trees | 0th | |

RPMM | Recursively Partitioned Mixture Model | 0th | |

RSNNS | Neural Networks using the Stuttgart Neural Network Simulator (SNNS) | 0th | |

RWeka | R/Weka Interface | 0th | |

RXshrink | Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression Methods | 0th | |

sda | Shrinkage Discriminant Analysis and CAT Score Variable Selection | 0th | |

SIS | Sure Independence Screening | 0th | |

stabs | Stability Selection with Error Control | 0th | |

SuperLearner | Super Learner Prediction | 0th | |

svmpath | The SVM Path Algorithm | 0th | |

tgp | Bayesian Treed Gaussian Process Models | 0th | |

tree | Classification and Regression Trees | 0th | |

varSelRF | Variable Selection using Random Forests | 0th | |

vcrpart | Tree-Based Varying Coefficient Regression for Generalized Linear and Ordinal Mixed Models | 0th | |

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