Regularization for semiparametric additive hazards regression. [Deprecated]
Mining Association Rules and Frequent Itemsets
Extending Lasso Model Fitting to Big Data in R.
Bundle Methods for Regularized Risk Minimization Package.
A wrapper algorithm for all-relevant feature selection.
C5.0 Decision Trees and Rule-Based Models.
Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
Framework for fitting multiple caret models as well as creating ensembles of such models. [Deprecated]
Classification, regression, feature evaluation and ordinal evaluation.
Cox models by likelihood based boosting for a single survival endpoint or competing risks [Deprecated]
Rule- and Instance-Based Regression Modeling.
A data manipulation package that helps to solve the most common data manipulation problems.
Misc Functions of the Department of Statistics (), TU Wien
Multivariate Adaptive Regression Spline Models
Elastic-Net for Sparse Estimation and Sparse PCA.
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.
Evolutionary Learning of Globally Optimal Trees.
Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models.
Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the "forecast" package.
Flexible procedures for clustering.
Fuzzy Rule-based Systems for Classification and Regression Tasks. [Deprecated]
Generalized linear and additive models by likelihood based boosting. [Deprecated]
Boosting Methods for GAMLSS.
Generalized Boosted Regression Models.
Lasso and elastic-net regularized generalized linear models.
L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model.
Likelihood-based Boosting for Generalized mixed models. [Deprecated]
Fitting user specified models with Group Lasso penalty.
Regularization paths for regression models with grouped covariates.
ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.
A framework for fast, parallel, and distributed machine learning algorithms at scale -- Deeplearning, Random forests, GBM, KMeans, PCA, GLM.
Heteroscedastic Discriminant Analysis. [Deprecated]
binding to library - General purpose graph library.
Kernel-based Machine Learning Lab.
Classification and visualization.
Fast algorithms for best subset selection.
Least Angle Regression, Lasso and Forward Stagewise. [Deprecated]
L1 constrained estimation aka ‘lasso’.
Linear Predictive Models Based On The C/C++ Library.
Mapping, pruning, and graphing tree models. [Deprecated]
Blending regression models, using a greedy stepwise approach.
Machine Learning in R.
Regularization paths for SCAD- and MCP-penalized regression models.
Feed-forward Neural Networks and Multinomial Log-Linear Models. [Deprecated]
Pam: prediction analysis for microarrays. [Deprecated]
A Laboratory for Recursive Partytioning.
A Toolkit for Recursive Partytioning.
L1 (lasso and fused lasso) and L2 (ridge) estimation in GLMs and in the Cox model.
Penalized classification using Fisher's linear discriminant. [Deprecated]
Feature Selection SVM using penalty functions.
Quantile Regression Forests.
Breiman and Cutler's random forests for classification and regression.
Random Forests for Survival, Regression and Classification (RF-SRC).
Graphical user interface for data mining in R.
Shrunken Centroids Regularized Discriminant Analysis.
Relevant Dimension Estimation (RDE) in Feature Spaces. [Deprecated]
Regression Trees with Random Effects for Longitudinal (Panel) Data. [Deprecated]
Relaxed Lasso. [Deprecated]
R version of GENetic Optimization Using Derivatives
Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R.
Simpler use of data mining methods (e.g. NN and SVM) in classification and regression. [Deprecated]
Visualizing the performance of scoring classifiers. [Deprecated]
Data Analysis Using Rough Set and Fuzzy Rough Set Theories. [Deprecated]
Recursive Partitioning and Regression Trees.
Recursively Partitioned Mixture Model.
Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS).
Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression.
Shrinkage Discriminant Analysis and CAT Score Variable Selection. [Deprecated]
Learning Graphs from Data via Spectral Constraints.
Multi-algorithm ensemble learning packages.
the SVM Path algorithm. [Deprecated]
Two data science utilities in R from Microsoft: 1) Interactive Data Exploration, Analysis, and Reporting (IDEAR) ; 2) Automated Modeling and Reporting (AMR).
Bayesian treed Gaussian process models. [Deprecated]
Classification and regression trees.
Variable selection using random forests.
R binding for eXtreme Gradient Boosting (Tree) Library.