# 'Rcpp' Integration for the 'mlpack' Library

## Documentation for package ‘mlpack’ version 4.8.0-1

- DESCRIPTION file.

## Help Pages

mlpack-package | mlpack
adaboost | AdaBoost
adaboost_classify | AdaBoost Prediction
adaboost_probabilities | AdaBoost Probability Prediction
adaboost_train | AdaBoost
approx_kfn | Approximate furthest neighbor search
bayesian_linear_regression | BayesianLinearRegression
bayesian_linear_regression_predict | BayesianLinearRegression Prediction
bayesian_linear_regression_train | BayesianLinearRegression Training
cf | Collaborative Filtering
dbscan | DBSCAN clustering
decision_tree | Decision tree
decision_tree_classify | Decision tree Prediction
decision_tree_probabilities | Decision tree Prediction
decision_tree_train | Decision tree training
det | Density Estimation With Density Estimation Trees
emst | Fast Euclidean Minimum Spanning Tree
fastmks | FastMKS (Fast Max-Kernel Search)
gmm_generate | GMM Sample Generator
gmm_probability | GMM Probability Calculator
gmm_train | Gaussian Mixture Model (GMM) Training
hmm_generate | Hidden Markov Model (HMM) Sequence Generator
hmm_loglik | Hidden Markov Model (HMM) Sequence Log-Likelihood
hmm_train | Hidden Markov Model (HMM) Training
hmm_viterbi | Hidden Markov Model (HMM) Viterbi State Prediction
hoeffding_tree | Hoeffding trees
image_converter | Image Converter
kde | Kernel Density Estimation
kernel_pca | Kernel Principal Components Analysis
kfn | k-Furthest-Neighbors Search
kmeans | K-Means Clustering
knn | k-Nearest-Neighbors Search
krann | K-Rank-Approximate-Nearest-Neighbors (kRANN)
lars | LARS
lars_predict | LARS Prediction
lars_train | LARS Training
linear_regression | Simple Linear Regression and Prediction
linear_regression_predict | Linear Regression Prediction
linear_regression_train | Simple Linear Regression
linear_svm | Linear SVM is an L2-regularized support vector machine.
lmnn | Large Margin Nearest Neighbors (LMNN)
local_coordinate_coding | Local Coordinate Coding
logistic_regression | L2-regularized Logistic Regression and Prediction
logistic_regression_classify | L2-regularized Logistic Regression Classification
logistic_regression_probabilities | L2-regularized Logistic Regression Probabilities
logistic_regression_train | L2-regularized Logistic Regression Training and Prediction
lsh | K-Approximate-Nearest-Neighbor Search with LSH
mean_shift | Mean Shift Clustering
mlpack | mlpack
nbc | Parametric Naive Bayes Classifier
nca | Neighborhood Components Analysis (NCA)
nmf | Non-negative Matrix Factorization
pca | Principal Components Analysis
perceptron | Perceptron
predict.mlpack_adaboost | AdaBoost Prediction
predict.mlpack_bayesian_linear_regression | BayesianLinearRegression
predict.mlpack_lars | LARS Prediction
predict.mlpack_linear_regression | Linear Regression Prediction
predict.mlpack_logistic_regression | L2-regularized Logistic Regression Classification
predict.mlpack_random_forest | Random Forests
preprocess_binarize | Binarize Data
preprocess_describe | Descriptive Statistics
preprocess_one_hot_encoding | One Hot Encoding
preprocess_scale | Scale Data
preprocess_split | Split Data
radical | RADICAL
random_forest | Random Forests
random_forest_classify | Random Forests Prediction
random_forest_probabilities | Random Forests Probabilities
random_forest_train | Random Forests train
Serialize | Serialize/Unserialize an mlpack model.
softmax_regression | Softmax Regression
sparse_coding | Sparse Coding
test_r_binding | R binding test
Unserialize | Serialize/Unserialize an mlpack model.