Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing 
            comprehensive data preprocessing and support for a wide range of algorithms with hyperparameter tuning. 
            It offers performance metrics and visualization tools to facilitate efficient and effective machine learning workflows.

This is an extremely fast implementation of a Naive Bayes classifier. This 
    package is currently the only package that supports a Bernoulli distribution, a Multinomial 
    distribution, and a Gaussian distribution, making it suitable for both binary features, 
    frequency counts, and numerical features. Another feature is the support of a mix of 
    different event models. Only numerical variables are allowed, however, categorical variables 
    can be transformed into dummies and used with the Bernoulli distribution. 
    The implementation is largely based on the paper 
    "A comparison of event models for Naive Bayes anti-spam e-mail filtering" 
    written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be 
    submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.

We present an implementation of the algorithms required to simulate
  large-scale social networks and retrieve their most relevant metrics. Details 
  can be found in the accompanying scientific paper on the Journal 
  of Statistical Software, <doi:10.18637/jss.v096.i07>.

Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.

The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.

An implementation in 'Rcpp' / 'RcppArmadillo' of Partial Least Square algorithms. This package includes other functions to perform the double cross-validation and a fast correlation.