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.

A full set of fast data manipulation tools with a tidy
    front-end and a fast back-end using 'collapse' and 'cheapr'.

Read and write PNG images with arrays, rasters, native
    rasters, numeric arrays, integer arrays, raw vectors and indexed
    values.  This PNG encoder exposes configurable internal options
    enabling the user to select a speed-size tradeoff.  For example,
    disabling compression can speed up writing PNG by a factor of 50.
    Multiple image formats are supported including raster, native rasters,
    and integer and numeric arrays at color depths of 1, 2, 3 or 4. 16-bit
    images are also supported. This implementation uses the 'libspng' 'C'
    library which is available from
    <https://github.com/randy408/libspng/>.