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.

Finds the critical sample size ("critical point of stability") for a 
    correlation to stabilize in Schoenbrodt and Perugini's definition of 
    sequential stability (see <doi:10.1016/j.jrp.2013.05.009>).

'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.  
    It produces, for each sample, an html report and a compressed file containing the raw data. 
    If you have hundreds of samples, you are not going to open up each 'HTML' page. 
    You need some way of looking at these data in aggregate. 
    'fastqcr' Provides helper functions to easily parse, aggregate and analyze 
    'FastQC' reports for large numbers of samples. It provides a convenient solution for building 
    a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.