Functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models.

A simple set of wrapper functions for data.table::fread() that allows subsetting or
    filtering rows and selecting columns of table-formatted files too large for the available RAM.
    'b stands for 'big files'.
    bread makes heavy use of Unix commands like 'grep', 'sed', 'wc', 'awk' and 'cut'. They are available 
    by default in all Unix environments.
    For Windows, you need to install those commands externally in order to simulate a 
    Unix environment and make sure that the executables are in the Windows PATH variable.
    To my knowledge, the simplest ways are to install 'RTools', 'Git' or 'Cygwin'. If they have been 
    correctly installed (with the expected registry entries), they should be detected on loading
    the package and the correct directories will be added automatically to the PATH.

The goal of the package is to provide an easy-to-use method 
  for estimating degrees of relatedness (up to the second degree) 
  for extreme low-coverage data. The package also allows users to 
  quantify and visualise the level of confidence in the estimated 
  degrees of relatedness.

Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. 'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.

Model agnostic tool for decomposition of predictions from black boxes.
    Break Down Table shows contributions of every variable to a final prediction. 
    Break Down Plot presents variable contributions in a concise graphical way. 
    This package work for binary classifiers and general regression models.