Estimate large covariance matrices in approximate factor
        models by thresholding principal orthogonal complements.

Bayesian variable selection for regression models of under-reported
    count data as well as for (overdispersed) Poisson, negative binomal and
    binomial logit regression models using spike and slab priors.

The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.

Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) <doi: 10.1016/j.csda.2012.10.006>. It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution. The C code for fast Fourier transformation (FFT) is written by R Core Team (2019)<https://www.R-project.org/>, which implements the FFT algorithm in Singleton (1969) <doi: 10.1109/TAU.1969.1162042>.

Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.

Functions for obtaining the density, random deviates 
             and maximum likelihood estimates of the Poisson lognormal 
             distribution and the bivariate Poisson lognormal distribution.