Title: Bayesian Evaluation of Variant Involvement in Mendelian
Description: Bayesian inference procedures for evaluating evidence of involvement of variants in Mendelian disorders based on intuitive models of dominant and recessive inheritance.

Description: A fast integrative genetic association test for rare diseases.

Title: Bayesian Evaluation of Variant Involvement in Mendelian Disease

Author: Daniel Greene, Sylvia Richardson and Ernest Turro
Description: A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework.

Description: A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.

Imports: Rcpp (>= 0.12.3), Matrix, methods

Title: Compare the Goodness of Fit of Benford's and Blondeau Da Silva's
Maintainer: Blondeau Da Silva Stephane <Stephane.Blondeau-Da-Silva@ac-limoges.fr>
Description: Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).