**Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD** (**fitODBOD**)

Contains probability mass functions, cumulative mass functions, negative log likelihood value, parameter estimation and modeling data using Binomial Mixture distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial distributions (ABD).

**Discriminant Analysis via Projections** (**DAP**)

An implementation of Discriminant Analysis via Projections (DAP) method for high-dimensional binary classification in the case of unequal covariance matrices. See Irina Gaynanova and Tianying Wang (2018) <arXiv:1711.04817v2>.

**Estimating Controlled Direct Effects for Explaining Causal Findings** (**DirectEffects**)

A set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216>.

**Wilcoxon-Mann-Whitney Sample Size Planning** (**WMWssp**)

Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test that is needed for a given power and two sided type I error rate. The method works for metric data with and without ties, count data, ordered categorical data, and even dichotomous data. But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect. For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs – Using R and SAS, Springer Verlag, to appear.

**Fast Statistical Hypothesis Tests on Rows and Columns of Matrices** (**matrixTests**)

Functions to perform fast statistical hypothesis tests on rows/columns of matrices. The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use, 3) compatibility with tests implemented in R (like those available in the ‘stats’ package).

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