Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.

Performs dose assignment and trial simulation for the FBCRM (Fully Bayesian Continual Reassessment Method) and MFBCRM (Mixture Fully Bayesian Continual Reassessment Method) phase I clinical trial designs. These trial designs extend the Continual Reassessment Method (CRM) and Bayesian Model Averaging Continual Reassessment Method (BMA-CRM) by allowing the prior toxicity skeleton itself to be random, with posterior distributions obtained from Markov Chain Monte Carlo. On average, the FBCRM and MFBCRM methods outperformed the CRM and BMA-CRM methods in terms of selecting an optimal dose level across thousands of randomly generated simulation scenarios. Details on the methods and results of this simulation study are available on request, and the manuscript is currently under review.

We propose an objective Bayesian algorithm for searching the space
 of Gaussian directed acyclic graph (DAG) models. The algorithm uses moment
 fractional Bayes factors (MFBF) and is suitable for learning sparse graphs.
 The algorithm is implemented using Armadillo, an open-source C++ linear
 algebra library.

Fit a fractional binomial regression model and
 extended zero-inflated negative binomial regression 
 model to count data with excess zeros using maximum
 likelihood estimation.
 Compare zero-inflated regression models via Vuong 
 closeness test.

Implements the Mode Jumping Markov Chain Monte Carlo algorithm described in <doi:10.1016/j.csda.2018.05.020> and its Genetically Modified counterpart described in <doi:10.1613/jair.1.13047> as well as the sub-sampling versions described in <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.

Normalizes the data from a file containing the raw values of the SNP probes of microarray data by using the FISH probes and their corresponding copy number.