c(person("Lukas", "Vashold", role = c("aut", "cre"), email = "lukas.vashold@wu.ac.at", comment = c(ORCID = "0000-0002-3562-3414")),
person("Nikolas", "Kuschnig", role = c("aut"), comment = c(ORCID = "0000-0002-6642-2543")))
Lukas Vashold [aut, cre] (<https://orcid.org/0000-0002-3562-3414>),
Nikolas Kuschnig [aut] (<https://orcid.org/0000-0002-6642-2543>)
Functions to prepare tidy objects from estimated models via 'BVAR'
(see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and
visualisation thereof. Bridges the gap between estimating models with 'BVAR'
and plotting the results in a more sophisticated way with 'ggplot2' as well
as passing them on in a tidy format.

Bayesian Variant Selection: Bayesian Model Uncertainty
Techniques for Genetic Association Studies
The functions in this package focus on analyzing
case-control association studies involving a group of genetic
variants.  In particular, we are interested in modeling the
outcome variable as a function of a multivariate genetic
profile using Bayesian model uncertainty and variable selection
techniques.  The package incorporates functions to analyze data
sets involving common variants as well as extensions to model
rare variants via the Bayesian Risk Index (BRI) as well as
haplotypes.  Finally, the package also allows the incorporation
of external biological information to inform the marginal
inclusion probabilities via the iBMU.

Bayesian Variable Selection in High Dimensional Settings using
Non-Local Prior
Amir Nikooienejad [aut, cre], Valen E. Johnson [ths]
Variable/Feature selection in high or ultra-high dimensional
settings has gained a lot of attention recently specially in cancer genomic
studies. This package provides a Bayesian approach to tackle this problem,
where it exploits mixture of point masses at zero and nonlocal priors to
improve the performance of variable selection and coefficient estimation.
It performs variable selection for binary response and survival time
response datasets which are widely used in biostatistic and bioinformatics
community. Benefiting from parallel computing ability, it reports necessary
outcomes of Bayesian variable selection such as Highest Posterior
Probability Model (HPPM), Median Probability Model (MPM) and posterior
inclusion probability for each of the covariates in the model. The option
to use Bayesian Model Averaging (BMA) is also part of this package that can
be exploited for predictive power measurements in real datasets.