Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution.

Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.

Generate the James Blinding Index, as described in James et al (1996) 
             <https://pubmed.ncbi.nlm.nih.gov/8841652/> and the Bang Blinding Index, 
             as described in Bang et al (2004) <https://pubmed.ncbi.nlm.nih.gov/15020033/>.
             These are measures to assess whether or not satisfactory blinding has been 
             maintained in a randomized, controlled, clinical trial. These can be generated 
             for trial subjects, research coordinators and principal investigators, based 
             upon standardized questionnaires that have been administered, to assess whether
             or not they can correctly guess to which treatment arm (e.g. placebo or treatment) 
             subjects were assigned at randomization.

A GUI to correct measurement bias in DNA methylation
    analyses. The 'BiasCorrector' package just wraps the functions
    implemented in the 'R' package 'rBiasCorrection' into a shiny web
    application in order to make them more easily accessible. Publication:
    Kapsner et al. (2021) <doi:10.1002/ijc.33681>.

Statistical models of biased sampling in the form of 
  univariate and multivariate noncentral hypergeometric distributions, 
  including Wallenius' noncentral hypergeometric distribution and
  Fisher's noncentral hypergeometric distribution. 
  See vignette("UrnTheory") for explanation of these distributions.
  Literature: 
  Fog, A. (2008a). Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution, Communications in Statistics, Simulation and Computation, 37(2) <doi:10.1080/03610910701790269>.
  Fog, A. (2008b). Sampling methods for Wallenius’ and Fisher’s noncentral hypergeometric distributions, Communications in Statistics—Simulation and Computation, 37(2)
  <doi:10.1080/03610910701790236>.