Provide early termination phase II trial designs with a
    decreasingly informative prior (DIP) or a regular Bayesian prior
    chosen by the user. The program can determine the minimum planned
    sample size necessary to achieve the user-specified admissible
    designs. The program can also perform power and expected sample size
    calculations for the tests in early termination Phase II trials.
    See Wang C and Sabo RT (2022) <doi:10.18203/2349-3259.ijct20221110>;
    Sabo RT (2014) <doi:10.1080/10543406.2014.888441>.

Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.

Implements Bayesian Distribution Regression methods. This package contains functions for three estimators (non-asymptotic, semi-asymptotic and asymptotic) and related routines for Bayesian Distribution Regression in Huang and Tsyawo (2018) <doi:10.2139/ssrn.3048658> which is also the recommended reference to cite for this package. The functions can be grouped into three (3) categories. The first computes the logit likelihood function and posterior densities under uniform and normal priors. The second contains Independence and Random Walk Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithms as functions and the third category of functions are useful for semi-asymptotic and asymptotic Bayesian distribution regression inference.

The 'BayesDLMfMRI' package performs statistical analysis for task-based functional magnetic resonance imaging (fMRI) data at both individual and group levels. The analysis to detect brain activation at the individual level is based on modeling the fMRI signal using Matrix-Variate Dynamic Linear Models (MDLM). The analysis for the group stage is based on posterior distributions of the state parameter obtained from the modeling at the individual level. In this way, this package offers several R functions with different algorithms to perform inference on the state parameter to assess brain activation for both individual and group stages. Those functions allow for parallel computation when the analysis is performed for the entire brain as well as analysis at specific voxels when it is required.
             References: Cardona-Jiménez (2021) <doi:10.1016/j.csda.2021.107297>;
             Cardona-Jiménez (2021) <arXiv:2111.01318>.