Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2022) <doi:10.1002/env.2711>.

Implements Bayesian inference to detect signal from blinded 
    clinical trial when total number of adverse events of special 
    concerns and total risk exposures from all patients are available in the study.  
    For more details see the article by Mukhopadhyay et. al. (2018) 
    titled 'Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data', 
    in Pharmaceutical Statistics (to appear).

Provides a continuous date scale, omitting weekends and holidays.

Implements methods for building and analyzing models based on panel
    data as described in the paper by 
    Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>).
    The package provides functions to estimate dynamic panel data models
    and analyze the results of the estimation.

This is a special case of sparse matrices, used by coxme.

Provides methods to perform block diagonal covariance matrix detection using singular vectors ('BD-SVD'), which can be extended to inherently sparse principal component analysis ('IS-PCA'). The methods are described in Bauer (2025) <doi:10.1080/10618600.2024.2422985> and Bauer (2026) <doi:10.48550/arXiv.2510.03729>.

A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.

Analyze bioequivalence study data with industrial strength. The statistical core is delegated to the 'sasLM' package, which reproduces 'SAS' PROC GLM output; this covers the analysis of variance, the least square means, and the confidence interval of a 2x2 crossover study. Sample size could be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4 design, Balaam design, Two-sequence dual design, and William design.
             Reference: Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. 3rd ed. (2009, ISBN:978-1-58488-668-6).