Write blog posts and web pages in R Markdown. This package
    supports the static site generator 'Hugo' (<https://gohugo.io>) best,
    and it also supports 'Jekyll' (<https://jekyllrb.com>) and 'Hexo'
    (<https://hexo.io>).

It includes estimating the area under the concentrations
        versus time curve (AUC) and its standard error for data with
        Below the Limit of Quantification (BLOQ) observations. Two 
        approaches are implemented: direct estimation using censored maximum 
        likelihood, also by first imputing the BLOQ's 
        using various methods, then compute AUC and its standard 
        error using imputed data. Technical details can found in 
        Barnett, Helen Yvette, Helena Geys, Tom Jacobs, and Thomas Jaki. 
		"Methods for Non-Compartmental Pharmacokinetic Analysis With Observations 
		Below the Limit of Quantification." Statistics in Biopharmaceutical 
		Research (2020): 1-12.
        (available online: 
        <https://www.tandfonline.com/doi/full/10.1080/19466315.2019.1701546>).

Tools designed to make it easier for beginner and intermediate users to build and validate 
    binary logistic regression models. Includes bivariate analysis, comprehensive regression output, 
    model fit statistics, variable selection procedures, model validation techniques and a 'shiny' 
    app for interactive model building.

Provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.

Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial.

Due to a limited availability of observed high-resolution precipitation records with adequate length, simulations with stochastic precipitation models are used to generate series for subsequent studies [e.g. Khaliq and Cunmae, 1996, <doi:10.1016/0022-1694(95)02894-3>, Vandenberghe et al., 2011, <doi:10.1029/2009WR008388>]. This package contains an R implementation of the original Bartlett-Lewis rectangular pulse model (BLRPM), developed by Rodriguez-Iturbe et al. (1987) <doi:10.1098/rspa.1987.0039>. It contains a function for simulating a precipitation time series based on storms and cells generated by the model with given or estimated model parameters. Additionally BLRPM parameters can be estimated from a given or simulated precipitation time series. The model simulations can be plotted in a three-layer plot including an overview of generated storms and cells by the model (which can also be plotted individually), a continuous step-function and a discrete precipitation time series at a chosen aggregation level.