A method for the Bayesian functional linear regression model (scalar-on-function),
  including two estimators of the coefficient function and an estimator of its support.
  A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., 
  Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.

An all-encompassing R toolkit designed to streamline the
    process of calling various bioinformatics software and then performing data
    analysis and visualization in R. With 'blit', users can easily integrate a
    wide array of bioinformatics command line tools into their workflows,
    leveraging the power of R for sophisticated data manipulation and graphical
    representation.

Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.

Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013) <doi:10.1007/s11336-013-9328-2>. Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).

Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.

A chemical speciation and toxicity prediction model for the 
    toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) 
    engine was originally programmed in 'PowerBasic' by Robert Santore and 
    others. The main way the BLM can be used is to predict the toxicity of a 
    metal to an organism with a known sensitivity (i.e., it is known how much 
    of that metal must accumulate on that organism's biotic ligand to cause a 
    physiological effect in a certain percentage of the population, such as a 
    20% loss in reproduction or a 50% mortality rate). The second way the BLM 
    can be used is to estimate the chemical speciation of the metal and other 
    constituents in water, including estimating the amount of metal accumulated 
    to an organism's biotic ligand during a toxicity test. In the first 
    application of the BLM, the amount of metal associated with a toxicity 
    endpoint, or regulatory limit will be predicted, while in the 
    second application, the amount of metal is known and the portions of that 
    metal that exist in various forms will be determined. This version of the 
    engine has been re-structured to perform the calculations in a different 
    way that will make it more efficient in R, while also making it more 
    flexible and easier to maintain in the future. Because of this, it does 
    not currently match the desktop model exactly, but we hope to improve 
    this comparability in the future.