Advanced methods for a valuable quantitative environmental risk 
   assessment using Bayesian inference of survival and reproduction Data. Among
   others, it facilitates Bayesian inference of the general unified
   threshold model of survival (GUTS). See our companion paper 
   Baudrot and Charles (2021) <doi:10.21105/joss.03200>,
   as well as complementary details in Baudrot et al. (2018)
   <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al.
   (2017) <doi:10.1021/acs.est.6b05326>.

Advanced methods for a valuable quantitative environmental
    risk assessment using Bayesian inference of several type of
    toxicological data. 'binary' (e.g., survival, mobility), 'count'
    (e.g., reproduction) and 'continuous' (e.g., growth as length,
    weight).  Estimation procedures can be used without a deep knowledge
    of their underlying probabilistic model or inference methods. Rather,
    they were designed to behave as well as possible without requiring a
    user to provide values for some obscure parameters. That said, models
    can also be used as a first step to tailor new models for more
    specific situations.

Advanced methods for a valuable quantitative environmental risk 
   assessment using Bayesian inference of survival Data with toxicokinetics
   toxicodynamics (TKTD) models. Among others, it facilitates Bayesian inference of 
   the general unified threshold model of survival (GUTS). See models description
   in Jager et al. (2011) <doi:10.1021/es103092a> and implementation
   using Bayesian inference in Baudrot and Charles (2019)
   <doi:10.1038/s41598-019-47698-0>.

A toolkit for identifying potential mortalities and expelled tags in aquatic acoustic telemetry arrays. 
    Designed for arrays with non-overlapping receivers.

A collection of functions for the analysis of archaeological mortality
    data (on the topic see e.g. Chamberlain 2006 
    <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). 
    It takes demographic data in different formats and displays the result in a standard life table
    as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, 
    life expectancy, percentage of population). It also checks for possible biases in the age 
    structure and applies corrections to life tables.