The <http://standartox.uni-landau.de> database offers cleaned,
    harmonized and aggregated ecotoxicological test data, which can
    be used for assessing effects and risks of chemical concentrations
    found in the environment.

Easily create alerts, notifications, modals, info tips and loading 
    screens in 'Shiny'. Includes several options to customize alerts and 
    notifications by including text, icons, images and buttons. When wrapped 
    around a 'Shiny' output, loading screen is automatically displayed while 
    the output is being recalculated.

The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.

Implementation of popular mortality models using the 'rstan' 
    package, which provides the R interface to the 'Stan' C++ library for 
    Bayesian estimation. The package supports well-known models proposed in the 
    actuarial and demographic literature including the Lee-Carter (1992) 
    <doi:10.1080/01621459.1992.10475265> and the Cairns-Blake-Dowd (2006) 
    <doi:10.1111/j.1539-6975.2006.00195.x> models. By a simple call, the user 
    inputs deaths and exposures and the package outputs the MCMC simulations for
    each parameter, the log likelihoods and predictions. Moreover, the package 
    includes tools for model selection and Bayesian model averaging by leave 
    future-out validation.