Fits and simulates multi-optima Ornstein-Uhlenbeck models to phylogenetic comparative data using
    Bayesian reversible-jump methods. See Uyeda and Harmon (2014) <DOI:10.1093/sysbio/syu057>.

A toolkit for constructing, validating, and justifying Bayesian priors
  in clinical trial settings. Implements expert elicitation via quantile
  matching, the roulette method, and moment matching across six distribution
  families, linear and logarithmic expert pooling, prior-data conflict
  diagnostics including the Box p-value, surprise index, information
  divergence, and Mahalanobis distance, sensitivity analyses with tornado
  and influence heatmap plots, sceptical, robust, and power priors, and
  automated prior justification reports. Includes a fully modular 'Shiny'
  application for interactive use.
  Methods based on O'Hagan et al. (2006, ISBN:9780470029886),
  Box (1980) <doi:10.2307/2982063>,
  Oakley and O'Hagan (2010) <https://tonyohagan.co.uk/shelf/>,
  Schmidli et al. (2014) <doi:10.1111/biom.12242>,
  Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>,
  Spiegelhalter et al. (1994) <doi:10.2307/2983527>.

Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>).

Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.

Provides Bayesian age estimation for bioarchaeological skeletal 
    data using ordinal probit regression models implemented in 'JAGS' and 'NIMBLE'. 
    The package is designed to handle multiple ordinal traits of adult individuals
    and incorporates a Gompertz prior on age to reflect population-level mortality. 
    It accounts for estimation uncertainties and supports full customization of 
    model parameters and Markov Chain Monte Carlo settings. For more details
    see Müller-Scheeßel et al. (2026) <doi:10.1002/ajpa.70289>.

Enable users to evaluate long-term trends using a Generalized 
    Additive Modeling (GAM) approach. The model development includes selecting a 
    GAM structure to describe nonlinear seasonally-varying changes over time, 
    incorporation of hydrologic variability via either a river flow or salinity, 
    the use of an intervention to deal with method or laboratory changes 
    suspected to impact data values, and representation of left- and 
    interval-censored data. The approach has been applied to water quality data 
    in the Chesapeake Bay, a major estuary on the east coast of the United 
    States to provide insights to a range of management- and research-focused 
    questions.  Methodology described in Murphy (2019) 
    <doi:10.1016/j.envsoft.2019.03.027>.