Title: Bayesian Prediction of Stochastic Processes
Description: Bayesian estimation and prediction for stochastic processes based
on the Euler approximation. Considered processes are: jump diffusion,
(mixed) diffusion models, hidden (mixed) diffusion models, non-homogeneous
Poisson processes (NHPP), (mixed) regression models for comparison and a

Author: Emma Granqvist, Matthew Hartley and Richard J Morris
Description: Bayesian Spectrum Analysis of time series data

Title: A package for estimating age-specific survival from incomplete
Author: Fernando Colchero, Owen Jones, Maren Rebke
Description: This package estimates survival and mortality with covariates from capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots.

Description: This package estimates survival and mortality with
covariates from capture-recapture/recovery data in a Bayesian
framework when many individuals are of unknown age. It includes
tools for data checking, model diagnostics and outputs such as

Title: Age-Specific Survival Analysis from Incomplete
Description: Estimates survival and mortality with covariates from capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots.

Title: Bayesian Model to Archaeological Faunal Skeletal Profiles
Author: Ana B. Marín-Arroyo [aut], David Ocio [aut], Marco Vidal-Cordasco [cbt], Delphine Vettese [cbt]
Description: Tool to perform Bayesian inference of carcass processing/transport strategy and bone attrition from archaeofaunal skeletal profiles characterized by percentages of MAU (Minimum Anatomical Units). The approach is based on a generative model for skeletal profiles that replicates the two phases of formation of any faunal assemblage: initial accumulation as a function of human transport strategies and subsequent attrition.Two parameters define this model: 1) the transport preference (alpha), which can take any value between - 1 (mostly axial contribution) and 1 (mostly appendicular contribution) following strategies constructed as a function of butchering efficiency of different anatomical elements and the results of ethnographic studies, and 2) degree of attrition (beta), which can vary between 0 (no attrition) and 10 (maximum attrition) and relates the survivorship of bone elements to their maximum bone density. Starting from uniform prior probability distribution functions of alpha and beta, a Monte Carlo Markov Chain sampling based on a random walk Metropolis-Hasting algorithm is adopted to derive the posterior probability distribution functions, which are then available for interpretation. During this process, the likelihood of obtaining the observed percentages of MAU given a pair of parameter values is estimated by the inverse of the Chi2 statistic, multiplied by the proportion of elements within a 1 percent of the observed value. See Ana B. Marin-Arroyo, David Ocio (2018).<doi:10.1080/08912963.2017.1336620>.