Package: BaSkePro
Type: Package
Title: Bayesian Model to Archaeological Faunal Skeletal Profiles
Version: 0.1.0
Author: Ana B. Marín-Arroyo [aut], David Ocio [aut], Marco Vidal-Cordasco [cbt], Delphine Vettese [cbt]
Maintainer: Marco Vidal-Cordasco <marcovidalcordasco@gmail.com>
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>.
License: GPL-3
Encoding: UTF-8
Depends: MASS
NeedsCompilation: no
Packaged: 2022-02-16 14:42:49 UTC; vidalma
Repository: CRAN
Date/Publication: 2022-02-17 19:42:03 UTC
Built: R 4.1.3; ; 2023-04-17 13:42:47 UTC; windows
