Skip to content

This library provides a set of functions for the implementation of Monte Carlo based algorithms for the augmentation of both qualitative and quantitative data. This includes a series of Monte Carlo simulation functions, as well as an implementation of a Metropolis-Hastings variant of the Markov Chain Monte Carlo algorithm, with a series of funct…

License

Notifications You must be signed in to change notification settings

TIDOP-USAL/AugmentationMC

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

AugmentationMC

This library provides a set of functions for the implementation of Monte Carlo based algorithms for the augmentation of both qualitative and quantitative data. This includes a series of Monte Carlo simulation functions, as well as an implementation of a Metropolis-Hastings variant of the Markov Chain Monte Carlo algorithm, with a series of functions for the evaluation of chains.


Author

Lloyd A. Courtenay

Email

ladc1995@gmail.com

ORCID

https://orcid.org/0000-0002-4810-2001

Current Afiliations:

Universidad de Salamanca [USAL]


This code was designed and prepared for the study by: Courtenay, L.A.; Aramendi, J.; González-Aguilera, D. (Submitted) Recruiting a Skeleton Crew – Methods for Simulating and Augmenting Palaeoanthropological Data using Monte Carlo based Algorithms

TIDOP research group website: http://tidop.usal.es/


Dependencies:

  • abind

Comments, questions, doubts, suggestions and corrections can all be directed to L. A. Courtenay at the email provided above.

About

This library provides a set of functions for the implementation of Monte Carlo based algorithms for the augmentation of both qualitative and quantitative data. This includes a series of Monte Carlo simulation functions, as well as an implementation of a Metropolis-Hastings variant of the Markov Chain Monte Carlo algorithm, with a series of funct…

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • R 100.0%