geoBayes: The 'geoBayes' package

Description Details Author(s) References See Also Examples


Analysis of geostatistical data using Bayes and Empirical Bayes methods.


This package provides functions to fit geostatistical data. The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods.

Some demonstration examples are provided. Type demo(package = "geoBayes") to examine them.


Evangelos Evangelou <[email protected]> and Vivekananda Roy <[email protected]>


Roy, V., Evangelou, E. and Zhu, Z. (2014). Empirical Bayes methods for the transformed Gaussian random fields model with additive measurement errors. In Upadhyay, S. K., Singh, U., Dey, D. K., and Loganathan, A., editors, Current Trends in Bayesian Methodology with Applications, Boca Raton, FL, USA, CRC Press.

Roy, V., Evangelou, E., and Zhu, Z. (2015). Efficient estimation and prediction for the Bayesian spatial generalized linear mixed model with flexible link functions. Biometrics, 72(1), 289-298.

Evangelou, E., & Roy, V. (2019). Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling. Spatial Statistics, 29, 289-315.

Roy, V., & Evangelou, E. (2018). Selection of proposal distributions for generalized importance sampling estimators. arXiv preprint arXiv:1805.00829.

See Also

geoR, geoRglm


## Not run: 
demo(package = "geoBayes")
demo(rhizoctonia3, package = "geoBayes")

## End(Not run)

geoBayes documentation built on May 2, 2019, 3:14 a.m.