spTimer-package: Spatio-Temporal Bayesian Modelling using R

Description Details Author(s) References See Also


This package uses different hierarchical Bayesian spatio-temporal modelling strategies, namely:
(1) Gaussian processes (GP) models,
(2) Autoregressive (AR) models,
(3) Gaussian predictive processes (GPP) based autoregressive models for big-n problem.


Package: spTimer
Type: Package

The back-end code of this package is built under c language.
Main functions used:
> spT.Gibbs
> predict.spT
Some other important functions:
> spT.priors
> spT.initials
> spT.decay
> spT.time
Data descriptions:
> NYdata


K.S. Bakar & S.K. Sahu
Maintainer: K.S. Bakar <shuvo.bakar@gmail.com>


1. Bakar, K. S., & Sahu, S. K. (2015). sptimer: Spatio-temporal bayesian modelling using r. Journal of Statistical Software, 63(15), 1-32.
2. Sahu, S.K. & Bakar, K.S. (2012). Hierarchical Bayesian Autoregressive Models for Large Space Time Data with Applications to Ozone Concentration Modelling. Applied Stochastic Models in Business and Industry, 28, 395-415.
3. Sahu, S.K., Gelfand, A.E., & Holland, D.M. (2007). High-Resolution Space-Time Ozone Modelling for Assessing Trends. Journal of the American Statistical Association, 102, 1221-1234.
4. Bakar, K.S. (2012). Bayesian Analysis of Daily Maximum Ozone Levels. PhD Thesis, University of Southampton, Southampton, United Kingdom.

See Also

Packages 'spacetime', 'forecast'; 'spBayes'; 'maps'; 'MBA'; 'coda'; website: http://www.r-project.org/.

spTimer documentation built on July 2, 2020, 3:18 a.m.