spTimer (version 3.3)

spTimer-package: Spatio-Temporal Bayesian Modelling using R

Description

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.

Arguments

Details

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

References

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/.