spTimer (version 0.8)

spTimer-package: Spatio-Temporal Bayesian Modelling using R

Description

This package uses different hierarchical Bayesian spatio-temporal modelling strategies, namely the gaussian processes (GP) models, the autoregressive (AR) models, and models using Gaussian predictive processes (GPP) approximation to analyse space-time observations.

Details

ll{ 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

Bakar, K. S. and Sahu, S. K. (2013) spTimer: Spatio-Temporal Bayesian Modelling Using R. Technical Report, University of Southampton, UK. Sahu, S. K. and Bakar, K. S. (2012) A comparison of Bayesian Models for Daily Ozone Concentration Levels Statistical Methodology , 9, 144-157. Sahu, S. K. and Bakar, K. S. (2012) Hierarchical Bayesian auto-regressive models for large space time data with applications to ozone concentration modelling. Applied Stochastic Models in Business and Industry, 28, 395-415. Sahu, S. K., Bakar, K. S. and Awang, N. (2013) Bayesian Forecasting Using Hierarchical Spatio-temporal Models with Applications to Ozone Levels in the Eastern United States. Technical Report, University of Southampton. 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.

See Also

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