spTimer (version 0.02)

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) approximationto 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 > spT.prediction > spT.forecast > spT.priors > spT.initials > spT.decay > spT.time Some other functions: > spT.geodist > spT.grid.coords > spT.data.selection > spT.MCMC.stat > spT.MCMC.plot > spT.validation > spT.pCOVER > spT.segment.plot Data descriptions: > NYdata > NYsite

References

1. Sahu, S.K. & 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, In Press. 2. 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. 3. Bakar, K.S. & Sahu, S.K. (2012). spTimer: Spatio-Temporal Bayesian Modelling Using R. URL: http://www.southampton.ac.uk/~sks/research/papers/spTimeRpaper.pdf 4. Bakar, K.S. (2012). Bayesian Analysis of Daily Maximum Ozone Levels. PhD Thesis, University of Southampton, Southampton, United Kingdom.

See Also

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