spTimer-package

Spatio-Temporal Bayesian Modelling using R

Spatio-Temporal Bayesian Modelling using R

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

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

Author(s)

K.S. Bakar & S.K. Sahu

Maintainer: K.S. Bakar shuvo.bakar@gmail.com

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/.

  • Maintainer: K. Shuvo Bakar
  • License: GPL (>= 2)
  • Last published: 2024-09-08

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