Stem-package: Analysis of spatio-temporal hierarchical models

In Stem: Spatio-temporal models in R

Description

This package focuses on spatio-temporal hierarchical models. The package includes functions for maximum likelihood estimation (based on Kalman filtering and EM algorithm), for computing the parameter standard errors (using a parametric spatio-temporal bootstrap) and for spatial mapping.

Details

Package:	Stem
Type:	Package
Version:	1.0
Date:	2009-01-27
License:	GPL (>= 2)
LazyLoad:	yes

Author(s)

Michela Cameletti [e-mail]

References

Amisigo, B.A., Van De Giesen, N.C. (2005) Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series. Hydrology and Earth System Sciences 9, 209–224.

Fasso', A., Cameletti, M., Nicolis, O. (2007) Air quality monitoring using heterogeneous networks. Environmetrics 18, 245–264.

Fasso', A., Cameletti, M. (2007) A general spatio-temporal model for environmental data. Tech.rep. n.27 Graspa - The Italian Group of Environmental Statistics - http://www.graspa.org.

Fasso', A., Cameletti, M. (2009) A unified statistical approach for simulation, modelling, analysis and mapping of environmental data. Accepted for publication by Simulation: transaction of the Society for Modeling and Simulation International.

Mc Lachlan, G.J., Krishnan, T. (1997) The EM Algorithm and Extensions. Wiley, New York.

Shumway, R.H., Stoffer, D.S. (2006) Time Series Analysis and Its Applications: with R Examples. Springer, New York.

Xu, K., Wikle, C.K. (2007) Estimation of parameterized spatio-temporal dynamic models. Journal of Statistical Inference and Planning 137, 567–588.

Stem documentation built on May 2, 2019, 8:56 a.m.