R: Linear Mixed Effects Models with Non-stationary Stochastic...
lmenssp-package
R Documentation
Linear Mixed Effects Models with Non-stationary Stochastic Processes
Description
Obtains maximum likelihood estimates of the model parameters, filters, smooths
and forecasts random components of the model for the following
processes: 1) Brownian motion, 2) integrated Brownian motion, 3) integrated
Ornstein-Uhlenbeck process, 4) stationary process with powered correlation
function, 5) stationary process with Matern correlation function,
under multivariate normal and t response distributions. It also contains
miscellaneous functions for diagnostic checks, boostrap standard error calculation, etc.
Details
Package:
lmenssp
Type:
Package
Version:
1.1
Date:
2015-07-17
License:
GPL (>=2)
References
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Oxford University Press: Oxford.
Diggle PJ, Ribeiro PJ Jr. (2007) Model-based Geostatistics. Springer-Verlag: New York.
Diggle PJ, Sousa I, Asar O (2015) Real time monitoring of progression towards renal failure in primary care patients.
Biostatistics, 16(3), 522-536.
Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics, 38, 134-147.
Matern B. (1960) Spatial Variation. Statens Skogsforsningsinstitut, Stockholm.
Pinheiro JC, Liu C, Wu YN. (2001) Efficient algorithms for robust estimation in linear mixed-effects
models using the multivariate t distribution. Journal of Computational and Graphical Statistics10, 249-276.
Ross SM (1996) Stochastic processes. John Wiley & Sons, New Jersey.
Taylor JMG, Cumberland WG, Sy JP (1994) A Stochastic Model for Analysis of Longitudinal AIDS Data.
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