Description Details Setup Classes Methods Functions Acknowledgement Start-up-Banner Demos Author(s) References
robKalman provides routines for robust Kalman filtering. Currently, the ACM-filter and the rLS-Filter are provided.
Package: | robKalman |
Version: | 0.2.1 |
Date: | 2009-03-18 |
Depends: | R(>= 2.3.0), methods, graphics, startupmsg, dse1, dse2, MASS |
Imports: | stats |
LazyLoad: | yes |
License: | LGPL-3 |
URL: | http://distr.r-forge.r-project.org/ |
We work in the setup of the time-invariant, linear, Gaussian state space model (ti-l-G-SSM) with p dimensional states x_t and q dimensional observations y_t, with initial condition
x_0 ~ N_p(a,S),
state equation
x_t = F x_{t-1} + v_t, v_t ~ N_p(0,Q), t>=1,
observation equation
y_t = Z x_t + e_t, e_t ~ N_q(0,V), t>=1,
and where all random variable x_0, v_t, e_t are independent.
yet to be filled
yet to be filled
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | general recursive filters
+recursiveFilter
-KalmanFilter
-rLSFilter:
*rLS.AO.Filter
*rLS.IO.Filter
-ACMfilter
-mACMfilter
ACMfilter:
+ACMfilt
GM-estimators for AR models
+arGM
utilities:
+Euclidnorm,
+rcvmvnorm,
+Huberize,
+limitS
simulation of AO contaminated state space models
+simulateState,
+simulateObs,
|
We thank Paul Gilbert for their help in preparing this package.
You may suppress the start-up banner/message completely by setting options("StartupBanner"="off")
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If option "StartupBanner"
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The same can be achieved by wrapping the library
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call into
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or onlytypeStartupMessages(.,atypes="version")
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As for general packageStartupMessage
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the start-up banner by wrapping the library
or require
call into suppressPackageStartupMessages()
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Demos are available — see demo(package="robKalman")
Peter Ruckdeschel (Maintainer) Peter.Ruckdeschel@itwm.fraunhofer.de,
Bernhard Spangl bernhard.spangl@boku.ac.at,
Martin, R.D. and Zeh, J.E. (1978): Generalized M-estimates for Autoregression Including Small-sample Efficiency Robustness
Martin, R.D. (1979): Approximate Conditional-mean Type Smoothers and Interpolators.
Martin, R.D. (1980): Robust Estimation of Autoregressive Models.
Martin, R.D. (1981): Robust Methods for Time Series
Martin, R.D. and Thomson, D.J. (1982): Robust-resistent Spectrum Estimation.
Ruckdeschel, P. (2001) Ans\"atze zur Robustifizierung des
Kalman Filters. Bayreuther Mathematische Schriften, Vol. 64.
Spangl, B. (2008): On Robust Spectral Density
Estimation. PhD Thesis at Technical University, Vienna.
Stockinger, N. and Dutter, R. (1987): Robust Time Series Analysis: A Survey.
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