LMfilteR: Filter Methods for Parameter Estimation in Linear and Non Linear Regression Models

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

Depends: R (≥ 3.6.0)
Imports: MASS (≥ 7.3-50), stats (≥ 3.5.1)
Published: 2023-02-02
DOI: 10.32614/CRAN.package.LMfilteR
Author: Christian Llano Robayo [aut, cre], Nazrul Shaikh [aut], Pegu Nilutpal [aut]
Maintainer: Christian Llano Robayo <info at cecareus.com>
BugReports: https://github.com/ChrissCod/LMfilteR/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: LMfilteR results


Reference manual: LMfilteR.pdf


Package source: LMfilteR_0.1.3.1.tar.gz
Windows binaries: r-devel: LMfilteR_0.1.3.1.zip, r-release: LMfilteR_0.1.3.1.zip, r-oldrel: LMfilteR_0.1.3.1.zip
macOS binaries: r-release (arm64): LMfilteR_0.1.3.1.tgz, r-oldrel (arm64): LMfilteR_0.1.3.1.tgz, r-release (x86_64): LMfilteR_0.1.3.1.tgz, r-oldrel (x86_64): LMfilteR_0.1.3.1.tgz
Old sources: LMfilteR archive


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