EM.Fuzzy: EM Algorithm for Maximum Likelihood Estimation by Non-Precise Information

The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) <doi:10.1016/j.fss.2011.05.022>.

Version:	1.0
Imports:	FuzzyNumbers, DISTRIB
Published:	2018-08-16
DOI:	10.32614/CRAN.package.EM.Fuzzy
Author:	Abbas Parchami (Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran)
Maintainer:	Abbas Parchami <parchami at uk.ac.ir>
License:	LGPL (≥ 3)
NeedsCompilation:	no
CRAN checks:	EM.Fuzzy results

Documentation:

Reference manual:

EM.Fuzzy.pdf

Downloads:

Package source:	EM.Fuzzy_1.0.tar.gz
Windows binaries:	r-devel: EM.Fuzzy_1.0.zip, r-release: EM.Fuzzy_1.0.zip, r-oldrel: EM.Fuzzy_1.0.zip
macOS binaries:	r-release (arm64): EM.Fuzzy_1.0.tgz, r-oldrel (arm64): EM.Fuzzy_1.0.tgz, r-release (x86_64): EM.Fuzzy_1.0.tgz, r-oldrel (x86_64): EM.Fuzzy_1.0.tgz

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