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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 |
| Reference manual: | EM.Fuzzy.pdf |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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