univariateML: Maximum Likelihood Estimation for Univariate Densities

User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.

Version: 1.1.0
Depends: R (≥ 2.10)
Imports: assertthat, extraDistr, tibble, logitnorm, actuar, nakagami, fGarch
Suggests: testthat, knitr, covr, rmarkdown, markdown, copula, dplyr
Published: 2020-08-05
Author: Jonas Moss ORCID iD [aut, cre], Thomas Nagler [ctb]
Maintainer: Jonas Moss <jonas.gjertsen at gmail.com>
BugReports: https://github.com/JonasMoss/univariateML/issues
License: MIT + file LICENSE
URL: https://github.com/JonasMoss/univariateML, https://univariateml.netlify.com/
NeedsCompilation: no
Citation: univariateML citation info
Materials: README
CRAN checks: univariateML results

Downloads:

Reference manual: univariateML.pdf
Vignettes: Copula Modeling
Distributions
Overview of univariateML
Package source: univariateML_1.1.0.tar.gz
Windows binaries: r-devel: univariateML_1.1.0.zip, r-release: univariateML_1.1.0.zip, r-oldrel: univariateML_1.1.0.zip
macOS binaries: r-release: univariateML_1.1.0.tgz, r-oldrel: univariateML_1.1.0.tgz
Old sources: univariateML archive

Reverse dependencies:

Reverse imports: kdensity

Linking:

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