Package: univariateML 1.5.0

univariateML: Maximum Likelihood Estimation for Univariate Densities

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

Authors:Jonas Moss [aut, cre], Thomas Nagler [ctb], Chitu Okoli [ctb]

univariateML_1.5.0.tar.gz
univariateML_1.5.0.zip(r-4.7)univariateML_1.5.0.zip(r-4.6)univariateML_1.5.0.zip(r-4.5)
univariateML_1.5.0.tgz(r-4.6-any)univariateML_1.5.0.tgz(r-4.5-any)
univariateML_1.5.0.tar.gz(r-4.7-any)univariateML_1.5.0.tar.gz(r-4.6-any)
univariateML_1.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
univariateML/json (API)

# Install 'univariateML' in R:
install.packages('univariateML', repos = c('https://jonasmoss.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jonasmoss/univariateml/issues

Pkgdown/docs site:https://jonasmoss.github.io

Datasets:
  • abalone - Abalone data
  • corbet - Frequencies of butterflies collected in Malaya
  • egypt - Mortality data from ancient Egypt

On CRAN:

Conda:

densityestimationmaximum-likelihood

7.42 score 9 stars 9 packages 72 scripts 941 downloads 56 exports 48 dependencies

Last updated from:c0b9949bbc. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK322
source / vignettesOK232
linux-release-x86_64OK191
macos-release-arm64OK133
macos-oldrel-arm64OK92
windows-develOK186
windows-releaseOK94
windows-oldrelOK100
wasm-releaseOK109

Exports:bootstrapmldmlmlbetamlbetaprmlbinommlburrmlcauchymldunifmlexpmlfatiguemlgammamlgedmlgeommlgompertzmlgumbelmlinvburrmlinvgammamlinvgaussmlinvweibullmlkumarmllaplacemllgammamllgsermlllogismllnormmllogismllogitnormmllomaxmlnakamlnbinommlnormmlparalogismlparetomlpoismlpowermlrayleighmlsgedmlsnormmlsstdmlstdmlunifmlweibullmlzipmlzipfmodel_selectpmlppmllineppmlplotppmlpointsqmlqqmllineqqmlplotqqmlpointsrmlunivariateML_metadataunivariateML_models

Dependencies:actuarassertthatbbmlebdsmatrixclicvarexpintextraDistrfastICAfBasicsfGarchgbutilsgluegssGUILDSintervalslatticelifecyclelogitnormmagrittrMASSMatrixmvtnormnakagaminloptrnumDerivpillarpkgconfigpoilogpoweRlawpracmarbibutilsRcppRcppArmadilloRcppParallelRdpackRfastrlangsadsspatialstabledisttibbletimeDatetimeSeriesutf8vctrsVGAMzigg

Copula Modeling

Rendered fromcopula.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2025-03-03
Started: 2019-11-01

Distributions

Rendered fromdistributions.Rmdusingknitr::knitron May 28 2026.

Last update: 2025-02-17
Started: 2019-11-01

Overview of univariateML

Rendered fromoverview.Rmdusingknitr::knitron May 28 2026.

Last update: 2025-03-03
Started: 2019-11-01

Readme and manuals

Help Manual

Help pageTopics
univariateMLunivariateML-package univariateML
Abalone dataabalone
Parametric Bootstrap on Distributions Fitted with Maximum Likelihoodbootstrapml
Confidence Intervals for Maximum Likelihood Estimatesconfint.univariateML
Frequencies of butterflies collected in Malayacorbet
Mortality data from ancient Egyptegypt
Returns appropriate starting valueget_start
Inverse digamma functioninverse_digamma
Maximum likelihood estimated distributiondml MaximumLikelihoodDistribution pml qml rml
Beta distribution maximum likelihood estimationmlbeta
Beta prime distribution maximum likelihood estimationmlbetapr
Binomial distribution maximum likelihood estimationmlbinom
Burr distribution maximum likelihood estimationmlburr
Cauchy distribution maximum likelihood estimationmlcauchy
Discrete uniform distribution maximum likelihood estimationmldunif
Exponential distribution maximum likelihood estimationmlexp
Gamma distribution maximum likelihood estimationmlfatigue
Gamma distribution maximum likelihood estimationmlgamma
Generalized Error distribution maximum likelihood estimationmlged
Geometric distribution maximum likelihood estimationmlgeom
Gompertz distribution maximum likelihood estimationmlgompertz
Gumbel distribution maximum likelihood estimationmlgumbel
Inverse Burr distribution maximum likelihood estimationmlinvburr
Inverse Gamma distribution maximum likelihood estimationmlinvgamma
Inverse Gaussian (Wald) maximum likelihood estimationmlinvgauss
Inverse Weibull distribution maximum likelihood estimationmlinvweibull
Kumaraswamy distribution maximum likelihood estimationmlkumar
Laplace distribution maximum likelihood estimationmllaplace
Log-gamma distribution maximum likelihood estimationmllgamma
Logarithmic series distribution maximum likelihood estimationmllgser
Log-logistic distribution maximum likelihood estimationmlllogis
Log-normal distribution maximum likelihood estimationmllnorm
Logistic distribution maximum likelihood estimationmllogis
Logit-Normal distribution maximum likelihood estimationmllogitnorm
Lomax distribution maximum likelihood estimationmllomax
Nakagami distribution maximum likelihood estimationmlnaka
Negative binomial distribution maximum likelihood estimationmlnbinom
Normal distribution maximum likelihood estimationmlnorm
Paralogistic distribution maximum likelihood estimationmlparalogis
Pareto distribution maximum likelihood estimationmlpareto
Poisson distribution maximum likelihood estimationmlpois
Power distribution maximum likelihood estimationmlpower
Rayleigh distribution maximum likelihood estimationmlrayleigh
Skew Generalized Error distribution maximum likelihood estimationmlsged
Skew Normal distribution maximum likelihood estimationmlsnorm
Skew Student t-distribution maximum likelihood estimationmlsstd
Student-t distribution maximum likelihood estimationmlstd
Uniform distribution maximum likelihood estimationmlunif
Weibull distribution maximum likelihood estimationmlweibull
Zero-inflated Poisson distribution maximum likelihood estimationmlzip
Zipf distribution maximum likelihood estimationmlzipf
Fit multiple models and select the best fitmodel_select
Plot, Lines and Points Methods for Maximum Likelihood Estimateslines.univariateML plot.univariateML points.univariateML
Probability Plots Using Maximum Likelihood Estimatesppmlline ppmlplot ppmlpoints ProbabilityPlots qqmlline qqmlplot qqmlpoints
Construct 'univariateML' object.univariateML_construct
Metadata for 'univariateML' models.univariateML_metadata
Implemented modelsunivariateML_models