Package: bbmle 1.0.26

bbmle: Tools for General Maximum Likelihood Estimation

Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.

Authors:Ben Bolker [aut, cre], R Development Core Team [aut], Iago Giné-Vázquez [ctb]

bbmle_1.0.26.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bbmle/json (API)

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

Bug tracker:https://github.com/bbolker/bbmle/issues

On CRAN:

Conda:

13.23 score 25 stars 152 packages 1.6k scripts 32k downloads 114 mentions 45 exports 6 dependencies

Last updated from:6ed2e79e4e. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING257
source / vignettesOK257
linux-release-x86_64WARNING236
macos-release-arm64WARNING166
macos-oldrel-arm64WARNING119
windows-develWARNING172
windows-releaseWARNING177
windows-oldrelWARNING178
wasm-releaseOK124

Exports:AICAICcAICctabAICtabanovaBICtabcall.to.charcoefconfintdeviancednorm_nformulaICtablogLikmle2mle2.optionsnamedropnobsparnamesparnames<-plotpop_pred_samppredictproffunprofileqAICqAICcrelist2residualssbetasbetabinomsbinomsimulatesliceslice1Dslice2DsliceOldslnormsnbinomsnormspoisstdErsummaryupdatevcov

Dependencies:bdsmatrixlatticeMASSMatrixmvtnormnumDeriv

Examples for enhanced mle code
Example: Orobanche/overdispersed binomial | Example: reed frog size predation | Newer stuff | Technical details
Rendered frommle2.Rnwusingknitr::knitr

Last update: 2024-10-07
Started: 2016-11-17

quasi: notes on quasi-likelihood/qAIC analysis inR
Rendered fromquasi.Rnwusingknitr::knitr

Last update: 2022-05-06
Started: 2016-11-17

Readme and manuals

Help Manual

Help pageTopics
convert profile to data frameas.data.frame.profile.mle2 coerce,profile.mle2,data.frame-method coerce,profile.mle2-method
Log likelihoods and model selection for mle2 objectsAIC,mle2-method AIC-methods AICc AICc,ANY,mle2,logLik-method AICc,ANY-method AICc,logLik-method AICc,mle2-method AICc-methods anova,mle2-method BIC-methods logLik,mle2-method logLik-methods qAIC qAIC,ANY,mle2,logLik-method qAIC,ANY-method qAIC,logLik-method qAIC,mle2-method qAIC-methods qAICc qAICc,ANY,mle2,logLik-method qAICc,ANY-method qAICc,logLik-method qAICc,mle2-method qAICc-methods
Convert calls to charactercall.to.char
Normal distribution with profiled-out standard deviationdnorm_n
extract model namesget.mnames
Compute table of information criteria and auxiliary infoAICctab AICtab BICtab ICtab print.ICtab
Maximum Likelihood Estimationcalc_mle2_function mle mle2
Class "mle2". Result of Maximum Likelihood Estimation.coef,mle2-method coerce,mle,mle2-method deviance,mle2-method formula,mle2-method mle2-class show,mle2-method slice,mle2-method stdEr stdEr,mle2-method summary,mle2-method update,mle2-method vcov,mle2-method
Options for maximum likelihood estimationmle2.options
drop unneeded names from list elementsnamedrop
get and set parameter namesparnames parnames<-
generate population prediction sample from parameterspop_pred_samp
Predicted values from an mle2 fitgfun predict,mle2-method predict-methods residuals,mle2-method simulate,mle2-method
Likelihood profilesproffun profile,mle2-method profile-methods profile.mle2
Methods for likelihood profilesconfint,mle2-method confint,profile.mle2-method confint.mle2 plot,profile.mle2,missing-method plot,profile.mle2-method plot.profile.mle2 profile.mle2-class show,profile.mle2-method
reconstruct the structure of a listrelist2
Abstract definitions of distributionssbeta sbetabinom sbinom slnorm snbinom snorm spois
Calculate likelihood "slices"slice slice1D slice2D sliceOld slicetrans
likelihood-surface slicesslice.mle2-class
Wrap strings at white space and + symbolsstrwrapx
Class "summary.mle2", summary of "mle2" objectscoef,summary.mle2-method show,summary.mle2-method summary.mle2-class