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OneStep: One-Step Estimation

Provide principally an eponymic function that numerically computes the Le Cam's one-step estimator for an independent and identically distributed sample. One-step estimation is asymptotically efficient (see L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) and can be computed faster than the maximum likelihood estimator for large observation samples, see e.g. Brouste et al. (2021) <doi:10.32614/RJ-2021-044>.

Version: 0.9.4
Depends: fitdistrplus, numDeriv, parallel, extraDistr
Suggests: actuar
Published: 2024-10-17
DOI: 10.32614/CRAN.package.OneStep
Author: Alexandre Brouste ORCID iD [aut], Christophe Dutang ORCID iD [aut, cre], Darel Noutsa Mieniedou [ctb]
Maintainer: Christophe Dutang <dutangc at gmail.com>
Contact: Alexandre Brouste, Christophe Dutang <onestep@univ-lemans.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://journal.r-project.org/archive/2021/RJ-2021-044/
NeedsCompilation: no
Citation: OneStep citation info
Materials: README NEWS
In views: Distributions
CRAN checks: OneStep results

Documentation:

Reference manual: OneStep.pdf

Downloads:

Package source: OneStep_0.9.4.tar.gz
Windows binaries: r-devel: OneStep_0.9.4.zip, r-release: OneStep_0.9.4.zip, r-oldrel: OneStep_0.9.4.zip
macOS binaries: r-release (arm64): OneStep_0.9.4.tgz, r-oldrel (arm64): OneStep_0.9.4.tgz, r-release (x86_64): OneStep_0.9.4.tgz, r-oldrel (x86_64): OneStep_0.9.4.tgz
Old sources: OneStep archive

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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.