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:	0.9.3
Depends:	fitdistrplus, numDeriv, parallel, extraDistr
Suggests:	actuar
Published:	2024-02-23
Author:	Alexandre Brouste [aut], Christophe Dutang [aut, cre], Darel Noutsa Mieniedou [ctb]
Maintainer:	Christophe Dutang <christophe.dutang at ensimag.fr>
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:	NEWS
In views:	Distributions
CRAN checks:	OneStep results

Documentation:

Reference manual:

OneStep.pdf

Downloads:

Package source:	OneStep_0.9.3.tar.gz
Windows binaries:	r-devel: OneStep_0.9.3.zip, r-release: OneStep_0.9.3.zip, r-oldrel: OneStep_0.9.3.zip
macOS binaries:	r-release (arm64): OneStep_0.9.3.tgz, r-oldrel (arm64): OneStep_0.9.3.tgz, r-release (x86_64): OneStep_0.9.3.tgz, r-oldrel (x86_64): OneStep_0.9.3.tgz
Old sources:	OneStep archive

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