r-cran-clubsandwich 0.4.0-1 source package in Ubuntu

Changelog

r-cran-clubsandwich (0.4.0-1) unstable; urgency=medium

  * Team upload.
  * New upstream version

 -- Dylan Aïssi <email address hidden>  Mon, 30 Dec 2019 22:38:12 +0100

Upload details

Uploaded by:
Debian R Packages Maintainers on 2019-12-31
Uploaded to:
Sid
Original maintainer:
Debian R Packages Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Focal release on 2020-01-18 universe misc

Builds

Focal: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-clubsandwich_0.4.0-1.dsc 2.2 KiB 7f1f152c3ca46e2f11f9aaf203d57eba4c3dda950086490153d6515eeaa98e3a
r-cran-clubsandwich_0.4.0.orig.tar.gz 267.5 KiB 8df58272e0b9ea010fb8f683ea0f4a27eb32224e858dc966ff5a43e5ea46e6cf
r-cran-clubsandwich_0.4.0-1.debian.tar.xz 2.6 KiB 3c70de8bc18f7348a662355f067f179bec744a792740d6c6107d0b1d550b2e8a

Available diffs

No changes file available.

Binary packages built by this source

r-cran-clubsandwich: GNU R cluster-robust (Sandwich) variance estimators with small-sample

 Corrections Provides several cluster-robust variance estimators
 (i.e., sandwich estimators) for ordinary and weighted least
 squares linear regression models, including the bias-reduced
 linearization estimator introduced by Bell and McCaffrey (2002)
 <http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf>
 and developed further by Pustejovsky and Tipton (2017)
 <DOI:10.1080/07350015.2016.1247004>. The package includes
 functions for estimating the variance- covariance matrix and for
 testing single- and multiple- contrast hypotheses based on Wald
 test statistics. Tests of single regression coefficients use
 Satterthwaite or saddle-point corrections. Tests of multiple-contrast
 hypotheses use an approximation to Hotelling's T-squared
 distribution. Methods are provided for a variety of fitted models,
 including lm() and mlm objects, glm(), ivreg() (from package
 'AER'), plm() (from package 'plm'), gls() and lme() (from 'nlme'),
 robu() (from 'robumeta'), and rma.uni() and rma.mv() (from
 'metafor').