r-cran-clubsandwich 0.5.0-1 source package in Ubuntu

Changelog

r-cran-clubsandwich (0.5.0-1) unstable; urgency=medium

  * New upstream version
  * debhelper-compat 13 (routine-update)

 -- Andreas Tille <email address hidden>  Tue, 08 Sep 2020 21:24:26 +0200

Upload details

Uploaded by:
Debian R Packages Maintainers on 2020-09-08
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

Builds

Hirsute: [FULLYBUILT] amd64

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File Size SHA-256 Checksum
r-cran-clubsandwich_0.5.0-1.dsc 2.2 KiB 638f9eee8b5d120b6d59b905acd4555c9074babb0c596347f542c9c015e8e708
r-cran-clubsandwich_0.5.0.orig.tar.gz 311.2 KiB a70e8538e09b9475e0152378a66b00780c2c70bf1912dd022416634d558c2d1a
r-cran-clubsandwich_0.5.0-1.debian.tar.xz 3.0 KiB 975c68fe5b6045f59eaafe8fe4778bd8c5c3613904306c8092cd6194b9f0f94e

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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').