r-cran-clubsandwich 0.4.2-2build1 source package in Ubuntu

Changelog

r-cran-clubsandwich (0.4.2-2build1) groovy; urgency=medium

  * No-change rebuild against r-api-4.0

 -- Graham Inggs <email address hidden>  Sat, 30 May 2020 19:18:45 +0000

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Uploaded by:
Graham Inggs on 2020-05-30
Uploaded to:
Groovy
Original maintainer:
Debian R Packages Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

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Series Pocket Published Component Section
Groovy release on 2020-06-26 universe misc

Builds

Groovy: [FULLYBUILT] amd64

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File Size SHA-256 Checksum
r-cran-clubsandwich_0.4.2.orig.tar.gz 267.0 KiB c138daa443b3773dc91d2a87ee1c2b6d53c5cd869ddf35ff1c910209ac20be7e
r-cran-clubsandwich_0.4.2-2build1.debian.tar.xz 3.0 KiB d341712c0eab82bc1b5a4b2d7883290ccb745e42663ef1ebd8835a48cbd2bff0
r-cran-clubsandwich_0.4.2-2build1.dsc 2.2 KiB 2e6d5f9866a5b30f98027db11b3521d26c959b07dcc635a7696c136a6f9ccf13

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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').