r-cran-clubsandwich 0.4.2-1 source package in Ubuntu

Changelog

r-cran-clubsandwich (0.4.2-1) unstable; urgency=medium

  * Team upload.
  * New upstream version

 -- Dylan Aïssi <email address hidden>  Sat, 18 Apr 2020 23:12:12 +0200

Upload details

Uploaded by:
Debian R Packages Maintainers
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

Groovy: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-clubsandwich_0.4.2-1.dsc 2.2 KiB 11c801d18cfa434aafcfe7a6a5f16c290f50efef1777b2578db36c1eb4b77854
r-cran-clubsandwich_0.4.2.orig.tar.gz 267.0 KiB c138daa443b3773dc91d2a87ee1c2b6d53c5cd869ddf35ff1c910209ac20be7e
r-cran-clubsandwich_0.4.2-1.debian.tar.xz 2.9 KiB b79ccb9702f5c24e885ed7779259cf6ba84888da6fdc16cc8e642154146bba6a

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').