estimatr

Fast Estimators for Design-Based Inference

Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.

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Description file content

Package
estimatr
Type
Package
Title
Fast Estimators for Design-Based Inference
Version
0.12
Date
2018-09-05
Description
Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) .
URL
BugReports
https://github.com/DeclareDesign/estimatr/issues
License
MIT + file LICENSE
Depends
R (>= 3.4.0)
Imports
Rcpp (>= 0.12.16), methods, Formula, rlang (>= 0.2.0)
LinkingTo
Rcpp, RcppEigen
RoxygenNote
6.0.1
LazyData
true
Suggests
RcppEigen, fabricatr (>= 0.4), randomizr (>= 0.16), testthat, sandwich, clubSandwich, margins, prediction, lfe, AER, ivpack, stargazer, car
Enhances
broom, texreg
NeedsCompilation
yes
Packaged
2018-09-15 19:19:43 UTC; luke
Author
Graeme Blair [aut, cre], Jasper Cooper [aut], Alexander Coppock [aut], Macartan Humphreys [aut], Luke Sonnet [aut], Neal Fultz [ctb]
Maintainer
Graeme Blair
Repository
CRAN
Date/Publication
2018-09-15 22:50:03 UTC

install.packages('estimatr')

0.12

a month ago

https://declaredesign.org/r/estimatr/

Graeme Blair

MIT + file LICENSE

Depends on

R (>= 3.4.0)

Imports

Rcpp (>= 0.12.16), methods, Formula, rlang (>= 0.2.0)

Suggests

RcppEigen, fabricatr (>= 0.4), randomizr (>= 0.16), testthat, sandwich, clubSandwich, margins, prediction, lfe, AER, ivpack, stargazer, car

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