dfadjust 1.1.0
New Features
- Allow argument
ell to be shorter than covariate
dimension. In this case, ell specifies which subset of
covariates to compute standard errors for.
Minor improvements and fixes
- Use
collapse::fsum instead of tapply calls
to improve speed
- Check that covariates are not collinear, drop the collinear
ones
dfadjust 1.0.5
Minor improvements and
fixes
- Fix inaccuracies about theoretical properties of the variance
estimator in package vignette
dfadjust 1.0.4
Minor improvements and
fixes
- Adjust tolerance in unit tests so there are no issues on M1 Mac
dfadjust 1.0.3
Minor improvements and
fixes
- Fix incorrect computation of p-values in the
print.dfadjustSE method
dfadjust 1.0.2
Minor improvements and
fixes
- Fix incorrect computation of CR2 variance estimator and degrees of
freedom adjustment if data not sorted by cluster
dfadjust 1.0.1
Minor improvements and
fixes
- Fix problem with failing tests when platform didn’t use long
double
dfadjust 1.0.0
New Features
- The function
dfadjustSE implements small-sample degrees
of freedom adjustment discussed in Imbens and Kolesár
(2016), using both heteroskedasticity-robust and clustered standard
errors. For clustered standard errors, the package implements both the
Imbens and Kolesár (2016) and the Bell and McCaffrey (2002, Survey
Methodology) degrees of freedom adjustments.
- This implementation can handle models with fixed effects, as well as
datasets with a large number of observations (for
heteroskedasticity-robust standard errors) or datasets with large
clusters (for clustered standard errors)