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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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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