Changes in version 1.1.0 (2024-12-18) 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 Changes in version 1.0.5 (2023-10-04) Minor improvements and fixes - Fix inaccuracies about theoretical properties of the variance estimator in package vignette Changes in version 1.0.4 (2022-04-24) Minor improvements and fixes - Adjust tolerance in unit tests so there are no issues on M1 Mac Changes in version 1.0.3 (2021-08-10) Minor improvements and fixes - Fix incorrect computation of p-values in the print.dfadjustSE method Changes in version 1.0.2 (2021-02-24) Minor improvements and fixes - Fix incorrect computation of CR2 variance estimator and degrees of freedom adjustment if data not sorted by cluster Changes in version 1.0.1 (2019-12-16) Minor improvements and fixes - Fix problem with failing tests when platform didn't use long double Changes in version 1.0.0 (2019-08-23) 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)