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estimatr: Fast Estimators for Design-Based Inference

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Hi! I hope this finds you well. Several tests rely on randomizr, a suggested package. For cases where this package might not be installed, it would be nice if these...

Hi, the Debian R packaging team has packages estimatr for Debian. On some architectures it fails its test with: ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-lm-robust-fes.R:589'): FEs handle collinear covariates...

When I do the following call: reg |t|) CI Lower treat:as.factor(Year)2000 -0.1229535 0.11031 -1.11464 0.3157 -0.40651 treat:as.factor(Year)2001 -0.1504377 0.10413 -1.44472 0.2082 -0.41811 treat:as.factor(Year)2002 -0.1176582 0.10318 -1.14032 0.3058 -0.38289 treat:as.factor(Year)2003 -0.0696706...

bug

If you take the example from the documentation and add log() around variables, it works fine with logs around the regressors but will not work with a log around the...

bug

Currently https://declaredesign.org/r/estimatr/articles/mathematical-notes.html only documents the form of covariance estimators for linear models with a single outcome. However, `lm_robust()` supports multiple outcomes. It would be helpful to document the computation that...

Hello, I have found that `model.frame()` gives unexpected results when used with `iv_robust()`, possibly due to it being unable to parse the model formula. With a factor variable in the...

- changes to tests per CRAN email - version bump to mirror DeclareDesign

- Version bump for first post-0.12 release bump - Adds CR3 standard error estimators to both `lm_robust` and `iv_robust` (closes issue #261) - Updates documentation and tests (lots of test...

@jwbowers and Lula Chen reported that with small N and many values of a discrete covariate lm_lin does not return an answer. I reproduced it below, and it is not...

bug

**Have added** - [x] confint.lm - [x] nobs.lm - [x] predict.lm - fix this to use the right variance - [x] print.lm - [x] summary.lm - [x] tidy.lm - [x]...

lm_robust
Priority: Low