Comments (6)
WOW! So fast!
I have so many unit tests written for tbl_regression already, and I am just re-purposing them for broom.helpers! 😆 There are so many different scenarios....it took a long time to address all these edge cases.
I am so excited for the package! Thank you for your amazing work on it!
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tested added and fixed
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Thanks for all these checks
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FYI, I finished integrating broom.helpers into gtsummary and ran all the checks. It looks like everything is working perfectly!
ddsjoberg/gtsummary#606
Thanks for taking the time to write the awesome package! 🥇 You've made gtsummary more robust, and simplified the internals significantly. 💯
Once broom.helpers is on CRAN, I'll ask someone from my team to do more checking and merge the updates! Hopefully, the first CRAN submission comes back with a small nit-picky change so the re-submission incorporates the updates from the last few days! 🕺 🕺 🕺
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Let's see
I just resubmitted version 1.0.0 as I didn't have feedback yet from CRAN
🤞
Thanks for your feedback and all the new additional checks
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Related Issues (20)
- Required change to stay on CRAN
- Release broom.helpers version 1.14.0
- Support for mmrm models HOT 8
- Support for MASS::contr.sdif() contrasts
- Support for zero-inflated models
- beta regression is not supported yet HOT 2
- Considering a `tidy_post_fun` argument to `tidy_plus_plus()` HOT 2
- Support with coxphf model HOT 9
- Bug fixes and improvements for mixed models HOT 1
- tbl_regression (package gtsummary) and ggcoef_model (package ggstats) not working on the output of a replicate svyglm model HOT 2
- fantastic support of multivariate quantile regression for any quantile HOT 1
- Support for survival::cch model? HOT 5
- Avg_comparisons and nlme::lme() models HOT 1
- order of variable levels with marginal tidiers HOT 8
- Release broom.helpers 1.15.0
- Take into account (id) when computing model_get_n() for coxph models
- `marginaleffects::datagridcf()` is deprecated
- Do you know the status of the {margins} pkg? HOT 1
- Explore better support of VGAM::vglm() models
- Execution time
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