roughly added the design

[clarkliming]

No description provided.

[clarkliming]

for negative binomial we need MASS::glm.nb

[clarkliming]

the reason to have "unbiased prediction" is that we may encounter cases that the unbiased prediction itself is sufficient and the variance can be ontained from Jackknife/bootstrapping, especially when we want to combine the usage with some multiple imputation methods

[github-actions]

Code Coverage Summary

Filename      Stmts    Miss  Cover    Missing
----------  -------  ------  -------  ---------
R/hello.R         2       0  100.00%
TOTAL             2       0  100.00%

Diff against main

Filename      Stmts    Miss  Cover
----------  -------  ------  -------
R/hello.R       -18      -6  +30.00%
TOTAL           -18      -6  +30.00%

Results for commit: e957bbb

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[github-actions]

Unit Test Performance Difference

Test Suite	$Status$	Time on main	$±Time$	$±Tests$	$±Skipped$	$±Failures$	$±Errors$
api	💀	$4.20$	$-4.20$	$-1$	$0$	$0$	$0$
shiny	💀	$3.51$	$-3.51$	$-1$	$0$	$0$	$0$

Additional test case details

Test Suite	$Status$	Time on main	$±Time$	Test Case
api	💀	$4.20$	$-4.20$	API_greets_the_person
shiny	💀	$3.51$	$-3.51$	The_Shiny_App_returns_a_proper_greeting

Results for commit 2d780cc

♻️ This comment has been updated with latest results.

[clarkliming]

merging the design as there is no comments/questions.