Glmnet defaults

[PhilBoileau]

Description

When the super learning arguments are set to NULL in unicate() and
sunicate(), cross-validated elastic net regressions are fit to estimate
nuisance parameters instead of the previously used default sl3 super
learners. This speeds up computation time, and makes it easier to apply these
methods to small datasets by default. This doesn't affect the asymptotic
behaviour of the unicate() estimator, either.

Addresses issue #9

Type of change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to
  not work as expected)
• This change requires a documentation update

Testing

New unit tests for internals and integration tests for unicate() and
sunicate() to evaluate default behaviour.

Checklist

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• Any dependent changes have been merged and published in downstream modules

[github-actions]

Code Coverage Summary

Filename                                  Stmts    Miss  Cover    Missing
--------------------------------------  -------  ------  -------  ---------
R/compute_coefs_tbl.R                         8       0  100.00%
R/estimate_univariate_cates.R               199       6  96.98%   335-340
R/estimate_univariate_survival_cates.R      287       6  97.91%   482-487
R/perform_inference.R                        16       0  100.00%
R/prep_data.R                                56       0  100.00%
R/prep_long_data.R                          160       1  99.38%   248
R/sunicate.R                                 12       0  100.00%
R/unicate.R                                  11       0  100.00%
TOTAL                                       749      13  98.26%

Results for commit: f8dc80d

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results