Harmonize with CRAN

Pass CRAN checks and harmonize with cvAUC, ROCR.

Improving power

Improve power by being smarter about sample-splitting (and when it needs to be done).

Enhanced estimation of cross-fitted AUC

Harmonize cross-fitted AUC estimation with cross-fitted deviance and R-squared estimation -- namely, that components of the estimator that don't involve regression (or machine learning) don't have to be estimated using cross-fitting (e.g., the variance of the outcome; or the probability that the outcome equals 1).

Allow IPW estimation in two-phase samples

Previously, only AIPW estimation was available for cases where the data arise from a two-phase sample; now IPW estimation is available as well.

Bugfixes

• Update all tests to use glm rather than ranger or xgboost (improves package build and check speed and removes some dependencies)
• Internal bugfixes (mostly for cross-fitted VIM estimation)
• Update links and DOIs throughout vignettes and documentation

Use package `stats` versions of internal functions, bugfixes

• Use stats::plogis and stats::qlogis for scale = "logistic"; this increases stability and R compatibility
• Minor bugfixes (e.g., if different number of observations are in each of the sample-splitting folds for hypothesis testing, there is no longer an error)

Allow IPC weighting, CIs to be computed on non-identity scale

v2.1.1.1

bump news, description

Update inverse probability weighting behavior

v2.1.1

change name of internal return variable for ipc weights in sp_vim

Add SPVIM

Add Shapley Population Variable Importance Measures (SPVIM) as an additional option for defining variable importance.

Clean up and pass CRAN checks

Remove some unnecessary functions (e.g., cv_vim_nodonsker -- this has been superseded by cv_vim) and clean up \examples to pass CRAN checks.