add auc plotting

[alkaZeltser]

In this PR:

• Added a new function analyze.pgs.binary.predictiveness which given a PGS and phenotypes runs a logistic regression and returns statistics (OR, p-value, AUC) describing how well the PGS predicts the phenotype. It also automatically plots a receiver-operator-curve.
• Added manual for new function
• Added unit tests for new function

• I have read the code review guidelines and the code review best practice on GitHub check-list.

• The name of the branch is meaningful and well formatted following the standards, using [AD_username (or 5 letters of AD if AD is too long)-[brief_description_of_branch].

• I have set up or verified the branch protection rule following the github standards before opening this pull request.

• I have added the changes included in this pull request to NEWS under the next release version or unreleased, and updated the date.

• I have updated the version number in metadata.yaml and DESCRIPTION.

• Both R CMD build and R CMD check run successfully.

Testing Results

All unit tests PASS
Also R CMD check passes locally where scales is installed.
The CICD check fails because scales is not installed in the image and since it is a suggested dependency it is not automatically installed by the package.

[alkaZeltser]

@rhughwhite @jarbet This is the last feature that I'm adding to the package before I introduce a large braking change that will increment the version. Please take a look ASAP so that I can move forward with the next set of PRs with large changes.