Calculating the stability of random forest with certain numbers of trees. The non-linear relationship between stability and numbers of trees is described using a logistic regression model and used to estimate the optimal number of trees.

Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. 
             The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

Provides methods for determining optimum plot size and shape in field 
    experiments using Fairfield-Smith's variance law approach. It will evaluate 
    field variability, determine optimum plot size and shape and study fertility 
    trends across the field.

Provides a robust workflow for optimal cut-point analysis in
    time-to-event ('survival') data. Functions determine the optimal number
    of cut-points via find_cutpoint_number(), find their precise locations
    via find_cutpoint() using systematic or genetic algorithms (via the
    'rgenoud' package), and validate stability via bootstrapping using
    validate_cutpoint(). Features include covariate adjustment, parallel
    processing, and an extensible S3 plotting engine for clinical
    dashboards and diagnostics.

Optimal testing under general dependence. The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure.