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benkeser
nlpred:Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples
Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), <doi:10.1080/01621459.2019.1668794>). Cross-validated area under the receiver operating characteristics curve (LeDell, Petersen, van der Laan (2015), <doi:10.1214/15-EJS1035>) and other metrics are included.
Maintained by David Benkeser. Last updated 3 years ago.
auccross-validationestimating-equationsmachine-learningtmle
3 stars 4.18 score 6 scripts
cran
mpower:Power Analysis via Monte Carlo Simulation for Correlated Data
A flexible framework for power analysis using Monte Carlo simulation for settings in which considerations of the correlations between predictors are important. Users can set up a data generative model that preserves dependence structures among predictors given existing data (continuous, binary, or ordinal). Users can also generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This package includes several statistical models common in environmental mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.
Maintained by Phuc H. Nguyen. Last updated 3 years ago.
1 stars 1.70 score