Suggests: covr, crayon, devtools, knitr, Matrix, modeldata (>= 0.0.2),
recipes (>= 0.1.8), rmarkdown (>= 2.3), roxygen2, testthat (>=

Imports: glue, rlang (>= 0.4.2), tibble, vctrs (>= 0.3.0)

generally goes into providing an implementation for a new method that
is efficient, fast, and correct, but often less emphasis is put on the
user interface. A good interface requires specialized knowledge about
S3 methods and formulas, which the average package developer might not
have.  The goal of 'hardhat' is to reduce the burden around building
new modeling packages by providing functionality for preprocessing,

Title: Harmonic Mean p-Values and Model Averaging by Mean Maximum
Description: The harmonic mean p-value (HMP) test simply and instantly combines p-values and corrects for multiple testing while controlling the family-wise error rate in a way that is more powerful than common alternatives including Bonferroni and Simes procedures, more stringent than controlling the false discovery rate, and is robust to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is almost certain to be significant and conversely when the superset is non-significant, the constituent tests are almost certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem.

Description: The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the family-wise error rate in a way that is more powerful than common alternatives including Bonferroni and Simes procedures, more stringent than controlling the false discovery rate, and is robust to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is almost certain to be significant and conversely when the superset is non-significant, the constituent tests are almost certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation.