p_value_functions: Overview of p-value combination functions

In confMeta: Confidence Curves and P-Value Functions for Meta-Analysis

Overview of p-value combination functions

Description

The p_value_functions family is useful to combine individual study effect estimates and standard errors into a single meta-analytical p-value function. These functions are the building blocks for the confMeta function.

Details

Vectorization over mu: All of the p-value functions in this package are vectorized over the mu argument. This means that you can pass a sequence of null values (e.g., seq(-2, 2, length.out = 1000)) and the function will evaluate the combined p-value for every point. This is specifically designed to construct p-value functions and invert the tests to find confidence intervals at any desired level.

Available Methods:

• p_edgington: Classical Edgington method (Sum of p-values).

• p_edgington_w: Weighted Edgington method.

• p_fisher: Fisher's method (Log-product of p-values).

• p_pearson: Pearson's method (Log-product of complements).

• p_tippett: Tippett's method (Minimum p-value).

• p_wilkinson: Wilkinson's method (Maximum p-value).

• p_stouffer: Stouffer's method.

• p_hmean: Harmonic mean method.

References

Held, L, Hofmann, F, Pawel, S. (2025). A comparison of combined p-value functions for meta-analysis. Research Synthesis Methods, 16:758-785. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1017/rsm.2025.26")}

See Also

confMeta

confMeta documentation built on June 10, 2026, 1:06 a.m.