predictive_value() computes the positive or negative predictive value of a
diagnostic test, showing sensitivity, specificity and prevalence as separate
steps. Supplying prevalence applies the test to a population other than the
one in the table, via Bayes' theorem, which is how a test validated in a
clinic is assessed as a population screen.confint() no longer silently ignores its level argument. Intervals are
computed when the measure is computed, because the derivation displays the
arithmetic that produced them, so level still cannot change the result --
but it now warns and names the fix (conf_level on the measure) instead of
returning an interval at a different level than the one asked for.
Step labels no longer paste a custom exposure label directly after "the",
which produced text such as "Odds of Ill among the ate potato salad". Labels
now name the epidemiological role and give the user's label in parentheses:
"Odds of Ill among the exposed (Ate potato salad)". Output is unchanged when
the default exposure labels are used.
First release.
epi2x2() builds a 2 x 2 table in standard epidemiological orientation.risk_ratio(), odds_ratio() and risk_difference() return the measure
together with the full worked derivation.attributable_fraction() computes the fraction among the exposed or in the
population, showing that the direct, Levin and Miettinen formulas agree.epi_strata() and collapse_strata() handle stratified data.mh_odds_ratio() and mh_risk_ratio() pool across strata, printing the
stratum weights and the crude estimate alongside the adjusted one.homogeneity() implements the Breslow-Day test with Tarone's correction.check_work() compares a hand calculation against every step of a
derivation and reports where it diverged.steps_table() returns a derivation as a data frame.options(epibyhand.verbose = ) and
options(epibyhand.digits = ).