pg-correlationslisted

# PG Correlations Use for association analyses between continuous or ordinal variables. ## Load Read: - `../../references/supervision-gates.md` - `../../references/pingouin-api-quickref.md` - `../../references/apa-output-template.md` if writing results. ## Function Choice - Two continuous variables, linear association -> `pg.corr(..., method="pearson")`. - Ordinal or monotonic/non-normal association -> `method="spearman"` or `method="kendall"`. - Association controlling covariates -> `pg.partial_corr`. - Many variables -> `pg.pairwise_corr` with `padjust`. - Repeated observations per participant -> `pg.rm_corr`. - Nonlinear dependence screening -> `pg.distance_corr` if appropriate. ## Required Inputs - Variables to correlate. - Whether observations are independent. - Covariates, if partial correlation is requested. - Multiple-comparison family and correction. - Hypothesis direction; default to two-sided. ## Code Patterns Basic correlation: ```python res = pg.corr(x=df["x"], y=df["y"], method="pearson", alternative="two-sided").round(3) pg.print_table(res) ``` Partial correlation: ```python res = pg.partial_corr(data=df, x="x", y="y", covar=["age", "baseline"], method="pearson").round(3) ``` Pairwise correlations: ```python res = pg.pairwise_corr(data=df, columns=["x", "y", "z"], method="spearman", padjust="holm").round(3) ``` Repeated-measures correlation: ```python res = pg.rm_c