API

pymare.core: Core objects

Core classes and functions.

core.Dataset([y, v, X, n, data, X_names, ...])	Container for input data and arguments to estimators.
core.meta_regression([y, v, X, n, data, ...])	Fit the standard meta-regression/meta-analysis model to provided data.

pymare.estimators: Meta-analytic algorithms

Estimators for meta-analyses and meta-regressions.

estimators.WeightedLeastSquares([tau2])	Weighted least-squares meta-regression.
estimators.DerSimonianLaird()	DerSimonian-Laird meta-regression estimator.
estimators.VarianceBasedLikelihoodEstimator([...])	Likelihood-based estimator for estimates with known variances.
estimators.SampleSizeBasedLikelihoodEstimator([...])	Likelihood-based estimator for data with known sample sizes but unknown sampling variances.
estimators.StanMetaRegression(**sampling_kwargs)	Bayesian meta-regression estimator using Stan.
estimators.Hedges()	Hedges meta-regression estimator.
estimators.StoufferCombinationTest([mode])	Stouffer's Z-score meta-analysis method.
estimators.FisherCombinationTest([mode])	Fisher's method for combining p-values.
estimators.estimators.BaseEstimator()	A base class for Estimators.

pymare.results: Meta-analytic results

Tools for representing and manipulating meta-regression results.

results.MetaRegressionResults(estimator, ...)	Container for results generated by PyMARE meta-regression estimators.
results.CombinationTestResults(estimator, ...)	Container for results generated by p-value combination methods.
results.PermutationTestResults(results, ...)	Lightweight container to hold and display permutation test results.
results.BayesianMetaRegressionResults(data, ...)	Container for MCMC sampling-based PyMARE meta-regression estimators.

pymare.effectsize: Effect size computation/conversion

Tools for converting between effect-size measures.

effectsize.OneSampleEffectSizeConverter([...])	Effect size converter for metric involving a single group/set of scores.
effectsize.TwoSampleEffectSizeConverter([...])	Effect size converter for two-sample comparisons.
effectsize.Expression(expression[, ...])	Represent a single statistical expression.
effectsize.solve_system(system[, known_vars])	Solve and evaluate a system of SymPy equations given known inputs.
effectsize.select_expressions(target, known_vars)	Select a minimal system of expressions needed to solve for the target.
effectsize.compute_measure(measure[, data, ...])	Auto-detect and apply the right converter class.

pymare.stats: Miscellaneous statistical functions

Miscellaneous statistical functions.

stats.weighted_least_squares(y, v, X[, ...])	Perform 2-D weighted least squares.
stats.ensure_2d(arr)	Ensure the passed array has 2 dimensions.
stats.q_profile(y, v, X[, alpha])	Get the CI for tau^2 via the Q-Profile method.
stats.q_gen(y, v, X, tau2)	Calculate a generalized form of Cochran's Q-statistic.
stats.bonferroni(p_values)	Perform Bonferroni correction on p values.
stats.fdr(p_values[, q, method])	Perform FDR correction on p values.

pymare.datasets: Meta-analytic datasets

Open meta-analytic datasets.

datasets.michael2013()

Load a dataset of studies on the persuasive power of a brain image.

pymare.utils: Miscellaneous utility functions

Miscellaneous utility functions.

utils.get_resource_path()

Return the path to general resources, terminated with separator.