nimare.dataset: Dataset IO

Classes for representing datasets of images and/or coordinates.

dataset.Dataset(source[, target, mask])

Storage container for a coordinate- and/or image-based meta-analytic dataset/database.

nimare.meta: Meta-analytic algorithms

For more information about the components of coordinate-based meta-analysis in NiMARE, see Coordinate-based meta-analysis in NiMARE.

Coordinate-, image-, and effect-size-based meta-analysis estimators.


Image-based meta-analysis estimators.


CBMA methods from the activation likelihood estimation (ALE) family.


CBMA methods from the multilevel kernel density analysis (MKDA) family.


CBMA methods from the ALE and MKDA families.


Kernel transformers for CBMA algorithms.


Coordinate Based Meta Regression Methods.

nimare.results: Meta-analytic results

Tools for managing meta-analytic results.

results.MetaResult(estimator[, corrector, ...])

Base class for meta-analytic results.

nimare.correct: Multiple comparisons correction

Multiple comparisons correction methods.


Perform family-wise error rate correction on a meta-analysis.

correct.FDRCorrector([method, alpha])

Perform false discovery rate correction on a meta-analysis.

nimare.diagnostics: Diagnostics

Methods for diagnosing problems in meta-analytic datasets or analyses.


Remove coordinates outside of the Dataset's mask from the Dataset.

diagnostics.Jackknife([target_image, ...])

Run a jackknife analysis on a meta-analysis result.

diagnostics.FocusCounter([target_image, ...])

Run a focus-count analysis on a coordinate-based meta-analysis result.

nimare.annotate: Automated annotation

Automated annotation tools.


Automated annotation of Cognitive Atlas labels.


Topic modeling with generalized correspondence latent Dirichlet allocation.


Topic modeling with latent Dirichlet allocation.


Text extraction tools.


Utility functions for ontology tools.

nimare.decode: Functional characterization analysis

For more information about functional characterization analysis, see Meta-analytic functional decoding.

Functional decoding tools.


Methods for decoding subsets of voxels or experiments into text.


Methods for decoding unthresholded brain maps into text.


Methods for encoding text into brain maps.


Base classes for the decode module.

nimare.io: Tools for ingesting data in other formats

Input/Output operations.

io.convert_neurosynth_to_dict(...[, ...])

Convert Neurosynth/NeuroQuery database files to a dictionary.

io.convert_neurosynth_to_json(...[, ...])

Convert Neurosynth/NeuroQuery dataset text file to a NiMARE json file.

io.convert_neurosynth_to_dataset(...[, ...])

Convert Neurosynth/NeuroQuery database files into NiMARE Dataset.


Convert Sleuth text file to a dictionary.

io.convert_sleuth_to_json(text_file, out_file)

Convert Sleuth output text file into json.

io.convert_sleuth_to_dataset(text_file[, target])

Convert Sleuth output text file into NiMARE Dataset.

io.convert_neurovault_to_dataset(...[, ...])

Convert a group of NeuroVault collections into a NiMARE Dataset.

nimare.nimads: NeuroImaging Meta-Analysis Data Structure

NIMADS-related classes for NiMARE.

nimads.Studyset(source[, target_space, ...])

A collection of studies for meta-analysis.

nimads.Annotation(source, studyset)

A collection of labels and associated weights from the same Annotator.


A collection of Analyses from the same paper.


A single statistical contrast from a Study.

nimads.Condition(condition, weight)

A condition within an Analysis.


A single statistical map from an Analysis.


A single peak coordinate from an Analysis.

nimare.transforms: Data transforms

Miscellaneous spatial and statistical transforms.

transforms.ImageTransformer(target[, overwrite])

A class to create new images from existing ones within a Dataset.


Transformer from images to coordinates.

transforms.transform_images(images_df, ...)

Generate images of a given type from other image types and write out to files.

transforms.resolve_transforms(target, ...)

Determine and apply the appropriate transforms to a target image type from available data.


Calculate degrees of freedom from a list of sample sizes using a simple heuristic.


Calculate appropriate sample size from a list of sample sizes using a simple heuristic.

transforms.sd_to_varcope(sd, sample_size)

Convert standard deviation to sampling variance.


Convert standard error values to sampling variance.


Convert "sample variance of the dataset" to "sampling variance".

transforms.t_and_varcope_to_beta(t, varcope)

Convert t-statistic to parameter estimate using sampling variance.

transforms.t_and_beta_to_varcope(t, beta)

Convert t-statistic to sampling variance using parameter estimate.

transforms.p_to_z(p[, tail])

Convert p-values to (unsigned) z-values.

transforms.t_to_z(t_values, dof)

Convert t-statistics to z-statistics.

transforms.z_to_t(z_values, dof)

Convert z-statistics to t-statistics.

transforms.z_to_p(z[, tail])

Convert z-values to p-values.

nimare.extract: Dataset and model fetching

For more information about fetching data from the internet, see Fetching resources from the internet.

Dataset and trained model downloading functions.

extract.fetch_neuroquery([data_dir, ...])

Download the latest data files from NeuroQuery.

extract.fetch_neurosynth([data_dir, ...])

Download the latest data files from NeuroSynth.

extract.download_nidm_pain([data_dir, overwrite])

Download NIDM Results for 21 pain studies from NeuroVault for tests.

extract.download_cognitive_atlas([data_dir, ...])

Download Cognitive Atlas ontology and extract IDs and relationships.

extract.download_abstracts(dataset, email)

Download the abstracts for a list of PubMed IDs.


Return the directories in which NiMARE looks for data.

nimare.stats: Statistical functions

Various statistical helper functions.

stats.one_way(data, n)

One-way chi-square test of independence.


Two-way chi-square test of independence.

stats.pearson(x, y)

Correlate row vector x with each row vector in 2D array y, quickly.

stats.null_to_p(test_value, null_array[, ...])

Return p-value for test value(s) against null array.

stats.nullhist_to_p(test_values, ...)

Return one-sided p-value for test value against null histogram.

nimare.generate: Data generation functions

Utilities for generating data for testing.

generate.create_coordinate_dataset([foci, ...])

Generate coordinate based dataset for meta analysis.


Download images from NeuroVault and use them to create a dataset.

nimare.utils: Utility functions and submodules

Utility functions for NiMARE.

utils.get_template([space, mask])

Load template file.


Get an initialized, fitted nilearn Masker instance from passed argument.


Return the path to general resources, terminated with separator.

utils.vox2mm(ijk, affine)

Convert matrix subscripts to coordinates.

utils.mm2vox(xyz, affine)

Convert coordinates to matrix subscripts.


Convert coordinates from Talairach space to MNI space.


Convert coordinates from MNI space Talairach space.

utils.unique_rows(ar[, return_counts])

Remove repeated rows from a 2D array.

nimare.workflows: Common workflows

Common meta-analytic workflows.

workflows.ale_sleuth_workflow(sleuth_file[, ...])

Perform ALE meta-analysis from Sleuth text file.

workflows.macm_workflow(dataset_file, mask_file)

Perform MACM with ALE algorithm.

workflows.cbma.CBMAWorkflow([estimator, ...])

Compose a coordinate-based meta-analysis workflow.


Base class for pairwise coordinate-based meta-analysis workflow methods.

workflows.ibma.IBMAWorkflow([estimator, ...])

Compose a coordinate-based meta-analysis workflow.


Perform a conjunction analysis.

nimare.reports: NiMARE report

Reports module.

reports.run_reports(results, out_dir)

Run the reports.

nimare.base: Base classes

Base classes for NiMARE.


Base class for NiMARE.


Estimators take in Datasets and return MetaResults.