API

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

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

meta.ibma	Image-based meta-analysis estimators
meta.cbma.ale	CBMA methods from the activation likelihood estimation (ALE) family.
meta.cbma.mkda	CBMA methods from the multilevel kernel density analysis (MKDA) family.
meta.cbma.base	CBMA methods from the ALE and MKDA families.
meta.kernel	Methods for estimating thresholded cluster maps from neuroimaging contrasts (Contrasts) from sets of foci and optional additional information (e.g., sample size and test statistic values).

nimare.results: Meta-analytic results

Base classes for datasets.

results.MetaResult(estimator, mask[, maps])

Base class for meta-analytic results.

nimare.correct: Multiple comparisons correction

Multiple comparisons correction methods

correct.FWECorrector([method])	Perform family-wise error rate correction on a meta-analysis.
correct.FDRCorrector([alpha, method])	Perform false discovery rate correction on a meta-analysis.

nimare.annotate: Automated annotation

Automated annotation tools

annotate.cogat	Automated annotation of Cognitive Atlas labels.
annotate.gclda	Topic modeling with generalized correspondence latent Dirichlet allocation.
annotate.lda	Topic modeling with latent Dirichlet allocation via MALLET.
annotate.text	Text extraction tools.
annotate.utils	Utility functions for ontology tools.

nimare.decode: Functional characterization analysis

Functional decoding tools

decode.discrete	Methods for decoding subsets of voxels (e.g., ROIs) or experiments (e.g., from meta-analytic clustering on a database) into text.
decode.continuous	Methods for decoding unthresholded brain maps into text.
decode.encode	Methods for encoding text into brain maps.

nimare.io: Input/Output

Input/Output operations.

io.convert_neurosynth_to_dict(text_file[, …])	Convert Neurosynth database files to a dictionary.
io.convert_neurosynth_to_json(text_file, …)	Convert Neurosynth dataset text file to a NiMARE json file.
io.convert_neurosynth_to_dataset(text_file)	Convert Neurosynth database files into dictionary and create NiMARE Dataset with dictionary.
io.convert_sleuth_to_dict(text_file)	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 dictionary and create NiMARE Dataset with dictionary.

nimare.transforms: Data transforms

Miscellaneous spatial and statistical transforms

transforms.transform_images(images_df, …)	Generate images of a given type, depending on compatible images of other types, and write out to files.
transforms.resolve_transforms(target, …)	Figure out the appropriate set of transforms for given available data to a target image type, and apply them.
transforms.sample_sizes_to_dof(sample_sizes)	A simple heuristic for calculating degrees of freedom from a list of sample sizes.
transforms.sample_sizes_to_sample_size(…)	A simple heuristic for appropriate sample size from a list of sample sizes.
transforms.sd_to_varcope(sd, sample_size)	Convert standard deviation to sampling variance.
transforms.se_to_varcope(se)	Convert standard error values to sampling variance.
transforms.samplevar_dataset_to_varcope(…)	Convert “sample variance of the dataset” (variance of the individual observations in a single sample) to “sampling variance” (variance of sampling distribution for the parameter).
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.vox2mm(ijk, affine)	Convert matrix subscripts to coordinates.
transforms.mm2vox(xyz, affine)	Convert coordinates to matrix subscripts.
transforms.tal2mni(coords)	Convert coordinates from Talairach space to MNI space.
transforms.mni2tal(coords)	Convert coordinates from MNI space Talairach space.

nimare.extract: Dataset and model fetching

Dataset and trained model downloading functions

extract.download_nidm_pain([data_dir, …])	Download NIDM Results for 21 pain studies from NeuroVault for tests.
extract.download_mallet([data_dir, …])	Download the MALLET toolbox for LDA topic modeling.
extract.download_cognitive_atlas([data_dir, …])	Download Cognitive Atlas ontology and combine Concepts, Tasks, and Disorders to create ID and relationship DataFrames.
extract.download_abstracts(dataset, email)	Download the abstracts for a list of PubMed IDs.
extract.download_peaks2maps_model([…])	Download the trained Peaks2Maps model from OHBM 2018.

nimare.stats: Statistical functions

Various statistical helper functions.

stats.one_way(data, n)	One-way chi-square test of independence.
stats.two_way(cells)	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[, tail])	Return p-value for test value against null array.
stats.nullhist_to_p(test_values, …)	Return one-sided p-value for test value against null histogram.
stats.fdr(p[, q])	Determine FDR threshold given a p value array and desired false discovery rate q.

nimare.utils: Utility functions and submodules

Utilities

utils.dict_to_df(id_df, data[, key])	Load a given data type in NIMADS-format dictionary into DataFrame.
utils.dict_to_coordinates(data, masker, space)	Load coordinates in NIMADS-format dictionary into DataFrame.
utils.validate_df(df)	Check that an input is a DataFrame and has a column for ‘id’.
utils.validate_images_df(image_df)	Check and update image paths in DataFrame.
utils.get_template([space, mask])	Load template file.
utils.get_masker(mask)	Get an initialized, fitted nilearn Masker instance from passed argument.
utils.listify(obj)	Wraps all non-list or tuple objects in a list; provides a simple way to accept flexible arguments.
utils.round2(ndarray)	Numpy rounds X.5 values to nearest even integer.
utils.get_resource_path()	Returns the path to general resources, terminated with separator.
utils.find_stem(arr)	Find longest common substring in array of strings.
utils.uk_to_us(text)	Convert UK spellings to US based on a converter.

nimare.workflows: Common workflows

Common meta-analytic workflows

workflows.ale_sleuth_workflow(sleuth_file[, …])	Perform ALE meta-analysis from Sleuth text file.
workflows.conperm_workflow(contrast_images)	Contrast permutation workflow.
workflows.macm_workflow(dataset_file, mask_file)	Perform MACM with ALE algorithm.
workflows.peaks2maps_workflow(sleuth_file[, …])	peaks2maps workflow
workflows.scale_workflow(dataset_file[, …])	Perform SCALE meta-analysis from Sleuth text file or NiMARE json file.

nimare.base: Base classes

Base classes for datasets.

base.NiMAREBase()	Base class for NiMARE.
base.Estimator()	Estimators take in Datasets and return MetaResults
base.MetaEstimator(*args, **kwargs)	Base class for meta-analysis methods in nimare.meta.
base.Transformer()	Transformers take in Datasets and return Datasets
base.Decoder()	Base class for decoders in nimare.decode.