API
nimare.dataset
: Dataset IO
Classes for representing datasets of images and/or coordinates.
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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. |
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CBMA methods from the activation likelihood estimation (ALE) family. |
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CBMA methods from the multilevel kernel density analysis (MKDA) family. |
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CBMA methods from the ALE and MKDA families. |
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Kernel transformers for CBMA algorithms. |
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Coordinate Based Meta Regression Methods. |
nimare.results
: Meta-analytic results
Tools for managing meta-analytic results.
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Base class for meta-analytic results. |
nimare.correct
: Multiple comparisons correction
Multiple comparisons correction methods.
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Perform family-wise error rate correction on a meta-analysis. |
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Perform false discovery rate correction on a meta-analysis. |
nimare.diagnostics
: Diagnostics
Methods for diagnosing problems in meta-analytic datasets or analyses.
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Remove coordinates outside of the Dataset's mask from the Dataset. |
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Run a jackknife analysis on a meta-analysis result. |
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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. |
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Topic modeling with generalized correspondence latent Dirichlet allocation. |
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Topic modeling with latent Dirichlet allocation. |
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Text extraction tools. |
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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. |
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Methods for decoding unthresholded brain maps into text. |
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Methods for encoding text into brain maps. |
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Base classes for the decode module. |
nimare.io
: Tools for ingesting data in other formats
Input/Output operations.
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Convert Neurosynth/NeuroQuery database files to a dictionary. |
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Convert Neurosynth/NeuroQuery dataset text file to a NiMARE json file. |
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Convert Neurosynth/NeuroQuery database files into NiMARE Dataset. |
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Convert Sleuth text file to a dictionary. |
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Convert Sleuth output text file into json. |
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Convert Sleuth output text file into NiMARE Dataset. |
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Convert a group of NeuroVault collections into a NiMARE Dataset. |
nimare.nimads
: NeuroImaging Meta-Analysis Data Structure
NIMADS-related classes for NiMARE.
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A collection of studies for meta-analysis. |
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A collection of labels and associated weights from the same Annotator. |
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A collection of Analyses from the same paper. |
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A single statistical contrast from a Study. |
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A condition within an Analysis. |
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A single statistical map from an Analysis. |
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A single peak coordinate from an Analysis. |
nimare.transforms
: Data transforms
Miscellaneous spatial and statistical transforms.
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A class to create new images from existing ones within a Dataset. |
Transformer from images to coordinates. |
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Generate images of a given type from other image types and write out to files. |
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Determine and apply the appropriate transforms to a target image type from available data. |
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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. |
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Convert standard deviation to sampling variance. |
Convert standard error values to sampling variance. |
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Convert "sample variance of the dataset" to "sampling variance". |
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Convert t-statistic to parameter estimate using sampling variance. |
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Convert t-statistic to sampling variance using parameter estimate. |
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Convert p-values to (unsigned) z-values. |
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Convert t-statistics to z-statistics. |
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Convert z-statistics to t-statistics. |
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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.
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Download the latest data files from NeuroQuery. |
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Download the latest data files from NeuroSynth. |
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Download NIDM Results for 21 pain studies from NeuroVault for tests. |
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Download Cognitive Atlas ontology and extract IDs and relationships. |
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Download the abstracts for a list of PubMed IDs. |
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Return the directories in which NiMARE looks for data. |
nimare.stats
: Statistical functions
Various statistical helper functions.
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One-way chi-square test of independence. |
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Two-way chi-square test of independence. |
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Correlate row vector x with each row vector in 2D array y, quickly. |
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Return p-value for test value(s) against null array. |
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Return one-sided p-value for test value against null histogram. |
nimare.generate
: Data generation functions
Utilities for generating data for testing.
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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.
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Load template file. |
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Get an initialized, fitted nilearn Masker instance from passed argument. |
Return the path to general resources, terminated with separator. |
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Convert matrix subscripts to coordinates. |
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Convert coordinates to matrix subscripts. |
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Convert coordinates from Talairach space to MNI space. |
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Convert coordinates from MNI space Talairach space. |
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Remove repeated rows from a 2D array. |
nimare.workflows
: Common workflows
Common meta-analytic workflows.
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Perform ALE meta-analysis from Sleuth text file. |
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Perform MACM with ALE algorithm. |
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Compose a coordinate-based meta-analysis workflow. |
nimare.reports
: NiMARE report
Reports module.
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Run the reports. |
nimare.base
: Base classes
Base classes for NiMARE.
Base class for NiMARE. |
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Estimators take in Datasets and return MetaResults. |