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[ Module Hierarchy | Class Hierarchy ]

# Module Hierarchy

- mvpa: MultiVariate Pattern Analysis
  - mvpa.algorithms: Import helper for PyMVPA algorithms.
    - mvpa.algorithms.cvtranserror: Cross-validate a classifier on a dataset
    - mvpa.algorithms.hyperalignment: Hyperalignment of functional data to the common space
  - mvpa.atlases: Import helper for PyMVPA anatomical atlases
    - mvpa.atlases.base: Base classes for Anatomy atlases support
    - mvpa.atlases.fsl: FSL atlases interfaces
    - mvpa.atlases.transformation: Coordinate transformations
    - mvpa.atlases.warehouse: Collection of the known atlases
  - mvpa.base: Base functionality of PyMVPA
    - mvpa.base.config: Registry-like monster
    - mvpa.base.dochelpers: Various helpers to improve docstrings and textual output
    - mvpa.base.externals: Helper to verify presence of external libraries and modules
    - mvpa.base.info: Provide system and PyMVPA information useful while reporting bugs
    - mvpa.base.report: Creating simple PDF reports using reportlab
    - mvpa.base.report_dummy: Dummy report class, to just be there in case if reportlab is not available.
    - mvpa.base.verbosity: Verbose output and debugging facility
  - mvpa.clfs: Import helper for PyMVPA classifiers
    - mvpa.clfs._svmbase: Common to all SVM implementations functionality. For internal use only
    - mvpa.clfs.base: Base class for all classifiers.
    - mvpa.clfs.blr: Bayesian Linear Regression (BLR).
    - mvpa.clfs.distance: Distance functions to be used in kernels and elsewhere
    - mvpa.clfs.enet: Elastic-Net (ENET) regression classifier.
    - mvpa.clfs.glmnet: GLM-Net (GLMNET) regression classifier.
    - mvpa.clfs.gnb: Gaussian Naive Bayes Classifier
    - mvpa.clfs.gpr: Gaussian Process Regression (GPR).
    - mvpa.clfs.kernel: Kernels for Gaussian Process Regression and Classification.
    - mvpa.clfs.knn: k-Nearest-Neighbour classifier.
    - mvpa.clfs.lars: Least angle regression (LARS) classifier.
    - mvpa.clfs.libsmlrc: Wraper for the stepwise_regression function for SMLR.
      - mvpa.clfs.libsmlrc.ctypes_helper: Helpers for wrapping C libraries with ctypes.
    - mvpa.clfs.libsvmc: Classifiers provied by LibSVM library
      - mvpa.clfs.libsvmc._svm: Python interface to the SWIG-wrapped libsvm
      - mvpa.clfs.libsvmc.sens: Provide sensitivity measures for libsvm's SVM.
      - mvpa.clfs.libsvmc.svm: Wrap the libsvm package into a very simple class interface.
      - mvpa.clfs.libsvmc.svmc
    - mvpa.clfs.meta: Classes for meta classifiers -- classifiers which use other classifiers
    - mvpa.clfs.model_selector: Model selction.
    - mvpa.clfs.plr: Penalized logistic regression classifier.
    - mvpa.clfs.ridge: Ridge regression classifier.
    - mvpa.clfs.sg: Classifiers provied by shogun (sg) library
      - mvpa.clfs.sg.sens: Provide sensitivity measures for sg's SVM.
      - mvpa.clfs.sg.svm: Wrap the libsvm package into a very simple class interface.
    - mvpa.clfs.smlr: Sparse Multinomial Logistic Regression classifier.
    - mvpa.clfs.stats: Estimator for classifier error distributions.
    - mvpa.clfs.svm: Importer for the available SVM and SVR machines.
    - mvpa.clfs.transerror: Utility class to compute the transfer error of classifiers.
    - mvpa.clfs.warehouse: Collection of classifiers to ease the exploration.
  - mvpa.datasets: PyMVPA datasets and helper classes such as splitters
    - mvpa.datasets.base: Dataset container
    - mvpa.datasets.channel: Dataset handling data structured in channels.
    - mvpa.datasets.eep: Dataset that gets its samples from an EEP binary file
    - mvpa.datasets.event: Event-based dataset type
    - mvpa.datasets.mapped: Mapped dataset
    - mvpa.datasets.masked: Dataset with applied mask
    - mvpa.datasets.meta: Dataset container
    - mvpa.datasets.miscfx: Misc function performing operations on datasets.
    - mvpa.datasets.miscfx_sp: Misc function performing operations on datasets which are based on scipy
    - mvpa.datasets.nifti: Dataset that gets its samples from a NIfTI file
    - mvpa.datasets.splitters: Collection of dataset splitters.
  - mvpa.featsel: Feature selection methods.
    - mvpa.featsel.base: Feature selection base class and related stuff base classes and helpers.
    - mvpa.featsel.helpers
    - mvpa.featsel.ifs: Incremental feature search (IFS).
    - mvpa.featsel.rfe: Recursive feature elimination.
  - mvpa.mappers: PyMVPA mappers.
    - mvpa.mappers.array: Data mapper
    - mvpa.mappers.base: Data mapper
    - mvpa.mappers.boxcar: Data mapper
    - mvpa.mappers.ica: Data mapper
    - mvpa.mappers.lle: Local Linear Embedding Data mapper.
    - mvpa.mappers.mask: Data mapper which applies mask to the data
    - mvpa.mappers.metric: Classes and functions to provide sense of distances between sample points
    - mvpa.mappers.pca: Data mapper
    - mvpa.mappers.procrustean: Procrustean rotation mapper
    - mvpa.mappers.samplegroup: Data mapper
    - mvpa.mappers.som: Self-organizing map (SOM) mapper.
    - mvpa.mappers.svd: Singular-value decomposition mapper
    - mvpa.mappers.wavelet: Wavelet mappers
    - mvpa.mappers.zscore: Simple mapper to perform zscoring
  - mvpa.measures: PyMVPA measures.
    - mvpa.measures.anova: FeaturewiseDatasetMeasure performing a univariate ANOVA.
    - mvpa.measures.base: Base class for data measures: algorithms that quantify properties of datasets.
    - mvpa.measures.corrcoef: FeaturewiseDatasetMeasure of correlation with the labels.
    - mvpa.measures.corrstability: FeaturewiseDatasetMeasure of stability of labels across chunks based on correlation.
    - mvpa.measures.ds: Dissimilarity measure.
    - mvpa.measures.glm: The general linear model (GLM).
    - mvpa.measures.irelief: FeaturewiseDatasetMeasure performing multivariate Iterative RELIEF (I-RELIEF) algorithm. See : Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June 2007.
    - mvpa.measures.noiseperturbation: This is a FeaturewiseDatasetMeasure that uses a scalar DatasetMeasure and selective noise perturbation to compute a sensitivity map.
    - mvpa.measures.pls
    - mvpa.measures.searchlight: Implementation of the Searchlight algorithm
    - mvpa.measures.splitmeasure: This is a FeaturewiseDatasetMeasure that uses another FeaturewiseDatasetMeasure and runs it multiple times on differents splits of a Dataset.
  - mvpa.misc: Import helper for PyMVPA misc modules
    - mvpa.misc.args: Helpers for arguments handling.
    - mvpa.misc.attributes: Module with some special objects to be used as magic attributes with dedicated containers aka. `Collections`.
    - mvpa.misc.bv: Import helper for Brain Voyager
      - mvpa.misc.bv.base: Tiny snippets to interface with FSL easily.
    - mvpa.misc.cmdline: Common functions and options definitions for command line
    - mvpa.misc.data_generators: Miscelaneous data generators for unittests and demos
    - mvpa.misc.errorfx: Error functions helpers.
    - mvpa.misc.exceptions: Exception classes which might get thrown
    - mvpa.misc.fsl: Import helper for FSL
      - mvpa.misc.fsl.base: Tiny snippets to interface with FSL easily.
      - mvpa.misc.fsl.flobs: Wrapper around FSLs halfcosbasis to generate HRF kernels
      - mvpa.misc.fsl.melodic: Wrapper around the output of MELODIC (part of FSL)
    - mvpa.misc.fx: Misc. functions (in the mathematical sense)
    - mvpa.misc.io: Import helper for IO helpers
      - mvpa.misc.io.base: Some little helper for reading (and writing) common formats from and to disk.
      - mvpa.misc.io.eepbin: Reader for binary EEP files.
      - mvpa.misc.io.hamster: Helper for simple storage facility via cPickle and optionally zlib
      - mvpa.misc.io.meg: IO helper for MEG datasets.
    - mvpa.misc.param: Parameter representation
    - mvpa.misc.plot: Import helper for miscalaneouse PyMVPA plotting functions (mvpa.misc.plot)
      - mvpa.misc.plot.base: Misc. plotting helpers.
      - mvpa.misc.plot.erp: Basic ERP (here ERP = Event Related Plot ;-)) plotting
      - mvpa.misc.plot.mri: Basic (f)MRI plotting with ability to interactively perform thresholding
      - mvpa.misc.plot.topo: Plot parameter distributions on a head surface (topography plots).
    - mvpa.misc.state: Classes to control and store state information.
    - mvpa.misc.stats: Little statistics helper
    - mvpa.misc.support: Support function -- little helpers in everyday life
    - mvpa.misc.transformers: Simply functors that transform something.
    - mvpa.misc.vproperty: C++-like virtual properties
  - mvpa.suite: MultiVariate Pattern Analysis -- load helper
  - mvpa.support: Import helper for PyMVPA support modules
    - mvpa.support._copy: Generic (shallow and deep) copying operations.
    - mvpa.support.copy: Support for python's copy module.
    - mvpa.support.griddata: Import griddata with preference to the version from matplotlib
    - mvpa.support.stats: Fixer for rdist in scipy
  - mvpa.tests: Unit test interface for PyMVPA
    - mvpa.tests.main: Unit test console interface for PyMVPA
    - mvpa.tests.runner: Helper module to enable profiling of the testcase
    - mvpa.tests.test_args: Unit tests for PyMVPA args helpers
    - mvpa.tests.test_arraymapper: Unit tests for PyMVPA dense array mapper
    - mvpa.tests.test_atlases: Unit tests for PyMVPA atlases
    - mvpa.tests.test_base: Test some base functionality which did not make it into a separate unittests
    - mvpa.tests.test_boxcarmapper: Unit tests for PyMVPA Boxcar mapper
    - mvpa.tests.test_clf: Unit tests for PyMVPA basic Classifiers
    - mvpa.tests.test_clfcrossval: Unit tests for PyMVPA classifier cross-validation
    - mvpa.tests.test_cmdline: Unit tests for PyMVPA cmdline helpers
    - mvpa.tests.test_config: Unit tests for PyMVPA dense array mapper
    - mvpa.tests.test_datameasure: Unit tests for PyMVPA SplittingSensitivityAnalyzer
    - mvpa.tests.test_dataset: Unit tests for PyMVPA dataset handling
    - mvpa.tests.test_datasetfx: Unit tests for PyMVPA miscelaneouse functions operating on datasets
    - mvpa.tests.test_datasetfx_sp: Unit tests for PyMVPA miscelaneouse functions operating on datasets and requiring SciPy
    - mvpa.tests.test_datasetng: Tests for the dataset implementation
    - mvpa.tests.test_dochelpers: Unit tests for PyMVPA dochelpers
    - mvpa.tests.test_eepdataset: Unit tests for PyMVPA EEP dataset
    - mvpa.tests.test_enet: Unit tests for PyMVPA least angle regression (ENET) classifier
    - mvpa.tests.test_externals: Test externals checking
    - mvpa.tests.test_gnb: Unit tests for PyMVPA GNB classifier
    - mvpa.tests.test_gpr: Unit tests for PyMVPA GPR.
    - mvpa.tests.test_hamster: Unit tests for PyMVPA Hamster
    - mvpa.tests.test_hyperalignment: Unit tests for PyMVPA ...
    - mvpa.tests.test_icamapper: Unit tests for PyMVPA ICA mapper
    - mvpa.tests.test_ifs: Unit tests for PyMVPA incremental feature search.
    - mvpa.tests.test_iohelpers: Unit tests for PyMVPA IO helpers
    - mvpa.tests.test_kernel: Unit tests for PyMVPA kernels
    - mvpa.tests.test_knn: Unit tests for PyMVPA kNN classifier
    - mvpa.tests.test_lars: Unit tests for PyMVPA least angle regression (LARS) classifier
    - mvpa.tests.test_maskeddataset: Unit tests for PyMVPA pattern handling
    - mvpa.tests.test_meg: Unit tests for PyMVPA MEG stuff
    - mvpa.tests.test_metadataset: Unit tests for PyMVPA meta dataset handling
    - mvpa.tests.test_neighbor: Unit tests for PyMVPA metrics
    - mvpa.tests.test_niftidataset: Unit tests for PyMVPA nifti dataset
    - mvpa.tests.test_params: Unit tests for PyMVPA Parameter class.
    - mvpa.tests.test_pcamapper: Unit tests for PyMVPA PCA mapper
    - mvpa.tests.test_perturbsensana: Unit tests for PyMVPA perturbation sensitivity analyzer.
    - mvpa.tests.test_plr: Unit tests for PyMVPA logistic regression classifier
    - mvpa.tests.test_pls: Unit tests for Partial Least Squares measures.
    - mvpa.tests.test_procrust: Unit tests for PyMVPA Procrustean mapper
    - mvpa.tests.test_regr: Unit tests for PyMVPA Regressions
    - mvpa.tests.test_report: Unit tests for PyMVPA simple report facility
    - mvpa.tests.test_rfe: Unit tests for PyMVPA recursive feature elimination
    - mvpa.tests.test_ridge: Unit tests for PyMVPA ridge regression classifier
    - mvpa.tests.test_samplegroupmapper: Unit tests for PyMVPA SampleGroup mapper
    - mvpa.tests.test_searchlight: Unit tests for PyMVPA searchlight algorithm
    - mvpa.tests.test_smlr: Unit tests for PyMVPA sparse multinomial logistic regression classifier
    - mvpa.tests.test_som: Unit tests for PyMVPA SOM mapper
    - mvpa.tests.test_splitsensana: Unit tests for PyMVPA SplittingSensitivityAnalyzer
    - mvpa.tests.test_splitter: Unit tests for PyMVPA pattern handling
    - mvpa.tests.test_state: Unit tests for PyMVPA State parent class
    - mvpa.tests.test_stats: Unit tests for PyMVPA stats helpers
    - mvpa.tests.test_stats_sp: Unit tests for PyMVPA stats helpers -- those requiring scipy
    - mvpa.tests.test_suite: Unit test for PyMVPA mvpa.suite() of being loading ok
    - mvpa.tests.test_support: Unit tests for PyMVPA serial feature inclusion algorithm
    - mvpa.tests.test_svdmapper: Unit tests for PyMVPA SVD mapper
    - mvpa.tests.test_svm: Unit tests for SVM classifier
    - mvpa.tests.test_transerror: Unit tests for PyMVPA classifier cross-validation
    - mvpa.tests.test_transformers: Unit tests for PyMVPA transformers.
    - mvpa.tests.test_verbosity: Unit tests for PyMVPA verbose and debug output
    - mvpa.tests.test_waveletmapper: Unit tests for PyMVPA Wavelet mappers
    - mvpa.tests.test_zscoremapper: Unit tests for PyMVPA ZScore mapper
    - mvpa.tests.tests_warehouse: Miscelaneous functions/datasets to be used in the unit tests
    - mvpa.tests.tests_warehouse_clfs: Provides clfs dictionary with instances of all available classifiers.