How to use the nimare.due.due.dcite function in NiMARE

@due.dcite(Doi('10.1016/j.neuroimage.2010.02.048'),
           description='Validates the Lancaster MNI-to-Talairach and '
                       'Talairach-to-MNI transforms.')
def mni2tal(coords):
    """
    Python version of BrainMap's icbm_other2tal.m.
    This function converts coordinates from MNI space (normalized using
    templates other than those contained in SPM and FSL) to Talairach space
    using the icbm2tal transform developed and validated by Jack Lancaster at
    the Research Imaging Center in San Antonio, Texas.
    http://www3.interscience.wiley.com/cgi-bin/abstract/114104479/ABSTRACT
    FORMAT outpoints = icbm_other2tal(inpoints)
    Where inpoints is N by 3 or 3 by N matrix of coordinates
    (N being the number of points)
    ric.uthscsa.edu 3/14/07
    """
    # Find which dimensions are of size 3

@due.dcite(references.TEXT2BRAIN,
           description='Introduced text2brain models for annotation.')
def text2brain():
    """
    Perform text-to-image encoding with the text2brain model [1]_.

    Warnings
    --------
    This method is not yet implemented.

    References
    ----------
    .. [1] Dockès, Jérôme, et al. "Text to brain: predicting the spatial
        distribution of neuroimaging observations from text reports."
        International Conference on Medical Image Computing and
        Computer-Assisted Intervention. Springer, Cham, 2018.
        https://doi.org/10.1007/978-3-030-00931-1_67

@due.dcite(references.LANCASTER_TRANSFORM,
           description='Introduces the Lancaster MNI-to-Talairach transform, '
                       'as well as its inverse, the Talairach-to-MNI '
                       'transform.')
@due.dcite(references.LANCASTER_TRANSFORM_VALIDATION,
           description='Validates the Lancaster MNI-to-Talairach and '
                       'Talairach-to-MNI transforms.')
def mni2tal(coords):
    """
    Python version of BrainMap's icbm_other2tal.m.
    This function converts coordinates from MNI space (normalized using
    templates other than those contained in SPM and FSL) to Talairach space
    using the icbm2tal transform developed and validated by Jack Lancaster at
    the Research Imaging Center in San Antonio, Texas.
    http://www3.interscience.wiley.com/cgi-bin/abstract/114104479/ABSTRACT
    FORMAT outpoints = icbm_other2tal(inpoints)
    Where inpoints is N by 3 or 3 by N matrix of coordinates

@due.dcite(references.WEIGHTED_STOUFFERS, description='Weighted Stouffers citation.')
def weighted_stouffers(z_maps, sample_sizes, two_sided=True):
    """
    Run a Stouffer's image-based meta-analysis on z-statistic maps.

    Parameters
    ----------
    z_maps : (n_contrasts, n_voxels) :obj:`numpy.ndarray`
        A 2D array of z-statistic maps in the same space, after masking.
    sample_sizes : (n_contrasts,) :obj:`numpy.ndarray`
        A 1D array of sample sizes associated with contrasts in ``z_maps``.
        Must be in same order as rows in ``z_maps``.
    two_sided : :obj:`bool`, optional
        Whether to do a two- or one-sided test. Default is True.

    Returns
    -------

import os.path as op

import numpy as np
import pandas as pd
import nibabel as nib
from scipy.stats import multivariate_normal

from ...due import due
from ..base import AnnotationModel
from ...utils import get_template
from ... import references

LGR = logging.getLogger(__name__)


@due.dcite(references.GCLDAMODEL)
class GCLDAModel(AnnotationModel):
    """
    Generate a generalized correspondence latent Dirichlet allocation
    (GCLDA) [1]_ topic model.

    Parameters
    ----------
    count_df : :obj:`pandas.DataFrame`
        A DataFrame with feature counts for the model. The index is 'id',
        used for identifying studies. Other columns are features (e.g.,
        unigrams and bigrams from Neurosynth), where each value is the number
        of times the feature is found in a given article.
    coordinates_df : :obj:`pandas.DataFrame`, optional
        A DataFrame with a list of foci in the dataset. The index is 'id',
        used for identifying studies. Additional columns include 'x', 'y' and
        'z' (foci in standard space).