Welcome to Deep LVPM Documentation!
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Deep Latent Variable Path Modelling (DLVPM) is a method for multi‑view data integration using deep learning.  It constructs orthogonal latent variables that maximize correlation between different types of data and uses an explicit structural path matrix to define relationships between these latent factors. This documentation provides an overview of the DLVPM toolbox, installation instructions, step‑by‑step tutorials, and reference information about the core classes and layers.

If you find Deep LVPM useful, please consider giving the project a `star <https://github.com/alexjamesing/Deep_LVPM>`_ on GitHub to help others discover it.

.. toctree::
   :maxdepth: 2
   :hidden:

   introduction
   installation
   reference/structural_model
   tutorials/tcga
   tutorials/tcga_torch
   tutorials/mnist
   tutorials/siamese