Metadata-Version: 2.1
Name: decodanda
Version: 0.8.3
Summary: Geometric decoding of neural data with built-in best practices.
Home-page: https://github.com/lposani/decodanda
Author: Lorenzo Posani
Author-email: lorenzo.posani@gmail.com
Keywords: python,decoding,neuroscience,ccgp,neural activity,population activity,neural decoding,geometry
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
License-File: LICENSE.md
Requires-Dist: matplotlib (>=3.3.4)
Requires-Dist: numpy (>=1.20.1)
Requires-Dist: pandas (>=1.4.0)
Requires-Dist: scikit-learn (>=0.24.1)
Requires-Dist: scipy (>=1.6.1)
Requires-Dist: seaborn (>=0.11.1)
Requires-Dist: tqdm (>=4.58.0)
Requires-Dist: h5py (>=3.10.0)


Decodanda (dog latin for "to be decoded") is a best-practices-made-easy Python package for decoding neural data.
Decodanda is designed to expose a user-friendly and flexible interface for population activity decoding,
with a series of built-in best practices to avoid the most common pitfalls.
In addition, Decodanda exposes a series of functions to compute the Cross-Condition Generalization Performance (CCGP,
Bernardi et al. 2020) and Cross-Variable Interference (O'Neill et al 2025) 
for the geometrical analysis of neural population activity.
