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Merge pull request #210 from jakobrunge/developer
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updated readme and LPCMCI tutorial
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jakobrunge authored Apr 14, 2022
2 parents 26950d7 + abf0035 commit b5e0f0b
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2 changes: 2 additions & 0 deletions README.md
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Expand Up @@ -7,6 +7,8 @@ Version 5.0

[Documentation](https://jakobrunge.github.io/tigramite/)

[Tutorials](https://github.com/jakobrunge/tigramite/tree/master/tutorials/)

[Simple GUI](https://github.com/stbachinger/TigramiteGui)

## Overview
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2 changes: 1 addition & 1 deletion release_on_pip.sh
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#!/bin/bash

version="50115"
version="50116"

# Steps to move from github to pip
# (Make sure pypi token exists)
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -63,7 +63,7 @@ def run(self):
# Run the setup
setup(
name="tigramite",
version="5.0.1.16",
version="5.0.1.17",
packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
license="GNU General Public License v3.0",
description="Tigramite causal discovery for time series",
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2 changes: 1 addition & 1 deletion tutorials/tigramite_tutorial_latent-pcmci.ipynb
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"id": "4292aca1",
"metadata": {},
"source": [
"Based on the results of `run_bivci`, we choose to apply LPCMCI with `tau_max = 5`. We further choose `pc_alpha = 0.01` and apart from that do not modify the default hyperparameter settings."
"Based on the results of `run_bivci`, we choose to apply LPCMCI with `tau_max = 5` since after that lag, the lag functions decay to zero. Note that this is only a heuristic. In addition, you may also check scatter plots as done in the PCMCI tutorial in order to choose an appropriate conditional independence test. We further choose `pc_alpha = 0.01` and apart from that do not modify the default hyperparameter settings."
]
},
{
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