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        "description": "lad\n===\n|ci-badge| |cov-badge| |zenodo-badge|\n\n.. |ci-badge| image:: https://travis-ci.org/mirca/lad.svg?branch=master\n    :target: https://travis-ci.org/mirca/lad\n.. |cov-badge| image:: https://codecov.io/gh/mirca/lad/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/mirca/lad/branch/master/\n.. |zenodo-badge| image:: https://zenodo.org/badge/136721899.svg\n   :target: https://zenodo.org/badge/latestdoi/136721899\n\nLeast absolute deviations with L1 regularization using majorization-minimization.\nIn estimation theory terms, this is the Maximum A Posterior (MAP) estimator for\na Laplacian likelihood with Laplacian prior, i.e.\n\n.. image:: lad.png\n    :align: center\n\nThe MM algorithm update turns out to be\n\n.. image:: lad2.png\n    :align: center\n\nwhich belongs to the class of iterative reweighted least-squares: https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares\n\nPython Version\n--------------\n\nTo install the development version, proceed as follows::\n\n    git clone https://github.com/mirca/lad.git\n    pip install -e lad\n\nOr install the lastest version on PyPi::\n\n    pip install lad\n\nInstallation dependencies::\n\n    - tensorflow\n\nTest dependencies::\n\n    - numpy\n    - tensorflow\n    - pytest\n    - pytest-cov\n\nR version\n---------\n\nInside the R console, type::\n\n    devtools::install_github(\"mirca/lad/r/lad\")",
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