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        "author": "Xinwei Sun",
        "author_email": "sxwxiaoxiaohehe@pku.edu.cn",
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        "description": "Citing libra_py_001_05\n=============\n\nThe library libra_py is an academic project. The time and resources spent developing fastFM are therefore justified \nby the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry 'citation.bib <http://papers.nips.cc/paper/6288-split-lbi-an-iterative-regularization-path-with-structural-sparsity/bibtex>' ).\n\n    Huang, Chendi and Sun, Xinwei and Xiong, Jiechao and Yao, Yuan. \"Split LBI: An Iterative Regularization Path with Structural Sparsity\" Advances in Neural Information Processing Systems 29, pp. 3369--3377 (2016)\n\n\nlibra_py_001_05: A Package for sparsity problem\n============================================\n\n\n\nUsage\n-----\n.. code-block:: python\n\n    from libra_py_001_05 import lbi\n    obj = lbi.LB(X,y,family='gaussian')\n    obj.predict(X)\n\n\nTutorials and other information are available 'here <https://arxiv.org/abs/1604.05910>' and \n'here <https://www.sciencedirect.com/science/article/pii/S1063520316000038>'.\n\nThe R code is available as 'subrepository <https://cran.r-project.org/web/packages/Libra/index.html>'; the Matlab code is available as 'subrepository <https://github.com/yuany-pku/split-lbi>'.\n\nIf you have still **questions** after reading the documentation please open a issue at GitHub.\n\n+----------------+------------------+-----------------------------+\n| Family         | Solver           | Loss                        |\n+================+==================+=============================+\n| Gaussian       | LBI_Linear       | Square Loss                 |\n+----------------+------------------+-----------------------------+\n| Binomial       | LBI_Logit        | Logit Model                 |\n+----------------+------------------+-----------------------------+\n\n*Supported solvers and tasks*\n\nInstallation\n------------\n\n**binary install**\n\n``pip install libra_py_001_05``\n\n\nTests\n-----\nimport libra_py_001_05\n\n",
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        "keywords": "sparsity regularization path Lasso variable-selection",
        "license": "MIT",
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        "name": "libra_py_001_06",
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