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        "author": "Kevin Mendez, David Broadhurst",
        "author_email": "k.mendez@ecu.edu.au, d.broadhurst@ecu.edu.au",
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        "description": "<img src=\"cimcb_logo.png\" alt=\"drawing\" width=\"400\"/>\n\n# cimcb\ncimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data.\n\n## Installation\n\n### Dependencies\ncimcb requires:\n- Python (>=3.5)\n- Bokeh (>=1.0.0)\n- Keras\n- NumPy (>=1.12)\n- SciPy\n- scikit-learn\n- Statsmodels\n- TensorFlow\n- tqdm\n\n### User installation\nThe recommend way to install cimcb and dependencies is to using ``conda``:\n```console\nconda install -c cimcb cimcb\n```\nor ``pip``:\n```console\npip install cimcb\n```\nAlternatively, to install directly from github:\n```console\npip install https://github.com/KevinMMendez/cimcb/archive/master.zip\n```\n\n### Tutorial\nOpen with Binders:\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/KevinMMendez/BinderTutorial_Workflow/master?filepath=BinderTutorial_Workflow.ipynb)\n\n### API\nFor futher detail on the usage refer to the docstring.\n\n#### cimcb.model\n- [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm.\n- [PCR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCR.py#L8-L29): Principal component regression.\n- [PCLR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCLR.py#L8-L29): Principal component logistic regression.\n- [RF](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RF.py#L8-L9): Random forest.\n- [SVM](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/SVM.py#L8-L9): Support Vector Machine.\n- [RBF_NN](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RBF_NN.py#L8-L9): Radial basis function neural network.\n- [NN_LinearLinear](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLinear.py#L7-L8): 2 Layer linear-linear neural network.\n- [NN_LinearLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLogit.py#L7-L8): 2 Layer linear-logistic neural network.\n- [NN_LogitLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LogitLogit.py#L7-L8): 2 Layer logistic-logistic neural network.\n\n#### cimcb.plot\n- [boxplot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh.\n- [distribution](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh.\n- [pca](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh.\n- [permutation_test](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh.\n- [roc_plot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/roc.py#L11-L24): Creates a rocplot using Bokeh.\n- [scatter](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh.\n- [scatterCI](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh.\n\n#### cimcb.cross_val\n- [kfold](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics.\n\n#### cimcb.bootstrap\n- [Perc](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval.\n- [BC](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval.\n- [BCA](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BCA.py#L8-L36): Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval.\n\n#### cimcb.utils\n- [binary_metrics](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/binary_metrics.py#L5-L23): Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score.\n- [ci95_ellipse](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA.\n- [knnimpute](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance.\n- [load_dataXL](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/load_dataXL.py#L7-L29): Loads and validates the DataFile and PeakFile from an excel file.\n- [nested_getattr](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/nested_getattr.py#L4-L5): getattr for nested attributes.\n- [scale](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/scale.py#L4-L42): Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'.\n- [table_check](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/table_check.py#L4-L17): Error checking for DataTable and PeakTable (used in load_dataXL).\n- [univariate_2class](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class).\n- [wmean](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights.\n\n### License\ncimcb is licensed under the ___ license.\n\n### Authors\n- Kevin Mendez\n- [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en)\n\n### Correspondence\nProfessor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.\nE-mail: d.broadhurst@ecu.edu.au\n\n### Citation\nIf you would cite cimcb in a scientific publication, you can use the following: ___\n\n\n",
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