Metadata-Version: 2.1
Name: cimcb
Version: 1.0.39
Summary: This is a pre-release.
Home-page: https://github.com/KevinMMendez/cimcb
Author: Kevin Mendez, David Broadhurst
Author-email: k.mendez@ecu.edu.au, d.broadhurst@ecu.edu.au
License: http://www.apache.org/licenses/LICENSE-2.0.html
Description: <img src="cimcb_logo.png" alt="drawing" width="400"/>
        
        # cimcb
        cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data.
        
        ## Installation
        
        ### Dependencies
        cimcb requires:
        - Python (>=3.5)
        - Bokeh (>=1.0.0)
        - Keras
        - NumPy (>=1.12)
        - SciPy
        - scikit-learn
        - Statsmodels
        - TensorFlow
        - tqdm
        
        ### User installation
        The recommend way to install cimcb and dependencies is to using ``conda``:
        ```console
        conda install -c cimcb cimcb
        ```
        or ``pip``:
        ```console
        pip install cimcb
        ```
        Alternatively, to install directly from github:
        ```console
        pip install https://github.com/KevinMMendez/cimcb/archive/master.zip
        ```
        
        ### Tutorial
        Open with Binders:
        
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/KevinMMendez/BinderTutorial_Workflow/master?filepath=BinderTutorial_Workflow.ipynb)
        
        ### API
        For futher detail on the usage refer to the docstring.
        
        #### cimcb.model
        - [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm.
        - [PCR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCR.py#L8-L29): Principal component regression.
        - [PCLR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCLR.py#L8-L29): Principal component logistic regression.
        - [RF](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RF.py#L8-L9): Random forest.
        - [SVM](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/SVM.py#L8-L9): Support Vector Machine.
        - [RBF_NN](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RBF_NN.py#L8-L9): Radial basis function neural network.
        - [NN_LinearLinear](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLinear.py#L7-L8): 2 Layer linear-linear neural network.
        - [NN_LinearLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLogit.py#L7-L8): 2 Layer linear-logistic neural network.
        - [NN_LogitLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LogitLogit.py#L7-L8): 2 Layer logistic-logistic neural network.
        
        #### cimcb.plot
        - [boxplot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh.
        - [distribution](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh.
        - [pca](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh.
        - [permutation_test](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh.
        - [roc_plot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/roc.py#L11-L24): Creates a rocplot using Bokeh.
        - [scatter](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh.
        - [scatterCI](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh.
        
        #### cimcb.cross_val
        - [kfold](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics.
        
        #### cimcb.bootstrap
        - [Perc](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval.
        - [BC](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval.
        - [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.
        
        #### cimcb.utils
        - [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.
        - [ci95_ellipse](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA.
        - [knnimpute](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance.
        - [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.
        - [nested_getattr](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/nested_getattr.py#L4-L5): getattr for nested attributes.
        - [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'.
        - [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).
        - [univariate_2class](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class).
        - [wmean](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights.
        
        ### License
        cimcb is licensed under the ___ license.
        
        ### Authors
        - Kevin Mendez
        - [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en)
        
        ### Correspondence
        Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.
        E-mail: d.broadhurst@ecu.edu.au
        
        ### Citation
        If you would cite cimcb in a scientific publication, you can use the following: ___
        
Platform: UNKNOWN
Requires-Python: >=3.5
Description-Content-Type: text/markdown
