Metadata-Version: 2.1
Name: hypothetical
Version: 0.2.1
Summary: Hypothesis testing and other testing methods
Home-page: UNKNOWN
Author: Aaron Schlegel
Author-email: aaron@aaronschlegel.com
License: UNKNOWN
Description: # hypothetical - Hypothesis and Statistical Testing in Python
        
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        Python library for conducting hypothesis and other group comparison tests.
        
        ## Available Methods
        
        ### Analysis of Variance
        
        * One-way Analysis of Variance (ANOVA)
        * One-way Multivariate Analysis of Variance (MANOVA)
        
        ### Contingency Tables and Related Tests
        
        * Chi-square test of independence
        * Fisher's Exact Test
        * McNemar's Test of paired nominal data
        * Cochran's Q test
        
        ### Descriptive Statistics
        
        * Kurtosis
        * Skewness
        * Mean Absolute Deviation
        * Pearson Correlation
        * Spearman Correlation
        * Covariance
          - Several algorithms for computing the covariance and covariance matrix of 
            sample data are available
        * Variance
          - Several algorithms are also available for computing variance.
        * Simulation of Correlation Matrices
          - Multiple simulation algorithms are available for generating correlation matrices.
        
        ### Critical Value Tables and Lookup Functions
        
        * Chi-square statistic
        * r (one-sample runs test and Wald-Wolfowitz runs test) statistic 
        * Mann-Whitney U-statistic
        * Wilcoxon Rank Sum W-statistic
        
        ### Hypothesis Testing
        
        * Binomial Test
        * t-test
          - paired, one and two sample testing
        
        ### Nonparametric Methods
        
        * Friedman's test for repeated measures
        * Kruskal-Wallis (nonparametric equivalent of one-way ANOVA)
        * Mann-Whitney (two sample nonparametric variant of t-test)
        * Mood's Median test
        * One-sample Runs Test
        * Sign test of consistent differences between observation pairs
        * Wald-Wolfowitz Two-Sample Runs test
        * Wilcoxon Rank Sum Test (one sample nonparametric variant of paired and one-sample t-test)
        
        ### Normality and Goodness-of-Fit Tests
        
        * Chi-square one-sample goodness-of-fit
        * Jarque-Bera test
        
        ### Post-Hoc Analysis
        
        * Tukey's Honestly Significant Difference (HSD)
        * Games-Howell (nonparametric)
        
        ### Helpful Functions
        
        * Add noise to a correlation or other matrix
        * Tie Correction for ranked variables
        * Contingency table marginal sums
        * Contingency table expected frequencies
        * Runs and count of runs
        
        ## Goal
        
        The goal of the `hypothetical` library is to help bridge the gap in statistics and hypothesis testing 
        capabilities of Python closer to that of R. Python has absolutely come a long way with several popular and 
        amazing libraries that contain a myriad of statistics functions and methods, such as [`numpy`](http://www.numpy.org/), 
        [`pandas`](https://pandas.pydata.org/), and [`scipy`](https://www.scipy.org/); however, it is my humble opinion that 
        there is still more that can be done to make Python an even better language for data and statistics computation. Thus, 
        it is my hope with the `hypothetical` library to build on top of the wonderful Python packages listed earlier and 
        create an easy-to-use, feature complete, statistics library. At the end of the day, if the library helps a user 
        learn more about statistics or get the information they need in an easy way, then I consider that all the success 
        I need!
        
        ## Requirements
        
        * Python 3.5+
        * `numpy>=1.13.0`
        * `numpy_indexed>=0.3.5`
        * `pandas>=0.22.0`
        * `scipy>=1.1.0`
        * `statsmodels>=0.9.0`
        
        ## Installation
        
        ## License
        
        MIT
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
