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update for the article revision (1.8.1.14.)
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‎article/paper.md

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# Features
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`Statmanager-kr` was designed to be compatible with the wide range form of `Pandas.DataFrame`.
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## User-friendly Features
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### Setting the Language
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Learn more about the features in the *[official documentation](https://cslee145.notion.site/60cbfcbc90614fe990e02ab8340630cc?v=4991650ae5ce4427a215d1043802f5c0)*. The instructions for running each analysis are also described in *[the manual section of the official documentation](https://cslee145.notion.site/Statmanager-kr-Official-Documentation-74a610c12881402d96dc5d1654f97433#be93db7f4159419fa73eb324d6567793)*. *[Korean versions of the official documenation](https://cslee145.notion.site/fd776d4f9a4f4c9db2cf1bbe60726971?v=3b2b237555fc4cd3a41a8da337d80c01)* are also available.
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It is possible to change the language by adjusting the `language` parameter when creating an object of the Stat_Manager class. The supported languages are Korean ("kor") and English ("eng"), and the default is Korean.
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```python
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sm = Stat_Manager(df, language = 'eng')
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```
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### Other Methods
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Users can search for a specific usage by calling the `.howtouse()` method. It can also change the language with `.set_language()`, or change the dataframe by running `.change_dataframe()`.
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## Statistical Test
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The implementation of analysis in `Statmanager-kr` can be summarized as follows.
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| Objective | Analysis |
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| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| Check the normality assumption | Kolmogorov-Smirnov Test, Shapiro-Wilks Test, Z-Skewness & Z-Kurtosis Test |
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| Check the homoskedasticity assumption | Levene's Test, F<sub>max</sub> Test |
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| Frequency analysis | Chi-Squared Test, Fisher’s Exact Test |
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| Check the reliability of the scale | Calculating Cronbach’s Alpha |
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| Correlation analysis | Pearson’s r, Spearman’s rho, Kendall’s tau |
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| Comparison between groups | Independent Samples T-test, Yuen’s T-test, Welch’s T-test, Mann-whitney U test, Brunner-Munzel Test, One-way ANOVA, Kruskal Wallis Test, One-way ANCOVA |
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| Comparison within group | Dependent Samples T-test, Wilcoxon-Signed Rank Test, One-way Repeated Measures ANOVA, Friedman Test, Repeated Measures ANCOVA, |
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| Comparison by multiple ways | N-way ANOVA, N-way Mixed Repeated Measures ANOVA |
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| Regression analysis | Linear Regression, Logistic Regression |
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| etc | Bootstrapping percentile method |
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<br>Each analysis method has its own <b>"key"</b> that allows it to be used in the `.progress()` method. The analysis is performed by passing the key for each analysis method to the `method` parameter in the `.progress()` method, the variables to be analyzed to the `vars` parameter, and the group variables to the `group_vars` parameter.
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```python
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import pandas as pd
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from statmanager import Stat_Manager
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# 1. Reading the data
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df = pd.read_csv(r'testdata.csv', index_col = 'name')
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# 2. Creating object of Stat_Manager class
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sm = Stat_Manager(df)
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# 3. Running: check the difference in weight by sex
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sm.progress(method = 'ttest_ind', vars = 'weight', group_vars = 'sex')
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```
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<br>Also, if a post-hoc test is required, as in the case of a one-way ANOVA (key of one-way ANOVA is `f_oneway`), it can be conducted by simply providing `True` to the `posthoc` parameter.
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```python
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sm.progress(method = 'f_oneway', vars = 'income', group_vars = 'condition', posthoc = True)
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```
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### Keys and Related Informations
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The method-specific information needed to use the `.progress()` method can be found by using the `.howtouse()` method. The detailed information is summarized in the table below:
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| Key | Analysis | Required Parameters | Optional Parameters |
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| ------------------ | ----------------------------------- | -------------------- | --------------------------- |
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| `kstest` | Kolmogorov-Smirnov Test | `vars` | `group_vars` |
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| `shapiro` | Shapiro-Wilks Test | `vars` | `group_vars` |
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| `z_normal` | Z-Skewness & Z-Kurtosis test | `vars` | `group_vars` |
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| `levene` | Levene Test | `vars`, `group_vars` |
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| `fmax` | F<sub>max</sub> Test | `vars`, `group_vars` |
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| `chi2_contingency` | Chi-squared Test | `vars` |
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| `fisher` | Fisher's Exact Test | `vars` |
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| `pearsonr` | Pearson's r | `vars` |
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| `spearmanr` | Spearman's rho | `vars` |
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| `kendallt` | Kendall's tau | `vars` |
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| `ttest_ind` | Independent Samples T-test | `vars`, `group_vars` |
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| `ttest_rel` | Dependent Samples T-test | `vars` |
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| `ttest_ind_trim` | Yuen's Two Samples T-test | `vars`, `group_vars` |
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| `ttest_ind_welch` | Welch's Two Samples T-test | `vars`, `group_vars` |
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| `mannwhitneyu` | Mann-Whitney U Test | `vars`, `group_vars` |
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| `brunner` | Brunner-Munzel Test | `vars`, `group_vars` |
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| `wilcoxon` | Wilcoxon-Signed Rank Test | `vars` |
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| `bootstrap` | Boostrap Percentile Method | `vars` | `group_vars` |
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| `f_oneway` | One-way ANOVA | `vars`, `group_vars` | `posthoc`, `posthoc_method` |
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| `f_oneway_rm` | One-way Repeated Measures ANOVA | `vars` | `posthoc`, `posthoc_method` |
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| `kruskal` | Kruskal-Wallis Test | `vars`, `group_vars` | `posthoc`, `posthoc_method` |
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| `friedman` | Friedman Test | `vars` | `posthoc`, `posthoc_method` |
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| `f_nway` | N-way ANOVA | `vars`, `group_vars` | `posthoc`, `posthoc_method` |
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| `f_nway_rm` | N-way Mixed Repeated Measures ANOVA | `vars`, `group_vars` | `posthoc`, `posthoc_method` |
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| `linearr` | Linear Regression | `vars` |
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| `hier_linearr` | Hierarchical Linear Regression | `vars` |
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| `logisticr` | Logistic Regression | `vars` |
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| `oneway_ancova` | One-way ANCOVA | `vars`, `group_vars` |
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| `rm_ancova` | One-way Repeated Measures ANCOVA | `vars` |
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| `cronbach` | Calculating Cronbach's Alpha | `vars` |
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Also `Statmanager-kr` provides two post-hoc methods. It can be run by providing the key of the `posthoc_method` parameter as follows:
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| Key of `posthoc_method` | Method |
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| ----------------------- | --------------------- |
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| `bonf` | Bonferroni Correction |
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| `tukey` | Tukey HSD |
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## Visualization
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A figure is automatically generated for the results of the analysis when a `.figure()` is run as a chain method against a `.progress()`.
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```python
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# Running: check the difference in weight by sex with figure
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sm.progress(method = 'ttest_ind', vars = 'weight', group_vars = 'sex').figure()
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```
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# Acknowledgements
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‎statmanager/__init__.py

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from .posthoc_functions import *
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from .effectsize_functions import *
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__version__ = '1.8.1.13'
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__version__ = '1.8.1.14'
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__all__ = ['Stat_Manager']

‎statmanager/setup.py

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setup(
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name='statmanager-kr',
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version='1.8.1.13',
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version='1.8.1.14',
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license='MIT',
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long_description=description_markdown,
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long_description_content_type='text/markdown',

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