semvec/pypi-packages huggingface dataset:
RankBasedMonteCarlo 0.1.4
A set of Monte Carlo based tools to create test statistics for four non-parametric rank-based tests: Mann-Whitney (MW), Kruskal-Wallis (KW), Kolmogorov-Smirnov (KS) and Kuiper (K).
Requires Python: >=3.8
Keywords: Monte Carlo,Rank based,Statistics

# Rank Based Monte Carlo

This package produces the null hypothesis distribution of four non-parametric tests: Mann-Whitney (MW), Kruskal-Wallis (KW), Kolmogorov-Smirnov (KS) and Kuiper (K). 

In all cases, the distribution is created by initializing the class (MonteCarloMannWhitney, MonteCarloKruskalWallis, MonteCarloKolmogorovSmirnov, MonteCarloKuiper) from the package and calling the method PrintCriticalValueTable.  For instance, the KW test statistics can be accomplished with the following code:

~~~

from RankBasedMonteCarlo import MonteCarloKruskalWallis

if __name__ == '__main__':
    kw = MonteCarloKruskalWallis()
    criticalValues, pvalue = kw.PrintCriticalValueTable((6, 45, 30), 10000, 9)

~~~


The class is imported from the package on the first line.  Within the if statement, an instance of the class is created (kw) and the critical values and pvalue is determined on the next line.  The PrintCriticalValueTable method accepts the same parameters regardless of the statistical test:


1. ns : tuple : A tuple listing the number of observations per group.  For instance (6,5)
2. reps : int : The number of repetitions the process completes before producing critical values.  Default is 10,000.
3. observedValue : float : Optional value used to determine p-value
4. PrintToScreen : bool : Specifies if the critical values are printed to the screen.  Defaults to True.
5. cvs : list : Specifies a list of critical values.

## Installation

This package can be installed using pip or conda (Anaconda):

### Using Pip

~~~

pip install RankBasedMonteCarlo

~~~		

### Using Conda

~~~

conda install -c tazzben rankbasedmontecarlo

~~~
apidb 0.2.1
Autogenerate API based on DB structure directly from Python using ORM
Requires Python: >=3.6

# apidb
Autogenerate API based on DB structure directly from Python using ORM

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install apidb.

```bash
pip install apidb
```



## Usage
```python
from fastapi import FastAPI
import apidb.apidb_core as ad
import uvicorn

description = """
This is your API
"""

app = FastAPI(title="Example API",
    description=description,
    version="1.0.0",
    )

db_api_dict={'workspaces':'read','nodes':['read','create','update','delete']}
column_names=["workspace_name"]

ad.initialize_fastapi(app, db_api_dict, column_names, mysql)

@app.get("/api/v1/custom_workspaces")
def get_workspaces():
    """
    Returns all workspaces
    """
    return {"workspaces": []}

def run_api():
    uvicorn.run(app, host="0.0.0.0", port=8000)

if __name__=="__main__":
    run_api()
```
![obrazek](https://user-images.githubusercontent.com/29150831/214747236-0827330f-7c17-4749-a3ac-0b98003e741d.png)

## Current scope
Aims: Easy generation of API in Fastapi + Flask based on structure in DB (current dialects: MySQL, PostgreSQL, SQL Server, Mongo) or on series of predefined functions

Done: Flask and FastAPI simple example

Todo: Generalization


## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License
[MIT](https://choosealicense.com/licenses/mit/)
SmithWagnerCV 0.1.0
Produces critical values for value-added learning scores proposed in Smith and Wagner (2018) through Monte Carlo simulations.
Requires Python: >=3.8
Keywords: Monte Carlo,Statistics,Value-added Learning

# SmithWagnerCV
 
This module produces critical values for the disaggregated learning types as described in Smith and Wagner (2018) and Smith and White (2021). 

## Examples

Run a Monte Carlo Simulation of mu value of 0.1 and 25 students.

```python
from SmithWagnerCV import RunSimulation

d = RunSimulation(25, 0.1)

```

Simulate all combinations of [10,20] students and [0.1,0.5] mu values and return them as a dictionary

```python
from SmithWagnerCV import SimulationTable

d = SimulationTable([10,20], [0.1,0.5])

```
Simulate all combinations of [10,20] students and [0.1,0.5] mu values and save them to CSV files

```python
from SmithWagnerCV import SaveSimulationTable 

d = SaveSimulationTable([10,20], [0.1,0.5])

```

## Installation

Using the pip tool, you can install this module with the following command:

```
pip install SmithWagnerCV
```

Using the conda command you can type the following:

```
conda install -c tazzben smithwagnercv  
```