Metadata-Version: 2.1
Name: girth
Version: 0.3.7
Summary: A python package for Item Response Theory.
Home-page: https://eribean.github.io/girth/
Author: Ryan C. Sanchez
Author-email: rsanchez44@gatech.edu
License: MIT
Download-URL: https://github.com/eribean/girth/archive/v0.3.7.tar.gz
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        # **G**eorgia Tech **I**tem **R**esponse **Th**eory Package
        
        [![GIRTH](https://eribean.github.io/girth/featured-background_hubf3811d606e709c4b8d3b39f7338865e_285315_960x540_fill_q75_catmullrom_top.jpg)](https://eribean.github.io/girth/)
        
        Girth is a python package for estimating item response theory (IRT) parameters.  In addition, synthetic IRT data generation is supported. Below is a list of available functions, for more information visit the GIRTH [homepage](https://eribean.github.io/girth/).
        
        
        **Dichotomous Models**
        1. Rasch Model
           * Joint Maximum Likelihood
           * Conditional Likelihood
           * Marginal Maximum Likelihood
        2. One / Two Parameter Logistic Models
           * Joint Maximum Likelihood
           * Marginal Maximum Likelihood
        3. Three Parameter Logistic Models
           * Marginal Maximum Likelihood (No Optimization and Minimal Support)
        
        **Polytomous Models**
        1. Graded Response Model
           * Joint Maximum Likelihood
           * Marginal Maximum Likelihood
        2. Partial Credit Model
           * Joint Maximum Likelihood
           * Marginal Maximum Likelihood
        3. Graded Unfolding Model
           * Marginal Maximum Likelihood
        
        **Ablity Estimation**
        1. Dichotomous
           * Marginal Likelihood Estimation
           * Maximum a Posteriori Estimation
           * Expected a Posteriori Estimation
        2. Polytomous
           * Expected a Posteriori Estimation
        
        **Supported Synthetic Data Generation**
        1. Rasch / 1PL Models Dichotomous Models
        2. 2 PL Dichotomous Models
        3. 3 PL Dichotomous Models
        4. Graded Response Model Polytomous
        5. Partial Credit Model Polytomous
        6. Graded Unfolding Model Polytomous
        7. Multidimensional Dichotomous Models
        
        
        ## Installation
        Via pip
        ```
        pip install girth --upgrade
        ```
        
        From Source
        ```
        python setup.py install --prefix=path/to/your/installation
        ```
        
        ## Dependencies
        We recommend the anaconda environment which can be installed
        [here](https://www.anaconda.com/distribution/)
        
        * Python 3.7  
        * Numpy  
        * Scipy
        * Numba
        
        ## Usage
        ```python
        import numpy as np
        
        from girth import create_synthetic_irt_dichotomous
        from girth import twopl_mml
        
        # Create Synthetic Data
        difficulty = np.linspace(-2.5, 2.5, 10)
        discrimination = np.random.rand(10) + 0.5
        theta = np.random.randn(500)
        
        syn_data = create_synthetic_irt_dichotomous(difficulty, discrimination, theta)
        
        # Solve for parameters
        estimates = twopl_mml(syn_data)
        
        # Unpack estimates
        discrimination_estimates = estimates['Discrimination']
        difficulty_estimates = estimates['Difficulty']
        ```
        
        ## Unittests
        
        **Without** coverage.py module
        ```
        nosetests testing/
        ```
        
        **With** coverage.py module
        ```
        nosetests --with-coverage --cover-package=girth testing/
        ```
        
        ## Contact
        
        Ryan Sanchez  
        rsanchez44@gatech.edu
        
        ## License
        
        MIT License
        
        Copyright (c) 2020 Ryan Sanchez
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Keywords: IRT,Psychometrics,Item Response Theory
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
