Metadata-Version: 2.1
Name: humanleague
Version: 2.0.3
Summary: Microsynthesis using quasirandom sampling and/or IPF
Home-page: http://github.com/virgesmith/humanleague
Author: Andrew P Smith
Author-email: a.p.smith@leeds.ac.uk
License: UNKNOWN
Description: # humanleague
        
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        *humanleague* is a python and an R package for microsynthesising populations from marginal and (optionally) seed data. The core code is implemented in C++, and the current release is version 2.
        
        The package contains algorithms that use a number of different microsynthesis techniques:
        - [Iterative Proportional Fitting (IPF)](https://en.wikipedia.org/wiki/Iterative_proportional_fitting)
        - [Quasirandom Integer Sampling (QIS)](http://jasss.soc.surrey.ac.uk/20/4/14.html) (no seed population)
        - Quasirandom Integer Sampling of IPF (QISI): A combination of the two techniques whereby IPF solutions are used to sample an integer population.
        
        The latter provides a bridge between deterministic reweighting and combinatorial optimisation, offering advantages of both techniques:
        - generates high-entropy integral populations 
        - can be used to generate multiple populations for sensitivity analysis
        - goes some way to address the 'empty cells' issues that can occur in straight IPF
        - relatively fast compuation time
        
        The algorithms: 
        - support arbitrary dimensionality* for both the marginals and the seed.
        - produce statistical data to ascertain the likelihood/degeneracy of the population (where appropriate).
        
        [* excluding the legacy functions retained for backward compatibility with version 1.0.1]
        
        The package also contains the following utility functions:
        - a Sobol sequence generator
        - functionality to convert fractional to nearest-integer marginals (in 1D). This can also be achieved in multiple dimensions by using the QISI algorithm.
        - functionality to 'flatten' a population into a table: this converts a multidimensional array containing the population count for each state into a table listing individuals and their characteristics. 
        
        Version 1.0.1 reflects the work described in the [Quasirandom Integer Sampling (QIS)](http://jasss.soc.surrey.ac.uk/20/4/14.html) paper.
        
        ## R installation
        Official release:
        ```
        > install.packages("humanleague")
        ```
        For development version
        ```bash
        > devtools::install_github("virgesmith/humanleague")
        ```
        Or, for the legacy version
        ```bash
        > devtools::install_github("virgesmith/humanleague@1.0.1")
        ```
        ## python installation
        
        Requires Python 3 and numpy. PyPI package:
        ```bash
        python3 -m pip install humanleague
        ```
        [Conda pacakage is being worked on]
        
        ### Build and test (from local cloned repo)
        ```bash
        $ ./setup.py build
        ```
        ```bash
        $ python3 tests/test_all.py
        ```
        ### Install (from local repo)
        ```bash
        $ ./setup.py install
        ```
        The latter command may require admin rights. On linux, `sudo` is unnecessary if you have group (e.g. staff) write access to /usr/local/lib.
        
        ### Examples
        
        Consult the package documentation, e.g.
        ```
        > library(humanleague)
        > ?humanleague
        ```
        in R, or for python:
        ```
        >>> import humanleague as hl
        >>> help(hl)
        ```
        
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