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        "author": "Andrew P Smith",
        "author_email": "a.p.smith@leeds.ac.uk",
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        "description": "# humanleague\n\n[![CRAN\\_Status\\_Badge](https://www.r-pkg.org/badges/version/humanleague)](https://CRAN.R-project.org/package=humanleague)\n[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/grand-total/humanleague?color=black)](https://cran.r-project.org/package=humanleague)\n[![PyPI version](https://badge.fury.io/py/humanleague.svg)](https://badge.fury.io/py/humanleague)\n[![Travis Build Status](https://travis-ci.org/virgesmith/humanleague.png?branch=master)](https://travis-ci.org/virgesmith/humanleague)\n[![Appveyor Build status](https://ci.appveyor.com/api/projects/status/x9oypgryt21ndc3p?svg=true)](https://ci.appveyor.com/project/virgesmith/humanleague)\n[![codecov](https://codecov.io/gh/virgesmith/humanleague/branch/master/graph/badge.svg)](https://codecov.io/gh/virgesmith/humanleague)\n[![License](https://img.shields.io/github/license/mashape/apistatus.svg)](https://opensource.org/licenses/MIT)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1116318.svg)](https://doi.org/10.5281/zenodo.1116318)\n[![status](https://joss.theoj.org/papers/d5aaf6e1c2efed431c506762622473b4/status.svg)](https://joss.theoj.org/papers/d5aaf6e1c2efed431c506762622473b4)\n\n> ## Latest news: 2.1 release\n> - adds new functionality for multidimensional integerisation. \n> - deletes previously deprecated functionality `synthPop` and `synthPopG`.\n> ### Multidimensional integerisation\n> Building on the `prob2IntFreq` function - which takes a discrete probability distribution and a count, and returns the closest integer population to the distribution that sums to the count - a multidimensional equivalent `integerise` is introduced.\n> \n> In one dimension, for example:\n> ```python\n> >>> import numpy as np\n> >>> import humanleague\n> >>> p=np.array([0.1, 0.2, 0.3, 0.4])\n> >>> humanleague.prob2IntFreq(p, 11)\n> {'freq': array([1, 2, 3, 5]), 'rmse': 0.3535533905932736}\n> ```\n> produces the optimal (i.e. closest possible) integer population to the discrete distribution.\n>  \n> The `integerise` function generalises this problem and applies it to higher dimensions: given an n-dimensional array of real numbers where the 1-d marginal sums in every dimension are integral (and thus the total population is too), it attempts to find an integral array that also satisfies these constraints. \n\n> The QISI algorithm is repurposed to this end. As it is a sampling algorithm it cannot guarantee that a solution is found, and if so, whether the solution is optimal. If it fails this does not prove that a solution does not exist for the given input.\n\n> ```python\n> >>> a = np.array([[ 0.3,  1.2,  2. ,  1.5],\n>                   [ 0.6,  2.4,  4. ,  3. ],\n>                   [ 1.5,  6. , 10. ,  7.5],\n>                   [ 0.6,  2.4,  4. ,  3. ]])\n> # marginal sums\n> >> sum(a)\n> array([ 3., 12., 20., 15.])\n> >>> sum(a.T)\n> array([ 5., 10., 25., 10.])\n> # perform integerisation\n> >>> r = humanleague.integerise(a)\n> >>> r[\"conv\"]\n> True\n> >>> r[\"result\"]\n> array([[ 0,  2,  2,  1],\n>        [ 0,  3,  4,  3],\n>        [ 2,  6, 10,  7],\n>        [ 1,  1,  4,  4]])\n> >>> r[\"rmse\"]\n> 0.5766281297335398\n> # check marginals are preserved\n> >>> sum(r[\"result\"]) == sum(a)\n> array([ True,  True,  True,  True])\n> >>> sum(r[\"result\"].T) == sum(a.T)\n> array([ True,  True,  True,  True])\n> ```\n>\n> ### Removed functions\n> The functions `synthPop` and `synthPopG` implement restricted versions of algorithms that are available in other functions.\n>\n> Use `qis` ins place of `synthPop`, and `qisi` in place of `synthPopG`.\n\n### Introduction\n\n*humanleague* is a python and an R package for microsynthesising populations from marginal and (optionally) seed data. The package is implemented in C++ for performance.\n\nThe package contains algorithms that use a number of different microsynthesis techniques:\n- [Iterative Proportional Fitting (IPF)](https://en.wikipedia.org/wiki/Iterative_proportional_fitting)\n- [Quasirandom Integer Sampling (QIS)](http://jasss.soc.surrey.ac.uk/20/4/14.html) (no seed population)\n- Quasirandom Integer Sampling of IPF (QISI): A combination of the two techniques whereby the integral population is sampled (without replacement) from a distribution constructed from a dynamic IPF solution.\n\nThe latter provides a bridge between deterministic reweighting and combinatorial optimisation, offering advantages of both techniques:\n- generates high-entropy integral populations \n- can be used to generate multiple populations for sensitivity analysis\n- goes some way to address the 'empty cells' issues that can occur in straight IPF\n- relatively fast compuation time\n\nThe algorithms: \n- support arbitrary dimensionality* for both the marginals and the seed.\n- produce statistical data to ascertain the likelihood/degeneracy of the population (where appropriate).\n\nThe package also contains the following utility functions:\n- a Sobol sequence generator\n- construct a closest integer population from a discrete univariate probability distribution.\n- an algorithm for sampling an integer population from a discrete multivariate probability distribution, constrained to the marginal sums in every dimension.\n- 'flatten' a multidimensional population into a table: this converts a multidimensional array containing the population count for each state into a table listing individuals and their characteristics. \n\nVersion 1.0.1 reflects the work described in the [Quasirandom Integer Sampling (QIS)](http://jasss.soc.surrey.ac.uk/20/4/14.html) paper.\n\n## R installation\nOfficial release:\n```\n> install.packages(\"humanleague\")\n```\nFor development version\n```bash\n> devtools::install_github(\"virgesmith/humanleague\")\n```\nOr, for the legacy version\n```bash\n> devtools::install_github(\"virgesmith/humanleague@1.0.1\")\n```\n## python installation\n\nRequires Python 3 and numpy. PyPI package:\n```bash\npython3 -m pip install humanleague --user\n```\n[Conda-forge package is being worked on]\n\n### Build, install and test (from local cloned repo)\n```bash\n$ ./setup.py install --user\n```\n```bash\n$ ./setup.py test\n```\n### Examples\n\nConsult the package documentation, e.g.\n```\n> library(humanleague)\n> ?humanleague\n```\nin R, or for python:\n```\n>>> import humanleague as hl\n>>> help(hl)\n```",
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