{
    "info": {
        "author": "Jing Xu, Xinmin Li, Hua Liang",
        "author_email": "274762204@qq.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Text Processing"
        ],
        "description": "\n.. image:: https://img.shields.io/github/forks/badges/shields.svg?style=social&label=Fork   \n    :target: https://github.com/DataXujing/LN0SCIs/\n\n.. image:: https://img.shields.io/pypi/pyversions/Django.svg   \n\t:target: https://pypi.python.org/pypi/LN0SCIs\n\n.. image:: https://img.shields.io/cran/v/devtools.svg   \n\t:target: https://CRAN.R-project.org/package=LN0SCIs\n\n.. image:: https://img.shields.io/pypi/v/nine.svg   \n\t:target: https://pypi.python.org/pypi/LN0SCIs\n\n.. image:: https://github.com/DataXujing/LN0SCIs/raw/master/pic/log.png\n    :align: right\n\nLN0SCIs\n===============\n\n**Jing Xu, Xinmin Li, Hua Liang**\n\n\n\nIntroduction\n---------------\n\nThis Python package based on the paper of Simultaneous Confidence Intervals for Ratios of Means of Log-normal Populations with Zeros by Xu et al. It provides some methods for construct simultaneous confidence intervals for ratios of means of Log-normal populations with excess zeros. At last, we select 4 excellent methods which based on generalized pivotal quantity with order statistics and two-step MOVER intervals. For the convenience of use, we make a Python package called LN0SCIs, and it also has a R version package on CRAN: https://CRAN.R-project.org/package=LN0SCIs\n\n+ If you are a R User, you can install in your R kernal by Github:\n\n  - `devtools::install_github('DataXujing/LN0SCIs')`\n\n+ Or you can also install by CRAN:\n\n  - `install.packages('LN0SCIs')`\n\n+ If you are a Python user, you can \n\n  - `pip install LN0SCIs`\n\n\n\nMethods\n------------\n\nWe provaide four main functions in our LN0SCIs packages, FGW(),FGH(),MOVERW() and MOVERH(), if you want to deep understanding these four methods, you can read our paper: *Simultaneous Confidence Intervals for Ratios of Means of Log-normal Populations with Zeros*. the code we trust in GitHub. If you want to know how to realize them, you can read the source code.\n\n\nExamples\n---------\n\n+ FGW()\n\n::\n\n\n\tfrom LN0SCIs import *\n\t#Example1:\n\talpha = 0.05\n\tp = np.array([0.2,0.2,0.2])\n\tn = np.array([30,30,30])\n\tmu = np.array([0,0,0])\n\tsigma = np.array([1,1,1])\n\tN = 1000\n\tFGW(n,p,mu,sigma,N)\n\t#Example2:\n\tp = np.array([0.1,0.1,0.1,0.1])\n\tn = np.array([30,30,30,30])\n\tmu = np.array([0,0,0,0])\n\tsigma = np.array([1,1,1,1])\n\tC2 = np.array([[-1,1,0,0],[-1,0,1,0],[-1,0,0,1],[0,-1,1,0],[0,-1,0,1],[0,0,-1,1]])\n\tN = 1000\n\tFGW(n,p,mu,sigma,N,C2 = C2)\n\n  \n::\n\n\t====================Method: FGW=====================\n\tThe Simultaneous Confidence Intervals are:          \n             The1th CIs            The2th CIs            The3th CIs\n\t0  \u3010-0.843638,0.789044\u3011  \u3010-0.629208,1.075959\u3011  \u3010-0.604469,1.158544\u3011\n\t**********************Time**************************\n\tThe cost time is:0 secs\n\t====================Method: FGW=====================\n\tThe Simultaneous Confidence Intervals are:          \n             The1th CIs           The2th CIs           The3th CIs  \\\n\t0  \u3010-0.912169,1.578679\u3011  \u3010-1.02404,0.812882\u3011  \u3010-0.83778,1.382352\u3011   \n\n             The4th CIs            The5th CIs           The6th CIs  \n\t0  \u3010-1.597962,0.650222\u3011  \u3010-1.337939,1.203199\u3011  \u3010-0.546039,1.25945\u3011  \n\t**********************Time**************************\n\tThe cost time is:0 secs\n\n\n+ FGH()\n\n::\n\n\talpha = 0.05\n\tp = np.array([0.2,0.2,0.2])\n\tn = np.array([30,30,30])\n\tmu = np.array([0,0,0])\n\tsigma = np.array([1,1,1])\n\tN = 1000\n\tFGH(n,p,mu,sigma,N)\n\t#Example2:\n\tp = np.array([0.1,0.1,0.1,0.1])\n\tn = np.array([30,30,30,30])\n\tmu = np.array([0,0,0,0])\n\tsigma = np.array([1,1,1,1])\n\tC2 = np.array([[-1,1,0,0],[-1,0,1,0],[-1,0,0,1],[0,-1,1,0],[0,-1,0,1],[0,0,-1,1]])\n\tN = 1000\n\tFGH(n,p,mu,sigma,N,C2 = C2)\n\n::\n\n\t====================Method: FGH=====================\n\tThe Simultaneous Confidence Intervals are:          \n             The1th CIs            The2th CIs            The3th CIs\n\t0  \u3010-0.992276,1.455247\u3011  \u3010-0.703231,1.372774\u3011  \u3010-1.005873,1.124758\u3011\n\t**********************Time**************************\n\tThe cost time is:0 secs\n\t====================Method: FGH=====================\n\tThe Simultaneous Confidence Intervals are:          \n            The1th CIs            The2th CIs            The3th CIs  \\\n\t0  \u3010-1.62426,0.624984\u3011  \u3010-1.514528,0.553936\u3011  \u3010-1.565943,0.911157\u3011   \n\n            The4th CIs            The5th CIs           The6th CIs  \n\t0  \u3010-0.66646,1.010746\u3011  \u3010-0.829753,1.269381\u3011  \u3010-0.762683,1.07889\u3011  \n\t**********************Time**************************\n\tThe cost time is:0 secs\n\n\n+ MOVERW()\n\n\n::\n\n\n\talpha = 0.05\n\tp = np.array([0.2,0.2,0.2])\n\tn = np.array([30,30,30])\n\tmu = np.array([0,0,0])\n\tsigma = np.array([1,1,1])\n\tN = 1000\n\tMOVERW(n,p,mu,sigma,N)\n\t#Example2:\n\tp = np.array([0.1,0.1,0.1,0.1])\n\tn = np.array([30,30,30,30])\n\tmu = np.array([0,0,0,0])\n\tsigma = np.array([1,1,1,1])\n\tC2 = np.array([[-1,1,0,0],[-1,0,1,0],[-1,0,0,1],[0,-1,1,0],[0,-1,0,1],[0,0,-1,1]])\n\tN = 1000\n\tMOVERW(n,p,mu,sigma,N,C2 = C2)\n\n\n::\n\n\n\t====================Method: FGH=====================\n\tThe Simultaneous Confidence Intervals are:          \n             The1th CIs            The2th CIs            The3th CIs\n\t0  \u3010-1.103496,1.211033\u3011  \u3010-1.030952,0.888781\u3011  \u3010-1.314926,1.059975\u3011\n\t**********************Time**************************\n\tThe cost time is:0 secs\n\t====================Method: FGH=====================\n\tThe Simultaneous Confidence Intervals are:          \n            The1th CIs            The2th CIs            The3th CIs  \\\n\t0  \u3010-1.68825,0.349316\u3011  \u3010-1.270833,1.236153\u3011  \u3010-1.304731,1.053776\u3011   \n\n             The4th CIs            The5th CIs            The6th CIs  \n\t0  \u3010-0.349427,1.679719\u3011  \u3010-0.364992,1.484843\u3011  \u3010-1.294225,1.071433\u3011  \n\t**********************Time**************************\n\tThe cost time is:0 secs\n\n\n+ MOVERH()\n\n\n::\n\n\n\talpha = 0.05\n\tp = np.array([0.2,0.2,0.2])\n\tn = np.array([30,30,30])\n\tmu = np.array([0,0,0])\n\tsigma = np.array([1,1,1])\n\tN = 1000\n\tMOVERH(n,p,mu,sigma,N)\n\t#Example2:\n\tp = np.array([0.1,0.1,0.1,0.1])\n\tn = np.array([30,30,30,30])\n\tmu = np.array([0,0,0,0])\n\tsigma = np.array([1,1,1,1])\n\tC2 = np.array([[-1,1,0,0],[-1,0,1,0],[-1,0,0,1],[0,-1,1,0],[0,-1,0,1],[0,0,-1,1]])\n\tN = 1000\n\tMOVERH(n,p,mu,sigma,N,C2 = C2)\n\n\n::\n\n\t====================Method: FGH=====================\n\tThe Simultaneous Confidence Intervals are:          \n             The1th CIs            The2th CIs          The3th CIs\n\t0  \u3010-1.013305,0.765726\u3011  \u3010-1.152934,0.823283\u3011  \u3010-0.914194,0.8239\u3011\n\t**********************Time**************************\n\tThe cost time is:0 secs\n\t====================Method: FGH=====================\n\tThe Simultaneous Confidence Intervals are:          \n             The1th CIs            The2th CIs           The3th CIs  \\\n\t0  \u3010-0.681666,1.693927\u3011  \u3010-0.750657,1.458978\u3011  \u3010-1.21012,0.855608\u3011   \n\n             The4th CIs            The5th CIs            The6th CIs  \n\t0  \u3010-1.302431,1.003355\u3011  \u3010-1.762379,0.407925\u3011  \u3010-1.527028,0.467458\u3011  \n\t**********************Time**************************\n\tThe cost time is:0 secs\t\n\n\n\n\n\nSupports\n-----------\n\nTested on Python 2.7, 3.5, 3.6\n\n* pip install LN0SCIs\n* Download: https://pypi.python.org/pypi/LN0SCIs\n* Documentation: https://github.com/DataXujing/LN0SCIs\n* It has a R packages version which we have created, details you can see:  https://CRAN.R-project.org/package=LN0SCIs\n\nyou can log in Xujing's home page: https://dataxujing.coding.me or https://dataxujing.github.io to find the author(s), and if you want to learn more about simultaneous confidence intervals for the mixture distribution, you shou read the paper: Simulataneous Confidence Intervals for ratios of Means of Log-normal Populations with Zeros, which written by Jing Xu, Xinmin Li, and Hua Liang.\n\n",
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