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    "info": {
        "author": "Vladimir Emelianov",
        "author_email": "vlademelianov@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "This package will allow for easy building of yield curves using various methodologies found in academic literature. \nCurrently, this package only has the Nelson Siegal function programmed but other functions are being worked on at the moment.\n\nTo use:\nMake sure your dataframe consists of yields with column names denoting the maturity of the security in months.\nInitiate the function as such:\n  curve = NelsonSiegal(est_l=True)\n  curve.fit(data, params=[1,1,1,1], l=none)\n\nThe NelsonSiegal function contains one argument. est_l - this argument is basically asking if you would like to have the lambda estimated\nfrom the data using a non-linear least squares optimizer. Setting this to true will do so. Setting this to false means you will have to \nprovide your own value of lambda into the fit function.\nThe fit function contains three arguments. Data - this is just your dataframe, params - this is an array of parameters used as starting \nvalues in the optimisation function. Finally, l is just the value for lambda if you chose this option in the initiation function.\n\n",
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        "name": "yieldcurve",
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        "summary": "Estimates yield curves using various methodologies",
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