SDFC (Statistical Distribution Fit with Covariates)

Features

• python3 and R version
• Fit with (or without) covariates parametric laws (Normal, Exp, Gamma, GEV, GPD)
• MLE and Bayesian fit available for all laws
• Fit of non parametric distribution (QuantileRegression)
• Support for fit with fix parameters
• Support for user defined custom link function

Python instruction

Requires:

• python3
• Eigen
• numpy
• scipy
• pybind11

For python, just use the command:

python3 setup.py install

If the Eigen library is not found (needed for Quantile Regression), use:

python3 setup.py install eigen="path-to-eigen"

Note for quantile regression

The quantile regression is solved with the Frish-Newton algorithm, written in c++, depending of the Eigen c++ library. It is a re-written of the Fortran code of Koenker, available in the R package quantreg

Example

License

Copyright(c) 2020 Yoann Robin

This file is part of SDFC.

SDFC is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

SDFC is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with SDFC. If not, see https://www.gnu.org/licenses/.