- 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
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
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/.