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The goal of this package is to cover the most common steps in Loss Given Default (LGD) rating model development. The main procedures available are those that refer to bivariate and multivariate analysis. In particular two statistical methods for multivariate analysis are currently implemented – OLS regression and fractional logistic regression. Both methods are also available within different blockwise model designs and both have customized stepwise algorithms. Descriptions of these customized designs are available in Siddiqi (2016) <doi:10.1002/9781119282396.ch10> and Anderson, R.A. (2021) <doi:10.1093/oso/9780192844194.001.0001>. Although they are explained for PD model, the same designs are applicable for LGD model with different underlying regression methods (OLS and fractional logistic regression). To cover other important steps for LGD model development, it is recommended to use 'LGDtoolkit' package along with 'PDtoolkit', and 'monobin' (or 'monobinShiny') packages. Additionally, 'LGDtoolkit' provides set of procedures handy for initial and periodical model validation.
| Version: | 0.2.0 |
| Depends: | R (≥ 2.10) |
| Imports: | dplyr, monobin |
| Published: | 2023-05-30 |
| DOI: | 10.32614/CRAN.package.LGDtoolkit |
| Author: | Andrija Djurovic [aut, cre] |
| Maintainer: | Andrija Djurovic <djandrija at gmail.com> |
| License: | GPL (≥ 3) |
| URL: | https://github.com/andrija-djurovic/LGDtoolkit |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | LGDtoolkit results |
| Reference manual: | LGDtoolkit.pdf |
| Package source: | LGDtoolkit_0.2.0.tar.gz |
| Windows binaries: | r-devel: LGDtoolkit_0.2.0.zip, r-release: LGDtoolkit_0.2.0.zip, r-oldrel: LGDtoolkit_0.2.0.zip |
| macOS binaries: | r-release (arm64): LGDtoolkit_0.2.0.tgz, r-oldrel (arm64): LGDtoolkit_0.2.0.tgz, r-release (x86_64): LGDtoolkit_0.2.0.tgz, r-oldrel (x86_64): LGDtoolkit_0.2.0.tgz |
| Old sources: | LGDtoolkit archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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