Package: LGDtoolkit 0.2.0
LGDtoolkit: Collection of Tools for LGD Rating Model Development
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.
Authors:
LGDtoolkit_0.2.0.tar.gz
LGDtoolkit_0.2.0.zip(r-4.5)LGDtoolkit_0.2.0.zip(r-4.4)LGDtoolkit_0.2.0.zip(r-4.3)
LGDtoolkit_0.2.0.tgz(r-4.4-any)LGDtoolkit_0.2.0.tgz(r-4.3-any)
LGDtoolkit_0.2.0.tar.gz(r-4.5-noble)LGDtoolkit_0.2.0.tar.gz(r-4.4-noble)
LGDtoolkit_0.2.0.tgz(r-4.4-emscripten)LGDtoolkit_0.2.0.tgz(r-4.3-emscripten)
LGDtoolkit.pdf |LGDtoolkit.html✨
LGDtoolkit/json (API)
NEWS
| # Install 'LGDtoolkit' in R: |
| install.packages('LGDtoolkit', repos = c('https://andrija-djurovic.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andrija-djurovic/lgdtoolkit/issues
- lgd.ds.c - Synthetic modeling dataset
Last updated 3 months agofrom:1481c9b8cf. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 21 2024 |
| R-4.5-win | OK | Nov 21 2024 |
| R-4.5-linux | OK | Nov 21 2024 |
| R-4.4-win | OK | Nov 21 2024 |
| R-4.4-mac | OK | Nov 21 2024 |
| R-4.3-win | OK | Nov 21 2024 |
| R-4.3-mac | OK | Nov 21 2024 |
Exports:embedded.blocksensemble.blocksheterogeneityhomogeneityinteraction.transformerkfold.idxkfold.vldr.squaredrf.interaction.transformersc.mergestaged.blocksstepFWDstepRPC
Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestdplyrevaluatefansifarverfastmapfontawesomeforeignFormulafsgenericsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemonobinmunsellnlmennetpillarpkgconfigR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletidyselecttinytexutf8vctrsviridisviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Embedded blocks regression | embedded.blocks |
| Ensemble blocks regression | ensemble.blocks |
| Testing heterogeneity of the LGD rating model | heterogeneity |
| Testing homogeneity of the LGD rating model | homogeneity |
| Extract risk factors interaction from decision tree | interaction.transformer |
| Indices for K-fold validation | kfold.idx |
| K-fold model cross-validation | kfold.vld |
| Synthetic modeling dataset | lgd.ds.c |
| Coefficient of determination | r.squared |
| Extract interactions from random forest | rf.interaction.transformer |
| Special case merging procedure | sc.merge |
| Staged blocks regression | staged.blocks |
| Customized stepwise (OLS & fractional logistic) regression with p-value and trend check | stepFWD |
| Stepwise (OLS & fractional logistic) regression based on risk profile concept | stepRPC |
