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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# boostCopula: Gradient-Boosted Estimation of Generalized Linear Models for Conditional Vine Copulas
<!-- badges: start -->
[](https://CRAN.R-project.org/package=boostCopula) [](https://github.com/jobstdavid/boostCopula/actions) [](https://github.com/jobstdavid/boostCopula)
<!-- badges: end -->
An R package performing gradient-boosting estimation of generalized linear models (GLMs) for conditional vine copulas as described in the references.
## Installation
You can install the development version from GitHub with:
``` r
# install.packages("remotes")
remotes::install_github("jobstdavid/boostCopula")
```
## Package overview
### Overview of the most important functions
- `boostBiCopEst`: This function estimates a GLM for a conditional bivariate copula via gradient-boosting. It returns an object of class `boostBiCop`.
- `boostBiCopSelect`: This function performs a gradient-boosted estimation of a GLM for conditional bivariate copulas including copula family selection. It returns an object of class `boostBiCop`.
- `boostBiCopPDF`, `boostBiCopCDF`: These function calculate the probability density function (PDF) or cumulative distribution function (CDF) of an `boostBiCop` object, respectively.
- `boostBiCopSim`: This function simulates samples from an `boostBiCop` object.
- `boostRVineSeqEst`: This function sequentially estimates the GLMs for conditional bivariate copulas of a d-dimensional conditional R-vine copula model via gradient-boosting. It returns an object of class `boostRVineCopula`.
- `boostRVineCopSelect`: This function sequentially estimates the GLMs for conditional bivariate copulas of a d-dimensional conditional R-vine copula model via gradient-boosting. Additionally, it selects the best fitting copula family. It returns an object of class `boostRVineCopula`.
- `boostRVineStructureSelect`: This function sequentially selects the regular vine tree structure as well as the conditional bivariate copulas via a gradient-boosted estimation of GLMs for a d-dimensional conditional R-vine copula model. It returns an object of class `boostRVineCopula`.
- `boostRVinePDF`: This function calculates the probability density function (PDF) of an `boostRVineCopula` object.
- `boostRVineSim`: This function simulates samples from an `boostRVineCopula` object.
### Overview of the implemented copula families
| Copula family | `family` |
|:--------------------:|:-------------:|
| Gaussian | `1` |
| Double Clayton I copula (0 and 90 degrees) | `301` |
| Double Clayton II copula (0 and 270 degrees) | `302` |
| Double Clayton III copula (180 and 90 degrees) | `303` |
| Double Clayton IV copula (180 and 270 degrees) | `304` |
| Double Gumbel I copula (0 and 90 degrees) | `401` |
| Double Gumbel II copula (0 and 270 degrees) | `402` |
| Double Gumbel III copula (180 and 90 degrees) | `403` |
| Double Gumbel IV copula (180 and 270 degrees) | `404` |
## Example
### Load R-package and simulated data
```{r example1, eval = T}
# load package
library(boostCopula)
# load simulated data
data(data_bicop)
data(data_vinecop)
```
### Gradient-boosted estimation of a GLM for a conditional bivariate copula
```{r example2, eval = T, out.width = "75%", fig.align = "center"}
# fit object
object <- boostBiCopEst(formula = ~.,
U = data_bicop[, 1:2],
X = data_bicop[, -c(1:2)],
family = 301,
control = boost_control(deselection = "attributable"))
summary(object)
plot(object)
```
### Gradient-boosted estimation of a GLM for conditional bivariate copulas including copula family selection
```{r example3, eval = T}
# fit object
object <- boostBiCopSelect(formula = ~.,
U = data_bicop[, 1:2],
X = data_bicop[, -c(1:2)],
familyset = NA,
control = boost_control(deselection = "attributable"))
summary(object)
```
### Sequential gradient-boosted estimation of GLMs for a conditional R-vine copula model
```{r example4, eval = T}
# create boostRVineMatrix
Matrix <- c(5, 0, 0, 0, 0,
2, 2, 0, 0, 0,
3, 3, 3, 0, 0,
1, 4, 4, 4, 0,
4, 1, 1, 1, 1)
Family <- c(0, 0, 0, 0, 0,
404, 0, 0, 0, 0,
1, 404, 0, 0, 0,
401, 301, 304, 0, 0,
1, 401, 304, 301, 0)
Matrix <- matrix(Matrix, 5, 5, byrow = TRUE)
Family <- matrix(Family, nrow = 5, ncol = 5, byrow = TRUE)
Formula <- matrix("~ .", 5, 5, byrow = TRUE)
boostRVM <- boostRVineMatrix(Matrix = Matrix,
family = Family,
formula = Formula)
# fit object
object <- boostRVineSeqEst(U = data_vinecop[, 1:5],
X = data_vinecop[, -c(1:5)],
boostRVM = boostRVM,
control = boost_control(deselection = "attributable"))
```
## Contact
Feel free to contact [jobstd\@uni-hildesheim.de](mailto:jobstd@uni-hildesheim.de) if you have any questions or suggestions.
## References
Jobst, D., Möller, A., and Groß, J. (2024). Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas. doi: [10.48550/arXiv.2406.13500 ](https://arxiv.org/abs/2406.13500).