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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%"
)
```
# C-vine regression
<!-- badges: start -->
[](https://CRAN.R-project.org/package=cvinereg) [](https://github.com/jobstdavid/cvinereg/actions) [](https://github.com/jobstdavid/cvinereg)
<!-- badges: end -->
An R package for C-vine copula based mean and quantile regression.
## Installation
You can install the development version from GitHub with:
``` r
# install.packages("remotes")
remotes::install_github("jobstdavid/cvinereg")
```
## Example
```{r example1, eval = T}
library(cvinereg)
# simulate data
set.seed(2024)
x <- matrix(rnorm(200), 100, 2)
y <- x %*% c(1, -2)
data <- data.frame(y = y, x = x)
# fit vine regression model
(fit <- cvinereg(y ~ ., data))
# summary of fit
summary(fit)
# show marginal effects for all selected variables
plot_effects(fit)
```
## Contact
Feel free to contact [jobstd\@uni-hildesheim.de](mailto:jobstd@uni-hildesheim.de) if you have any questions or suggestions.
## References
* Tepegjozova and Czado (2019), D- and C-vine quantile regression for large data sets, https://mediatum.ub.tum.de/doc/1536070/1536070.pdf.
* Tepegjozova et al. (2022). Nonparametric C- and D-vine-based quantile regression, Dependence Modeling, 10(1), 1-21, https://doi.org/10.1515/demo-2022-0100.