An R package for C-vine copula based mean and quantile regression.
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("jobstdavid/cvinereg")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))
#> C-vine regression model: y | x.2, x.1
#> nobs = 100, edf = 3, cll = 67.44, caic = -128.88, cbic = -121.07
# summary of fit
summary(fit)
#> var edf cll caic cbic p_value
#> 1 y 0 -221.3855 442.7710 442.7710 NA
#> 2 x.2 1 79.0774 -156.1548 -153.5496 2.862975e-36
#> 3 x.1 2 209.7498 -415.4996 -410.2892 8.069092e-92
# show marginal effects for all selected variables
plot_effects(fit)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Feel free to contact jobstd@uni-hildesheim.de if you have any questions or suggestions.
- 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.