## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(rvinecopulib)
set.seed(201)

## ----construct----------------------------------------------------------------
cop <- bicop_dist(
  family = "clayton",
  rotation = 90,
  parameters = 2
)
cop

## ----evaluate-----------------------------------------------------------------
points <- rbind(c(0.2, 0.7), c(0.8, 0.3))
dbicop(points, cop)
pbicop(points, cop)
rbicop(4, cop)

## ----direct-------------------------------------------------------------------
dbicop(points, "gaussian", 0, 0.6)

## ----h-functions--------------------------------------------------------------
h <- hbicop(points, cond_var = 1, family = cop)
h
hbicop(cbind(points[, 1], h), cond_var = 1, family = cop, inverse = TRUE)

## ----dependence---------------------------------------------------------------
par_to_ktau("clayton", 90, 2)
get_ktau(cop)
blomqvist_beta(cop)
tail_dep(cop)

## ----selection----------------------------------------------------------------
u <- rbicop(300, "gumbel", 180, 2)
fit <- bicop(
  u,
  family_set = c("gaussian", "clayton", "gumbel", "frank"),
  selcrit = "bic"
)
fit
summary(fit)

## ----weights------------------------------------------------------------------
weights <- seq_len(nrow(u))
weighted_fit <- bicop(
  u,
  family_set = c("gaussian", "gumbel"),
  weights = weights
)
weighted_fit

## ----discrete-----------------------------------------------------------------
counts <- cbind(rpois(80, 2), rpois(80, 3))
u_discrete <- cbind(
  ppois(counts[, 1], 2),
  ppois(counts[, 2], 3),
  ppois(counts[, 1] - 1, 2),
  ppois(counts[, 2] - 1, 3)
)
fit_discrete <- bicop(
  u_discrete,
  var_types = c("d", "d"),
  family_set = "onepar"
)
fit_discrete

## ----vectorized---------------------------------------------------------------
rho <- seq(-0.7, 0.7, length.out = nrow(points))
dbicop(points, "gaussian", 0, parameters = rho)
rbicop(length(rho), "gaussian", 0, parameters = rho)