Calculate tail-weighted KL divergence for discrete distributions

Usage

tail_weighted_kl_divergence(
  P,
  Q,
  index = 1:min(c(length(Q), length(P))),
  index.spacing = "equal",
  parameter = list(lambda = 0.2, x0 = 30),
  cut = FALSE,
  inverse = FALSE,
  method = "constant",
  alignPQ = TRUE
)

Arguments

P: probability vector 2
Q: probability vector 1
index: a positive numeric vector containing probability spacing e.g., depth
index.spacing: whether index intervals are equally distant i.e., c(1,2,3,4....n), if "equal" than index is c(1,n)
parameter: list with lambda -> shape parameter (0 = constant weighting) & x0 -> curve offset (= inflexion point )
cut: if FALSE, 0 will be added to the shorter vector. If TRUE, the longer vector will be shortened at the end.
inverse: changes from right tail to left tail if TRUE
method: weighting function along index. Available options are: c("constant", "asymptotic", "linear, "exponential", "sigmoid", "gompertz","step")
alignPQ: if TRUE, index end values will be cut off in case of unequal length of P & Q so that length of P & Q is equal

Value

KL divergence, not symmetrical - changing the input order will change the result

Details

Kullback-Leibler Divergence