Update Release

Version 0.6.0

New Features

• distance() and all other individual information theory functions
  receive a new argument epsilon with default value epsilon = 0.00001 to treat cases where in individual distance or similarity computations
  yield x / 0 or 0 / 0. Instead of a hard coded epsilon, users can now set epsilon according to their input vectors. (Many thanks to Joshua McNeill #26 for this great question).
• three new functions dist_one_one(), dist_one_many(), dist_many_many() are added. They are fairly flexible intermediaries between distance() and single distance functions. dist_one_one() expects two vectors (probability density functions) and returns a single value. dist_one_many() expects one vector (a probability density function) and one matrix (a set of probability density functions), and returns a vector of values. dist_many_many() expects two matrices (two sets of probability density functions), and returns a matrix of values. (Many thanks to
  Jakub Nowosad, see #27, #28, and New Vignette Many_Distance)

Updates

• a new Vignette Comparing many probability density functions (Many thanks to
  Jakub Nowosad)
• dplyr package dependency was removed and replaced by the poorman
  due to the heavy dependency burden of dplyr, since philentropy
  only used dplyr::between() which is now poorman::between() (Many thanks to Patrice Kiener for this suggestion)
• distance(..., as.dist.obj = TRUE) now returns the same values as stats::dist() when working with 2 dimensional input matrices (2 vector inputs) (see #29) (Many thanks to
  Jakub Nowosad (@Nowosad))
  Example:

library(philentropy)

m1 = matrix(c(1, 2), ncol = 1)

dist(m1)
#> 1
#> 2 1
distance(m1, as.dist.obj = TRUE)
#> Metric: 'euclidean'; comparing: 2 vectors.
#> 1
#> 2 1