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distplyr 0.1.5

  • Updates to package infrastructure in the process of migrating to a new GitHub Organization.

distplyr 0.1.4

  • Fix graft distributions so that they can evaluate on NA.

distplyr 0.1.3

  • Default evaluation methods, and base distributional forms, have been moved to a new package, distionary. distplyr focusses on manipulation verbs only.
  • Math method now applies to finite distributions.
  • Ops methods are now available for arithmetic operations (+, -, *, and /) on a single distribution, along with the verbs shift(), multiply(), invert(), and flip().
  • graft_left() and graft_right() are fully functional, and slice_left() and slice_right() are now also available.

distplyr 0.1.2

  • If you have the tibble package installed, distplyr will now output tibbles wherever data frames were previously output.

Breaking changes

  • The get_ prefix has been removed from distributional quantities. get_mean() is now mean(), etc.
    • For now, the get_ prefix still holds for distributional representations, like get_cdf().
  • Make your own distribution object with distribution() instead of dst(), and checked with is_distribution().

distplyr 0.1.1

This patch both fixes some problems in the previous release, as well as offering a step towards a bigger expansion.

  • Some change in the functional representations:
    • Changed random number generation from randfn, a functional representation, to the realise() and realize() functions.
    • Changed probfn representation to be more specific: pmf or density
  • Added the enframe suite of functions.
  • Implement the beginnings of being able to specify your own distribution, with the set_ suite of functions, after making an empty distribution with dst().

Additionally, there’s some internal rearrangement, where the get functions call the eval functions, not vice versa.

distplyr 0.1.0

The first version of distplyr is now available! Its functionality is rather limited at the moment, but is still useful, especially for its capability to handle a discrete component of a distribution. Here are the main features:

  • Base distributions include step distributions, Gaussian, Uniform, and generalized Pareto.
  • Operations include grafting (right) and mixing
  • Distribution properties included are moment-related quantities, and extreme value index.
  • Distribution representations are mostly comprehensive, perhaps only missing mean excess function and moment generating function.

Take a look at the “Vision” vignette to get a sense of where this package is headed.