Distributions.jl

A Julia package for probability distributions and associated functions.

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7Mentions

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1.2KGitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

A Julia package for probability distributions and associated functions.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

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Also in these discussions

julialang/julia · linked in 3 of the same discussions
juliastats/statsbase.jl · linked in 3 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 8:03:49 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year7 dated mentions

Observed mentions: 2013-02-12 01:39:42 UTC to 2023-02-28 10:13:33 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
2013	2013-01-01 00:00:00 UTC	2014-01-01 00:00:00 UTC	1	1
2014	2014-01-01 00:00:00 UTC	2015-01-01 00:00:00 UTC	2	2
2015	2015-01-01 00:00:00 UTC	2016-01-01 00:00:00 UTC	0	0
2016	2016-01-01 00:00:00 UTC	2017-01-01 00:00:00 UTC	0	0
2017	2017-01-01 00:00:00 UTC	2018-01-01 00:00:00 UTC	0	0
2018	2018-01-01 00:00:00 UTC	2019-01-01 00:00:00 UTC	0	0
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	0	0
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	1	1
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	0	0
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	2	2
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	1	1

What Hacker News says

2 sources

idunning·Dec 29, 2014

I feel like people are perhaps a bit overly negative about the state of Julia's packages. Compared to other early-stage languages, I'd say our package ecosystem is vibrant and full of fantastic packages. Naturally perhaps they are more math/scientific focussed, so if you are looking for cutting-edge packages for web dev you won't find them yet (although there are packages!). See http://pkg.julialang.org/pulse.html, for example. We have over 470 packages in total that are registered, and on Julia 0.3 we have over 300 packages with tests that pass - and we run the tests in all registered packages every night. Some of my favorite packages (that I didn't make, of course :D) would include https://github.com/JuliaStats/Distributions.jl https://github.com/JuliaStats/StatsBase.jl https://github.com/pluskid/Mocha.jl (deep learning) https://github.

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com/stevengj/PyCall.jl The JuliaOpt stack of optimization packages (http://juliaopt.org) and then you get fun new ones like https://github.com/anthonyclays/RomanNumerals.jl

In Julia is awesome, but..

gajomi·Nov 30, 2014

I am a big fan of much of the work going on with statistics in Julia. I'd like to point out another (related) thing though that is wrong with the state of statistics in Julia, in my opinion. Actually my problem has less to do with statistics, per se, than the mathematical foundations of statistical calculations. There are, of course, many different approaches to statistical inference (the Bayesian vs. frequentist camps infamous among these), but the calculations all come down to reasoning about probabilities which is a well posed but not always easy task. The Julia developers, in their wisdom, have recognized this, and as such have put together Distributions.jl the purpose of which is to provide datatypes and utility functions over probability distributions (plus some vestigial methods about maximum likelihood inference, which thankfully

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stay out of the way). If you haven't seen it, check it out. It's got a nice design, I think. But there is presently a clear server limitation: the parameterized type hierarchy. The requirement is that every distribution have support over a set in which all members are either Univariate, Multivariate or Matrixvariate with elements in the fields being either Discrete or Continuous. This obviously misses the general picture of the kinds of sets from which one draws random variables, which plays into issues that the article mentions (if the probability distribution datatype can't model the data you have to do ad hoc things to account for it). Indeed a huge chunk of the issues currently open in the Distributions github page essential boil down to problems with representing the sets and/or spaces from which elements in the distribution are drawn: https://github.com/JuliaStats/Distributions.jl/issues/147 https://github.com/JuliaStats/Distributions.jl/issues/309 https://github.com/JuliaStats/Distributions.jl/issues/224 https://github.com/JuliaStats/Distributions.jl/issues/283 End users have a variety of well developed ideas in mind about the sets that their samples belong to and even the spaces from which they are drawn from which currently exceed the representational capacity of the existing types. In my opinion the way to fix this is to start a separate library that focuses on types and methods for representing and manipulating sets and spaces (i.e. topological information attached to sets). This could then be consumed by the Distributions people as well as others modeling things outside of probability.

In What's wrong with statistics in Julia?