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Learning Julia

You're reading from   Learning Julia Build high-performance applications for scientific computing

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781785883279
Length 316 pages
Edition 1st Edition
Languages
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Authors (2):
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Rahul Lakhanpal Rahul Lakhanpal
Author Profile Icon Rahul Lakhanpal
Rahul Lakhanpal
Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (11) Chapters Close

Preface 1. Understanding Julia's Ecosystem FREE CHAPTER 2. Programming Concepts with Julia 3. Functions in Julia 4. Understanding Types and Dispatch 5. Working with Control Flow 6. Interoperability and Metaprogramming 7. Numerical and Scientific Computation with Julia 8. Data Visualization and Graphics 9. Connecting with Databases 10. Julia’s Internals

Statistics

One of the strongest points of Julia has been its powerful support for statistical and mathematical functions. The Julia community has been very active in identifying the areas that can be covered using packages made purely in Julia, and hence we have now a group that deals purely in packages of Julia for stats. It's called JuliaStats, and can be found on GitHub at https://github.com/JuliaStats.

Some of the notable packages listed on the Julia stats page are as follows:

  • PDMats.jl: Uniform interface for positive definite matrices of various structures
  • Klara.jl: MCMC inference in Julia
  • StatsBase.jl: Basic statistics for Julia
  • HypothesisTests.jl: Hypothesis tests for Julia
  • ConjugatePriors.jl: A Julia package to support conjugate prior distributions
  • PGM.jl: A Julia framework for probabilistic graphical models
  • TimeSeries.jl: Time series toolkit for Julia
  • ...
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