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Implement the T-distribution via the F-distribution #149

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merged 6 commits into from
Nov 29, 2022
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andreasnoack
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Today I realized that

  1. I already implemented a pure Julia version of the F-distribution functions
  2. We can just implement the T(ν)-distribution via the F(1,ν)-distribution

Hence this PR. This is an alternative to #147

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codecov-commenter commented Nov 24, 2022

Codecov Report

Base: 59.85% // Head: 61.58% // Increases project coverage by +1.72% 🎉

Coverage data is based on head (2b93fad) compared to base (e5699db).
Patch coverage: 97.36% of modified lines in pull request are covered.

Additional details and impacted files
@@            Coverage Diff             @@
##           master     #149      +/-   ##
==========================================
+ Coverage   59.85%   61.58%   +1.72%     
==========================================
  Files          14       14              
  Lines         568      617      +49     
==========================================
+ Hits          340      380      +40     
- Misses        228      237       +9     
Impacted Files Coverage Δ
src/distrs/tdist.jl 97.72% <97.36%> (-2.28%) ⬇️
src/tvpack.jl 16.48% <0.00%> (+0.13%) ⬆️
src/misc.jl 88.88% <0.00%> (+0.65%) ⬆️

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@andreasnoack
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Thanks. I've pushed a commit that addresses the comments.

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@ararslan ararslan left a comment

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Very minor comments around consistency but otherwise looks great. Math checks out AFAICT and the tests are happy. Thanks!

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andreasnoack and others added 3 commits November 29, 2022 16:39
Co-authored-by: Alex Arslan <ararslan@comcast.net>
Co-authored-by: Alex Arslan <ararslan@comcast.net>
Co-authored-by: Alex Arslan <ararslan@comcast.net>
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4 participants