Merge pull request #7 from TuringLang/mt/ci_and_coveralls

Add travis and coveralls

Author: yebai

.travis.yml

@@ -0,0 +1,22 @@
+language: julia
+
+os:
+  - linux
+  - osx
+
+julia:
+  - 1.0
+  - 1.1
+  - 1.2
+  - nightly
+
+matrix:
+  allow_failures:
+    - julia: nightly
+  fast_finish: true
+
+notifications:
+  email: false
+
+after_success:
+  - julia -e 'using Pkg; cd(Pkg.dir("DistributionsAD")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(process_folder())'

README.md

@@ -1,5 +1,10 @@
 # DistributionsAD.jl
 
+[![Build Status](https://travis-ci.org/TuringLang/DistributionsAD.jl.svg?branch=master)](https://travis-ci.org/TuringLang/DistributionsAD.jl)
+
+[![Coverage Status](https://coveralls.io/repos/github/TuringLang/DistributionsAD.jl/badge.svg?branch=master)](https://coveralls.io/github/TuringLang/DistributionsAD.jl?branch=master)
+
+
 This package defines the necessary functions to enable automatic differentiation (AD) of the `logpdf` function from [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) using the packages [Tracker.jl](https://github.com/FluxML/Tracker.jl) and [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl). The goal of this package is to make the output of `logpdf` differentiable wrt all continuous parameters of a distribution as well as the random variable in the case of continuous distributions.
 
 AD of `logpdf` is fully supported and tested for the following distributions wrt all combinations of continuous variables (distribution parameters and/or the random variable) and using all defined distribution constructors: