Update index.html

Author: ViralBShah

index.html

@@ -77,9 +77,6 @@ <h3>Included packages</h3>
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 			<a href="https://github.com/JuliaDiff/ForwardDiff.jl"><strong>ForwardDiff</strong></a><br>
-			<a href="http://pkg.julialang.org/?pkg=ForwardDiff&ver=0.6">
-			  <img src="http://pkg.julialang.org/badges/ForwardDiff_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
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 			ForwardDiff provides user-friendly methods for performing forward-mode automatic differentiation on native Julia code. ForwardDiff offers several advanced features, such as nested differentiation, a non-allocating API, and a SIMD-vectorizable multidimensional dual number type.
@@ -89,9 +86,6 @@ <h3>Included packages</h3>
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 			<a href="https://github.com/JuliaDiff/ReverseDiff.jl"><strong>ReverseDiff</strong></a><br>
-			<a href="http://pkg.julialang.org/?pkg=ReverseDiff&ver=0.6">
-			  <img src="http://pkg.julialang.org/badges/ReverseDiff_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
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 			ReverseDiff provides user-friendly methods for performing reverse-mode automatic differentiation on native Julia code. ReverseDiff implements many linear algebra optimizations, flexible performance annotations, and composes well with ForwardDiff (enabling mixed-mode AD).
@@ -101,9 +95,6 @@ <h3>Included packages</h3>
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 			<a href="https://github.com/lbenet/TaylorSeries.jl"><strong>TaylorSeries</strong></a><br>
-			<a href="http://pkg.julialang.org/?pkg=TaylorSeries&ver=0.6">
-			  <img src="http://pkg.julialang.org/badges/TaylorSeries_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
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 			TaylorSeries implements truncated multivariate power series for high-order integration of ODEs and forward-mode automatic differentiation of arbitrary order derivatives via operator overloading.
@@ -113,9 +104,6 @@ <h3>Included packages</h3>
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               <a href="https://github.com/JuliaDiff/DualNumbers.jl"><strong>DualNumbers</strong></a><br>
-              <a href="http://pkg.julialang.org/?pkg=DualNumbers&ver=0.6">
-                <img src="http://pkg.julialang.org/badges/DualNumbers_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
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              DualNumbers provides a dual number type which can be used to take first-order scalar derivatives of native Julia code.
@@ -125,9 +113,6 @@ <h3>Included packages</h3>
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               <a href="https://github.com/JuliaDiff/HyperDualNumbers.jl"><strong>HyperDualNumbers</strong></a><br>
-              <a href="http://pkg.julialang.org/?pkg=HyperDualNumbers&ver=0.6">
-                <img src="http://pkg.julialang.org/badges/HyperDualNumbers_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
-              </a>
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              HyperDualNumbers provides a hyper-dual number type which can be used to take first-order and second-order scalar derivatives of native Julia code.
@@ -143,9 +128,6 @@ <h3>Related packages</h3>
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               <a href="https://github.com/gaika/AutoDiffSource.jl"><strong>AutoDiffSource</strong></a><br>
-              <a href="http://pkg.julialang.org/?pkg=AutoDiffSource&ver=0.6">
-                <img src="http://pkg.julialang.org/badges/AutoDiffSource_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
-              </a>
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 		Fast reverse mode differentiation for scalar and tensor functions using source code transformation.
@@ -155,9 +137,6 @@ <h3>Related packages</h3>
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               <a href="https://github.com/johnmyleswhite/Calculus.jl"><strong>Calculus</strong></a><br>
-              <a href="http://pkg.julialang.org/?pkg=Calculus&ver=0.6">
-                <img src="http://pkg.julialang.org/badges/Calculus_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
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               Provides methods for symbolic differentiation and finite-difference approximations.
@@ -167,9 +146,6 @@ <h3>Related packages</h3>
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               <a href="https://github.com/JuliaOpt/JuMP.jl"><strong>JuMP</strong></a><br>
-              <a href="http://pkg.julialang.org/?pkg=JuMP&ver=0.6">
-                <img src="http://pkg.julialang.org/badges/JuMP_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
-              </a>
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               An algebraic modeling language for optimization with an internal implementation of reverse-mode automatic differentiation for gradients and sparse Hessian matrices given closed-form expressions.
@@ -180,9 +156,6 @@ <h3>Related packages</h3>
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               <a href="https://github.com/jwmerrill/PowerSeries.jl"><strong>PowerSeries</strong></a><br>
-              <a href="http://pkg.julialang.org/?pkg=PowerSeries&ver=0.6">
-                <img src="http://pkg.julialang.org/badges/PowerSeries_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
-              </a>
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             <td>
               Implements truncated power series type which can be used for forward-mode automatic differentiation of arbitrary order derivatives via operator overloading.
@@ -192,9 +165,6 @@ <h3>Related packages</h3>
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             <td style="padding: 10px 10px">
               <a href="https://github.com/symengine/SymEngine.jl"><strong>SymEngine</strong></a><br>
-              <a href="http://pkg.julialang.org/?pkg=SymEngine&ver=0.6">
-                <img src="http://pkg.julialang.org/badges/SymEngine_0.6.svg" alt="Release testing status" style="margin-top: 2px;">
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               Implements symbolic differentiation.
@@ -216,7 +186,7 @@ <h2>Links</h2>
 
 <div class="sixteen columns">
       <hr />
-      <small>This website was made with <a href="http://www.getskeleton.com">Skeleton</a>, based on Iain Dunning's work on <a href="http://juliaopt.org">JuliaOpt</a>. Something wrong? Submit <a href="https://github.com/JuliaDiff/juliadiff.github.io/issues">issue</a>.
+      <small>This website was made with <a href="https://www.getskeleton.com">Skeleton</a>, based on Iain Dunning's work on <a href="http://juliaopt.org">JuliaOpt</a>. Something wrong? Submit <a href="https://github.com/JuliaDiff/juliadiff.github.io/issues">issue</a>.
     </div>
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