enforce naming consistency for FDist

src/univariate/continuous/fdist.jl

@@ -1,63 +1,64 @@
 immutable FDist <: ContinuousUnivariateDistribution
-    d1::Float64
-    d2::Float64
+    ν1::Float64
+    ν2::Float64
 
-    function FDist(d1::Real, d2::Real)
-        d1 > zero(d1) && d2 > zero(d2) || error("Degrees of freedom must be positive")
-        @compat new(Float64(d1), Float64(d2))
+    function FDist(ν1::Real, ν2::Real)
+        ν1 > zero(ν1) && ν2 > zero(ν2) ||
+            throw(ArgumentError("FDist: ν1 and ν2 must be positive."))
+        @compat new(Float64(ν1), Float64(ν2))
     end
 end
 
 @distr_support FDist 0.0 Inf
 
 #### Parameters
 
-params(d::FDist) = (d.d1, d.d2)
+params(d::FDist) = (d.ν1, d.ν2)
 
 
 #### Statistics
 
-mean(d::FDist) = (d2 = d.d2; d2 > 2.0 ? d2 / (d2 - 2.0) : NaN)
+mean(d::FDist) = (ν2 = d.ν2; ν2 > 2.0 ? ν2 / (ν2 - 2.0) : NaN)
 
-mode(d::FDist) = ((d1, d2) = params(d); d1 > 2.0 ? ((d1 - 2.0)/d1) * (d2 / (d2 + 2.0)) : 0.0)
+mode(d::FDist) = ((ν1, ν2) = params(d); ν1 > 2.0 ? ((ν1 - 2.0)/ν1) * (ν2 / (ν2 + 2.0)) : 0.0)
 
 function var(d::FDist)
-    (d1, d2) = params(d)
-    d2 > 4.0 ? 2.0 * d2^2 * (d1 + d2 - 2.0) / (d1 * (d2 - 2.0)^2 * (d2 - 4.0)) : NaN
+    (ν1, ν2) = params(d)
+    ν2 > 4.0 ? 2.0 * ν2^2 * (ν1 + ν2 - 2.0) / (ν1 * (ν2 - 2.0)^2 * (ν2 - 4.0)) : NaN
 end
 
 function skewness(d::FDist)
-    (d1, d2) = params(d)
-    if d2 > 6.0
-        return (2.0 * d1 + d2 - 2.0) * sqrt(8.0 * (d2 - 4.0)) / ((d2 - 6.0) * sqrt(d1 * (d1 + d2 - 2.0)))
+    (ν1, ν2) = params(d)
+    if ν2 > 6.0
+        return (2.0 * ν1 + ν2 - 2.0) * sqrt(8.0 * (ν2 - 4.0)) / ((ν2 - 6.0) * sqrt(ν1 * (ν1 + ν2 - 2.0)))
     else
         return NaN
     end
 end
 
 function kurtosis(d::FDist)
-    (d1, d2) = params(d)
-    if d2 > 8.0
-        a = d1 * (5. * d2 - 22.) * (d1 + d2 - 2.) + (d2 - 4.) * (d2 - 2.)^2
-        b = d1 * (d2 - 6.) * (d2 - 8.) * (d2 - 2.)
+    (ν1, ν2) = params(d)
+    if ν2 > 8.0
+        a = ν1 * (5. * ν2 - 22.) * (ν1 + ν2 - 2.) + (ν2 - 4.) * (ν2 - 2.)^2
+        b = ν1 * (ν2 - 6.) * (ν2 - 8.) * (ν2 - 2.)
         return 12. * a / b
     else
         return NaN
     end
 end
 
 function entropy(d::FDist)
-    (d1, d2) = params(d)
-    hd1 = d1 * 0.5
-    hd2 = d2 * 0.5
-    hs = (d1 + d2) * 0.5
-    return log(d2 / d1) + lgamma(hd1) + lgamma(hd2) - lgamma(hs) +
-        (1.0 - hd1) * digamma(hd1) + (-1.0 - hd2) * digamma(hd2) +
+    (ν1, ν2) = params(d)
+    hν1 = ν1 * 0.5
+    hν2 = ν2 * 0.5
+    hs = (ν1 + ν2) * 0.5
+    return log(ν2 / ν1) + lgamma(hν1) + lgamma(hν2) - lgamma(hs) +
+        (1.0 - hν1) * digamma(hν1) + (-1.0 - hν2) * digamma(hν2) +
         hs * digamma(hs)
 end
 
 #### Evaluation & Sampling
 
-@_delegate_statsfuns FDist fdist d1 d2
+@_delegate_statsfuns FDist fdist ν1 ν2
 
-rand(d::FDist) = StatsFuns.Rmath.fdistrand(d.d1, d.d2)
+rand(d::FDist) = StatsFuns.Rmath.fdistrand(d.ν1, d.ν2)