Address reviewer comments

src/univariate/continuous/lognormal.jl

@@ -158,7 +158,7 @@ end
 
 xval(d::LogNormal, z::Real) = exp(muladd(d.σ, z, d.μ))
 
-rand(rng::AbstractRNG, d::LogNormal) = xval(d,  randn(rng))
+rand(rng::AbstractRNG, d::LogNormal) = xval(d, randn(rng))
 function rand!(rng::AbstractRNG, d::LogNormal, A::AbstractArray{<:Real})
     randn!(rng, A)
     map!(Base.Fix1(xval, d), A, A)

src/univariate/continuous/pgeneralizedgaussian.jl

@@ -146,4 +146,4 @@ function rand(rng::AbstractRNG, d::PGeneralizedGaussian)
     else
         return d.μ + z
     end
-end
+end

test/fit.jl

@@ -369,7 +369,7 @@ end
     for func in funcs, dist in (Laplace, Laplace{Float64})
         d = fit(dist, func[2](dist(5.0, 3.0), N + 1))
         @test isa(d, dist)
-        @test isapprox(location(d), 5.0, atol=0.021)
+        @test isapprox(location(d), 5.0, atol=0.03)
         @test isapprox(scale(d)   , 3.0, atol=0.03)
     end
 end

test/testutils.jl

@@ -28,7 +28,8 @@ end
 # testing the implementation of a discrete univariate distribution
 #
 function test_distr(distr::DiscreteUnivariateDistribution, n::Int;
-                    testquan::Bool=true, rng::AbstractRNG=MersenneTwister(123))
+                    testquan::Bool=true, rng::AbstractRNG = Random.default_rng())
+
     test_range(distr)
     vs = get_evalsamples(distr, 0.00001)
 
@@ -39,8 +40,7 @@ function test_distr(distr::DiscreteUnivariateDistribution, n::Int;
 
     test_stats(distr, vs)
     test_samples(distr, n)
-    test_samples(distr, n, rng=rng)
-
+    test_samples(distr, n; rng=rng)
     test_params(distr)
 end
 
@@ -82,7 +82,6 @@ function test_distr(distr::ContinuousUnivariateDistribution, n::Int;
     allow_test_stats(distr) && test_stats(distr, xs)
     xs = test_samples(distr, n, rng=rng)
     allow_test_stats(distr) && test_stats(distr, xs)
-
     test_params(distr)
 end
 
@@ -103,6 +102,7 @@ function test_samples(s::Sampleable{Univariate, Discrete},      # the sampleable
                       q::Float64=1.0e-7,                        # confidence interval, 1 - q as confidence
                       verbose::Bool=false,                      # show intermediate info (for debugging)
                       rng::Union{AbstractRNG, Missing}=missing) # add an rng?
+
     # The basic idea
     # ------------------
     #   Generate n samples, and count the occurrences of each value within a reasonable range.
@@ -151,17 +151,17 @@ function test_samples(s::Sampleable{Univariate, Discrete},      # the sampleable
         Random.seed!(1234)
         samples2 = rand(s, n)
         Random.seed!(1234)
-        samples3 = map((_) -> rand(s), 1:n)
+        samples3 = [rand(s) for _ in 1:n]
         Random.seed!(1234)
-        samples4 = map((_) -> rand(s), 1:n)
+        samples4 = [rand(s) for _ in 1:n]
     else
         rng2 = deepcopy(rng)
         rng3 = deepcopy(rng)
         rng4 = deepcopy(rng)
         samples = rand(rng, s, n)
         samples2 = rand(rng2, s, n)
-        samples3 = map((_) -> rand(rng3, s), 1:n)
-        samples4 = map((_) -> rand(rng4, s), 1:n)
+        samples3 = [rand(rng3, s) for _ in 1:n]
+        samples4 = [rand(rng4, s) for _ in 1:n]
     end
     @test length(samples) == n
     @test samples2 == samples
@@ -176,27 +176,27 @@ function test_samples(s::Sampleable{Univariate, Discrete},      # the sampleable
             cnts[si - rmin + 1] += 1
         else
             vmin <= si <= vmax ||
-                error("Sample value out of valid range. (Vector Method)")
+                throw(DomainError(si, "sample generated by `rand(s, n)` is out of valid range [$vmin, $vmax]."))
         end
 
         @inbounds si_sc = samples3[i]
         if rmin <= si_sc <= rmax
             cnts_sc[si_sc - rmin + 1] += 1
         else
             vmin <= si_sc <= vmax ||
-                error("Sample value out of valid range. (Scalar Method)")
+                throw(DomainError(si, "sample generated by `[rand(s) for _ in 1:n]` is out of valid range [$vmin, $vmax]."))
         end
     end
 
     # check the counts
     for i = 1:m
         verbose && println("v = $(rmin+i-1) ==> ($(clb[i]), $(cub[i])): $(cnts[i])")
         clb[i] <= cnts[i] <= cub[i] ||
-            error("The counts are out of the confidence interval. (Vector Method)")
+            error("The counts of samples generated by `rand(s, n)` are out of the confidence interval.")
 
         verbose && println("v = $(rmin+i-1) ==> ($(clb[i]), $(cub[i])): $(cnts_sc[i])")
         clb[i] <= cnts_sc[i] <= cub[i] ||
-            error("The counts are out of the confidence interval. (Scalar Method)")
+            error("The counts of samples generated by `[rand(s) for _ in 1:n]` are out of the confidence interval.")
     end
     return samples
 end
@@ -273,17 +273,17 @@ function test_samples(s::Sampleable{Univariate, Continuous},    # the sampleable
         Random.seed!(1234)
         samples2 = rand(s, n)
         Random.seed!(1234)
-        samples3 = map((_) -> rand(s), 1:n)
+        samples3 = [rand(s) for _ in 1:n]
         Random.seed!(1234)
-        samples4 = map((_) -> rand(s), 1:n)
+        samples4 = [rand(s) for _ in 1:n]
     else
         rng2 = deepcopy(rng)
         rng3 = deepcopy(rng)
         rng4 = deepcopy(rng)
         samples = rand(rng, s, n)
         samples2 = rand(rng2, s, n)
-        samples3 = map((_) -> rand(rng3, s), 1:n)
-        samples4 = map((_) -> rand(rng4, s), 1:n)
+        samples3 = [rand(rng3, s) for _ in 1:n]
+        samples4 = [rand(rng4, s) for _ in 1:n]
     end
     @test length(samples) == n
     @test samples2 == samples
@@ -297,10 +297,10 @@ function test_samples(s::Sampleable{Univariate, Continuous},    # the sampleable
     for i = 1:n
         @inbounds si = samples[i]
         vmin <= si <= vmax ||
-            error("Sample value out of valid range. (Vector Method)")
+            throw(DomainError(si, "sample generated by `rand(s, n)` is out of valid range [$vmin, $vmax]."))
         @inbounds si_sc = samples3[i]
         vmin <= si_sc <= vmax ||
-            error("Sample value out of valid range. (Scalar Method)")
+            throw(DomainError(si, "sample generated by `[rand(s) for _ in 1:n]` is out of valid range [$vmin, $vmax]."))
     end
 
     # get counts
@@ -317,9 +317,9 @@ function test_samples(s::Sampleable{Univariate, Continuous},    # the sampleable
             @printf("[%.4f, %.4f) ==> (%d, %d): %d\n", edges[i], edges[i+1], clb[i], cub[i], cnts_sc[i])
         end
         clb[i] <= cnts[i] <= cub[i] ||
-            error("The counts are out of the confidence interval. (Vector Method)")
+            error("The counts of samples generated by `rand(s, n)` are out of the confidence interval.")
         clb[i] <= cnts_sc[i] <= cub[i] ||
-            error("The counts are out of the confidence interval. (Scalar Method)")
+            error("The counts of samples generated by `[rand(s) for _ in 1:n]` are out of the confidence interval.")
     end
     return samples
 end
@@ -623,7 +623,6 @@ end
 allow_test_stats(d::UnivariateDistribution) = true
 allow_test_stats(d::NoncentralBeta) = false
 allow_test_stats(::StudentizedRange) = false
-allow_test_stats(::LogitNormal) = false
 
 function test_stats(d::ContinuousUnivariateDistribution, xs::AbstractVector{Float64})
     # using Monte Carlo methods

test/univariate/continuous/exponential.jl

@@ -10,7 +10,7 @@ end
 
 test_cgf(Exponential(1), (0.9, -1, -100f0, -1e6))
 test_cgf(Exponential(0.91), (0.9, -1, -100f0, -1e6))
-test_cgf(Exponential(10  ), (0.08, -1, -100f0, -1e6))
+test_cgf(Exponential(10), (0.08, -1, -100f0, -1e6))
 
 # Sampling Tests
 @testset "Exponential sampling tests" begin

test/univariate/continuous/logitnormal.jl

@@ -72,4 +72,4 @@ end
     ]
         test_distr(d, 10^6)
     end
-end
+end

test/univariate/continuous/lognormal.jl

@@ -327,4 +327,4 @@ end
         ]
         test_distr(d, 10^6)
     end
-end
+end

test/univariate/continuous/normalcanon.jl

@@ -7,4 +7,4 @@
     ]
         test_distr(d, 10^6)
     end
-end
+end

test/univariate/continuous/pareto.jl

@@ -7,4 +7,4 @@
         ]
         test_distr(d, 10^6)
     end
-end
+end