Iâ€™m a little confused about whatâ€™s going on.

StatsFuns.jl currently has 14 core distributions.
Each distribution has exactly 10 properties: pdf/cdf/invcdf â€¦
It says:
â€śWe recommend using the Distributions.jl package for a more convenient interface.â€ť

Distributions.jl has \approx 80 distributions w/ several more in progress.
Each has different properties.
Some properties are not defined for that distribution, some still need to be added.
Distributions.jl allows users to create new distributions from existing ones via: truncationmixtureproductsconvolutionâ€¦
 One advantage of keeping distributions in the same repo is how easy it is to access all of them.
Suppose I got a new dataset & wanna see which â€śnamebrandâ€ť distribution best fits it. I can automatically fit all relevant distributions w/ a single package.
Code to fit all relevant distributions in Distributions.jl
using Distributions, Random, HypothesisTests;
Uni = subtypes(UnivariateDistribution)
#Cts_Uni = subtypes(ContinuousUnivariateDistribution)
DGP_True = LogNormal(17,7);
Random.seed!(123);
const d_train = rand(DGP_True, 1_000)
const d_test = rand(DGP_True, 1_000)
Er =[]; D_fit =[];
for d in Uni
println(d)
try
dd = "$(d)" > Meta.parse > eval
DĚ‚ = fit(dd, d_train)
Score = [loglikelihood(DĚ‚, d_test),
OneSampleADTest(d_test, DĚ‚) > pvalue,
ApproximateOneSampleKSTest(d_test, DĚ‚) > pvalue,
ExactOneSampleKSTest(d_test, DĚ‚) > pvalue,
#PowerDivergenceTest(d_test,lambda=1) Not working!!!
JarqueBeraTest(d_test) > pvalue #Only Normal
];
#Score = loglikelihood(DĚ‚, ds) #TODO: compute a better score.
push!(D_fit, [d, DĚ‚, Score])
catch e
println(e, d)
push!(Er, (d,e))
end
end
a=hcat(D_fit...)
M_names = a[1,:]; M_fit = a[2,:]; M_scores = a[3,:];
idx =sortperm(M_scores, rev=true);
Dfit_sort=hcat(M_names[idx], sort(M_scores, rev=true) )
Output
julia> Dfit_sort
11Ă—3 Array{Any,2}:
LogNormal â€¦ [20600.7, 0.823809, 0.789128, 0.781033, 0.0]
Gamma [21159.4, 6.0e7, 2.45426e68, 1.23247e69, 0.0]
Cauchy [24823.3, 6.0e7, 2.91142e213, 8.6107e227, 0.0]
InverseGaussian [26918.1, 6.0e7, 0.0, 0.0, 0.0]
Exponential [33380.3, 6.0e7, 0.0, 0.0, 0.0]
Normal â€¦ [40611.5, 6.0e7, 1.32495e213, 3.51792e227, 0.0]
Rayleigh [61404.6, 6.0e7, 0.0, 0.0, 0.0]
Laplace [2.03419e9, 6.0e7, 1.49234e138, 5.47197e144, 0.0]
DiscreteNonParametric [Inf, 6.0e7, 0.197933, 0.193494, 0.0]
Pareto [Inf, 6.0e7, 6.69184e108, 3.7704e111, 0.0]
Uniform â€¦ [Inf, 6.0e7, 0.0, 0.0, 0.0]