I’m not very well-versed in R but I find it quite horrible for statistics, for example in Julia if you want to compute the pdf of a Normal distribution with parameters (μ,σ) at value x you do :
pdf(Normal(μ,σ),x)
In R you do:
dnorm(x,μ,σ)
If you want to truncated Normal between zero and one you do:
pdf(Truncated(Normal(μ,σ),0,1),x)
In R you do:
google for a package
...
dtrunc(x, spec="norm", a=0, b=1, mean=μ, sd=σ)
If you want a mixture of two Gaussians:
MixtureModel([Normal(μ1,σ1), Normal(μ2,σ2)],[1/2,1/2])
In R you do:
google for a package
...
If you want a BetaBinomial:
pdf(BetaBinomial(n,α,β),x)
In R you do:
google for a package
...
In Julia you have nice atomic concepts that are composable, while in R you just have a bunch of functions with unreadable names and packages with no common semantics.
I would be curious to see how this translates in R:
[f(D) for f in [mean,std,entropy], D in [Normal(0,1), BetaBinomial(10,0.1,0.1), Truncated(Normal(0,1),0,1)]]
Ironically the biggest issue with Distributions.jl is that it uses Rmath, but hopefully that will get fixed in time.