Logistics.jl defines the data type Logistic which represents real numbers in [0, 1] (e.g. probabilities) with more numerically stable arithmetic operations.
The followings are some simple examples illustrating the usage of this package. Please see the documentation for more details of Logistics.jl.
julia> using Logistics
julia> x = Logistic(0)
Logistic{Float64}(0.0) ≈ 0.5
julia> y = logisticate(0.2)
Logistic{Float64}(-1.3862943611198906) ≈ 0.2
julia> x + y
Logistic{Float64}(0.8472978603872037) ≈ 0.7
julia> x - y
Logistic{Float64}(-0.8472978603872037) ≈ 0.3
julia> x * y
Logistic{Float64}(-2.197224577336219) ≈ 0.10000000000000002
julia> y / x
Logistic{Float64}(-0.40546510810816444) ≈ 0.4
julia> x ^ 2
Logistic{Float64}(-1.0986122886681098) ≈ 0.25
julia> mx = x ^ 10000
Logistic{Float64}(-6931.471805599453) ≈ 0.0
julia> my = y ^ 4307
Logistic{Float64}(-6931.84908885367) ≈ 0.0
julia> mx + my
Logistic{Float64}(-6930.949611751832) ≈ 0.0
julia> mx - my
Logistic{Float64}(-6932.629282338215) ≈ 0.0
julia> mx * my
Logistic{Float64}(-13863.320894453122) ≈ 0.0
julia> mx \ my
Logistic{Float64}(0.7801934845449816) ≈ 0.6857218127893691
julia> sqrt(mx)
Logistic{Float64}(-3465.7359027997263) ≈ 0.0
julia> cbrt(mx)
Logistic{Float64}(-2310.490601866484) ≈ 0.0
julia> log(y)
-1.6094379124341003
julia> logit(y)
-1.3862943611198906