EffectSizes.jl is a Julia package for effect size measures. Confidence intervals are assigned to effect sizes using the Normal distribution or by bootstrap resampling.
The package implements types for the following measures:
| Measure | Type |
|---|---|
| Cohen's d | CohenD |
| Hedge's g | HedgeG |
| Glass's Δ | GlassΔ |
julia> import Pkg; Pkg.add("EffectSizes");julia> using Random, EffectSizes; Random.seed!(1);
julia> xs = randn(10^3);
julia> ys = randn(10^3) .+ 0.5;
julia> es = CohenD(xs, ys, quantile=0.95); # normal CI (idealised distribution)
julia> typeof(es)
CohenD{Float64, ConfidenceInterval{Float64}}
julia> effectsize(es)
-0.5035709742336323
julia> quantile(es)
0.95
julia> ci = confint(es);
julia> typeof(ci)
ConfidenceInterval{Float64}
julia> confint(ci)
(-0.5926015897640895, -0.41454035870317507)
julia> es = CohenD(xs, ys, 10^4, quantile=0.95); # bootstrap CI (empirical distribution)
julia> effectsize(es) # effect size is the same
-0.5035709742336323
julia> typeof(es)
CohenD{Float64, BootstrapConfidenceInterval{Float64}}
julia> ci = confint(es); # confidence interval is different
julia> lower(ci)
-0.5919535584593746
julia> upper(ci)
-0.4155997394380884Ideas and PRs are very welcome.