johnmyleswhite

julia> import BenchmarkTools: @btime

julia> @inline _logistic_bounds(x::Float16) = (Float16(-16.64), Float16(7.625))
_logistic_bounds (generic function with 1 method)

julia> @inline _logistic_bounds(x::Float32) = (-103.27893f0, 16.635532f0)
_logistic_bounds (generic function with 2 methods)

julia> @inline _logistic_bounds(x::Float64) = (-744.4400719213812, 36.7368005696771)
_logistic_bounds (generic function with 3 methods)

johnmyleswhite

│ Row │ expr_type                      │ count  │ percentage  │
│     │ Any                            │ Int64  │ Float64     │
├─────┼────────────────────────────────┼────────┼─────────────┤
│ 1   │ ExprKind{:outer}               │ 1      │ 0.000226006 │
│ 2   │ ExprKind{:/=}                  │ 1      │ 0.000226006 │
│ 3   │ ExprKind{:typed_vcat}          │ 1      │ 0.000226006 │
│ 4   │ ExprKind{:.+=}                 │ 1      │ 0.000226006 │
│ 5   │ ExprKind{:%=}                  │ 3      │ 0.000678017 │
│ 6   │ ExprKind{:⊻=}                  │ 7      │ 0.00158204  │
│ 7   │ ExprKind{:.=}                  │ 8      │ 0.00180804  │

johnmyleswhite

import RCall: @R_str

function runtimes(n_reps)
    times = Array{Float64}(undef, n_reps)
    x = rand(10_000_000)
    for i in 1:n_reps
        times[i] = @elapsed sum(x)
    end
    times        
end

johnmyleswhite

> library("stringr")
> str_sub("ñ", start = -1)
[1] "̃"
> str_sub("ñ", start = -1)
[1] "ñ"

johnmyleswhite

function prob_f64(n)
    s = 0
    for _ in 1:n
        z_i = rand(UInt)
        x_i = reinterpret(Float64, z_i)
        s += Int(x_i == x_i + 1.0)
    end
    s, n
end

johnmyleswhite

function prob(n)
    s = 0
    for _ in 1:n
        x_i = rand(UInt)
        s += Int(Float64(x_i) === Float64(x_i + 1))
    end
    s, n
end

prob(1_000_000)

johnmyleswhite

function fixed_point(f, x0)
    x, x_old = f(x0), x0
    n = 1
    while x !== x_old
        x, x_old = f(x), x
        n += 1
    end
    (x, n)
end

johnmyleswhite

library("pwr")
library("ggplot2")

n_sims <- 1000L
n <- 10L
mu <- 0.5
sigma <- 1

true_power <- power.t.test(
    n = n,

johnmyleswhite

using BenchmarkTools

n = 10_000_000

x = rand(n)
y = [ifelse(iseven(i), missing, x[i]) for i in 1:n]

sum(x)
sum(y)

johnmyleswhite

# 0.6 Approach
using BenchmarkTools
using NullableArrays

n = 10_000_000

x = rand(n)
y = NullableArray{Float64}(n)
for i in 1:n
    if !iseven(i)