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

Univariate API

A new univariate distribution type should implement all of the following methods:

• Core constructors
  • MyDistribution{T}(args[...])
  • We need to clarify whether constructors should handle input validation or not. There are use cases in which people want to avoid input validation.
• params(d::MyDistribution{T})::Tuple: A tuple of the distribution's parameters in our canonical order.
• minimum(d::MyDistribution{T})::T: The lowest value in the support of MyDistribution.
• maximum(d::MyDistribution{T})::T: The highest value in the support of MyDistribution.

johnmyleswhite

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

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

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

johnmyleswhite

total = 0
N = 300

start_time = time()

for a in 0:(N - 1)
	for b in 0:(N - 1)
		for c in 0:(N - 1)
			if a^2 + b^2 == c^2
				total = total + 1

johnmyleswhite

# Generate (x, y) data with a sparse set of active predictors
# prob controls the frequency of predictors having zero effect
function simulate_date(n::Integer, p::Integer, prob::Real)
  x = randn(n, p)
	beta = randn(p)
	for j in 1:p
		if rand() < prob
			beta[j] = 0.0
		end
	end

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)

johnmyleswhite

using Distributions
using PyPlot

ϵ = 0.0001
n_grid = 10_000

ps = linspace(ϵ, 1 - ϵ, n_grid)

ss = [std(Bernoulli(p)) for p in ps]
sks = [skewness(Bernoulli(p)) for p in ps]

johnmyleswhite

using Distributions
using HypothesisTests

n_sims = 1_000_000
n = 2
x = Array{Float64}(n)
y = Array{Float64}(n)
p_d = Array{Float64}(n_sims)
for s in 1:n_sims
   for i in 1:n

johnmyleswhite

using Distributions
using HypothesisTests

n_sims = 1_000_000
n = 20
x = Array{Float64}(n)
y = Array{Float64}(n)
p_d = Array{Float64}(n_sims)
for s in 1:n_sims
    for i in 1:n