alstat

// Reference: http://stackoverflow.com/questions/4822471/count-number-of-lines-in-a-git-repository

$ git ls-files | xargs wc -l

alstat

y1 <- round(rnorm(n = 36, mean = 7, sd = 2)) # Simulate data from normal distribution
y2 <- round(rnorm(n = 36, mean = 21, sd = 6))
y3 <- round(rnorm(n = 36, mean = 50, sd = 8))
x <- rep(LETTERS[1:12], 3)
grp <- rep(c("Grp 1", "Grp 2", "Grp 3"), each = 12)
dat <- data.frame(grp, x, y1, y2, y3)

alstat

using Dates
using Distributions
using HypothesisTests: ADFTest, LjungBoxTest
using Knet
using Measures
using Plots
using PlotThemes
using Random
using StatsBase: autocor, pacf
using Turing

alstat

library(RCurl)
# Set SSL certs globally
options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl")))

library(twitteR)
reqURL <- "https://api.twitter.com/oauth/request_token"
accessURL <- "https://api.twitter.com/oauth/access_token"
authURL <- "https://api.twitter.com/oauth/authorize"
apiKey <-  "xxxxxxxxxxxxxxxxxxxxxx"
apiSecret <- "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

alstat

from matplotlib_venn import *
from matplotlib import pyplot as plt
v = venn2(subsets = {'10': 1, '01': 1, '11': 1}, set_labels = ('A', 'B'))
v.get_patch_by_id('10').set_alpha(0.5)
v.get_patch_by_id('10').set_color('orange')
v.get_patch_by_id('01').set_alpha(0.5)
v.get_patch_by_id('01').set_color('orange')
v.get_patch_by_id('11').set_alpha(0.75)
v.get_patch_by_id('11').set_color('orange')
v.get_label_by_id('10').set_text('')

alstat

set.seed(1)
x1 <- rnorm(10)
x2 <- rnorm(10)
out <- cbind(x1, x2)
colMeans(out)

# colMeans can be computed as follows as well
apply(out, 2, mean)

# apply - applies a mean function to column (indicated by 2) of out matrix

alstat

# Simulating the data
set.seed(73735911)
#set.seed(737377911)
n=100;nu=5;alpha=2;beta=2;sig=1;true=c(alpha,beta,nu)
x=rnorm(n,1,1)
y=alpha+beta*x+sig*rt(n,nu)

par(mfrow=c(1,1))
plot(x,y, col='blue3', pch=19)

alstat

using Turing, Distributions

# Import RDatasets.
using RDatasets

# Import MCMCChain, Plots, and StatPlots for visualizations and diagnostics.
using MCMCChain, Plots, StatPlots

# MLDataUtils provides a sample splitting tool that's very handy.
using MLDataUtils

alstat

# Configure Theme
kobe_theme <- function() {
  theme(
    plot.background = element_rect(fill = "#E2E2E3", colour = "#E2E2E3"),
    panel.background = element_rect(fill = "#E2E2E3"),
    panel.background = element_rect(fill = "white"),
    axis.text = element_text(colour = "#E7A922", family = "Impact"),
    plot.title = element_text(colour = "#552683", face = "bold", size = 18, vjust = 1, family = "Impact"),
    axis.title = element_text(colour = "#552683", face = "bold", size = 13, family = "Impact"),
    panel.grid.major.x = element_line(colour = "#E7A922"),

alstat

rz_helper = function() {
    y1 = rexp(1, 1) # step 1
    y2 = rexp(1, 1) # step 2

    # step 3
    while (y2 <= ((y1 - 1)^2)/2) {
        y1 = rexp(1, 1)
    }

    # step 4