gowerc

import pandas as pd
import numpy as np

dat = pd.DataFrame({
    "player1" : ["A", "A", "A", "A","A","B","B", "B", "C", "C", "C", "C", "C"],
    "player2" :[ "B", "C", "B", "B", "C", "A", "C", "C", "A", "B", "A", "B", "B"],
    "player1_won" : [ 1, 0, 1, 1, 1, 0, 1, 0 , 1, 0 , 0, 0, 0]
})

names = sorted(set(dat["player1"]))

gowerc

import aiohttp
import asyncio

URLS = [
    "http://www.laughfactory.com/jokes/clean-jokes/2",
    "http://www.laughfactory.com/jokes/clean-jokes/3",
    "http://www.laughfactory.com/jokes/clean-jokes/4",
    "http://www.laughfactory.com/jokes/clean-jokes/5",
    "http://www.laughfactory.com/jokes/clean-jokes/6",
    "http://www.laughfactory.com/jokes/clean-jokes/7",

gowerc

library(dplyr)
library(purrr)
library(parallel)
library(knitr)

set.seed(40130)

CIVS <- c("A", "B", "C", "D")
true_score_mean <- 0
true_score_sd <- 0.5

gowerc

###################################################
#
#
# Setup Dataset
#
#
#

library(tidyverse)
library(flexsurv)

gowerc

library(dplyr)
library(arrow)
library(stringi)
library(lubridate)

time_it <- function(expr) {
    start <- Sys.time()
    x <- eval(expr)
    stop <- Sys.time()
    tdiff <- round(as.numeric(difftime(stop, start, units = "sec")), 4)

gowerc

#############################
#
#
# Exploring the impact of completely random & independently
# distributed covariates
#
#
#############################

gowerc

# Relevant Links
#
#   CRAN Server builds
#       https://cran.r-project.org/web/checks/check_flavors.html#r-devel-linux-x86_64-debian-clang
#
#   clang + devel specific instructions
#       https://www.stats.ox.ac.uk/pub/bdr/Rconfig/r-devel-linux-x86_64-fedora-clang
#
#   Instructions for building R from source
#       https://docs.posit.co/resources/install-r-source/

gowerc

#######################################
# Classes
#######################################
#
# The class function is how you construct objects in python. There are several key concepts to
# understand
#
# General syntax:
#     class <object name>(<object to inherit from>):
#         <define class wide attributes>

gowerc

##################################
#
#
#  This simple script aims to highlight several non-normal distributions
#  and transformations of distributions where 95% of the distribution
#  can be found within the interval mu +- 1.96*sigma
#  Results can be found as comments inline with the code
#

gowerc

library(brms)
library(dplyr)

bform <- 
    bf(y1 ~ age, family = gaussian() )  + 
    bf(y2 ~ age + y1, family = gaussian()) +
    set_rescor(FALSE)

df <- tibble(