Christopher Gandrud christophergandrud

## e.divGG.R
#' Function for creating nonparamentric multiple change point plots with
#' estimates from the ecp package.
#'
#' @param data A data frame with the covariates and time variable.
#' @param Vars A character vector listing the names of the variates from
#' \code{data} to include in the nonparametric multiple change point analysis.
#' @param TimeVar A character string naming the time variable in \code{data}.
#' Must be in a format handled by POSIX, e.g. YYYY-MM-DD.
#' @param sig.lvl The level at which to sequentially test if a proposed change
#' point is statistically significant.

## source_GitHubData.R
#####################
# R function for downloading plain-text data from GitHub
# Christopher Gandrud
# 7 January 2013
#####################

# source_GitHubData is directly based on source_url from the Hadley Wickham's devtools package

source_GitHubData <-function(url, sep = ",", header = TRUE)
{

## create_lags_diffs.R
# Create lag and difference variables for a grouped time series (grouped by a variable called "country")
#
# @param df a data frame containing the variable to lag and lead
# @param x a column name in `df` to lag and lead
# @param lag integer period to lag and difference
#
# @importFrom tsibble difference
# @importFrom dplyr %>% lag

create_lags_diffs <- function(df, x, lag = 1) {

## simulate_power_distributed_discrete.R
library(xfun)
pkg_attach2("tidyverse", "poweRlaw", "furrr")
plan(multiprocess)


# Simulate from a discrete power law
# @param counter numeric, to enable mapping
# @param n integer, number of draws
# @param xmin numeric, minimum value subject to power law
# @param alpha, scaling parameter

## a_cc_iv_2019.csv

          
            Y
            X
            Z
            A

            
              1.41349020936189
              Signup
              3.76363446527701
              0

            
              -1.19511457323204
              No Signup
              -3.81402465182499
              1

            
              1.87668885146666
              Signup
              0.712012451131932
              1

            
              1.8385401566156
              No Signup
              -0.774724331043617
              1

            
              3.02728268507929
              Signup
              3.25513469702395
              1

            
              2.88424256922274
              Signup
              -0.863068694934284
              1

            
              -0.387965283366912
              No Signup
              -2.59568803608236
              0

            
              0.577546833602056
              Signup
              -0.277480406497884
              0

            
              2.72670763084368
              Signup
              0.181542158647839
              1

## r_stan_setup.sh
########################################################
# Set up RStudio and JAGS on an Amazon EC2 instance
# Using Ubuntu 64-bit
# Christopher Gandrud
# 16 December 2014
# Partially from http://blog.yhathq.com/posts/r-in-the-cloud-part-1.html
# See yhat for EC2 instance set up
########################################################

# In your terminal navigate to key pair

## poisson_mle_sims_bias_comp.R

library(pacman)

p_load(MASS, tidyverse)

# set seed
set.seed(4321)

# set simulation paramters
n <- 100  # sample size

## transformation_bias_blog.R

library(pacman)

p_load(MASS, tidyverse)

# set seed
set.seed(8764)

# set simulation paramters
n <- 100  # sample size

## quick_mod_to_intuition_plot.R
set.seed(19743)

library(tidyverse)
library(ggrepel)
library(forcats)

tau <- function(x) {
    x^2
}

## leg_violence_predict.R
####################
# Create relogit predicted probabilities using Zelig and ggplot2
# Two Sword Lengths: Losers' Consent and Violence in National Legislatures (Working Paper 2012)
# Christopher Gandrud
# Updated 26 April 2012
###################

## Load required packages
library(RCurl)
library(Zelig)
	#' Function for creating nonparamentric multiple change point plots with
	#' estimates from the ecp package.
	#'
	#' @param data A data frame with the covariates and time variable.
	#' @param Vars A character vector listing the names of the variates from
	#' \code{data} to include in the nonparametric multiple change point analysis.
	#' @param TimeVar A character string naming the time variable in \code{data}.
	#' Must be in a format handled by POSIX, e.g. YYYY-MM-DD.
	#' @param sig.lvl The level at which to sequentially test if a proposed change
	#' point is statistically significant.
	#####################
	# R function for downloading plain-text data from GitHub
	# Christopher Gandrud
	# 7 January 2013
	#####################

	# source_GitHubData is directly based on source_url from the Hadley Wickham's devtools package

	source_GitHubData <-function(url, sep = ",", header = TRUE)
	{
	# Create lag and difference variables for a grouped time series (grouped by a variable called "country")
	#
	# @param df a data frame containing the variable to lag and lead
	# @param x a column name in `df` to lag and lead
	# @param lag integer period to lag and difference
	#
	# @importFrom tsibble difference
	# @importFrom dplyr %>% lag

	create_lags_diffs <- function(df, x, lag = 1) {
	library(xfun)
	pkg_attach2("tidyverse", "poweRlaw", "furrr")
	plan(multiprocess)


	# Simulate from a discrete power law
	# @param counter numeric, to enable mapping
	# @param n integer, number of draws
	# @param xmin numeric, minimum value subject to power law
	# @param alpha, scaling parameter
Y	X	Z	A
1.41349020936189	Signup	3.76363446527701	0
-1.19511457323204	No Signup	-3.81402465182499	1
1.87668885146666	Signup	0.712012451131932	1
1.8385401566156	No Signup	-0.774724331043617	1
3.02728268507929	Signup	3.25513469702395	1
2.88424256922274	Signup	-0.863068694934284	1
-0.387965283366912	No Signup	-2.59568803608236	0
0.577546833602056	Signup	-0.277480406497884	0
2.72670763084368	Signup	0.181542158647839	1
	########################################################
	# Set up RStudio and JAGS on an Amazon EC2 instance
	# Using Ubuntu 64-bit
	# Christopher Gandrud
	# 16 December 2014
	# Partially from http://blog.yhathq.com/posts/r-in-the-cloud-part-1.html
	# See yhat for EC2 instance set up
	########################################################

	# In your terminal navigate to key pair

	library(pacman)

	p_load(MASS, tidyverse)

	# set seed
	set.seed(4321)

	# set simulation paramters
	n <- 100 # sample size
	set.seed(19743)

	library(tidyverse)
	library(ggrepel)
	library(forcats)

	tau <- function(x) {
	x^2
	}
	####################
	# Create relogit predicted probabilities using Zelig and ggplot2
	# Two Sword Lengths: Losers' Consent and Violence in National Legislatures (Working Paper 2012)
	# Christopher Gandrud
	# Updated 26 April 2012
	###################

	## Load required packages
	library(RCurl)
	library(Zelig)