alexpghayes

library(tidyverse)
library(tidygraph)
library(netmediate)

data <- read.csv("final_regime.IGO.sanction.csv") |> 
  # remove some columns of missing data to avoid downstream issues & unnnecessary columns
  select(-1, -AMCOW, -MOPAN, - year, -version)

node_data <- data |> 
  select(country, ccode, polity2, democracy, sanction) |> 

alexpghayes

library(tidyverse)
library(tidygraph)
library(netmediate)

data <- read_csv("final_regime.IGO.sanction.csv") |> 
  # remove some columns of missing data to avoid downstream issues
  select(-1, -AMCOW, -MOPAN)

node_data <- data |> 
  select(country, ccode, polity2, democracy, sanction)

alexpghayes

``` r
library(tidyverse)
library(glue)
library(unglue)
#> Warning: package 'unglue' was built under R version 4.2.3

"~/../Desktop/patty-restaurants.txt" |> 
  readLines() |> 
  unglue_data("- [{visited}] {name}") |> 
  arrange(visited, name) |> 

alexpghayes

# rotate Y to align it to X
mypro <- function(X, Y) {
  XY <- crossprod(X, Y)
  s <- svd(XY)
  rotation <- s$v %*% t(s$u)
  Yrot <- Y %*% rotation
  diff <- X - Yrot

  colwise_loss <- Matrix::colSums(diff^2)
  two_infty_loss <- max(sqrt(Matrix::rowSums(diff^2)))

alexpghayes

library(tidyverse)
library(qgam)
library(gratia)
library(furrr)
library(mgcv)

plan(multisession, workers = 5)

hap <- read_csv("https://osf.io/download/kpnjf/")

alexpghayes

x <- c(
  -0.54660789, 2.43686553, 1.24397260, -0.73957557, 2.33131378,
  -0.97341652, -0.48911950, 0.35658630, -0.77824579, 1.55796356,
  0.10641176, 0.89764371, 0.64903636, 1.89822592, -0.35568742,
  1.52124163, 0.01328800, 1.33453143, -1.08829020, 0.83033763,
  0.68018922, 1.93419047, 1.14073253, 2.56650893, 1.90859128,
  1.39105347, 1.37547130, -0.07223141, 0.43142589, 1.78035482,
  1.90837367, 1.84173396, -0.09760825, 1.66610817, 0.03379679,
  0.97277154, 0.78599263, 0.75538460, 1.23006396, 0.83001685
)

alexpghayes

library(tidyverse)

data <- read_csv("data.csv")
X <- as.matrix(data)  # i presume this is the starting state of the data

plot_at_threshold <- function(mat, threshold) {
  
  colnames(mat) <- 1:NCOL(mat)
  rownames(mat) <- 1:NROW(mat)
  

alexpghayes

library(sandwich)

n <- 10
d <- 3

p <- 4

X <- matrix(
  rnorm(n * d),

alexpghayes

library(fastRG)
library(estimatr)
library(tidyverse)

set.seed(27)

get_one_estimate <- function() {
  k <- 5
  n <- 1000
  B <- matrix(stats::runif(k * k), nrow = k, ncol = k)

alexpghayes

``` r
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union