SachaEpskamp

# Input:
payout <- 35
winSpaces <- 1
allSpaces <- 37
bet <- 10

# Probability:
p <- winSpaces / allSpaces

# Expected value:

SachaEpskamp

library("qgraph")
library("bootnet")

# Number of nodes:
nNode <- 10

# Make a list with intervals:
intervals <- replicate(nNode,sort(runif(2,0.5,1)),simplify = FALSE)

# Rescale intervals so they represent difference:

SachaEpskamp

library("qgraph")
library("bootnet")

set.seed(1)

# Number of nodes:
nNode <- 10

# random point estimates:
pointests <- runif(nNode,0,1)

SachaEpskamp

# Load network:
library("maps")
library("qgraph")

map("world", fill=TRUE, col="white", bg="lightblue", ylim=c(-60, 90), mar=c(0,0,0,0))

# Some network with 4 nodes:
Adj <- matrix(1,4,4)

# Layout using coordinates:

SachaEpskamp

# Packages needed:
library("qgraph")
library("dplyr")

# Create a deck:
createDeck <- function(){
  data.frame(
    card = 1:60,
    type = rep(c("land","spell","spell"), length = 60)
  )

SachaEpskamp

# Install from github:
devtools::install_github("sachaepskamp/qgraph")
library("qgraph")

# Example network to play with:
# Load data:
library("psychTools")
data(bfi)

# Compute polychoric correlations:

SachaEpskamp

compareNetworks <- function(true,est, directed = FALSE){
  cor0 <- function(x,y,...){
    if (sum(!is.na(x)) < 2 || sum(!is.na(y)) < 2 || sd(x,na.rm=TRUE)==0 | sd(y,na.rm=TRUE) == 0){
      return(0)
    } else {
      return(cor(x,y,...))
    }
  }
  
  bias <- function(x,y) mean(abs(x-y),na.rm=TRUE)

SachaEpskamp

library("parSim")

parSim(
  ### SIMULATION CONDITIONS
  # Vary sample size:
  sampleSize = c(250, 500, 1000),
  
  # Vary missingness:
  missing = c(0, 0.1, 0.25),
  

SachaEpskamp

library("parSim")

parSim(
  ### SIMULATION CONDITIONS
  # Vary sample size:
  sampleSize = c(250, 500, 1000),
  
  # Vary missingness:
  missing = c(0, 0.1, 0.25),
  

SachaEpskamp

library("SEset")
library("qgraph")
library("pcalg")

# For true DAG:
A <- matrix(c(
  0,0,0,
  0.25,0,0,
  0,0.25,0
),3,3,byrow=TRUE)