andrewheiss

library(tidyverse)
library(tidybayes)
library(brms)
library(ggtext)

priors <- c(prior(normal(20, 5), class = Intercept),
            prior(normal(0, 2), class = b),
            prior(exponential(1), class = sigma),
            prior(exponential(1), class = sd),

vincentarelbundock

library(fixest)
library(data.table)
library(insight)

bboot_vab <- function(model, reps = 1e3, conf.level = .95, cluster = NULL) {
    data <- insight::get_data(model)
    setDT(data)
    if (!is.null(cluster)) {
        if (anyNA(data[[cluster]])) {
            stop("The cluster variable cannot include missing values.")

grantmcdermott

# Context: https://twitter.com/grant_mcdermott/status/1487528757418102787

library(data.table)
library(fixest)

bboot =
  function(object, reps = 100L, cluster = NULL, ...) {
    
    fixest_obj = inherits(object, c('fixest', 'fixest_multi'))
    

nhatley

#code from start to finish 

##install or update packages if neccessary
#install.packages("tidyverse")
#install.packages("haven") 

##load packages in 
library(tidyverse) #loads all "core" tidyverse packages like dplyr, tidyr, forcats, and ggplot2
library(haven) 

mainambui

# Arguments:
#  - model = a clmm model
#  - modelAvg = a clmm model average (object of class averaging)
#  - newdata = a dataframe of new data to apply the model to
# Returns a dataframe of predicted probabilities for each row and response level
fake.predict.clmm <- function(modelAvg, newdata) {
  # Actual prediction function
  pred <- function(eta, theta, cat = 1:(length(theta) + 1), inv.link = plogis) {
    Theta <- c(-1000, theta, 1000)
    sapply(cat, function(j) inv.link(Theta[j + 1] - eta) - inv.link(Theta[j] - eta))

casallas

Package	Ordinal	Multinomial	Partial proportional odds	Scale Effects	Random Effects
nnet	no	multinom	N/A	N/A	No
MASS	polr	No	No	No	No
ordinal	clm/clm2	all X's in nominal (may not converge)	offending X's in nominal	via scale	clmm/clmm2
VGAM	yes	?	?	?	No
MCMCglmm	yes	?	?	?	?
Mixcat	yes	?	?		npmlt
mlogit	?	mlogit	N/A	N/