mcfrank

library(wordbankr)
library(langcog)
library(tidyverse)
library(brms)
library(forcats)
library(survey)
library(gamlss)
theme_set(theme_mikabr())
font <- theme_mikabr()$text$family

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# relies on zoo package

# function to find the baseline value
baseline_looking <- function (lts) {
  # select lts > 12s
  lts_12 <- lts[lts > 12 & !is.na(lts)]
 
  # sum the first three
  baseline <- sum(lts_12[1:3], na.rm=TRUE)
 

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library(tidyverse)
library(assertthat)

shuffle_data <- function(d) {
  d$participant_id <- shuffle(d$participant_id)
  
  return(d)
}

get_knower_level <- function (q, r, technique = "perfect") {

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# approach from
# https://www.medrxiv.org/content/10.1101/2022.11.02.22281762v1.full.pdf

library(tidyverse)
library(survival)
library(ggsurvfit)
#devtools::install_github("maxlinde/baymedr")
library(baymedr)
# library(BayesSurvival)

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---
title: "mtcars example markdown"
author: "Mike Frank"
date: "2023-03-21"
output: 
  html_document:
    toc: true
    toc_float: true
---

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library(tidyverse)
library(lme4)

sgf <- read_csv("https://raw.githubusercontent.com/langcog/experimentology/main/data/tidyverse/stiller_scales_data.csv") |>
  mutate(age_group = cut(age, 2:5, include.lowest = TRUE), 
         condition_f = factor(ifelse(condition == "Label", 
                                     "Experimental", "Control")),
         age_centered = age - mean(age))

mod1 <- glmer(correct ~ age * condition + (1|subid) + (1|item), 

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# starts at line 716 of paper.Rmd

d_lmer_scale <- d %>%
  filter(trial_type != "train") %>%
  mutate(log_lt = log(looking_time),
         age_mo = scale(age_mo, scale = FALSE),
         trial_num = trial_num - 8.5, 
         item = paste0(stimulus_num, trial_type)) %>%
  filter(!is.na(log_lt), !is.infinite(log_lt))

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library(tidyverse)
library(gamlss)

d <- readxl::read_excel("sample_data.xlsx")

# model
max_vocab <- max(d$`Vocabulary production`)

# transformation to 0-1 for beta model
# note that beta data cannot be exactly 0 or 1, it may be necessary to add/subtract .001 for data including 0s and 1s

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# delta rule formula
# (i-o) o (1-o)

library(tidyverse)
d <- expand_grid(i =  seq(0.05,.95,.05),
            o = seq(0.05,.95,.05)) |>
  mutate(last_part = o * (1-o),
         first_part = abs(i-o),
         delta = abs((i-o) * o * (1-o)), 
         divergence = i * log(i/o)) 

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library(rscopus)
library(tidyverse)
library(igraph)
library(GGally)

faculty <- read_csv("faculty.csv") 

faculty$au_id <- NA
for (i in 1:nrow(faculty)) {
  print(i)