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library(tidyverse)
library(lubridate)
d <- read_csv("psychjobwiki.csv")

ms <- d %>%
  group_by(year, category) %>%
  count() %>%
  left_join(d %>% group_by(year) %>% count() %>% rename(total = n)) %>%
  mutate(prop = n / total)

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library(tidyverse)
library(BayesFactor)

# simulation-based frequentist power analysis with known ES
ES <- .5
n_sims <- 100
ns <- c(2, 5, 10, 20, 50, 100)

sims <- as.tibble(expand.grid(sim = 1:n_sims, 
                      n = ns)) %>%

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conditions <- read_csv("condition_means.csv")

generate_data <- function(condition_df, n_subs = 40, n_trials = 3) {
  tibble(speaker = condition_df$speaker, 
         prior = condition_df$prior,
         info = condition_df$info, 
         experiment = condition_df$experiment,
         correct = rbinom(1, n_subs * n_trials, 
                          condition_df$mean_correct), 
         subid = rep(1:n_subs, n_trials))

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library(tidyverse)
library(viridis)
library(knitr)

d <- read_csv("AuthorList.csv")

authors <- d %>%
  group_by(author) %>%
  count

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from Bio import Entrez

Entrez.email = "[email protected]"

def get_links_term(term):
	links = Entrez.esearch(db="pubmed", retmax = 5000, term=term)
	record = Entrez.read(links)
	link_list = record[u'IdList']
	return link_list

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# very quick re-implementation of a loose version of Experiment 1 from 
# Twomey & Westermann 2017

# Mike Frank ([email protected])

library(autoencoder)
library(tidyverse)

# need this to evaluate pairwise distance in training sets
euclidean_pairwise <- function (x) {

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library(tidyverse)
d <- read_csv("~/Desktop/med_table_shavit.csv") %>%
  arrange(age_grp, kinship, donation, soc_dist)

# first plot
ggplot(d, 
       aes(x = soc_dist, y = med_amnt, col = age_grp)) + 
  geom_point() + 
  facet_grid(kinship ~ donation) + 
  geom_smooth(method="lm", formula = y ~ poly(x, 2), 

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library(tidyverse)
library(purrr)

n1 = 12
n2 = 30
nsims = 10

get_chi2 <- function(x) {
  p1 <- rbinom(1, size = n1, prob = x$p1)
  p2 <- rbinom(1, size = n2, prob = x$p2)

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library(purr)
library(tidyverse)
library(stringr)


# constants
locations <- 1:3
timestep <- 1
t_end <- 6
t <- seq(0, t_end, timestep)