Dpananos

library(tidyverse)
library(lubridate)
library(rtweet)

user.name = 'PhDemetri'
#Get user's most recent tweets
user = get_timeline(user = user.name, n = 5000)

#Clean the data.

Dpananos

library(tidyverse)

n.samples = 25

set.seed(5)
rerun(20,rnorm(n.samples)) %>% 
  map_dfr(~data_frame(data = list(.x)), .id = 'samples') %>% 
  mutate(mu = map_dbl(data,mean), 
         se = map_dbl(data,~sd(.x)/sqrt(length(.x))), 
         top = mu +1.96*se,

Dpananos

library(tidyverse)

N = 1000

#So imagine we have all the data we need for people with low birth weights

race = sample(c('Caucasian','AfricanAmerican'), size = N, replace = T) #Race of people who have low birth weight
birthweight = sample(c('Low','Normal'), size = N, replace = T)
social_class = sample(c('Upper','Middle','Lower'), size = N, replace = T) #Social class of people with low birth weight.  These are the strata

Dpananos

library(tidyverse)
library(lubridate)

#Make fake data
dates2019 <- seq(ymd('2019-01-01'), ymd('2019-12-31'), by ='1 week')
dates2020 <- seq(ymd('2020-01-01'), ymd('2020-12-31'), by ='1 week')

admission <- sample(dates2019, size = 10)
discharge <- sample(dates2020, size = 10)

Dpananos

library(tidyverse)

logit <- function(p) log(p/(1-p))

simulate_trial<-function(N, effect_size){  
  catheter_diameter = sample(c(-1,0,1), replace = T, size = N)
  vaso_band = rbinom(N, 1, 0.5)
  
  X = model.matrix(~catheter_diameter*vaso_band)
  

Dpananos

import numpy as np
from sklearn.svm import SVR
from sklearn.pipeline import Pipeline
from sklearn.compose import TransformedTargetRegressor

from scipy.stats import beta
from scipy.special import expit, logit

np.random.seed(0)
# Generate features

Dpananos

import string
import numpy as np
import networkx
import pickle
import pandas as pd

#First, we need to determine the set of legal moves in the game.
# The grid is 4x4, and we can move in any direction so long as we don't 
# retrace our steps.
# This means we take a simple walk on a graph

Dpananos

library(palmerpenguins)
library(rstanarm)
library(tidybayes)
library(tidyverse)
library(brms)

minsmaxs = penguins %>%
           drop_na() %>% 
           group_by(species) %>% 
           summarise(low = min(flipper_length_mm),

Dpananos

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from scipy.special import expit, logit
from itertools import product
import pandas as pd
import seaborn as sns

def make_regression_data(n, alpha, sigma):
    

Dpananos

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


x = np.random.normal(size = 10)
y = 2*x + 1 + np.random.normal(0, 0.3, size=x.size)

grid = np.linspace(-12, 20, 25)
b0, b1 = np.meshgrid(grid, grid)