Dpananos

import numpy as np
from statsmodels.nonparametric.smoothers_lowess import lowess

from sklearn.datasets import load_breast_cancer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold, RepeatedKFold, GridSearchCV, cross_val_score
from sklearn.metrics import make_scorer, brier_score_loss
from sklearn.utils import resample

Dpananos

# LRT
data = c(27,24,3,14,9,14,4,6,7,8)
m = matrix(data, nrow = 2)

theta_null = colSums(m)/sum(m)
theta_1 = m[1,]/sum(m[1,])
theta_2 = m[2,]/sum(m[2,])

L0 = dmultinom(colSums(m), prob = theta_null, log=T)
L1 = dmultinom(m[1,], prob = theta_1, log=T) + dmultinom(m[2,], prob = theta_2, log=T)

Dpananos

library(tidyverse)

set.seed(7)
N = 1000
x = rnorm(N)
p = plogis(0.2*x - 0.8)
y = rbinom(N, 1, p)

tibble(x, y) %>%  
  mutate(z = cut_number(x, 5)) %>% 

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)

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

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 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

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

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

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)