baogorek

library(dplyr)
library(tidyr)
library(ggplot2)
library(brms)
library(qcc)

#set.seed(1569)  # First 100
set.seed(8536)  # Next 1000

# B <- 100 # First time through

baogorek

library(dplyr)
library(tidyr)


sim_df <- bind_rows(
    tibble(group='A', y=rnorm(100, mean=33)),
    tibble(group='B', y=rnorm(10000, mean=30)),
    tibble(group='C', y=rnorm(100, mean=33.1))
)

baogorek

library(dplyr)

# Structural Equations ---------------------------------------------------
get_subject_affinity <- function(N) {
  # u ~ Uniform(1, 20)
  runif(N, min=1, max=20)
}

get_time_resources <- function(N) {
  # w ~ Gamma(3, 1)

baogorek

# See Wiki @ https://github.com/USEPA/useeior/Wiki for install instructions
library(useeior)

seeAvailableModels()
# explanation of model names can be found at
# https://github.com/USEPA/USEEIO/blob/master/VersioningScheme.md
# LCI: direct perspective life cycle inventory result
# LCIA: life cycle impact assessment

# Build USEEIO v2.0-GHG

baogorek

# pip install ananke-causal
from ananke import graphs
from ananke import identification
from ananke.estimation import CausalEffect

import numpy as np
import pandas as pd

# Simulate front-door situation with confounder Z
N = 100000

baogorek

import pandas as pd
import numpy as np
import patsy

import tensorflow as tf

class SleepReg(tf.Module):
    def __init__(self, sleepdata_path):
        """Initializing tensorflow variables and other necessary matrices"""
        # These two TensorFlow variables show up in the trainable_variables

baogorek

N <- 10000
theta <- 1.4

e <- rnorm(N)

e_lag <- c(NA, e[1:(N-1)])

y <- e - theta * e_lag

acf(y[2:N])

baogorek

baogorek

library(dplyr)
library(ggplot2)
library(splines)

days_grid <- 0:180
interior_knots <- c(2, 6, 25)
#interior_knots <- c(6, 25, 80)

my_spline <- ns(days_grid, knots = interior_knots)

baogorek

library(dplyr)
library(gridExtra)
library(ggplot2)

exp_decay <- function(t, tau) {
    exp(-t / tau)
}

convolve_training<- function(training, n, tau) {
   sum(training[1:(n - 1)] * exp_decay((n - 1):1, tau))