pip3 install pipenv
pipenv shell
| library(tidyverse) | |
| library(nlme) | |
| library(rms) | |
| library(lmerTest) | |
| library(emmeans) | |
| data("fev_data", package = "mmrm") | |
| data <- fev_data %>% | |
| as_tibble() %>% | |
| mutate(ARMCD = as.character(ARMCD)) %>% |
| library(survival) | |
| library(dplyr) | |
| library(modelbased) | |
| library(ggplot2) | |
| # Recode the status into 0 for censoring | |
| data <- survival::lung %>% | |
| select(time, status, age, sex) %>% | |
| mutate( | |
| status = status - 1, # Status is coded as 1=dead 2=alive |
| # Install necessary packages if not already installed | |
| install.packages("svglite") | |
| install.packages("ggplot2") | |
| # Load the ggplot2 library | |
| library(ggplot2) | |
| # Create a basic boxplot of Sepal Length by Species | |
| p <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + | |
| geom_boxplot() + |
| library(mice) | |
| library(gtsummary) | |
| # impute the data | |
| df_imputed <- mice::mice(trial, m = 2, seed = 123) | |
| # build the model list | |
| imputed_model_list <- purrr::map( | |
| 1:df_imputed$m, | |
| ~ lm(age ~ marker + grade, complete(df_imputed, .x)) |
| from math import comb | |
| import numpy as np | |
| def kravchuck(k: int, x: int, n: int, q: int) -> int: | |
| """ | |
| Kravchuck polynomials of the form K_k(x,n,q) where k = 0,...,n and q | |
| must be a prime power. | |
| """ | |
| k_values = [ |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| def collatz(x): | |
| if x % 2 == 0: | |
| return(x/2) | |
| else: | |
| return((3*x)+1) |