JonasMoss

#!/usr/bin/env python3

# Prompt to o3-mini-high: The file url = "https://raw.githubusercontent.com/dwyl/english-words/master/words.txt" contains "all" english words. I want a Python script that prints out the longest word with letters in alphabetical order (e.g., "abc") AND reverse alphabetical order (e.g., "cba"). Convert all words to lowercase and replace hyphens with nothing before doing this. The script should be executable immediately, so you need to provide code for downloading the file.

import urllib.request


def is_alphabetical(word):
    return list(word) == sorted(word)

JonasMoss

### Here's some more info about default values! 
### Especially when they are difficult to understand.

### An easy example of default values.
def f(x, a = True): 
    if a:
        return (x + 1)
    if not a:
        return (x + 2)

JonasMoss

JonasMoss

JonasMoss

First Reading

On the Consistency Rule in Causal Inference Axiom, Definition, Assumption, or Theorem? (Pearl, 2010, 4 page) One of the big problems with the causality literature is the terminology and the lack of foundationas for everyone to agree on.(Think about a vector space -- everyone agrees what it is. That's where we want to be.) The consistency rule appears to me to be the corner-stone of an axiomatic development of causality theory.

The following papers are mentioned in the Pearl paper and are a part of the assignment:

1. The consistency statement in causal inference: a definition or an assumption

2. Concerning the consistency assumption in causal inference

JonasMoss

#' Hide non-function variables from function.
#' 
#' @param ... Named functions and function definitions.
#' @return Nothing.

H = function(...) {
  
  function_names = names(as.list(substitute((...)))[-1])
  function_defs = list(...)
  envir = parent.env(parent.frame())

JonasMoss

# Create a covariance matrix for the covariates.
rho12 = -0.1
rho13 = 0.65
rho23 = -0.3

covariance = matrix(c(1,     rho12, rho13,
                      rho12, 1,     rho23,
                      rho13, rho23, 1), nrow = 3)

# Simulate a linear regression with all betas equal to 1.

JonasMoss

#' Graph of number of tries needed to obtain K successes.
#' @param K number of studies.
#' @return NULL.

plotter = function(K){
  kk = 0:(K*70)
  plot(kk + K, dnbinom(kk, K, 0.05), bty = "l", type = "b", pch = 20,
       xlab = "Number of studies", 
       ylab = "Probability",
       main =  paste0("Number of studies before ", K, " successes"))

JonasMoss

#' Find the cumulative maximal streak length in a vector of bools.
#' 
#' @param bools Logical vector.
#' @return An integer vector. The \code{i}th element is the maximal streak
#' length in \code{x[1:i]}.
#' @example
#'     bools1 = c(FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE)
#'     streaks(bools1) [1] 0 1 1 1 2 3 3
#'     
#'     bools2 = c(FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE)