kylebutts

triangle_info <- function(A, B, C) {
  # Helper function to compute distance between two points
  dist <- function(P, Q) sqrt((P[1] - Q[1])^2 + (P[2] - Q[2])^2)
  
  # Side lengths
  dist_AB <- dist(A, B)
  dist_AC <- dist(A, C)
  dist_BC <- dist(B, C)
  
  # Law of Cosines for each angle

kylebutts

Dev

1. Download the files with gh repo clone posit-dev/positron in some directory
2. npm i from the main directory to install packages
3. npm run watch from the main directory to compile client and extensions (watch-client and watch-extensions). This will run continuously in the background, so you need a second terminal window

Running tests

The npm test-extension takes way too long as it runs all tests. The -g and -l options do not seem to work. Instead, I recommend opening the positron folder with vscode and it will prompt you to install a task running extension.

kylebutts

library(tidyverse)
library(spatstat)
#> Loading required package: spatstat.data
#> Loading required package: spatstat.univar
#> spatstat.univar 3.1-1
#> Loading required package: spatstat.geom
#> spatstat.geom 3.3-5
#> Loading required package: spatstat.random
#> spatstat.random 3.3-2

kylebutts

Law of Rare Events / Poisson Limit Theorem

This famous theorem states that when you have:

• A large number of independent trials ($n \to \infty$)
• Each trial has a small probability of success ($p \to 0$)
• The expected number of successes ($\lambda = np$) remains constant

$\implies$ the number of successes $X$ follows a Poisson distribution with parameter $\lambda$

kylebutts

* https://github.com/py-econometrics/pyfixest/issues/672
. use "~/Downloads/census2000_5pc.dta", clear
(5% Extract from 2000 US Census (from Abadie et al., 2023))

. 
. // set 1
. b1x2 ln_earnings, x1all(educ) x2all(hours)

                                                      Number of obs = 2632838

kylebutts

library(ggplot2)
beta <- seq(-0.2, 0.2, by = 0.001)
exp_minus_1 <- exp(beta) - 1

(plot_pct_change_appxoimation <- ggplot() +
  geom_line(
 aes(x = beta, y = beta, color = "approx"),

kylebutts

# Modified to use `sf` from this article:
# https://rud.is/b/2014/11/16/moving-the-earth-well-alaska-hawaii-with-r/
library(sf)
#> Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
library(tigris)
#> To enable caching of data, set `options(tigris_use_cache = TRUE)`
#> in your R script or .Rprofile.
library(rmapshaper)
library(ggplot2)

kylebutts

# %%
library(tidyverse)
library(fixest)
library(binsreg)
library(patchwork)

set.seed(20240829)
x <- runif(500)
w <- rnorm(n = 500, mean = x, sd = 1)
y <- sin(x) + w * 2 + rnorm(500, mean = 0, sd = 1)

kylebutts

This code shows the problem of post-selection inference following the review article Post-Selection Inference

library(tidyverse)
library(fixest)

Data generation process:

• $X = (X_1, X_2, X_3)'$ is multi-variate normal with non-diagonal covaraince

kylebutts

% https://brand.uark.edu/graphic-identity/official-colors.php
\definecolor{cardinal_red}{HTML}{9D2235}
\definecolor{apple_blossom}{HTML}{FFFFFF}
\definecolor{quartz}{HTML}{F2F2F4}
\definecolor{gray_squirrel}{HTML}{C7C8CA}
\definecolor{spoofers_stone}{HTML}{424242}
\definecolor{black_whetstone}{HTML}{000000}
\definecolor{diana_butterfly}{HTML}{2B5269}
\definecolor{ozark_mountains}{HTML}{3F7F7F}
\definecolor{birdsfoot_violet}{HTML}{2F1332}