rasmusab

bowling_animation_urls <- c(
  "0.01" = "https://i.imgur.com/Kn8CQbj.gif",
  "0.05" = "https://i.imgur.com/HFTKdDL.gif",
  "0.1" = "https://i.imgur.com/8Sw54Mz.gif",
  "1" = "https://i.imgur.com/kjROdhj.gif"
)

# We need to download the animations to the session temp dirercorty to display 
# them in the Rstudio Viever pane
bowling_animation_html <- sapply(bowling_animation_urls, function(url) {

kennedymwavu

setHook("rstudio.sessionInit", function(newSession) {
  cat('\f')
  
  hour <- format(Sys.time(), format = '%H') |> as.numeric() |> as.character()
  
  morning <- 0:11
  afternoon <- 12:16
  evening <- c(17:23)
  
  day_hrs <- rep(

tnagler

Problem statement from Alex Hayes

i have (x, y) pairs generated from two smooth curves, but i don't know which curve each point comes from

is there a way to recover the original curves?https://t.co/h9kL2JEeEV pic.twitter.com/tzy4O1VnrT

— alex hayes (@alexpghayes) June 28, 2022

library(tidyverse)

x <- seq(0, 10, length.out = 100)

unobserved <- tibble(

0xabad1dea

Risk Assessment of GitHub Copilot

0xabad1dea, July 2021

this is a rough draft and may be updated with more examples

GitHub was kind enough to grant me swift access to the Copilot test phase despite me @'ing them several hundred times about ICE. I would like to examine it not in terms of productivity, but security. How risky is it to allow an AI to write some or all of your code?

Ultimately, a human being must take responsibility for every line of code that is committed. AI should not be used for "responsibility washing." However, Copilot is a tool, and workers need their tools to be reliable. A carpenter doesn't have to

teunbrand

library(ggplot2)
library(tuneR)

midi <- tuneR::readMidi("https://www.8notes.com/school/midi/piano/beethoven_ode_to_joy.mid")


df <- tuneR::getMidiNotes(midi)

# Difference between first two notes
mult <- 960

eggie5

# Generalized Matrix Factorization
def GMF(num_users, num_items, k, reg):
    """Models the interaction between user/item LV w/ linear dot-product"""
    print("using dot-product interaction term")

    seed = 1
    uid = Input((1,), name="uid")
    iid = Input((1,), name="iid")

    item_bias = Embedding(

eric-pedersen

library(mgcv)

# annual diameter of women’s skirts at the hem 1866 to 1911
# Hipel and McLeod, 1994
skirts <- scan("http://robjhyndman.com/tsdldata/roberts/skirts.dat",skip=5)
skirtseries <- data.frame(year=1866:1911, diam=skirts)

# prediction grid
pd <- data.frame(year=seq(1864, 1960, by=1))

dill

# extrapolating into the future with B-splines
#  based on code in ?smooth.construct.bs.smooth.spec

library(mgcv)

# annual diameter of women’s skirts at the hem 1866 to 1911
# Hipel and McLeod, 1994
skirts <- scan("http://robjhyndman.com/tsdldata/roberts/skirts.dat",skip=5)
skirtseries <- data.frame(year=1866:1911, diam=skirts)

tjmahr

super_split <- function(data, ...) {
  dots <- rlang::quos(...)
  for (var in seq_along(dots)) {
    var_name <- rlang::as_name(dots[[var]])
    data <- purrr:::map_depth(
      data, 
      var - 1, 
      function(xs) split(xs, xs[var_name])
    ) 

stablemarkets

data {
  int<lower=0> num_obs; // number of observations
  real y[num_obs]; // Observed value
  int time[num_obs]; // time
  
}
parameters {
  //weakly uninformative priors, K on log base 10 scale
  vector[100] log_K; 
  real<lower=0,upper=100> p0;