ntamas

#!/usr/bin/env python

from numpy import arange, array, bincount, ndarray, ones, where
from numpy.random import seed, random, randint

__author__ = "Tamas Nepusz, Denis Bzowy"
__version__ = "27jul2011"

class WalkerRandomSampling(object):
    """Walker's alias method for random objects with different probablities.

jrnold

library("ggplot2")

#' Draw Normal Distribution Density with an area shaded in.
#'
#' @param lb Lower bound of the shaded area. Use \code{-Inf} for a left tail.
#' @param ub Upper bound of the shaded area. Use \code{Inf} for a right tail.
#' @param mean Mean of the normal distribution
#' @param sd Standard deviation of the normal distribution
#' @param limits Lower and upper bounds on the x-axis of the area displayed.
#' @return ggplot object.

jennybc

#' ---
#' output:
#'   html_document:
#'     keep_md: TRUE
#' ---

#+ include = FALSE
library(dplyr)

#' Responses to [my

stevenpollack

R package development "best practices"

The core of this tutorial gist lies in bestPracticesWalkThrough.R. Running assumes you have the following packages at versions equal (or above) those specified

library('devtools') # 1.9.1
library('testthat') # 0.11.0
library('stringr')  # 1.0.0
library('git2r') # 0.12.1

fredbenenson

layout	title	description	tags
default	SQL Style Guide	A guide to writing clean, clear, and consistent SQL.	data process

Purpose

mike-lawrence

functions{
	// GP: computes noiseless Gaussian Process
	vector GP(real volatility, real amplitude, vector normal01, int n_x, real[] x ) {
		matrix[n_x,n_x] cov_mat ;
		real amplitude_sq_plus_jitter ;
		amplitude_sq_plus_jitter = amplitude^2 + 1e-6 ;
		cov_mat = cov_exp_quad(x, amplitude, 1/volatility) ;
		for(i in 1:n_x){
			cov_mat[i,i] = amplitude_sq_plus_jitter ;
		}

noamross

library(rtweet) #rtweet API creds should already be set up
library(stringi)
library(dplyr)

friends = get_friends(user="noamross")
followers = get_followers("noamross")
tweeps_id = distinct(bind_rows(friends, followers))
tweeps_info = lookup_users(tweeps_id$user_id)

# A regex for a visit to Durham

DavisVaughan

// [[Rcpp::depends(xtensor)]]

#include <numeric>
#include "xtensor/xmath.hpp"
#include "xtensor-r/rarray.hpp"
#include <Rcpp.h>

using namespace Rcpp;

// [[Rcpp::plugins(cpp14)]]

thomasp85

library(future)
trim_model <- function(model, predictor = predict, ..., ignore_warnings = TRUE) {
  # Cache the correct output
  true_pred <- predictor(model, ...)
  
  # Treat prediction warnings as errors?
  if (!ignore_warnings) {
    old_ops <- options(warn = 2)
    on.exit(options(old_ops))
  }

khakieconomics

library(rstanarm); library(tidyverse)
options(mc.cores = parallel::detectCores())
set.seed(42)
data("radon")
head(treatment_sample)

# Some levels have no variance in the outcomes, making likelihood estimates impossible
# Adding a tiny bit of noise fixes the problem
radon$log_uranium <- rnorm(nrow(radon), radon$log_uranium, 0.05)