mblondel

# Authors: Mathieu Blondel, Vlad Niculae
# License: BSD 3 clause

import numpy as np


def _gen_pairs(gen, max_iter, max_inner, random_state, verbose):
    rng = np.random.RandomState(random_state)

    # if tuple, interpret as randn

wmay

---
title: "Latex engine test"
author: "William May"
date: "December 1, 2019"
output: pdf_document
---

```{r setup, include=FALSE}
library(knitr)
opts_chunk$set(echo = TRUE)

stephensrmmartin

functions {
  real matrix_f_lpdf(matrix cov, real nu, real delta){
    int k = cols(cov);
    return(lmgamma(k, (nu + delta + k - 1)/2) - (lmgamma(k, nu/2) + lmgamma(k, (delta + k - 1)/2) + log(1)) + log_determinant(cov)*((nu -k - 1)/2) - (nu + delta + k - 1)/2 * log_determinant(cov + diag_matrix(rep_vector(1, k))));
  }
  
  real matrix_f_fast_lpdf(matrix cov, real nu, real delta){
    int k = cols(cov);
    real log_det_cov = 2*sum(log(diagonal(cholesky_decompose(cov))));
    real I_Sig_log_det = 2*sum(log(diagonal(cholesky_decompose(diag_matrix(rep_vector(1,k)) + cov))));

khakieconomics

data {
  int N; // number of individuals
  int T; // number of time periods
  matrix[N, T] Y; // outcome matrix; missing entries set to -9.0
  int K; // rank of matrix
}
parameters {
  matrix[N, K] M; // individual loadings
  matrix[T, K] U; // time factors
  real<lower = 0> sigma; // error scale

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)

ahalterman

import spacy
nlp = spacy.load("en_core_web_lg")

with open("scraped.json", "r") as f:
    news = json.load(f)
news = [i['body'] for i in news]
processed_docs = list(nlp.pipe(news))

verb_list = ["launch", "begin", "initiate", "start"]
dobj_list = ["attack", "offensive", "operation", "assault"]

bfbraum

# Template for running and plotting a very simple agent-based model in R
# Professor Bear Braumoeller, Department of Political Science, Ohio State

# This code creates a 20x20 grid of 0s and 1s, which represent values of some
# variable held by agents in those cells. It then chooses two adjacent cells,
# the first at random and the second at random from among the first cell's
# neighbors, and applies a simple rule -- the first cell takes on the value
# of the second. It iterates this cell selection and rule application 1,000
# times, displays the result, and tracks the fraction of 1s in the matrix
# over time.

rmcelreath

# functions for plotting garden of forking data plots

library(rethinking)

polar2screen <- function( dist, origin, theta ) {
  ## takes dist, angle and origin and returns x and y of destination point
  vx <- cos(theta) * dist;
  vy <- sin(theta) * dist;
  c( origin[1]+vx , origin[2]+vy );
}

mbjoseph

# Simulation script for factor analysis ala Leung & Drton (2016) ----------
library(rstan)
library(bayesplot)

m <- 5 # dimension of observed data (e.g., # traits)
k <- 2 # number of latent factors
n <- 100 # number of sample units (e.g., # species)

# residual variance matrix (is diagonal)
Omega <- diag(.3 + abs(rnorm(m, sd = .3)))