chezou

# -*- coding: utf-8 -*-
"""
given a word and visualize near words
original source code is https://github.com/nishio/mycorpus/blob/master/vis.py
"""
import word2vec_boostpython as w2v
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import matplotlib.font_manager

tokestermw

"""
preprocess-twitter.py

python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu

Translation of Ruby script to create features for GloVe vectors for Twitter data.

jcheng5

library(ggplot2)
library(shiny)

# Call ggbrush with a ggplot2 object, and the dimensions which
# should be brushed (try "xy" for scatter, "x" for histogram).
# The plot will show in RStudio Viewer or your web browser, and
# any observations selected by the user will be returned.
ggbrush <- function(plotExpr, direction = c("xy", "x", "y")) {
  
  # See below for definition of dialogPage function

fonnesbeck

Horizons in Probabilistic Programming and Bayesian Analysis

Representations:

• Hierarchical models
• Hidden Markov models
• Graphical models
• Non-parametric Bayes (distributions over functions)

Inference Approaches:

bishboria

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

hrbrmstr

leeper

# Check URLs in a document

## This code will extract URLs from a text document using regex,
## then execute an HTTP HEAD request on each and report whether
## the request failed, whether a redirect occurred, etc. It might
## be useful for cleaning up linkrot.

if (!require("httr")) {
    install.packages("httr", repos = "http://cran.rstudio.com/")
}

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)))

mattblackwell

group.mean <- function(x, group) {
  out <- tapply(x, group, mean, na.rm = TRUE)
  out[group]
}

data(state.fips, package = "maps")
state.fips <- unique(state.fips[,c("fips","abb")])
state.fips$abb <- as.character(state.fips$abb)
state.fips <- rbind(state.fips, c(2, "AK"))
state.fips <- rbind(state.fips, c(15, "HI"))

benmarwick

# metadata
data_source <- "https://docs.google.com/spreadsheets/d/1xa0iLqYKz8x9Yc_rfhtmSOJQ2EGgeUVjvV4A8LsIaxY/htmlview?sle=true#gid=0"
data_collectors <- "Jeremy Pressman (@djpressman, U of Connecticut) and\nErica Chenoweth (@EricaChenoweth, U of Denver)"
code_gist <- "https://gist.github.com/benmarwick/a1ac9c7235ebef542824512162ff2f44"


#  ------------------------------------------------------------------------

# read in data from google sheets to get a data frame