even4void

#!/bin/bash

GIST_RECV_URL=http://gist.github.com/%s.txt;
GIST_POST_URL=http://gist.github.com/gists;
GIST_DEL_URL=http://gist.github.com/delete;
GIST_DESC_URL=http://gist.github.com/gists/%s/update_description;
GITHUB_USER=`git config --global github.user | tr -d \\n`;
GITHUB_TOKEN=`git config --global github.token | tr -d \\n`;
GETINDEX="gistfile1";

even4void

# Time-stamp: <2010-08-09 12:10:54 chl>

#
# Some illustrations of splines fitting.
#
# The example used throughout this script comes from
# Kooperberg & LeBlanc, Multivariate Nonparametric Regression,
# in _High-Dimensional Data Analysis in Cancer Research_
# (Ed. X. Li and R. Xu), Chapter 3, p. 45.
#

even4void

# Time-stamp: <2010-08-10 11:51:15 chl>

#
# Illustration of LASSO penalization in multiple regression.
#
# The exemple is largely inspired from Harezlak, Tchetgen
# and Li, Variable selection in regression -- Estimation,
# Prediction, Sparsity, Inference,
# in _High-Dimensional Data Analysis in Cancer Research_
# (Ed. X. Li and R. Xu), Chapter 2, pp. 13--33.

even4void

class Stack:
  def __init__(self):
    self.items = []
  def isEmpty(self):
    return self.items == []
  def push(self, item):
    self.items.append(item)
  def pop(self):
    return self.items.pop()
  def peek(self):

even4void

#!/usr/bin/env ruby
require("gsl")

x = 2.0

P = GSL::Cdf::ugaussian_P(x);
printf("prob(x < %f) = %f\n", x, P);

Q = GSL::Cdf::ugaussian_Q(x);
printf("prob(x > %f) = %f\n", x, Q);

even4void

#! /usr/bin/env ruby
require 'statsample'
sample=200
a=sample.times.collect {rand}.to_scale
b=sample.times.collect {rand}.to_scale
c=sample.times.collect {rand}.to_scale
d=sample.times.collect {rand}.to_scale

ds={'a'=>a,'b'=>b,'c'=>c,'d'=>d}.to_dataset

even4void

#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>

int main(void) {
  char * line = NULL;
  size_t len = 0;

even4void

#!/usr/bin/env python
from pylab import *
  
dt = 0.0005
t = arange(0.0, 20.0, dt)
s1 = sin(2*pi*100*t)
s2 = 2*sin(2*pi*400*t)
  
# create a transient "chirp"
mask = where(logical_and(t>10, t>12), 1.0, 0.0)

even4void

cronbach.alpha <- function(data, na.action="omit", signif.level=.05) {
  # computes Cronbach's Alpha
  # * Input *
  #    data         : a n*k matrix, where each column hold one item responses'
  #                   vector
  #    na.action    : if "omit", then handle missing value in cov/var
  #                   calculation [default]
  #    signif.level : type I error
  # * Output *
  #    a list with statistic and confidence interval

even4void

import graph;
import patterns;

size(300,200,IgnoreAspect);

real xmin=-4,xmax=4;
real ymin=0,ymax=1;
real PI=3.141593;
real sd=.6;
int a=-1, b=1;