casallas

• RWeka: wrapper for Weka, they provide convenience functions for some of weka's classifiers/regressors, e.g. J48. The others have a more complex procedure to make the call. http://cran.r-project.org/web/packages/RWeka

• caret: wrapper for many classifiers and predictors. One can also create a wrapper for a custom classifier. Their [r-forge website]http://caret.r-forge.r-project.org/] is a nice place to start, there is also a slideshow tutorial by Max Kuhn and even a book (which I haven't read BTW).

• C50: The C5.0 tree classifier (not sure which advantages it has over C4.5) http://cran.r-project.org/web/packages/C50/C50.pdf

casallas

NHST

• Descriptions and myths: http://yatani.jp/HCIstats/NHST
• Statistical significance
  • Marginal significance: http://andrewgelman.com/2013/11/06/marginally-significant/
  • Gelman: p-values and multiple comparisons http://www.slate.com/articles/health_and_science/science/2013/07/statistics_and_psychology_multiple_comparisons_give_spurious_results.single.html
  • Gelman: vs. Bayesian inference http://andrewgelman.com/2013/10/29/talk-amsterdam-tomorrow-wed-29-oct-can-use-bayesian-methods-resolve-current-crisis-statistically-significant-research-findings-dont-hold/
  • Gelman. (2013) The problem with p-values is how they're used. http://www.stat.columbia.edu/~gelman/research/published/murtaugh2.pdf
    • (note that Gelman says that the same problem can be found on the usage of AIC and CIs). Some background on this article can be found in this blog post
  • Dance of the p-values https://www.youtube.com/watch?v=ez4DgdurRPg

casallas

Package	Ordinal	Multinomial	Partial proportional odds	Scale Effects	Random Effects
nnet	no	multinom	N/A	N/A	No
MASS	polr	No	No	No	No
ordinal	clm/clm2	all X's in nominal (may not converge)	offending X's in nominal	via scale	clmm/clmm2
VGAM	yes	?	?	?	No
MCMCglmm	yes	?	?	?	?
Mixcat	yes	?	?		npmlt
mlogit	?	mlogit	N/A	N/

casallas

# t-test
## t(df) = t.comp, p < alpha
## Compare computed t to tabulated t
qt(1-alpha, df) < t.comp

# F-test
## F(df1, df2) = f.comp, p < alpha
## Compare computed f to tabulated f
qf(1-alpha, df1, df2) < f.comp

casallas

#!/usr/bin/env ruby

ARGV.each do|arg|
  `wget https://github.com/textmate/#{arg}.tmbundle/archive/master.zip -O #{arg}.tmbundle.zip`
  `unzip #{arg}.tmbundle.zip`
  `mv #{arg}.tmbundle-master ~/Library/Application\\ Support/TextMate/Managed/Bundles/#{arg}.tmbundle`
  `rm #{arg}.tmbundle.zip`
end

casallas

casallas

function wLoad()
{
	/*Calculate the svg bounds in client terms once the window is loaded*/
	svgCBound = backRect.getBoundingClientRect();
	/*Go to the question start so that less whitespace is shown, in case of small resolutions*/
	location.href = "#questStart";
	/*Play the animation*/
	replay();
}
function checkBounds()

casallas

#!/bin/bash

# Install homebrew (taken from http://brew.sh/)
ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

# Add brew to your path
echo "# Prepend homebrew bins to your PATH" >> ~/.bash_profile
echo "export PATH=/usr/local/sbin:/usr/local/bin:/usr/local/Library/Contributions/examples:$PATH" >> ~/.bash_profile

# Tap casallas/vr

casallas

# The python built-in option py_compile
python -m py_compile *.py
# Check for errors in python files
pylint -E *.py
# Check for warnings, but still ignore some aesthetic ones
pylint --include-ids=y -d W0312 -d C0103 -d C0111 -d C0301 -d C0324 -d W0614 -d W0401 *.py

casallas

WereMac:Formula juan$ brew install -v autogen
==> Downloading http://ftpmirror.gnu.org/autogen/rel5.14/autogen-5.14.tar.gz
File already downloaded in /Users/juan/Library/Caches/Homebrew
/usr/bin/tar xf /Users/juan/Library/Caches/Homebrew/autogen-5.14.tar.gz
==> ./configure --disable-debug --disable-dependency-tracking --prefix=/usr/local/Cellar/autogen/5.14
./configure --disable-debug --disable-dependency-tracking --prefix=/usr/local/Cellar/autogen/5.14
checking build system type... x86_64-apple-darwin10.8.0
checking host system type... x86_64-apple-darwin10.8.0
checking target system type... x86_64-apple-darwin10.8.0
*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*