kylebgorman

#!/usr/bin/env python
# 
# TIMIT+.py: make TIMIT bearable to use
# Kyle Gorman <[email protected]
# 
# To use this: 
# 1. place in the same directory as a copy of TIMIT
# 2. install SoX and textgrid.py
# 3. run ./TIMIT+.py
# 

kylebgorman

// A cool way to draw a martini glass; this was on the final of CS 115 at U of I

#include <stdio.h>

void drawcup(int N, int offset) {

    // base case
    if (offset > N)
        return;

kylebgorman

#!/usr/bin/env python
# 
# Copyright (c) 2012 Kyle Gorman
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

kylebgorman

/** 
 *  Copyright (C) 2012 Kyle Gorman
 *  All rights reserved.
 *
 *  Redistribution and use in source and binary forms, with or without
 *  modification, are permitted provided that the following conditions are met:
 *
 *  1. Redistributions of source code must retain the above copyright notice, 
 *     this list of conditions and the following disclaimer.
 *  2. Redistributions in binary form must reproduce the above copyright 

kylebgorman

syn on
set ruler
" tab stuff
set expandtab
set hlsearch
set tabstop=4
" scrolling
set scrolloff=5
" backspace over everythign
set backspace=indent,eol,start

kylebgorman

# autoloess.R: compute loess metaparameters automatically
# Kyle Gorman <[email protected]>

aicc.loess <- function(fit) {
    # compute AIC_C for a LOESS fit, from:
    # 
    # Hurvich, C.M., Simonoff, J.S., and Tsai, C. L. 1998. Smoothing 
    # parameter selection in nonparametric regression using an improved 
    # Akaike Information Criterion. Journal of the Royal Statistical 
    # Society B 60: 271–293.

kylebgorman

#!/usr/bin/env Rscript
# lda-match.R: Perform group matching via backward selection using a heuristic based on Fisher's 
# linear discriminant
# Kyle Gorman <[email protected]>

require(MASS)

lda.match <- function(x, grouping, term.fnc=univariate.all) {
    # Create a matched group via backward selection using a heuristic
    # based on Fisher's linear discriminant. Observations are removed

kylebgorman

# compute means
mu <- mean(covariate)
mu.group <- tapply(covariate, condition, mean)
# locate observations driving the correlation
correlate <- ((covariate < mu.group) == (mu.group < mu))
# compute order, filtering out non-correlates
ord <- order(covariate)
ord <- ord[correlate[ord]]
# split on condition and reverse for those above
search.space <- split(ord, condition[ord])

kylebgorman

#!/usr/bin/env python -O
#
# Copyright (c) 2013 Kyle Gorman
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to

kylebgorman

# Copyright (c) 2013-2022 Kyle Gorman
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#