XXIV Computer Programs by Chapter and Section 12.4 fourn fast Fourier transform in multidimensions 12.5 rlft3 FFT of real data in two or three dimensions 12.6 fourfs FFT for huge data sets on external media 12.6 fourew rewind and permute files,used by fourfs 13.1 convlv convolution or deconvolution of data using FFT 132 correl correlation or autocorrelation of data using FFT 13.4 spctrm power spectrum estimation using FFT 13.6 memcof evaluate maximum entropy(MEM)coefficients http://www.nr. readable files Permission is 13.6 fixrts reflect roots of a polynomial into unit circle 13.6 predic linear prediction using MEM coefficients 83 13.7 evlmem power spectral estimation from MEM coefficients granted for 13.8 period power spectrum of unevenly sampled data 13.8 fasper power spectrum of unevenly sampled larger data sets (including this one) 13.8 spread extirpolate value into array,used by fasper 11-800-872 13.9 dftcor compute endpoint corrections for Fourier integrals from NUMERICAL RECIPES IN 13.9 dftint high-accuracy Fourier integrals 13.10 wt1 one-dimensional discrete wavelet transform 13.10 daub4 Daubechies 4-coefficient wavelet filter 13.10 pwtset initialize coefficients for pwt (North America tusers to make one paper THE 13.10 pwt partial wavelet transform 1988-1992 by Cambridge University Press.Programs 13.10 wtn multidimensional discrete wavelet transform only),or 14.1 moment calculate moments of a data set send to any server computer,is strictly prohibited. copy for their 14.2 ttest Student's t-test for difference of means 14.2 avevar calculate mean and variance of a data set 14.2 Copyright(C) tutest Student's t-test for means,case of unequal variances 14.2 tptest Student's t-test for means,case of paired data 14.2 ftest F-test for difference of variances email to directcustsen 14.3 chsone chi-square test for difference between data and model 14.3 chstwo chi-square test for difference between two data sets 14.3 ksone Kolmogorov-Smirnov test of data against model v@cambri 14.3 kstwo Kolmogorov-Smirnov test between two data sets 14.3 probks Kolmogorov-Smirnov probability function 1988-1992 by Numerical Recipes ART OF SCIENTIFIC COMPUTING(ISBN 0-521-43108-5) 14.4 cntab1 contingency table analysis using chi-square 14.4 cntab2 contingency table analysis using entropy measure 14.5 pearsn Pearson's correlation between two data sets 14.6 spear Spearman's rank correlation between two data sets Software. 14.6 crank replaces array elements by their rank ridge.org (outside North America). 14.6 kendl1 correlation between two data sets,Kendall's tau 14.6 kend12 contingency table analysis using Kendall's tau 14.7 ks2d1s K-S test in two dimensions,data vs.model 14.7 quadct count points by quadrants,used by ks2d1s 14.7 quadv1 quadrant probabilities,used by ks2d1s 14.7 ks2d2s K-S test in two dimensions,data vs.data 14.8 savgol Savitzky-Golay smoothing coefficientsxxiv Computer Programs by Chapter and Section Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copyin Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) g of machine￾readable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America). 12.4 fourn fast Fourier transform in multidimensions 12.5 rlft3 FFT of real data in two or three dimensions 12.6 fourfs FFT for huge data sets on external media 12.6 fourew rewind and permute files, used by fourfs 13.1 convlv convolution or deconvolution of data using FFT 13.2 correl correlation or autocorrelation of data using FFT 13.4 spctrm power spectrum estimation using FFT 13.6 memcof evaluate maximum entropy (MEM) coefficients 13.6 fixrts reflect roots of a polynomial into unit circle 13.6 predic linear prediction using MEM coefficients 13.7 evlmem power spectral estimation from MEM coefficients 13.8 period power spectrum of unevenly sampled data 13.8 fasper power spectrum of unevenly sampled larger data sets 13.8 spread extirpolate value into array, used by fasper 13.9 dftcor compute endpoint corrections for Fourier integrals 13.9 dftint high-accuracy Fourier integrals 13.10 wt1 one-dimensional discrete wavelet transform 13.10 daub4 Daubechies 4-coefficient wavelet filter 13.10 pwtset initialize coefficients for pwt 13.10 pwt partial wavelet transform 13.10 wtn multidimensional discrete wavelet transform 14.1 moment calculate moments of a data set 14.2 ttest Student’s t-test for difference of means 14.2 avevar calculate mean and variance of a data set 14.2 tutest Student’s t-test for means, case of unequal variances 14.2 tptest Student’s t-test for means, case of paired data 14.2 ftest F-test for difference of variances 14.3 chsone chi-square test for difference between data and model 14.3 chstwo chi-square test for difference between two data sets 14.3 ksone Kolmogorov-Smirnov test of data against model 14.3 kstwo Kolmogorov-Smirnov test between two data sets 14.3 probks Kolmogorov-Smirnov probability function 14.4 cntab1 contingency table analysis using chi-square 14.4 cntab2 contingency table analysis using entropy measure 14.5 pearsn Pearson’s correlation between two data sets 14.6 spear Spearman’s rank correlation between two data sets 14.6 crank replaces array elements by their rank 14.6 kendl1 correlation between two data sets, Kendall’s tau 14.6 kendl2 contingency table analysis using Kendall’s tau 14.7 ks2d1s K–S test in two dimensions, data vs. model 14.7 quadct count points by quadrants, used by ks2d1s 14.7 quadvl quadrant probabilities, used by ks2d1s 14.7 ks2d2s K–S test in two dimensions, data vs. data 14.8 savgol Savitzky-Golay smoothing coefficients