Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

factor - FGNcov - flipaxes - free - fwttin
factor factor performs a Factor Analysis for x (principal component, principal axes). For each method you can interactively between two different criteria for the factors. At the end you get a draftman plot of the the chosen factors.
factorial Computes the factorial for all values in an array.
FARcov Procedure for the calculation of the covariance function of a fractional ARIMA(0,d,0) model, uses the Fast Fourier Transform
FARgk Calculation of the gk for Simulation of fractional ARIMA(0,d,0) with FARx
FARx Simulation of a series of fractional ARIMA(0,d,0) by a method proposed by Davies and Harte
fastint fastint estimates the additive components and their derivatives of an additive model using a modification of the integration estimator plus a one step backfit, see Kim, Linton and Hengartner (1997) and Linton (1996)
FBMx Calculation of a series of fractional Brownian motion, after a method proposed by Davies and Harte
fft fft computes the Fast Fourier Transformation of a complex vector.
fgenci auxiliary quantlet for cointegration
FGNchol Simulation of a series standard fractional Brownian Motion by the exact cholesky decomposition
FGNcov Calculation of the aotocovariance function of fractional Gaussian noise
FGNgk Calculation of a series gk for the calculation of fractional Gaussian noise by a method proposed by Davies and Harte
FGNx Simulation of a series of fractional Gaussian noise (not standard fractional Gaussian noise) by a method proposed by Davies and Harte
filltime generates vector of time points starting from t with length n and granulation gran (default month). The difference between two timepoints is given by step.
final Supporting Quantlet for cartsplit
finalshow shows the final visualization of the network
financemain loads the libraries needed for the macros in finance
financetest self test of extreme value module
fittail transforms location and scale parameter of GP distribution from fit to exceedances to tail fit.
fivenum computes the five number summary consisting of the minimum and maximum, the quartiles and the median.
flipaxes adds a menubutton th change the axes of a display
floatinf provides information about real numbers within the interval [.5,0) in the form of x=a*10^b, b is bounded by -20
floor floor gives the next smaller integer value of the elements of an array.
fncovci auxiliary quantlet for cointegration
fnrici auxiliary quantlet for cointegration
fnyzci auxiliary quantlet for cointegration
fnzzci auxiliary quantlet for cointegration
forec2 Forecasting in VAR Models with undifferencing
forecast Forecasting in VAR Models
fracbrown the result*normal(2*p*nu+1) gives a fractional brownian motion with respect to alpha=2*H ( H = Hurst coefficent)
free free removes global objects. It is convenient to delete big objects which consume a lot of memory. If free is invoked without arguments all objects are deleted.
freecolor freecolor deallocates the colors that were allocated by user or by system.

frequencies provides frequency tables for the selected variables of the data set
frequency provides frequency tables for all columns of a matrix. An additional vector of name strings can be given to identify columns by names.
func func loads files and executes them. If necessary the suffix xpl is appended.

fwt fwt computes the Fast Wavelet Transformation of a vector.
fwt2 The algorithm fwt2 is designed for 2 dimensional wavelet transformation. It mainly corresponds to dwt for the one dimensional case. If wished it works with the tensor product of one dimensional wavelet transforms.
fwtin fwtin computes the Fast Wavelet Transformation of all circular shifts of the vector x.
fwtinshift fwtinshift retrieves the wavelet coefficients for a given shift of the Fast Wavelet Transformation of all circular shifts (fwtin) of a vector.
fwttin Generates the translation invariant estimate of x with automatic hardthresholding. It is well-known that nonlinear wavelet estimators are not translation-invariant: if we shift the underlying data set by a small amount, apply nonlinear thresholding and shift the estimator back, then we usually obtain an estimator different from the estimator without the shifting and backshifting operation. To get rid of this, we average over several estimators obtained by shifting, nonlinear thresholding and backshifting.

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 17.8.2000