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- table2
- computes a two way table from two-dimensional data.
- tableN
- tableN returns a N way table for N-dimensional data.
- TailCoeffCopula
- calculates the lower and upper tail-dependence coefficients for various copulae.
- TailCoeffEstimation
- estimates the upper or lower tail-dependence coefficient via a bivariate empirical copula.
- TailCoeffEstimElliptical
- estimates the upper (=lower) tail-dependence coefficient for an elliptically contoured distribution.
- TailCoeffLookup
- auxiliary table-search quantlet for TailCoeff* quantlets.
- taills
- Estimates the tail index of fat-tailed distributions
- tan
- Returns the tangent in radian of the elements of an array.
- tanh
- Returns the hyperbolic tangent of the elements of an array.
- tautstring
- auxiliary quantlet for denreg and pmreg, core of taut string method
- tdiff
- calculates the d'th difference of the time series x. Furthermore, it allows to calculate the s'th seasonal difference. In terms of the backshift (or lag) operator B, the series y_t = (1-B^s)^d x_t is generated. The default values are s=1 and d=1, which generate the first differences of the series x
- tgarsim
- plots the difference between call option prices calculated by the Black & Scholes model and between risk neutral GARCH or Treshold GARCH models.
- timeplot
- Generates a display that shows the time series x in multiple windows with user-specified maximum length per window. It is possible to label the abscissa in a yearly format. However, you can not specify the periodicity of the labels (in that case, use timeplotlabel).
- timeplotlabel
- Generates a display that shows the time series x. The abscissa is labelled automatically, when the labels have the format year:1. One may specify the periodicity of the labels.
- timesmain
- loads the libraries needed for the quantlets in times
- timestest
- executes some tests for the quantlets defined in times library
- TimeVarAddModel2
- estimates a dynamic factor model from the form: yt = m0(u) + bt1*m1(u) + bt2*m2(u)... btL*mL(u), where m0 to mL are 2-dimensional invariant basis functions on the grid u and bt0=1. bt1 to btL are scalar weights depending on time T. After estimation, the functions m are orthogonalized under the empi
- tobit
- 2-step estimation of a Tobit model
- tourasimov
- Computes a rotation matrix based on the paper by Asimov (1985).
- tourlittle
- Computes a little tour rotation matrix.
- tourrandom
- Computes a random rotation matrix.
- trans
- trans transposes matrices. This function is equal to the operator '
- transform
- Transforms the given dataset.
- tree
- generates from a binary tree an output for plotting.
- tri
- tri computes the triweight kernel, multivariate
- trian
- trian computes the triangular kernel, multivariate
- trimper
- trims a given percentage of a (binned) data matrix
- ttest
- runs a t-test
- tw1d
- The teachware quantlet tw1d shows a histogram of the user-defined data and offers an interactive visual analysis of this data by means of box plots (for mean and median) and QQ-plots. Transformations may be applied to the data in order to study the change in distribution and box plots.
- twaremain
- loads necessary quantlets in order to execute the teachware tware.lib.
- twaretest
- Executes some tests for the quantlets defined in the teachware tware.lib.
- twavemain
- Starts the twave lesson when library("twave") is called and generates the global constant twavec which allows to jump immediately to a single task.
- twboxcox
- allows to find interactively the best parameter for your data for a Box-Cox transformation.
- twboxcoxintroduction
- generates the introductory text for twboxcox
- twboxcoxloop
- main loop for twboxcox
- twclt
- teachware quantlet twclt shows a discrete four point distribution and simulates repeated sampling from this apparently non normal distribution. The variation of the observed mean values around the true mean value (standardized by scale) is shown in a plot. The user may interactively change the numb
- twles1
- Shows the functions approximation by wavelets. You can choose between different wavelet base, different number of father wavelet coefficients, different functions and different views to the mother wavelet coefficients.
- twles2
- Compares the data compression of wavelets with fourier basis. You can choose between different wavelet base, different number of father wavelet coefficients, different functions and different views to the mother wavelet coefficients.
- twles3
- Compares the approximation of sines with different frequencies by wavelets. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
- twles4
- Shows the approximation of a sine function which changes its frequency. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
- twles5
- Shows how a hard threshold behaves on the true function and the true function plus noise. You can choose between different wavelet base, different number of father wavelet coefficients, different functions different views to the mother wavelet coefficients, hard threshold by hand and automatically.
- twles6
- Shows how a soft threshold behaves on the true function and the true function plus noise. You can choose between different wavelet base, different number of father wavelet coefficients, different functions different views to the mother wavelet coefficients, soft threshold by hand and automatically.
- twles7
- Shows how a hard threshold behaves on an image and an image plus noise. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
- twles8
- Shows the father and mother wavelet for a given basis. You can choose between different wavelet base.
- twles9
- Shows in the left window the true function plus noise and in the right a translation invariant estimator with k=4*log_2(n) shifts.
- twlesson
- Starts the twave lessons either interactively or a specific lesson.
- twlinreg
- teachware quantlet twlinreg gives visual insight into how least squares simple linear regression works, and the relationship between the regression of Y on X, X on Y, and total regression.
- twnormalize
- teachware quantlet twnormalize shows the distribution of binomials B(n1, p), B(n2, p) and B(n3, p) with increasing n1, n2, n3. One may shift the distribution by the mean value and divide by the standard deviation in order to study the normalizing effect. In addition a normal density may be graphica
- twpearson
- the teachware quantlet twpearson gives a visual demonstration of the form of the Pearson correlation coefficient. In particular, it shows why the product moment gives a measure of "dependence", and why it is essential to "normalize", i.e. to subtract means, and divide by standard deviations, to pre
- twpvalue
- teachware quantlet twpvalue computes the p-value of a B(n, p) distribution
- twrandomsample
- the teachware quantlet twrandomsample asks for a distribution of the numbers {1, 2, 3, 4}, displays a bar chart of the entered values and calculates a test for H0: p{2,3} = 0.5, the hypothesis of uniform distribution.
- twskew
- teachware quantlet shows effects on skewness and kurtosis by contamination of a normal distribution
- twtest
- teachware quantlet shows error type I and II in testing simple hypotheses

(C) MD*TECH Method and Data Technologies, 05.02.2006 | Impressum |