cabs | Absolute value of a complex array |
cadd | Complex addition of two arrays |
callbull | calculates the results of a Bull Call Spread for the context of option pricing |
canbw | does the canonical bandwith transformation of a bandwith value of kernel K1 into an equivalent bandwidth for Kernel K2. |
canker | does the canonical bandwith transformation of a bandwith value of kernel K1 into an equivalent bandwidth for Kernel K2. |
cartcv | Performs cross validation for the CART: subtracts from the data in a given number of ways a test set, with the rest of the data a regression tree is formed and a sequence of subtrees is pruned from the initial tree. For each tree, the test set is used to calculate the prediction error. |
cartsplit | Computes a regression tree. |
cartsplitopt | sets optional parameters for cartsplit (spliting of for classification and regression trees) |
cartsplitout | |
case |
Inside a switch-endsw block case controls the execution of an alternative. If the condition of case is true, the following block is executed similar to an if-endif statement. The keyword break serves as end marker of case and leaves the switch block at the position of endsw. When break is omitted, the next consecutive case is processed. If the program's counter comes to default, the following block is executed in any case.
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categorize | creates dummy variables from a data with respect to distinct realizations. The default reference category is the minimal value in each column. Alternatively, categorization can be done by giving a value or the index (rank among the realizations) in a column. |
cceil | Computes ceil for a complex array |
cconj | Conjugated array |
ccos | Complex cosine |
ccosh | Complex hyperbolic cosine |
cdfb | Returns the values of the beta-distribution function with parameters a and b for the elements of an array. |
cdfbin | computes the cumulative distribution function of a binomial distribution |
cdfc | Returns the values of the chi-quare distribution function with d degrees of freedom for the elements of an array. |
cdff | Returns the values of the F-distribution function with d1 and d2 degrees of freedom for the elements of an array. |
cdfn | Returns the values of the standard normal distribution function for the elements of an array. |
cdft | Returns the values of the t-distribution function with d degrees of freedom for the elements of an array. |
cdfx | cdfx returns the value of the extreme value and generalized Pareto distribution functions for elements of a vector. |
cdiv | Complex division |
ceil | Returns the smallest integer value greater or equal to each element of an array. |
cexp | Complex exponential |
cfc1diff | Forecasting undifferenced time series in VAR models |
cfloor | Computes floor for a complex array |
changename | changes the names of selected variables |
changetype | changes the types of selected variables and shows if they excluded or included |
char | Convert numbers to ASCII characters in strings. |
chbase | Changes interactively the wavelet coeffients. You may choose between Haar, Daubechies2 and Coiflet2. |
chfunc | Generates specific functions (Jump, Up-down, Sine, Freq. sine and Doppler). If all entries of sel are zero then you can choose interactively the function otherwise the selected function will be generated. |
chol | computes the Cholesky decomposition of a symmetric, positive definite matrix |
chold | The function chold is calculating the triangularisation and the Cholesky decomposition of x into matrices b and d, so that b'*d*b = x. |
choosegroup | selction of group variables (discrete type) |
choosevariable | selction of variables |
choosevariable2 | selction of variables to transform |
choosevariablep | selction of variables in regression context |
choosevariableX | selction of X variables in regression context |
chview | Enforces a specific view of the wavelet mother coefficients (Standard, Ordered, Circle and Partial sum). If none is selected then the old view will be returned. |
ciirboot | Computes two sided bootstrap confidence intervals for impulse responses for a K-dimensional VAR(p) by resampling the estimated residuals. The confidence intervals are computed using Halls (1993 ???) and Efrons (1992 ???) methodology. |
cimag | Extracts imaginary part of a complex array |
cinv | Complex inverse matrix |
cir | displays the yield curve for given parameters under the model of Cox/Ingersoll/Ross (1985) |
cln | Complex natural logarithm |
cmatdiv | Computes the complex solution of A x = b |
cmatmul | Computes the complex matrix multiplication of X and Y |
cmul | Complex multiplication |
cobfidenceb | computes the 95% confidence intervals for each beta after regression analysis |
coeffba | auxiliary macro for full VAR model analysis |
coeffest | estimates the coefficients of a full VAR model |
coeffss | estimates parameters of Subset VAR |
collinearity | performs a collineatity diagnostic after regression analysis and shows the eigenvalues and condition indices of the independent variables |
colorcube | Displays a multi-color cube |
cols | Returns the number of columns in an array. |
committee |
This macro computes a committee of networks with nets of
the form single
layer feedforward perceptron. The macro can be used alone
or in connection with the library ISTA. The standalone version
also needs the parameter data. Just choose 0 for the input.
The number of nets to build the committee can be chosen.
The data will be splitted with this number to build the
different datasets. The weight for the cases for the training
of the net can be chosen, the numbers of hidden units and
additional information concerning the weights of the units.
Different optional parameters can be chosen to
build the architektur of the network. The choice holds for
every single net. The default values are chosen in
order to solve a linear regression problem. The optional
parameters constits of 8 values. Boolean values for linear
output, entropy error function, log probability models and
for skip connections (direkt links). The fifth values is
the maximum value for the starting weights, the sixth is the
weight decay, the seventh the maximum number of iterations
and the the last value generates the output concerning the
architekur of the net if it is equal to one.
The output consits of the Error and MSE of the single nets
and for all cases. Additionally the R^2 for the average of
the nets and the R^2 of the committee are shown.
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comp | Checks whether an object has a specific component or not. If the first argument is a string, the object with the specified name is regarded as a list object. |
complex | Generates a complex array |
confidencey | computes the 95% confidence intervals for the unstandardized predicted values after regression analysis |
conting | crosses two categorical variables (for instance partitions from cluster analysis) and builds up contingency table |
contmax | computes a linkage table between the rows and columns of a contingency table by maximum value of correspondence. The number of correspondences is the minimum of number or dimensions of the contingency table. |
contour2 | contour2 computes lines and points for a contourplot of a three dimensional dataset at level c |
contour3 | contour3 computes lines and points for a contourplot of a four dimensional dataset at level c |
conv |
conv performs the convolution of a step kernel function and a function over a p-dimensional equidistant grid.
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cor2dist | transforms the values of the upper triangle of a correlation matrix into distances, and it stores these distances into a vector regarding the sequence described in agglom |
corr | Computes the correlation (Bravais-Pearson) structure of a given array. |
corresp | corresp executes Correspondence Analysis which analyses and describes a contingency table cross-tabulations) in terms of a reduced number of dimensions. Correspondence Analysis can be viewed as finding the best simultaneous representation of two sets that comprise the rows and columns of a data matrix, in order to obtain a summary description for large tables (cross-tabulations). This technique can be helpful in finding important underlying characteristics which might not be directly observed in the data. Graphical visualizations provide an insight and understanding tool for interpreting the data. |
corrint | computes the correlation integral for time series |
cos | Returns the cosine in radian of the elements of an array. |
cosh | Returns the hyperbolic cosine of the elements of an array. |
cosi | cosi computes the cosine kernel, multivariate |
countNaN | Counts how many missing values (NaN) are in an array. |
countNotNumber | Counts how many elements of an array are missing values, infinity or -infinity (NaN,Inf or -Inf). |
cov | Computes the covariance structure of a given array. |
covabc | Covariance matrix of C=A*B, Reduced Rank VAR Model |
covabrr | covariance matrix A*B, reduced rank VAR model |
covarr | covariance matrix A, reduced rank VAR model |
covbrr | Covariance matrix B, reduced rank VAR Model |
covcheck | checks if the covariance matrix is singular |
covfore2 | Computes forecast MSE matrix for undiff. time series |
covforec | calculates the forecast MSE matrix for VAR models |
covmatrix | computes the covariance matrix for beta after regression analysis |
covmlrr | covariance matrix used for reduced rank models |
covmwgen | generates covariance matrix of the mean in VAR |
covres | auxiliary macro for full VAR models |
cplot | Plot of x and y in absolute space |
cplotfunc | Plots a function of x and y-space |
cpolar | Complex numbers in polar coordinates |
creal | Extracts real part of an complex array |
createcolor |
createcolor allocates the colors for user
|
createdisplay |
createdisplay create a display for further plotting the datasets or texts.
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createportnumber | |
criterss | calculates selection criteria for Subset VAR |
crosstable | computes pairwise crosstables from all columns of a data matrix, gives the result of a Chi-square independence test and computes contingency coefficients. |
csin | Complex sine |
csinh | Complex sine hyperbolicus |
csort | csort sorts the rows of a complex matrix with respect to the absolute value of the complex numbers. If a column c is specified the rows of the matrix will be ordered with respect to the elements of column c in ascending (descending) order. |
csortcol | sorts with respect to either a real part of a column or an imaginary part of a column c. If 1 <= c <= cols(xr) it sorts after the real part of x, if cols(xr) < c <= cols(xr)+cols(xi) it sorts the imaginary part after column c-cols(xr). |
csqrt | Complex squareroot |
csub | Complex subtraction two arrays of complex numbers |
ctan | Complex tangens |
ctanh | Complex tangens hyperbolicus |
cumprod | cumprod computes the cumulative product of the elements in an array regarding a given dimension. |
cumsum | cumsum computes the cumulative sum of the elements an array regarding a given dimension. |
cv | runs a cross validation over the hidden units |
cvdec | runs a cross validation over the weight decay |