RA RB RC RD RE RF RG RH RI RJ RK RL RM RN RO RP RQ RR RS RT RU RV RW RX RY RZ

- randbin
- computes random numbers based on the binomial distribution
- randmix2exp
- generates pseudo random variables with mixture of two exponentials distribution.
- randomize
- Sets the seed of the pseudorandom number generator.
- randomize2
- Sets the seed of the pseudo-random number generator
- randomnumbers
- computes n p-dimensional independent random vectors
- randx
- randx generates a vector of pseudo random variables with extreme value and generalized Pareto distribution.
- ranexp2
- Ranexp2 generates arrays up to eight dimensions of pseudo random variables with the standard exponential distribution.
- rank
- Computes the rank vector of a given vector.
- rankcorr
- computes rank correlation coefficients according to Spearman and Kendall. In the case of ties, corrected versions are computed.
- rankm
- Computes the rank r of a matrix x.
- ranpois
- generates arrays (up to 8 dimensions) of pseudo random variables with Poisson distribution.
- rbfinfo
- shows information about the given network
- rbfload
- loads a saved RBF network from a file
- rbfpredict
- predicts the output of given RBF neural network
- rbfsave
- saves given radial basis function network into the given file
- rbftest
- tests the given rbfnet network
- rbftrain
- trains a radial basis function neural network
- rbftrain2
- trains a radial basis function neural network
- Rdenbest
- evaluates a kernel estimate of an integrated squared density (derivative) using the normal kernel for a (vector of) bandwidth(s) h. This quantlet is a variation of Rdenxest and uses linearly prebinned data for faster computation.
- Rdenxest
- evaluates a kernel estimate of an integrated squared density (derivative) using the normal kernel for a (vector of) bandwidth(s) h.
- rdl1
- Computes RDL1 estimate --- a weighted L1-estimator of y on on continuous variables x and binary variables xdum with weights min(1, p/(RD^2)); RD contains the robust distances obtained by the MVE estimator for x.
- read
- read is a command to read data from a file. Each column of the file will be interpreted as a vector of numbers.
- readascii
- readascii is a command to read ASCII data from a file.
- readcomponent
- internal routine for readlist
- readcond
- reads the data according to a condition which is explicitly stated as a string.
- readcsv
- reads numerical data from a csv file
- readcsvm
- readcsvm reads mixed data from a CSV file
- readevent
- readevent reads a key- or a mouse- event while a program is running. An "event" is a stroke of a key or a click of a mouse button. readevent will be mainly useful for letting XploRe know whether such an event has occured and to get some special information like the coordinates where the mouse click
- readlist
- Reads a composed object as ASCII data from a set of files. All elements of the composed object have to be numerical matrices or textvectors !
- readm
- readm reads mixed data from a file.
- readshow
- shows the visualization of a feedforward neural network
- readshow2
- shows the visualization of a feedforward neural network
- readvalue
- asks for one or more input values via a dialog box and reads them.
- readxls
- reads numerical data from a MS Excel file
- readxlsm
- reads mixed data from a MS Excel file
- reca
- RECA (REgression CAlibration) is a method in which replacing the unobserved x by its expected value E(x|w,z) and then to perform a standard analysis.
- recode
- allocates categories 1,2,...,L to intervals of categories. The upper bounds of the intervals have to be specified. It is an useful tool to join classes and hence to collaps contingency tables.
- reduce
- Deletes all dimensions with only a single component.
- redun
- calculates a single redundance and a redundance vector for dpls quantlet as a measure of goodness.
- regbwcrit
- determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice
- regbwsel
- interactive tool for bandwidth selection in univariate kernel regression estimation.
- regcb
- computes uniform confidence bands with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator. The computation uses WARPing.
- regci
- computes pointwise confidence intervals with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator. The computation uses WARPing.
- regest
- computes the Nadaraya-Watson estimator for univariate regression. The computation uses WARPing.
- regestp
- Nadaraya-Watson estimator for multivariate regression. The computation uses WARPing.
- regxbwcrit
- determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice
- regxbwsel
- interactive tool for bandwidth selection in univariate kernel regression estimation.
- regxcb
- computes uniform confidence bands with a pre-specified confidence level for univariate regression using the Nadaraya-Watson estimator.
- regxci
- computes pointwise confidence intervals with a pre-specified confidence level for univariate regression using the Nadaraya-Watson estimator.
- regxest
- computes the Nadaraya-Watson estimator for univariate regression.
- regxestp
- computes the Nadaraya-Watson estimator for multivariate regression.
- relation
- Computes the relation coefficients (chi^2, contingency, corrected contingency, spearman rank, bravais-pearson) for the data x.
- relationchi2
- Computes the Chi^2 coefficients for discrete data.
- relationcont
- Computes the contingency coefficient for discrete data.
- relationcorr
- Computes the bravais-pearson correlation for metric data.
- relationcorrcont
- Computes the corrected contingency coefficient for discrete data.
- relationrank
- Computes the rank correlation of spearman for ordinal data.
- repa
- computes the radial symmetric epanechnikov kernel
- replace
- Replaces values by other values.
- replicdata
- replicdata reduces a matrix x to its distinct rows and gives the number of replications of each rows in the original dataset. An optional second matrix y can be given, the rows of y are summed up accordingly. replicdata does in fact the same as discrete but provides an additional index vector to id
- resclass
- shows the residuals in case of the classification
- resclass2
- shows the residuals in case of the classification
- reshape
- reshape transforms an array into a new one with given dimensions.
- residuen
- calculates residuals for VAR models
- resplots2
- auxiliary quantlet for spdest2, plots the residuals.
- resreg
- shows the residuals in case of the regression
- resreg2
- shows the residuals in case of the regression
- rev
- reverts the order of the rows of the input matrix
- rgb2hls
- converts an RGB colour vector into an HLS vector.
- rgb2lab
- converts an RGB colour vector into an L*a*b* vector.
- rgenss
- generates the restriction matrix for Subset VAR
- rICfil
- Calculates a filtered time serie (uni- or multivariate) using a robust, recursive Filter based on LS-optimality, the rLS-filter. Additionally to the Kalman-Filter one needs to specify the degree of robustness one wants to achieve; this is done either by specifying a clipping height or by specifying
- rici
- auxiliary quantlet for cointegration
- rint
- rint gives the next nearest integer value of the elements of an array.
- rkernpq
- Computes the radial kernel of the form: C (1-r^q)^p.
- rlogspline
- random samples from a logspline density - auxiliary quantlet for logspline density estimation
- rlsbnorm
- Auxiliary routine for rlsfil: solves E [ |X-MYw_b(MY)|^2]=(1+e)E [ |X-MY|^2] - if possible - by MC-integration for X ~ N_n(0,Sigt), v ~ N_m(0,Q) indep. M = Sigt H'(Q+HSigt H')^{-1} Y = HX+v, w_b(x)=min(1,b/|x|)
- rlsbnorm1
- Auxiliary routine for rlsfil: solves E [ |X-MYw_b(MY)|^2]=(1+e)E [ |X-MY|^2] - if possible - by numerical integration for X ~ N(0,Sigt), v ~ N(0,Q) indep. M=Sigt H'(Q+HSigt H')^{-1} Y=HX+v, w_b(x)=min(1,b/|x|)
- rlsfil
- Calculates a filtered time serie (uni- or multivariate) using a robust, recursive Filter based on LS-optimality, the rLS-filter. additionally to the Kalman-Filter one needs to specify the degree of robustness one wants to achieve; this is done either by specifying a clipping height or by specifying
- rmed
- rmed computes the running median of y using the optimal median smoothing algorithm of Haerdle and Steiger (1990).
- rndBurr
- generates a vector of pseudo random variables with Burr distribution.
- rndexp
- generates a vector of pseudo random variables with exponential distribution.
- rndgamma
- generates a vector of pseudo random variables with gamma distribution.
- rndgengamma
- generates a vector or matrix of pseudo random variables with generalized gamma distribution.
- rndgeom
- generates a vector or matrix of pseudo random variables with geometric distribution.
- rndhyp
- generates arrays up to eight dimensions of pseudo random variables with hyperbolic (HYP) distribution.
- rndln
- generates a vector of pseudo random variables with lognormal distribution.
- rndmixexp
- generates a vector of pseudo random variables with mixture of exponentials distributions.
- rndnig
- generates arrays up to eight dimensions of pseudo random variables with Normal Inverse Gaussian (NIG) distribution.
- rndPareto
- generates a vector of pseudo random variables with Pareto distribution.
- rndsstab
- generates arrays up to eight dimensions of pseudo-random variables with symmetric stable distribution.
- rndstab
- generates arrays up to eight dimensions of pseudo-random variables with stable distribution.
- rndtrbeta
- generates a vector or matrix of pseudo random variables with transformed beta distribution.
- rndWeibull
- generates a vector of pseudo random variables with Weibull distribution.
- roblm
- Semiparametric average periodogram estimator of the degree of long memory of a time series. The first argument of the quantlet is the series, the second optional argument is a strictly positive constant q, which is also strictly less than one. The third optional argument is the bandwidth vector m.
- robmest
- calculates M-estimators in linear model
- robtechmain
- Main routine of robtech library.
- robwhittle
- Semiparametric Gaussian estimator of the degree of long memory of a time series, based on the Whittle estimator. The first argument is the series, the second argument is the vector of bandwidths, i.e., the number of frequencies after zero that are considered. By default, the bandwidth vector m = n/
- rootsci
- calculates characteristic roots of VAR operator
- rot2mat
- Computes an orthonormal matrix from a set of Givens rotations.
- rotationmatrix
- auxiliary quantlet for plotgt, rotates the rotation cosinus matrix of the graphic
- round
- Rounds to a given precision. If the precision is omitted the nearest integer is displayed.
- rows
- rows returns the number of rows in an array.
- rqfit
- Performs quantile regression of y on x using the original simplex approach of Barrodale-Roberts/Koenker-d'Orey.
- rqua
- computes the radial quartic kernel
- rrstest
- Computes the regression rankscore test of a linear hypothesis based on the dual quantile regression process. It tests the hypothesis that b1 = 0 in the quantile regression model y = x0'b0 + x1'b1 + u. Test statistic is asymptotically Chi-squared with rank(x1) degrees of freedom.
- rtri
- computes the radial symmetric triweight kernel
- rtrian
- computes the radial symmetric triangular kernel
- runcv
- runs a cross validation and estimates the generalization error
- runcv2
- runs a cross validation and estimates the generalization error
- runi
- computes the radial symmetric uniform kernel
- runinit
- initializes the training andtest dataset, the errors and the weights in the network
- runinit2
- initializes the training andtest dataset, the errors and the weights in the network
- runnet
- runs a network with prespecified optimization method
- runnet2
- runs a network with prespecified optimization method
- runnew
- optimize a neural network by a quadratic approximation
- runnew2
- optimize a neural network by a quadratic approximation
- runqsa
- optimizes a neural network by a stochastic search
- runqsa2
- optimizes a neural network by a stochastic search
- runsa
- optimizes a neural network by Boltzman annealing
- runsa2
- optimizes a neural network by Boltzman annealing
- runshow
- visualizes a neural network during optimization
- runshow2
- visualizes a neural network during optimization
- rvlm
- Calculation of the rescaled variance test for I(0) against long-memory alternatives. The statistic is the centered kpss statistic based on the deviation from the mean. The limit distribution of this statistic is a Brownian bridge whose distribution is related to the distribution of the Kolmogorov s

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