Statistics
Produce a hierarchical clustering dendrogram
Return the distance between any two rows in X.
"tovector")
anova1
barttest
nbin*
use pascal_*
nlinfit
ranksum
signrank
signtest
ttest
ttest2
unid*
use discrete_*
ztest
For each element of X, returns the CDF at X of the beta distribution with parameters A and B, i.e., PROB (beta (A, B) <= X).
For each component of X, compute the quantile (the inverse of the CDF) at X of the Beta distribution with parameters A and B.
For each element of X, returns the PDF at X of the beta distribution with parameters A and B.
Return an R by C or `size (SZ)' matrix of random samples from the Beta distribution with parameters A and B.
For each element of X, compute the CDF at X of the binomial distribution with parameters N and P.
For each element of X, compute the quantile at X of the binomial distribution with parameters N and P.
For each element of X, compute the probability density function (PDF) at X of the binomial distribution with parameters N and P.
Return an R by C or a `size (SZ)' matrix of random samples from the binomial distribution with parameters N and P.
For each element of X, compute the cumulative distribution function (CDF) at X of the chisquare distribution with N degrees of freedom.
For each element of X, compute the quantile (the inverse of the CDF) at X of the chisquare distribution with N degrees of freedom.
For each element of X, compute the probability density function (PDF) at X of the chisquare distribution with N degrees of freedom.
Return an R by C or a `size (SZ)' matrix of random samples from the chisquare distribution with N degrees of freedom.
For each element of X, compute the cumulative distribution function (CDF) at X of the exponential distribution with parameter LAMBDA.
For each element of X, compute the quantile (the inverse of the CDF) at X of the exponential distribution with parameter LAMBDA.
For each element of X, compute the probability density function (PDF) of the exponential distribution with parameter LAMBDA.
Return an R by C matrix of random samples from the exponential distribution with parameter LAMBDA, which must be a scalar or of size R by C.
For each element of X, compute the CDF at X of the F distribution with M and N degrees of freedom, i.e., PROB (F (M, N) <= X).
For each component of X, compute the quantile (the inverse of the CDF) at X of the F distribution with parameters M and N.
For each element of X, compute the probability density function (PDF) at X of the F distribution with M and N degrees of freedom.
Return an R by C matrix of random samples from the F distribution with M and N degrees of freedom.
For each element of X, compute the cumulative distribution function (CDF) at X of the Gamma distribution with parameters A and B.
For each component of X, compute the quantile (the inverse of the CDF) at X of the Gamma distribution with parameters A and B.
For each element of X, return the probability density function (PDF) at X of the Gamma distribution with parameters A and B.
Return an R by C or a `size (SZ)' matrix of random samples from the Gamma distribution with parameters A and B.
For each element of X, compute the CDF at X of the geometric distribution with parameter P.
For each element of X, compute the quantile at X of the geometric distribution with parameter P.
For each element of X, compute the probability density function (PDF) at X of the geometric distribution with parameter P.
Return an R by C matrix of random samples from the geometric distribution with parameter P, which must be a scalar or of size R by C.
Compute the cumulative distribution function (CDF) at X of the hypergeometric distribution with parameters M, T, and N.
For each element of X, compute the quantile at X of the hypergeometric distribution with parameters M, T, and N.
Compute the probability density function (PDF) at X of the hypergeometric distribution with parameters M, T, and N.
Return an R by C matrix of random samples from the hypergeometric distribution with parameters M, T, and N.
For each element of X, compute the cumulative distribution function (CDF) at X of the lognormal distribution with parameters A and V.
For each element of X, compute the quantile (the inverse of the CDF) at X of the lognormal distribution with parameters A and V.
For each element of X, compute the probability density function (PDF) at X of the lognormal distribution with parameters A and V.
Return an R by C matrix of random samples from the lognormal distribution with parameters A and V.
For each element of X, compute the cumulative distribution function (CDF) at X of the normal distribution with mean M and variance V.
For each element of X, compute the quantile (the inverse of the CDF) at X of the normal distribution with mean M and variance V.
For each element of X, compute the probability density function (PDF) at X of the normal distribution with mean M and variance V.
Return an R by C or `size (SZ)' matrix of random samples from the normal distribution with parameters M and V.
For each element of X, compute the cumulative distribution function (CDF) at X of the Poisson distribution with parameter lambda.
For each component of X, compute the quantile (the inverse of the CDF) at X of the Poisson distribution with parameter LAMBDA.
For each element of X, compute the probability density function (PDF) at X of the poisson distribution with parameter LAMBDA.
Return an R by C matrix of random samples from the Poisson distribution with parameter LAMBDA, which must be a scalar or of size R by C.
For each element of X, compute the CDF at X of the t (Student) distribution with N degrees of freedom, i.e., PROB (t(N) <= X).
For each component of X, compute the quantile (the inverse of the CDF) at X of the t (Student) distribution with parameter N.
For each element of X, compute the probability density function (PDF) at X of the T (Student) distribution with N degrees of freedom.
Return an R by C matrix of random samples from the t (Student) distribution with N degrees of freedom.
Return the CDF at X of the uniform distribution on [A, B], i.e., PROB (uniform (A, B) <= x).
For each element of X, compute the quantile (the inverse of the CDF) at X of the uniform distribution on [A, B].
For each element of X, compute the PDF at X of the uniform distribution on [A, B].
Return an R by C or a `size (SZ)' matrix of random samples from the uniform distribution on [A, B].
Compute the cumulative distribution function (CDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE, which is
Compute the quantile (the inverse of the CDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE.
Compute the probability density function (PDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE which is given by
Return an R by C matrix of random samples from the Weibull distribution with parameters SCALE and SHAPE which must be scalar or of size R by C.
Compute the cumulative distribution function (CDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE, which is
Compute the quantile (the inverse of the CDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE.
Compute the probability density function (PDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE which is given by
Return an R by C matrix of random samples from the Weibull distribution with parameters SCALE and SHAPE which must be scalar or of size R by C.
Return a simulated realization of the D-dimensional Wiener Process on the interval [0, T].
Create an array by accumulating the elements of a vector into the positions defined by their subscripts.
Return the complementary log-log function of X, defined as
calculates the correlation matrix X and Y can contain missing values encoded with NaN.
calculates the correlation matrix from pairwise correlations.
covariance matrix X and Y can contain missing values encoded with NaN.
Cumulative product of elements along dimension DIM.
Cumulative sum of elements along dimension DIM.
Cumulative numerical integration using trapezoidal method.
Create categorical data out of numerical or continuous data by cutting into intervals.
calculates the geomentric mean of data elements.
calculates the harmonic mean of data elements.
hist does not work with the Grace graphic interface.
Produce histogram counts.
calculates the interquartile range Missing values (encoded as NaN) are ignored.
Compute Kendall's TAU for each of the variables specified by the input arguments.
estimates the kurtosis
For each component of P, return the logit of P defined as
estimates the Mean Absolute deviation
Return the Mahalanobis' D-square distance between the multivariate samples X and Y, which must have the same number of components (columns), but may have a different number of observations (rows).
For a vector argument, return the maximum value.
calculates the mean of data elements.
calculates the mean of the squares
data elements,
For a vector argument, return the minimum value.
Count the most frequently appearing value.
estimates the p-th moment
nanfunc
not implemented
Find the maximal element while ignoring NaN values.
same as SUM but ignores NaN's.
Compute the median of data while ignoring NaN values.
Find the minimal element while ignoring NaN values.
same as STD but ignores NaN's.
same as SUM but ignores NaN's.
Compute the variance while ignoring NaN values.
calculates the percentiles of histograms and sample arrays.
For each component of P, return the probit (the quantile of the standard normal distribution) of P.
Product of elements along dimension DIM.
If X is a vector, return the range, i.e., the difference between the maximum and the minimum, of the input data.
gives the rank of each element in a vector.
Find the lengths of all sequences of common values.
Count the upward runs along the first non-singleton dimension of X of length 1, 2, ..., N-1 and greater than or equal to N.
estimates the skewness
Spearman's rank correlation coefficient.
If X is a matrix, return a matrix with the minimum, first quartile, median, third quartile, maximum, mean, standard deviation, skewness and kurtosis of the columns of X as its columns.
calculates the standard deviation.
If X is a vector, subtract its mean and divide by its standard deviation.
Sum of elements along dimension DIM.
calculates the sum of squares.
Create a contingency table T from data vectors.
Compute a frequency table.
Numerical integration using trapezoidal method.
calculates the trimmed mean by removing the upper and lower (p/2)% samples.
Return the different values in a column vector, arranged in ascending order.
calculates the variance.
removes the mean and normalizes the data to a variance of 1.
Return the CDF for the given Anderson-Darling coefficient A computed from N values sampled from a distribution.
For each element of X, returns the CDF at X of the beta distribution with parameters A and B, i.e., PROB (beta (A, B) <= X).
For each component of X, compute the quantile (the inverse of the CDF) at X of the Beta distribution with parameters A and B.
For each element of X, returns the PDF at X of the beta distribution with parameters A and B.
Return an R by C or `size (SZ)' matrix of random samples from the Beta distribution with parameters A and B.
Compute mean and variance of the beta distribution.
For each element of X, compute the CDF at X of the binomial distribution with parameters N and P.
For each element of X, compute the quantile at X of the binomial distribution with parameters N and P.
For each element of X, compute the probability density function (PDF) at X of the binomial distribution with parameters N and P.
Return an R by C or a `size (SZ)' matrix of random samples from the binomial distribution with parameters N and P.
Compute mean and variance of the binomial distribution.
For each element of X, compute the cumulative distribution function (CDF) at X of the Cauchy distribution with location parameter LAMBDA and scale parameter SIGMA.
For each element of X, compute the quantile (the inverse of the CDF) at X of the Cauchy distribution with location parameter LAMBDA and scale parameter SIGMA.
For each element of X, compute the probability density function (PDF) at X of the Cauchy distribution with location parameter LAMBDA and scale parameter SIGMA > 0.
Return an R by C or a `size (SZ)' matrix of random samples from the Cauchy distribution with parameters LAMBDA and SIGMA which must both be scalar or of size R by C.
For each element of X, compute the cumulative distribution function (CDF) at X of the chisquare distribution with N degrees of freedom.
For each element of X, compute the quantile (the inverse of the CDF) at X of the chisquare distribution with N degrees of freedom.
For each element of X, compute the probability density function (PDF) at X of the chisquare distribution with N degrees of freedom.
Return an R by C or a `size (SZ)' matrix of random samples from the chisquare distribution with N degrees of freedom.
Compute mean and variance of the chi-square distribution.
Returns confidence level of multinomial parameters estimated p = x / sum(x) with predefined confidence interval B.
Compute the cumulative distribution function of a copula family.
Compute the probability density function of a copula family.
For each element of X, compute the cumulative distribution function (CDF) at X of a univariate discrete distribution which assumes the values in V with probabilities P.
For each component of X, compute the quantile (the inverse of the CDF) at X of the univariate distribution which assumes the values in V with probabilities P.
For each element of X, compute the probability density function (PDF) at X of a univariate discrete distribution which assumes the values in V with probabilities P.
Generate a row vector containing a random sample of size N from the univariate distribution which assumes the values in V with probabilities P.
For each element of X, compute the cumulative distribution function (CDF) at X of the empirical distribution obtained from the univariate sample DATA.
For each element of X, compute the quantile (the inverse of the CDF) at X of the empirical distribution obtained from the univariate sample DATA.
For each element of X, compute the probability density function (PDF) at X of the empirical distribution obtained from the univariate sample DATA.
Generate a bootstrap sample of size N from the empirical distribution obtained from the univariate sample DATA.
For each element of X, compute the cumulative distribution function (CDF) at X of the exponential distribution with mean LAMBDA.
For each element of X, compute the quantile (the inverse of the CDF) at X of the exponential distribution with mean LAMBDA.
For each element of X, compute the probability density function (PDF) of the exponential distribution with mean LAMBDA.
Return an R by C matrix of random samples from the exponential distribution with mean LAMBDA, which must be a scalar or of size R by C.
Compute mean and variance of the exponential distribution.
For each element of X, compute the CDF at X of the F distribution with M and N degrees of freedom, i.e., PROB (F (M, N) <= X).
For each component of X, compute the quantile (the inverse of the CDF) at X of the F distribution with parameters M and N.
For each element of X, compute the probability density function (PDF) at X of the F distribution with M and N degrees of freedom.
Return an R by C matrix of random samples from the F distribution with M and N degrees of freedom.
Compute mean and variance of the F distribution.
For each element of X, compute the cumulative distribution function (CDF) at X of the Gamma distribution with parameters A and B.
For each component of X, compute the quantile (the inverse of the CDF) at X of the Gamma distribution with parameters A and B.
Calculates the negative log-likelihood function for the Gamma distribution over vector R, with the given parameters A and B.
For each element of X, return the probability density function (PDF) at X of the Gamma distribution with parameters A and B.
Return an R by C or a `size (SZ)' matrix of random samples from the Gamma distribution with parameters A and B.
Compute mean and variance of the gamma distribution.
For each element of X, compute the CDF at X of the geometric distribution with parameter P.
For each element of X, compute the quantile at X of the geometric distribution with parameter P.
For each element of X, compute the probability density function (PDF) at X of the geometric distribution with parameter P.
Return an R by C matrix of random samples from the geometric distribution with parameter P, which must be a scalar or of size R by C.
Compute mean and variance of the geometric distribution.
Compute the cumulative distribution function (CDF) at X of the hypergeometric distribution with parameters T, M, and N.
For each element of X, compute the quantile at X of the hypergeometric distribution with parameters T, M, and N.
Compute the probability density function (PDF) at X of the hypergeometric distribution with parameters T, M, and N.
Return an R by C matrix of random samples from the hypergeometric distribution with parameters T, M, and N.
Compute mean and variance of the hypergeometric distribution.
For each element of X, compute the cumulative distribution function (CDF) at X of the Johnson SU distribution with shape parameters ALPHA1 and ALPHA2.
For each element of X, compute the probability density function (PDF) at X of the Johnson SU distribution with shape parameters ALPHA1 and ALPHA2.
Return the CDF at X of the Kolmogorov-Smirnov distribution,
For each element of X, compute the cumulative distribution function (CDF) at X of the Laplace distribution.
For each element of X, compute the quantile (the inverse of the CDF) at X of the Laplace distribution.
For each element of X, compute the probability density function (PDF) at X of the Laplace distribution.
Return an R by C matrix of random numbers from the Laplace distribution.
For each component of X, compute the CDF at X of the logistic distribution.
For each component of X, compute the quantile (the inverse of the CDF) at X of the logistic distribution.
For each component of X, compute the PDF at X of the logistic distribution.
Return an R by C matrix of random numbers from the logistic distribution.
For each element of X, compute the cumulative distribution function (CDF) at X of the lognormal distribution with parameters MU and SIGMA.
For each element of X, compute the quantile (the inverse of the CDF) at X of the lognormal distribution with parameters MU and SIGMA.
For each element of X, compute the probability density function (PDF) at X of the lognormal distribution with parameters MU and SIGMA.
Return an R by C matrix of random samples from the lognormal distribution with parameters MU and SIGMA.
Compute mean and variance of the lognormal distribution.
Compute the cumulative distribution function of the multivariate normal distribution.
Compute multivariate normal pdf for X given mean MU and covariance matrix SIGMA.
Draw N random D-dimensional vectors from a multivariate Gaussian distribution with mean MU(NxD) and covariance matrix SIGMA(DxD).
Compute the cumulative distribution function of the multivariate Student's t distribution.
For each element of X, compute the CDF at x of the Pascal (negative binomial) distribution with parameters N and P.
For each element of X, compute the quantile at X of the Pascal (negative binomial) distribution with parameters N and P.
For each element of X, compute the probability density function (PDF) at X of the Pascal (negative binomial) distribution with parameters N and P.
Return an R by C matrix of random samples from the Pascal (negative binomial) distribution with parameters N and P.
Compute mean and variance of the negative binomial distribution.
returns normal cumulative distribtion function
returns inverse cumulative function of the normal distribution
returns normal probability density
Return an R by C or `size (SZ)' matrix of random samples from the normal distribution with parameters mean M and standard deviation S.
Compute mean and variance of the normal distribution.
For each element of X, compute the CDF at x of the Pascal (negative binomial) distribution with parameters N and P.
For each element of X, compute the quantile at X of the Pascal (negative binomial) distribution with parameters N and P.
For each element of X, compute the probability density function (PDF) at X of the Pascal (negative binomial) distribution with parameters N and P.
Return an R by C matrix of random samples from the Pascal (negative binomial) distribution with parameters N and P.
For each element of X, compute the cumulative distribution function (CDF) at X of the Poisson distribution with parameter lambda.
For each component of X, compute the quantile (the inverse of the CDF) at X of the Poisson distribution with parameter LAMBDA.
For each element of X, compute the probability density function (PDF) at X of the poisson distribution with parameter LAMBDA.
Return an R by C matrix of random samples from the Poisson distribution with parameter LAMBDA, which must be a scalar or of size R by C.
Compute mean and variance of the Poisson distribution.
Generates pseudo-random numbers from a given one-, two-, or three-parameter distribution.
Compute the cumulative distribution function of the Rayleigh distribution.
Compute the quantile of the Rayleigh distribution.
Compute the probability density function of the Rayleigh distribution.
Generate a matrix of random samples from the Rayleigh distribution.
Compute mean and variance of the Rayleigh distribution.
For each component of X, compute the CDF of the standard normal distribution at X.
For each component of X, compute the quantile (the inverse of the CDF) at X of the standard normal distribution.
For each element of X, compute the probability density function (PDF) of the standard normal distribution at X.
Return an R by C or `size (SZ)' matrix of random numbers from the standard normal distribution.
returns student cumulative distribtion function
returns inverse cumulative function of the student distribution
returns student probability density
Return an R by C matrix of random samples from the t (Student) distribution with N degrees of freedom.
Compute mean and variance of the t (Student) distribution.
For each element of X, compute the cumulative distribution function (CDF) at X of a univariate discrete distribution which assumes the values in V with equal probability.
For each component of X, compute the quantile (the inverse of the CDF) at X of the univariate discrete distribution which assumes the values in V with equal probability
For each element of X, compute the probability density function (PDF) at X of a univariate discrete distribution which assumes the values in V with equal probability.
Return random values from discrete uniform distribution, with maximum value(s) given by the integer MX, which may be a scalar or multidimensional array.
Compute mean and variance of the discrete uniform distribution.
Return the CDF at X of the uniform distribution on [A, B], i.e., PROB (uniform (A, B) <= x).
For each element of X, compute the quantile (the inverse of the CDF) at X of the uniform distribution on [A, B].
For each element of X, compute the PDF at X of the uniform distribution on [A, B].
Return an R by C or a `size (SZ)' matrix of random samples from the uniform distribution on [A, B].
Compute mean and variance of the continuous uniform distribution.
Compute mean and variance of the Weibull distribution.
Compute the cumulative distribution function (CDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE, which is
Compute the quantile (the inverse of the CDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE.
Compute the probability density function (PDF) at X of the Weibull distribution with shape parameter SCALE and scale parameter SHAPE which is given by
Return an R by C matrix of random samples from the Weibull distribution with parameters SCALE and SHAPE which must be scalar or of size R by C.
Return a simulated realization of the D-dimensional Wiener Process on the interval [0, T].
full-factor design with n binary terms.
full-factor design with n binary terms.
Full factorial design.
Full factorial design.
Construct a Hadamard matrix HN of size N-by-N.
Finds the maximumlikelihood estimator for the Gamma distribution for R See also: gampdf, gaminv, gamrnd, gamlike.
Test the hypothesis that X is selected from the given distribution using the Anderson-Darling test.
Perform a Bartlett test for the homogeneity of variances in the data vectors X1, X2, ..., XK, where K > 1.
Given two samples X and Y, perform a chisquare test for homogeneity of the null hypothesis that X and Y come from the same distribution, based on the partition induced by the (strictly increasing) entries of C.
Perform a chi-square test for independence based on the contingency table X.
Test whether two samples X and Y come from uncorrelated populations.
Perform an F test for the null hypothesis rr * b = r in a classical normal regression model y = X * b + e.
For a sample X from a multivariate normal distribution with unknown mean and covariance matrix, test the null hypothesis that `mean (X) == M'.
For two samples X from multivariate normal distributions with the same number of variables (columns), unknown means and unknown equal covariance matrices, test the null hypothesis `mean (X) == mean (Y)'.
Perform a Kolmogorov-Smirnov test of the null hypothesis that the sample X comes from the (continuous) distribution dist.
Perform a 2-sample Kolmogorov-Smirnov test of the null hypothesis that the samples X and Y come from the same (continuous) distribution.
Perform a Kruskal-Wallis one-factor "analysis of variance".
Perform a one-way multivariate analysis of variance (MANOVA).
For a square contingency table X of data cross-classified on the row and column variables, McNemar's test can be used for testing the null hypothesis of symmetry of the classification probabilities.
If X1 and N1 are the counts of successes and trials in one sample, and X2 and N2 those in a second one, test the null hypothesis that the success probabilities P1 and P2 are the same.
Perform a chi-square test with 6 degrees of freedom based on the upward runs in the columns of X.
For two matched-pair samples X and Y, perform a sign test of the null hypothesis PROB (X > Y) == PROB (X < Y) == 1/2.
For a sample X from a normal distribution with unknown mean and variance, perform a t-test of the null hypothesis `mean (X) == M'.
For two samples x and y from normal distributions with unknown means and unknown equal variances, perform a two-sample t-test of the null hypothesis of equal means.
Perform an t test for the null hypothesis `RR * B = R' in a classical normal regression model `Y = X * B + E'.
For two samples X and Y, perform a Mann-Whitney U-test of the null hypothesis PROB (X > Y) == 1/2 == PROB (X < Y).
For two samples X and Y from normal distributions with unknown means and unknown variances, perform an F-test of the null hypothesis of equal variances.
For two samples X and Y from normal distributions with unknown means and unknown and not necessarily equal variances, perform a Welch test of the null hypothesis of equal means.
For two matched-pair sample vectors X and Y, perform a Wilcoxon signed-rank test of the null hypothesis PROB (X > Y) == 1/2.
Perform a Z-test of the null hypothesis `mean (X) == M' for a sample X from a normal distribution with unknown mean and known variance V.
For two samples X and Y from normal distributions with unknown means and known variances V_X and V_Y, perform a Z-test of the hypothesis of equal means.
Perform a one-way analysis of variance (ANOVA).
Perform a multi-way analysis of variance (ANOVA).
Perform a multi-way analysis of variance (ANOVA).
Generalized least squares estimation for the multivariate model y = x b + e with mean (e) = 0 and cov (vec (e)) = (s^2) o, where y is a t by p matrix, x is a t by k matrix, b is a k by p matrix, e is a t by p matrix, and o is a t p by t p matrix.
Ordinary least squares estimation for the multivariate model y = x b + e with mean (e) = 0 and cov (vec (e)) = kron (s, I).
Produce prediction intervals for the fitted y.
Return the coefficients of a polynomial P(X) of degree N that minimizes the least-squares-error of the fit.
Evaluate the polynomial at of the specified values for X.
Compute principal components of X.
Compute principal components of X.
Multiple Linear Regression using Least Squares Fit of Y on X with the model `y = X * beta + e'.
Return the coefficients of a polynomial P(X) of degree N that minimizes `sumsq (p(x(i)) - y(i))', to best fit the data in the least squares sense.
Solve a potentially over-determined system with uncertainty in the values.
hmmdecode
not implemented
Estimate the matrix of transition probabilities and the matrix of output probabilities of a given sequence of outputs and states generated by a hidden Markov model.
Generate an output sequence and hidden states of a hidden Markov model.
Use the Viterbi algorithm to find the Viterbi path of a hidden Markov model given a sequence of outputs.
Perform ordinal logistic regression.
Called by logistic_regression.
Calculates likelihood for the ordinal logistic regression model.
Levenberg-Marquardt nonlinear regression of f(x,p) to y(x).
Produce a box plot.
Produce a box plot.
Plot histogram with superimposed fitted normal density.
Plot histogram with superimposed fitted normal density.
Produce a normal probability plot for each column of X.
Produce a normal probability plot for each column of X.
pareto does not work with the Grace graphic interface.
pareto does not work with the Grace graphic interface.
Perform a PP-plot (probability plot).
Perform a QQ-plot (quantile plot).
scatter does not work with the Grace graphic interface.
scatter does not work with the Grace graphic interface.
Read case names from an ascii file.
Write case names to an ascii file.
Read tabular data from an ascii file.
Write tabular data to an ascii file.
