Return the CDF for the given Anderson-Darling coefficient A computed from n values sampled from a distribution.
Test the hypothesis that x is selected from the given distribution using the Anderson-Darling test.
Perform a multi-way analysis of variance (ANOVA).
Compute mean and variance of the beta distribution.
Compute mean and variance of the binomial distribution.
The box plot is a graphical display that simultaneously describes several
Compute mean and variance of the chi-square distribution.
Compute mean and variance of the exponential distribution.
full-factor design with n binary terms.
Compute mean and variance of the F distribution.
full factorial design with choices 1 through n_i for each factor i
Finds the maximumlikelihood estimator for the Gamma distribution for R See also: gampdf, gaminv, gamrnd, gamlike.
Calculates the negative log-likelihood function for the Gamma distribution over vector R, with the given parameters A and B.
Compute mean and variance of the gamma distribution.
Compatibility function --- same as mean(x,'g')
Compute mean and variance of the geometric distribution.
Compatibility function --- same as mean(x,"h")
Histogram with superimposed fitted normal density.
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.
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 clusters generated from a distance vector created by the pdist function.
Compute mean and variance of the lognormal distribution.
mean absolute deviation of X
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).
nanmax is identical to the max function except that NaN values are are ignored.
nanmean is identical to the mean function except that NaN values are ignored.
nanmedian is identical to the median function except that NaN values are ignored.
nanmin is identical to the min function except that NaN values are are ignored.
nanstd is identical to the std function except that NaN values are ignored.
nansum is identical to the sum function except that NaN values are treated as 0 and so ignored.
nanstd is identical to the var function except that NaN values are ignored.
Compute mean and variance of the negative binomial distribution.
Produce a normal probability plot for each column of X.
Compute mean and variance of the normal distribution.
DISTFUN)
Compute mean and variance of the Poisson distribution.
Computes the value associated with the P-th percentile of X.
Compute principal components of X
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.
Multiple Linear Regression using Least Squares Fit of Y on X with the model `y = X * beta + e'.
"tovector")
Frequency table.
mean of x excluding highest and lowest p% of the data
Compute mean and variance of the t (Student) distribution.
Compute mean and variance of the discrete uniform distribution.
Compute mean and variance of the continuous uniform distribution.
Compute mean and variance of the Weibull distribution.
compute the z-score of each element of X relative to the data in the columns of X.
