Syntax

Arguments and performed function

W = mean(X $[$, d$]$);

W is a matrix with entries equal to the sample mean of the entries of X across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the mean is computed. The default value for d is 1, for which the mean is calculated over rows.

W = var(X $[$, d$]$);

W is a matrix with entries equal to the sample variance of the entries of X across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the mean is computed. The default value for d is 1, for which the variance is calculated over rows.

W = sd(X $[$, d$]$);

W is a matrix with entries equal to the sample standard deviation of the entries of X across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the standard deviation is computed. The default value for d is 1, for which the standard deviation is calculated over rows.

W = min(X $[$, d$]$);

W is a matrix with entries equal to the minimum of the entries of X across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the minimum is computed. The default value for d is 1, for which the minimum is calculated over rows.

W = max(X $[$, d$]$ );

W is a matrix with entries equal to the maximum of the entries of X across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the maximum is computed. The default value for d is 1, for which the maximum is calculated over rows.

W = median(X $[$ ,d$]$);

W is a matrix with entries equal to the median of the entries of X across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the median is computed. The default value for d is 1, for which the median is calculated over rows.

W = tabulate(v $[$ ,m$]$);

W is a matrix that contains information on the distribution of the values in vector v. W has three columns:

m is an optional argument, specifying the maximum number of unique values in v beyond which an error is produced.

• v must be a vector or a dataset with a single row or column
• m must a positive integer. The default value for m is 20.

W = cov(X $[$, d$]$ );

W is the sample covariance matrix of the variables contained in X, with the variables organized across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension according to which the variables in X are organized. The default value for d is 1, in which case each column of X is treated as a variable.

W = corr(X $[$, d$]$ );

W is the sample correlation matrix of the variables contained in X, with the variables organized across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension according to which the variables in X are organized. The default value for d is 1, in which case each column of X is treated as a variable.

W = ceil(X);

W is a matrix with dimensions equal to those of X and entries obtained by rounding off the entries of X upwards to the nearest integer. The function works element-wise.

• X must be a matrix or dataset

W = floor(X);

W is a matrix with dimensions equal to those of X and entries obtained by rounding off the entries of X downwards to the nearest integer. The function works element-wise.

• X must be a matrix or dataset

W = round(X);

W is a matrix with dimensions equal to those of X and entries obtained by rounding off the entries of X to the nearest integer. The function works element-wise.

• X must be a matrix or dataset

W = sort(X  $[$, d$]$);

W is a matrix with dimensions equal to those of X and entries obtained by sorting, in ascending order, the values of X across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the sorting should be done. The default value for d is 1, for which the entries of each column of X are sorted in ascending order.

see also sortrows and sortd

W = sortrows(X  $[$, d$]$);

W is a matrix with dimensions equal to those of X and entries obtained by sorting the rows of X, in ascending order, according to the values contained in the columns, the indices of which are provided in vector d.

• X must be a matrix or dataset
• d must be vector of integers with maximum value not greater than the number of columns of X. The default value for d is 1, for which the rows of of X are sorted in ascending order according to the values contained in the first column. If d contains more than one index, then the rows of X are sorted first according to the first index, and in case of duplicate values in the respective column, according to second index, and so on.

see also sort and sortd

W = sum(X  $[$, d$]$);

W is a matrix with entries equal to the sum of the entries of X across dimension d.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the sum is computed. The default value for d is 1, for which the sum is calculated over rows.

W = logsumexp(X  $[$, d$]$);

W is a matrix with entries equal to the logarithm of the sum of the exponential of the entries of X across dimension d:
${\mathtt{W}}_j=log{\sum }_{i}exp\left\{{\mathtt{X}}_{ij}\right\}$
when d is one (or not provided).
The function is provided to guard against overflow when calculating quantities of this form, which appear frequently in the calculation of log-marginal likelihoods.

• X must be a matrix or dataset
• d must be either 1 or 2, indicating the dimension across which the sum is computed. The default value for d is 1, for which the sum is calculated over rows.