

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:

the ﬁrst column contains the unique values of v, sorted from smallest to
largest

the second column lists the number of times each corresponding unique value
in the ﬁrst column appears in v

the third column lists the number of entries in v smaller than or equal to
the corresponding value in the ﬁrst column (cumulative sum of the second
column)
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 elementwise.
 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
elementwise.
 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 elementwise.
 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 logmarginal 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.



