Descriptive statistics and computation

Descriptive statistics

aggregate	Aggregates the data in the columns of a matrix based upon a column containing group ids with a choice of method.
dstatmt	Computes descriptive statistics of a dataset, dataframe, or matrix.
frequency	Generate frequency table.
JarqueBera	Computes the Jarque-Bera goodness-of-fit test
kurtosis	Computes the sample kurtosis.
maxc	Computes maximum value of each column of a matrix.
meanc	Computes mean value of each column of a matrix.
median	Computes medians of the columns of a matrix.
minc	Computes minimum value of each column of a matrix.
modec	Computes mode of each column of a matrix.
quantile	Computes quantiles from each column in a matrix, given specified probabilities.
../skew	Computes the sample skew.
stdc	Computes the sample standard deviation of the elements in each column of a matrix.
tabulate	Computes and returns two-way tables of frequencies.
vcm, vcx	Computes an unbiased estimate of a variance-covariance matrix from a matrix \(x\) or a moment matrix, \(x'x\).

Computation

cumprodc	Computes the cumulative products of the columns of a matrix.
cumsumc	Computes the cumulative sums of the columns of a matrix.
prodc	Computes the products of all elements in each column of a matrix.
prodr	Computes the products of all elements in each row of a matrix.
sumc	Computes the sum of each column of a matrix or the sum across the second-fastest moving dimension of an L-dimensional array.
sumr	Computes the sum of each row of a matrix or the sum of the fastest moving dimension of an L-dimensional array.

maxindc	Returns a column vector containing the index (i.e., row number) of the maximum element in each column of a matrix.
minindc	Returns a column vector containing the index (i.e., row number) of the smallest element in each column of a matrix.

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Data cleaning

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Estimation methods