@capplyranks Reorder the rows of the matrix using a vector of ranks.

@columnextract Extract column from matrix.

@columns Number of columns in matrix object or group.

@convert Converts series or group to a vector or matrix after removing NAs.

@eqna Test for equality of data objects, treating NAs and null strings as ordinary and not missing values.

@explode Square matrix from a sym matrix object.

@fill Vector initialized from a list of values.

@filledmatrix Matrix initialized with scalar value.

@filledrowvector Rowvector initialized with scalar value.

@filledsym Sym initialized with scalar value.

@filledvector Vector initialized with scalar value.

@getmaindiagonal Extract main diagonal from matrix.

@grid Vector containing equally spaced grid of values.

@hcat Vertically concatenate matrices.

@identity Identity matrix

@implode Creates sym from lower triangle of square matrix.

@implodeu Creates sym from upper triangle of square matrix.

@isna Test for missing values.

@lower Lowercase representation of a string, or lower triangular matrix of a matrix.

@makediagonal Create a matrix with vector placed on a diagonal.

@mnrnd Matrix of normal random numbers.

@neqna Inequality test (NAs and blanks treated as values, not missing values).

@ones Matrix or vector of ones.

@permute Permutation of matrix.

@range Vector of sequential integers.

@ranks Ranking of values.

@rapplyranks Reorder the columns of a matrix using a vector of ranks.

@resample Randomly draw from the rows of the matrix.

@rmvnorm Multivariate normal random draws.

@rowextract Extract rowvector from matrix object.

@rows Number of rows.

@rwish Wishart random draw.

@subextract Extract submatrix from matrix object.

@scale Scale rows or columns of matrix.

@seq Vector containing arithmetic sequence.

@seqm Vector containing geometric sequence.

@sfill Create a string vector from a list of strings.

@sort Sort elements of data object.

@unvec Unstack vector into a matrix.

@unvech Unstack vector into lower triangle of sym.

@uniquevals Vector or svector of unique values of object.

@unitvector Extracts column from an identity matrix.

@upper Uppercase representation of a string; or upper triangular matrix of a matrix.

@vcat Vertically concatenate matrices.

@vec Vectorize (stack columns of) matrix.

@vech Vectorize (stack columns of) lower triangle of matrix.

@zeros Matrix or vector of zeros.

@cholesky Cholesky factor of matrix.

@commute Commutation matrix.

@cond Condition number of square matrix or sym.

@det Determinant of matrix.

@duplic Duplication matrix.

@duplicinv Inverse duplication matrix.

@eigenvalues Vector of eigenvalues of a sym.

@eigenvectors Matrix whose columns contain the eigenvectors of a matrix.

@elimin Elimination matrix.

@inner Inner product.

@inverse Inverse of matrix.

@issingular Test matrix for singularity.

@kronecker Kronecker product.

@lu LU decomposition of a matrix.

@norm Norm of series or matrix object.

@outer Outer product of vectors or series.

@pinverse Moore-Penrose pseudo-inverse of matrix.

@qform Quadratic form.

@qr QR decomposition.

@rank Rank of a matrix.

@rsweep Reverse sweep operator.

@solvesystem Solve system of linear equations.

@svd Singular value decomposition (economy) of matrix.

@svdfull Singular value decomposition (full) of matrix.

@sweep Sweep operator.

@trace Computes the trace of a square matrix or sym.

@transpose Transpose of a matrix object.

@unvec Unstack vector into a matrix.

@unvech Unstack vector into lower triangle of sym.

@vec Vectorize (stack columns of) matrix.

@vech Vectorize (stack columns of) lower triangle of matrix.

@columns Number of columns in matrix object or group.

@cor Correlation of two vectors or series, or between the columns of a matrix or series in a group.

@cov Covariance (non-d.f.corrected) of two vectors or series, or between the columns of a matrix or series in a group.

@covp Covariance (non-d.f. corrected) of two vectors or series, or between the columns of a matrix or series in a group.

@covs Covariance (d.f. corrected) of two vectors or series, or between the columns of a matrix or series in a group.

@first The first non-missing value in the vector or series.

@gmean Geometric mean.

@hmean Harmonic mean.

@ifirst Index of the first non-missing value in the vector or series.

@ilast Index of the last non-missing value in the vector or series.

@imax Index of maximum value.

@imaxes Indices of maximum value (multiple).

@imin Index of minimum value.

@imins Indices of minimum value (multiple).

@inner Inner product.

@intercept Intercept from a trend regression.

@kurt Kurtosis.

@last The last non-missing value in the vector or series.

@mae Mean of absolute error (difference) between series.

@mape Mean absolute percentage error (difference) between series.

@max Maximum value.

@maxes Maximum values (multiple).

@mean Arithmetic mean.

@median Median.

@min Minimum value.

@mins Minimum values (multiple).

@mse Mean of square error (difference) between series.

@nas Number of missing observations.

@norm Norm of series or matrix object.

@obs Number of observations.

@prod Product.

@quantile Empirical quantile.

@regress Perform an OLS regression on the first column of a matrix versus the remaining columns.

@rmse Root of the mean of square error (difference) between series.

@rows Number of rows.

@skew Skewness.

@smape Symmetric mean absolute percentage error (difference) between series.

@stdev Sample standard deviation (d.f. adjusted).

@stdevp Population standard deviation (no d.f. adjustment).

@stdevs Sample standard deviation (d.f. adjusted).

@stdize Standardized data (using sample standard deviation).

@stdizep Standardized data (using population standard deviation).

@sum Arithmetic sum.

@sumsq Arithmetic sum of squares.

@theil Theil inequality coefficient (difference) between series.

@trendcoef Trend coefficient from detrending regression.

@trmean Trimmed mean.

@uniquevals Vector or svector of unique values of object.

@var Population variance (no d.f. adjustment).

@varp Population variance (no d.f. adjustment).

@vars Sample variance (d.f. adjusted).

@cfirst First non-missing value in each column of a matrix.

@cifirst Index of the first non-missing value in each column of a matrix.

@cilast Index of the last non-missing value in each column of a matrix.

@cimax Index of the maximal value in each column of a matrix.

@cimin Index of the maximal value in each column of a matrix.

@cintercept Intercept from a trend regression performed on each column of a matrix.

@clast Last non-missing value in each column of the matrix.

@cmax Maximal value in each column of a matrix.

@cmean Mean in each column of a matrix.

@cmedian Median of each column of a matrix.

@cmin Minimal value for each column of the matrix.

@cnas Number of NA values in each column of a matrix.

@cobs Number of non-NA values in each column of a matrix.

@cprod Product of elements in each column of a matrix.

@cquantile Quantile of each column of a matrix.

@cstdev Sample standard deviation (d.f. corrected) of each column of a matrix.

@cstdevp Population standard deviation (non-d.f. corrected) of each column of a matrix.

@cstdevs Sample standard deviation (non-d.f. corrected) of each column of a matrix.

@csum Sum of the values in each column of a matrix.

@csumsq Sum of the squared values in each column of a matrix.

@ctrendcoef Slope from a trend regression on each column of a matrix.

@ctrmean Trimmed mean of each column of a matrix .

@cvar Population variance of each column of a matrix.

@cvarp Population variance of each column of a matrix.

@cvars Sample variance of each column of a matrix.

@ediv Element by element division of two matrices.

@eeq Element by element equality comparison of two data objects.

@eeqna Element by element equality comparison of two data objects with NAs treated as ordinary value for comparison.

@ege Element by element tests for whether the elements in the data objects are greater than or equal to corresponding elements in another data object.

@egt Element by element tests for whether the elements in the data object strictly greater than corresponding elements in another data object.

@einv Element by element inverses of a matrix.

@ele Element by element tests for whether the elements in the data object are less than or equal to corresponding elements in another data object.

@elt Element by element tests for whether the elements in the data object are strictly less than corresponding elements in another data object.

@emax Element by element maximums of two conformable data objects.

@emin Element by element minimums of two conformable data objects.

@emult Element by element multiplication of two matrix objects.

@eneq Element by element inequality comparison of two data objects.

@eneqna Element by element inequality comparison of two data objects with NAs treated as ordinary value for comparison.

@epow Raises each element in a matrix to a power.

@erecode Element by element recode of data objects.

@capplyranks Reorder the rows of the matrix using a vector of ranks.

@demean Compute deviations from the mean of the data object.

@detrend Compute deviations from the trend of the data object.

@dupselem Identifier for the observation within the set of duplicates.

@dupsid Identifier for the duplicates group for the observation.

@dupsobs Number of observations in the corresponding duplicates group.

@permute Permutation of matrix.

@ranks Ranking of values.

@rapplyranks Reorder the columns of a matrix using a vector of ranks.

@resample Randomly draw from the rows of the matrix.

@transpose Transpose of a matrix object.

@colcumprod Cumulative products for each column of a matrix.

@colcumsum Cumulative sums for each column of a matrix.

@coldemean Demean each column of a matrix.

@coldetrend Detrend each column of a matrix.

@colpctiles Percentile values for each column of a matrix.

@colranks Ranks of each column of the matrix.

@colsort Sort each column of the matrix.

@colstdize Standardize each column using the sample (d.f. corrected) standard deviation.

@colstdizep Standardize each column using the population (non-d.f. corrected) standard deviation.

@rowranks Matrix where each row contains ranks of the column values.

@rowsort Matrix where each row contains sorted columns.