Returns values of the empirical cumulative probability distribution function for Y, which can be a vector or a list. Cumulative probability is the proportion of data values less than or equal to the value of QuantVec.

If L is the Cholesky root of an nxn matrix A, then after calling cholUpdate L is replaced with the Cholesky root of A+V*C*V' where C is an mxm symmetric matrix and V is an n*m matrix.

Calculates the correlation matrix of the data in the matrix argument.

A matrix that contains the data. If the data has m rows and n columns, the result is an m-by-m matrix.

Calculates the covariance matrix of the data in the matrix argument.

A matrix that contains the data. If the data has m rows and n columns, the result is an m-by-n matrix.

Creates design columns for a vector of values.

Missing values in the levelsList argument are not ignored. For example:

A version of Design for making full-rank versions of design matrices for nominal effects.

Missing values in the levelsList argument are not ignored. For example:

A version of Design for making full-rank versions of design matrices for ordinal effects.

Missing values in the levelsList argument are not ignored. For example:

Efficiently update an X´X matrix.

The third argument controls whether the row or rows defined in the second argument, X, are added to or deleted from the matrix A. 1 means to add the row or rows and -1 means to delete the row or rows.

The value used to populate the matrix. If value is not specified, 1 is used.

Returns a matrix. If position is not specified, returns the n nearest rows and distances to all rows. If position is specified, returns the n nearest rows and distances to either a point or a row. Stop is either n or {n, limit}. Position is a point that is described as a row vector for the coordinate of a row, or as the number of a row.

Remove either the row specified by number or the rows specified by vector. Returns the number of rows that were removed. Rows that were already removed are ignored.

Re-insert either the row specified by number or the rows specified by vector. Returns the number of rows that were inserted. Rows that were already inserted are ignored.

Creates a matrix of subscript positions where A is nonzero and nonmissing. For the two-argument function, Loc returns a matrix of positions where item is found within list.

Creates a matrix of subscript positions where the values of A have values less than or equal to the values in B. A must be a matrix sorted in ascending order.

Constructs an n-by-m matrix from a list of n lists of m row values or from the number of rows and columns.

Matrix Mult() allows only two arguments, while using the * operator enables you to multiply several matrices.

Imputes missing values in yVec based on the mean and covariance.

Orthonormalizes the columns of matrix A using the Gram Schmidt method. Centered(0) makes the columns to sum to zero. Scaled(1) makes them unit length.

Set to false (0) to use the decimal separator for your locale. Set to true (1) to always use a period (.) as a separator. The default value is false (0).

Returns a vector of indices that, used as a subscript to the original vector, sorts the vector by rank. Excludes missing values. Lists of numbers or strings are supported in addition to matrices.

Returns a vector of ranks of the values of vector, low to high as 1 to n, ties arbitrary. Lists of numbers or strings are supported in addition to matrices.

Returns a vector of ranks of the values of vector, but ranks for ties are averaged. Lists of numbers or strings are supported in addition to matrices.

Reshapes the matrix A across rows to the specified dimensions. Each value from the matrix A is placed row-by-row into the re-shaped matrix.

If ncol is not specified, the number of columns is whatever is necessary to fit all of the original values of the matrix into the reshaped matrix.

Solves a linear system. In other words, x=inverse(A)*b.

Returns a copy of a list or matrix source with the items in ascending order.

Returns a copy of a list or matrix source with the items in descending order.

Evaluates the spline predictions using the coef matrix, where coef is in the same form as Spline Coef().