mat,stat: refer to SymmetricDim in docs
MODIFY
mat/cholesky.go
mat/cholesky.go
@@ -477,7 +477,7 @@ func (c *Cholesky) Scale(f float64, orig *Cholesky) {
// that k > w' A^-1 w. If this condition does not hold then ExtendVecSym will
// return false and the receiver will not be updated.
//
-// ExtendVecSym will panic if v.Len() != a.Symmetric()+1 or if a does not contain
+// ExtendVecSym will panic if v.Len() != a.SymmetricDim()+1 or if a does not contain
// a valid decomposition.
func (c *Cholesky) ExtendVecSym(a *Cholesky, v Vector) (ok bool) {
n := a.SymmetricDim()

MODIFY
stat/distmv/normal.go
stat/distmv/normal.go
@@ -86,7 +86,7 @@ func NewNormalChol(mu []float64, chol *mat.Cholesky, src rand.Source) *Normal {
// NewNormalPrecision creates a new Normal distribution with the given mean and
// precision matrix (inverse of the covariance matrix). NewNormalPrecision
-// panics if len(mu) is not equal to prec.Symmetric(). If the precision matrix
+// panics if len(mu) is not equal to prec.SymmetricDim(). If the precision matrix
// is not positive-definite, NewNormalPrecision returns nil for norm and false
// for ok.
func NewNormalPrecision(mu []float64, prec *mat.SymDense, src rand.Source) (norm *Normal, ok bool) {

MODIFY
stat/distmv/studentst.go
stat/distmv/studentst.go
@@ -49,7 +49,7 @@ type StudentsT struct {
// NewStudentsT creates a new StudentsT with the given nu, mu, and sigma
// parameters.
//
-// NewStudentsT panics if len(mu) == 0, or if len(mu) != sigma.Symmetric(). If
+// NewStudentsT panics if len(mu) == 0, or if len(mu) != sigma.SymmetricDim(). If
// the covariance matrix is not positive-definite, nil is returned and ok is false.
func NewStudentsT(mu []float64, sigma mat.Symmetric, nu float64, src rand.Source) (dist *StudentsT, ok bool) {
if len(mu) == 0 {

mat,stat: refer to SymmetricDim in docs