pdNatural class,
  representing a general positive-definite matrix, using a natural
  parametrization . If the matrix associated with object is of
  dimension $n$, it is represented by $n(n+1)/2$
  parameters. Letting $\sigma_{ij}$ denote the $ij$-th
  element of the underlying positive definite matrix and
  $\rho_{ij}=\sigma_{i}/\sqrt{\sigma_{ii}\sigma_{jj}},\;i\neq j$ denote the associated
  "correlations", the "natural" parameters are given by
  $\sqrt{\sigma_{ii}}, \;i=1,\ldots,n$ and
  $\log((1+\rho_{ij})/(1-\rho_{ij})),\; i \neq
    j$. Note that all
  natural parameters are individually unrestricted, but not jointly
  unrestricted (meaning that not all unrestricted vectors would give
  positive-definite matrices). Therefore, this parametrization should
  NOT be used for optimization. It is mostly used for deriving
  approximate confidence intervals on parameters following the
  optimization of an objective function. When value is
  numeric(0), an uninitialized pdMat object, a one-sided
  formula, or a vector of character strings, object is returned
  as an uninitialized pdSymm object (with just some of its
  attributes and its class defined) and needs to have its coefficients
  assigned later, generally using the coef or matrix replacement
  functions. If value is an initialized pdMat object,
  object will be constructed from
  as.matrix(value). Finally, if value is a numeric 
  vector, it is assumed to represent the natural parameters of the
  underlying positive-definite matrix.pdNatural(value, form, nam, data)pdMat object, a positive-definite
   matrix, a one-sided linear formula (with variables separated by
   +), a vector of character strings, or a numeric
   object. Because
   factors may be present in form, the formula needs to be
   evaluated on a data.frame to resolve the names itvalue and form. It is used to
   obtain the levels for factors, which affect the
   dimensions and the row/column names of the underlying matrix. pdNatural object representing a general positive-definite
  matrix in natural parametrization, also inheriting from class
  pdMat.as.matrix.pdMat,
  coef.pdMat,
  pdClasses,
  matrix<-.pdMat