pdInd: Constuctor for Positive-Definite Matrix With Zero Covariances...
In gmonette/Tcells: Fits Tcell dynamic models with mixed models

This function is a constructor for the pdInd class, representing a positive-definite matrix with zero covariances except possibly in the first row and column. If the matrix associated with object is of dimension $n$, it is represented by $n + (n-1)$ unrestricted parameters representing a lower-triangular log-Cholesky decomposition. The first $n$ parameters are the logs of the diagonal elements of the matrix and the last $n-1$ components are the $n-1$ remaining elements of the lower-triangular decomposition corresponding the to the possibly non-zero covariances in the first row.

1 2	pdInd(value = numeric(0), form = NULL, nam = NULL, data = sys.parent(), cov = NULL, zero = NULL)

`value`	an option initialization value, which can be any of the following ...
`form`	an optional one-sided linear formula specifying the row/column names for the matrix represented by `object`.
`nam`	and optional vector of character strings specifying the row/column names for the matrix represented by `object`.
`data`	and optional data frame i which to evaluate the variables names in `value` and `form`. ...
`cov`	optional position in lower triangle of covariances that are estimated and, thus, possibly non-zero. The default is that the covariances in the first column are estimated and possibly non-zero.
`zero`	optional way of specifying covariances constrained to be equal to zero. Only the lower triangular portion of the `zero` is used. The elements that are equal to 0 corresponds to the pattern of elements that are constrained to zero in the covariance matrix.