Enable More Complex Error Structures in ParmEst and Pyomo.DoE

[slilonfe5]

Fixes # .

Summary/Motivation:

1. Currently, ParmEst and Pyomo.DoE support only independent and identically distributed (i.i.d.) measurement errors. This PR adds support for correlated measurement errors by enabling the construction of a full measurement-error covariance matrix (MCM), allowing correlations across time and between measurements collected at the same time point. The new implementation also supports proportional (heteroskedastic) error structures.
2. Currently, the calculation of the parameter covariance/Fisher information matrix in ParmEst/Pyomo.DoE is not supported for indexed parameters. This PR develops the capability to compute these matrices for indexed parameters.

Changes proposed in this PR:

• Update _compute_jacobian, _kaug_FIM, and _cov_at_theta in ParmEst to support covariance matrix estimation for indexed parameters
• Add a helper function in both ParmEst and Pyomo.DoE that builds the MCM from the user-supplied standard deviation and covariances
• Update SSE_weighted in ParmEst to use the MCM in computing the "SSE_weighted" objective function
• Update _kaug_FIM and _finite_difference_FIM in ParmEst to use the MCM in computing the parameter covariance matrix
• Update _kaug_FIM and _sequential_FIM in Pyomo.DoE to use the MCM
• Update create_doe_model in Pyomo.DoE to use the MCM
• Replicate ParmEst's _expanded_unknown_parameter_info in Pyomo.DoE
• Update all appearances of model.unknown_parameters or model.scenario_blocks.unknown_parameters to use the expanded parameter objects
• Add tests in test_parmest.py and test_doe_solve.py to exercise the new capabilities

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[slilonfe5]

@blnicho @mrmundt @jsiirola @adowling2. This PR is ready for review. This PR enables parameter covariance and Fisher Information Matrix estimation for indexed parameters across ParmEst and Pyomo.DoE. Additionally, it adds support for complex measurement-error structures (such as correlated or proportional errors) by building a full measurement-error covariance matrix for parameter estimation and optimal experimental design.

[slilonfe5]

@blnicho, just a kind reminder to send me your comments (I remember you mentioned that you've already started looking at this some weeks ago). Thanks

[slilonfe5]

@mrmundt, I will also appreciate your review of this PR while I wait for Bethany's comments. Thank you.

[mrmundt]

Thanks for this! There is an actual typo, some nitpicks, and a question about a paper.

[slilonfe5]

@mrmundt, thank you for your comments. I have addressed all of them. @blnicho, I look forward to receiving your comments. Thank you