[WIP] Converting *_ldlt_* functions to work with varmat

[bbbales2]

Summary

This is a first attempt at converting all the ldlt functions to work with varmat (and also all the multivariate distribution functions that use the *_ldlt_* functions)

Side Effects

This is a backwards compatibility breaking change because I'm removing LDLT_alloc and changing how LDLT_factor works, so it's a version bump afaik. @syclik @rok-cesnovar (just so a couple people know)

Release notes

Update *_ldlt_* functions to work with var<mat>

Checklist

• Math issue Make functions with custom autodiff var<mat> friendly #2101

• Copyright holder: Columbia University

  The copyright holder is typically you or your assignee, such as a university or company. By submitting this pull request, the copyright holder is agreeing to the license the submitted work under the following licenses:
  - Code: BSD 3-clause (https://opensource.org/licenses/BSD-3-Clause)
  - Documentation: CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

• the basic tests are passing

  • unit tests pass (to run, use: ./runTests.py test/unit)
  • header checks pass, (make test-headers)
  • dependencies checks pass, (make test-math-dependencies)
  • docs build, (make doxygen)
  • code passes the built in C++ standards checks (make cpplint)

• the code is written in idiomatic C++ and changes are documented in the doxygen

• the new changes are tested

[bbbales2]

@SteveBronder I told you I was working on the ldlt functions for the pull that is updating all the distributions to work with var<mat> (#2214) since the ldlt functions are used in the multivariate distributions.

Now that I've gone through and had a look, let's keep the ldlt and multivariate distribution stuff separate from #2214. It's complicated enough and self-contained.

I've gone through and changed them in a way that I think would work with var<mat>, but I haven't added var<mat> support yet. I want to try some more refactoring and simplification (the fundamental types in rev and prim are still kinda hairy, and maybe I made a mistake by trying to refactor LDLT_factor and not just replacing it where it is used in the distributions anyway).

Anyway, there's a branch for this now.