Documentation updates

Extended documentation - more to come.

Switch to BlockArrays for penalized least squares

• Use BlockArrays types for the A and L members of LinearMixedModel
• Create ScalarFactorReTerm and VectorFactorReTerm types
• Create UniformBlockDiagonal for diagonal blocks associated with VectorFactorReTerm
• Add more benchmarks
• Add tests
• Clean up code to reduce allocation

Fix loglikelihood calculation with weights

• Fixes #89
• Adds more benchmarks
• Introduces and uses model_response(mf::ModelFrame, d::Distribution) to convert a binary PooledDataArray response to a 0/1 floating point vector. This should be done in DataFrames and a pull request will be made for this.
• pass the random number generator to the simulate! method
• methods for A_rdiv_Bc!, etc. that are now in Base are commented with if VERSION < ...
• allow a contrasts specification in lmm and glmm

Fold wttrms and Λ into trms

• reformulate the LinearMixedModel type by incorporating the wttrms and Λ members into the trms member.
• create AbstractTerm with subtypes MatrixTerm and FactorReTerm
• add some benchmarks using the BenchmarkTools package
• remove some of the instances of method definitions for functions from Base with signatures of Base classes only
  • BLAS-like in-place linear algebra with scalar multipliers are now called e.g. αβA_mul_Bc!
  • the remaining problematic methods are operations with Diagonal for which I plan to create a PR on the julia repository after consulting with Tony and Andreas

Lower Cholesky formulation

Travis failures are timeouts on julia-0.6.0-pre. Once the dust settles on the julia new release I will check for bottlenecks.

Last release before v0.8.0

Incorporate a couple of commits on the master branch prior to major changes from merging the LowerCholesky branch.

Allow 3 or more nested factors

v0.7.6

Fix correlation store in bootstrap!

Return a DataFrame from bootstrap

The bootstrap function now returns a data frame with columns corresponding to individual parameters.

Correct the calculation of the conditional std. dev. of the r.e.

Correct the calculation of conditional std dev of r.e.

* Initialize pars to optsum.initial, not optsum.final

* clean up logic in optimize for GLMM - still needs work

* Restore model at the end of the bootstrap

* Use Cholesky factor not product in `condVar`

* Need to square diagonals of Cholesky factor

Failures on v0.6.0-dev are new and likely not to be unique to this package.

Fix bug introduced in v0.7.2

In the fit! method for LinearMixedModel objects the parameters were initialized to optsum.final not optsum.initial. This is not a problem for newly created objects because final is a copy of initial, But it does cause a problem for simulations such as a parametric bootstrap.