Use BlockedSparse type to reduce allocation

• Reduce memory allocation and garbage collection in models with multiple vector-valued random effects
• Add tests and test coverage
• Add benchmarks

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.