Flexible Bayesian penalized regression modelling

-Fix count regression for Matlab 2020a and 2020b releases.

-Updated bayesreg help file

-Updated "Cite As" field

-Added support for count regression via Poisson and geometric regression models
-Added sparsity adaptive log-t shrinkage prior (option 'logt')
-Improved sparsification [br_sparsify]

-Updated the "Cite As" field in the toolbox description

-Added function "br_sparsify()" to sparsify posterior coefficient estimates; three sparsification methods currently available (see "br_example15")
-Improved br_summary() printing of categorical data (see "br_example5")
-Minor updates and fixes

-improved sampling speed for large design matrices
-improved sampling speed when block sampling with Gaussian data
-improved sampling efficiency of the horseshoe+ sampler

-Display the Widely Applicable Akaike's Information Criterion (WAIC) instead of DIC in summary output
-Implemented block sampling of betas for data with large numbers of predictors (options 'blocksample and 'blocksize')

- written a new parallelised C++ implementation of sampling code for logistic regression
- efficient MATLAB implementation of logistic regression sampling included; works even when MEX files are not available but not as fast

- Added option ‘groups’ which allows grouping of variables into potentially overlapping groups
- Grouping works with HS, HS+ and lasso
- Fixed a bug with g priors and logistic models
- Updated examples to demonstrate grouping and toolbox description

- Tidied up the summary display
- Added support for MATLAB tables
- Added support for categorical predictors
- Added a prediction function
- Updated and improved the example scripts
- Fix bug in computation of R2

Version 1.2
-This version implements Zellner's g-prior for linear and logistic regression. The g-prior only works with full rank matrices. The examples in "examples_bayesreg.m" have been updated to include a g-prior example.

Version 1.1
-Moved all display code to a separate function called "summary()". Now the summary table can be produced on demand after sampling.
-Updated "examples_bayesreg.m" to include examples of the new "summary()" command.

Updated description to include links to the full version of the toolbox.