NEWS.md
This is a very large update which adds a number of features and major speed improvements. We now depend on the TensorFlow Probability Python package, and use functionality in that package wherever possible. Sampling a simple model now takes ~10s, rather than ~2m (>10x speedup).
dim<-()
now always rearranges elements in column-major order (R-style, not Python-style)future
package for execution of MCMC chains on remote machines. Note: it is not advised to use future
for parallel execution of chains on the same machine, that is now automatically handled by greta.one_by_one
argument to MCMC can handle serious numerical errors (such as failed matrix inversions) as ‘bad’ samplesextra_samples()
function to continue sampling from a model.calculate()
works on the output of MCMC, to enable post-hoc posterior predictionabind()
, aperm()
, apply()
, chol2inv()
, cov2cor()
, eigen()
, identity()
, kronecker()
, rdist()
, and tapply()
(thanks to @jdyen)greta_array()
opt()
and mcmc()
as objects, with defined tuning parameters. The control
argument to these functions is now defunct.x[2, 3]
, rather than x.6
)plot.greta_model()
now returns a DiagrammeR::grViz
object (thanks to @flyaflya). This is less modifiable, but renders the plot more much consistently across different environments and notebook types. The DiagrammeR
dgr_graph
object use to create the grViz
object is included as an attribute of this object, named "dgr_graph"
.Minor patch to handle an API change in the progress package. No changes in functionality.
calculate()
function to compute the values of greta arrays conditional on provided values for othersimultilogit()
transformchains
argument to model()
forwardsolve()
and backsolve()
colSums()
, rowSums()
, colMeans()
, and rowMeans()
dim<-()
to reshape greta arrayssweep()
now handles greta array STATS
when x
is numeric.internals
object to enable extension packagesAPI changes:
define_model()
, an alias for model()