reinstall_greta_deps(), which helps with starting
from a clean slate when installing greta dependencies (#524)future and parallely packages
error when a CPU with only one core is provided (#513, #516).multiprocess as it is deprecated in
the future package (#394)Python is now initialised when a greta_array is
created (#468).
head and tail S3 methods for greta_array are now
consistent with head and tail methods for R versions 3 and 4 (#384).
greta_mcmc_list objects (returned by
mcmc()) are now no longer modified by operations (like
coda::gelman.diag()).
joint distributions of uniform variables now have the correct constraints when sampling (#377).
array-scalar dispatch with 3D arrays is now less buggy (#298).
greta now provides R versions of all of R’s
primitive functions (I think), to prevent them from silently not
executing (#317).
Uses Sys.unsetenv("RETICULATE_PYTHON") in
.onload on package startup, to prevent an issue introduced
with the “ghost orchid” version of RStudio where they do not find the
current version of RStudio. See #444 for more
details.
Internal change to code to ensure future continues
to support parallelisation of chains. See #447 for more
details.
greta now depends on future version
1.22.1, tensorflow (the R package) 2.7.0, and
parallelly 1.29.0. This should see no changes on the user
side.
Now depends on R >= 3.1.0 (#386)
chol2inv.greta_array() now warns user about LINPACK
argument being ignored, and also reminds user it has been deprecated
since R 3.1
calculate() now accepts multiple greta arrays for
which to calculate values, via the ... argument. As a
consequence any other arguments must now be named.
A number of optimiser methods are now deprecated, since they will
be unavailable when greta moves to using TensorFlow v2.0:
powell(), cg(), newton_cg(),
l_bfgs_b(), tnc(), cobyla(), and
slsqp().
dirichlet() now returns a variable (rather than an
operation) greta array, and the graphs created by
lkj_correlation() and wishart() are now
simpler as cholesky-shaped variables are now available
internally.
Adds the reinstall_greta_env(),
reinstall_miniconda(), remove_greta_env(), and
remove_miniconda() helper functions for helping
installation get to “clean slate” (#443).
greta currently doesn’t work on Apple Silicon (M1
Macs) as they need to use TF 2.0, which is currently being implemented.
greta now throws an error if M1 macs are detected and
directs users to https://github.com/greta-dev/greta/issues/458
(#487)
New install_greta_deps() - provides installation of
python dependencies (#417). This saves exact versions of Python (3.7),
and the python modules NumPy (1.16.4), Tensorflow (1.14.0), and
Tensorflow Probability (0.7.0) into a conda environment, “greta-env”.
When initialising Python, greta now searches for this conda environment
first, which presents a great advantage as it isolates these exact
versions of these modules from other Python installations. It is not
required to use the conda environment, “greta-env”. Overall this means
that users can run the function install_greta_deps(),
follow the prompts, and have all the python modules they need installed,
without contaminating other software that use different python
modules.
calculate() now enables simulation of greta array
values from their priors, optionally conditioned on fixed values or
posterior samples. This enables prior and posterior predictive checking
of models, and simulation of data.
A simulate() method for greta models is now also
provided, to simulate the values of all greta arrays in a model from
their priors.
variable() now accepts arrays for upper
and lower, enabling users to define variables with
different constraints.
There are three new variable constructor functions:
cholesky_variable(), simplex_variable(), and
ordered_variable(), for variables with these constraints
but no probability distribution.
New chol2symm() is the inverse of
chol().
mcmc(), stashed_samples(), and
calculate() now return objects of class
greta_mcmc_list which inherit from coda’s
mcmc.list class, but enable custom greta methods for
manipulating mcmc outputs, including a window()
function.
mcmc() and calculate() now have a
trace_batch_size argument enabling users to trade-off
computation speed versus memory requirements when calculating posterior
samples for target greta arrays (#236).
Many message, warning, and error prompts have been replaced internally with the {cli} R package for nicer printing. This is a minor change that should result in a more pleasant user experience (#423 #425).
Internally, where sensible, greta now uses the
glue package to create messages/ouputs (#378).
New FAQ page and updated installation instructions for installing Python dependencies (#424)
New greta_sitrep() function to generate a situation
report of the software that is available for use, and also initialising
python so greta is ready to use. (#441)
This release is predominantly a patch to make greta work with recent versions of TensorFlow and TensorFlow Probability, which were not backward compatible with the versions on which greta previously depended. From this release forward, greta will depend on specific (rather than minimum) versions of these two pieces of software to avoid it breaking if more changes are made to the APIS of these packages.
greta now (only) works with TensorFlow 1.14.0 and TensorFlow Probability 0.7.0 (#289, #290)
behaviour of the pb_update argument to
mcmc() has been changed slightly to avoid a bad interaction
with thinning (#284)
various edits to the documentation to fix spelling mistakes and typos
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 predictionmixture() and joint() distribution
constructorsabind(), 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.
model(),
%*%<- for
assignmentn_cores argument to model()calculate() function to compute the values of
greta arrays conditional on provided values for othersimultilogit() transformchains argument to model()cumsum() and cumprod()
functionsforwardsolve() 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()