Simple and Scalable Statistical Modelling in R

Write statistical models in R and fit them by MCMC on CPUs and GPUs, using Google TensorFlow (see < https://greta-dev.github.io/greta> for more information).


News

greta 0.3.0 (in development)

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).

Fixes:

operation bugs

  • dim<-() now always rearranges elements in column-major order (R-style, not Python-style)

performance bugs

  • removed excessive checking of TF installation by operation greta arrays (was slowing down greta array creation for complex models)
  • sped up detection of sub-DAGs in model creation (was slowing down model definition for complex models)
  • reduced passing between R, Python, and TensorFlow during sampling (was slowing down sampling)

New Functionality:

inference methods

  • 18 new optimisers have been added
  • initial values can now be passed for some or all parameters
  • 2 new MCMC samplers have been added: random-walk Metropolis-Hastings (thanks to @michaelquinn32) and slice sampling
  • improved tuning of MCMC during warmup (thanks to @martiningram)
  • integration with the 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.
  • the one_by_one argument to MCMC can handle serious numerical errors (such as failed matrix inversions) as 'bad' samples
  • new extra_samples() function to continue sampling from a model.
  • calculate() works on the output of MCMC, to enable post-hoc posterior prediction

distributions

  • multivariate distributions now accept matrices of parameter values
  • added mixture() and joint() distribution constructors

operations

  • added functions: abind(), aperm(), apply(), chol2inv(), cov2cor(), eigen(), identity(), kronecker(), rdist(), and tapply() (thanks to @jdyen)
  • we now automatically skip operations if possible, e.g. computing binomial and poisson densities with log-, logit- or probit-transformed parameters where they exist, or skipping cholesky decomposition of a matrix if it was created from its cholesky factor. This increases numerical stability as well as speed.

misc

  • ability to change the colour of the model plot (thanks to @dirmeier)
  • ability to reshape greta arrays using greta_array()

API changes:

inference methods

  • mcmc now runs 4 chains (simultaneously on all available cores), 1000 warmup steps, and 1000 samples by default
  • optimisation and mcmc methods are now passed to opt() and mcmc() as objects, with defined tuning parameters. The control argument to these functions is now defunct.
  • columns names for parameters now give the array indices for each scalar rather than a number (i.e. x[2, 3], rather than x.6)

distributions

  • multivariate distributions now define each realisation as a row, and parameters must therefore have the same orientation

misc

  • 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".

documentation

  • lots more model examples (thanks to @leehazel, @dirmeier, @jdyen)
  • two analysis case studies (thanks to @ShirinG, Tiphaine Martin, @mmulvahill, @michaelquinn32, @revodavid)
  • new and improved pkgdown website (thanks to @pteetor)

testing

  • added tests of the validity of posterior samples drawn by MCMC (for known distributions and with Geweke tests)

greta 0.2.5

Minor patch to handle an API change in the progress package. No changes in functionality.

greta 0.2.4

Fixes:

  • improved error checking/messages in model(), %*%
  • switched docs and examples to always use <- for assignment
  • fixed the n_cores argument to model()

New functionality:

  • added a calculate() function to compute the values of greta arrays conditional on provided values for others
  • added imultilogit() transform
  • added a chains argument to model()
  • improved HMC self-tuning, including a diagonal euclidean metric

greta 0.2.3

Fixes:

  • fixed breaking change in extraDistr API (caused test errors on CRAN builds)
  • added dontrun statements to pass CRAN checks on winbuilder
  • fixed breaking change in tensorflow API (1-based indexing)

New functionality:

  • added cumsum() and cumprod() functions

greta 0.2.2

New functionality:

  • added forwardsolve() and backsolve()
  • added colSums(), rowSums(), colMeans(), and rowMeans()
  • added dim<-() to reshape greta arrays
  • sweep() now handles greta array STATS when x is numeric

greta 0.2.1

New functionality:

  • export internal functions via .internals object to enable extension packages

API changes:

  • removed the deprecated define_model(), an alias for model()
  • removed the dynamics module, to be replaced by the gretaDynamics package

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("greta")

0.3.0 by Nick Golding, 5 months ago


https://github.com/greta-dev/greta


Report a bug at https://github.com/greta-dev/greta/issues


Browse source code at https://github.com/cran/greta


Authors: Nick Golding [aut, cre] , Simon Dirmeier [ctb] , Adam Fleischhacker [ctb] , Shirin Glander [ctb] , Martin Ingram [ctb] , Lee Hazel [ctb] , Tiphaine Martin [ctb] , Matt Mulvahill [ctb] , Michael Quinn [ctb] , David Smith [ctb] , Paul Teetor [ctb] , Jian Yen [ctb]


Documentation:   PDF Manual  


Apache License 2.0 license


Imports R6, tensorflow, reticulate, progress, future, coda, methods

Suggests knitr, rmarkdown, DiagrammeR, bayesplot, lattice, testthat, mvtnorm, MCMCpack, rmutil, extraDistr, truncdist, tidyverse, fields, MASS, abind

System requirements: Python (>= 2.7.0) with header files and shared library; TensorFlow (>= 1.10; https://www.tensorflow.org/); Tensorflow Probability (>=0.3.0; https://www.tensorflow.org/probability/)


See at CRAN