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# Changelog

## 0.4.0

* 2x faster on GPU, due to parallelizing better the tree sampling step.
* Uses less memory, now can do $n=100\,000$ with $10\,000$ trees on a V100. This was mostly an excessively large batch size for counting datapoints per leaf.
* The Metropolis-Hastings ratio is saved only for the proposed move.
* The grow and prune moves are merged into one object.

## 0.3.0

* 2-3x faster on CPU.
* Uses less memory.
* Add `initkw` argument to `BART.gbart` for advanced configuration of the MCMC initialization.
* Modified the automatic determination of `sigest` in `BART.gbart` to match the one of the R package.
* Add `usequants=False` option to `BART.gbart`, which is now the default.
* New function `prepcovars.uniform_splits_from_matrix`.
* Add `sum_trees=False` option to `grove.evaluate_forest` to evaluate separately each tree.
* Support non-batched arguments in `jaxext.autobatch`.
* Fix a bug with empty arrays in `jaxext.autobatch`.
* New option in `mcmcstep.init` to save the acceptance ratios.
* Separate batching options for residuals and counts.
* Sweeping changes to the tree move internals, more computations are parallel across trees.
* Added support for `dbarts` in the unit tests.

## 0.2.1

* Fix a bug that prevented using bart in a compiled function.

## 0.2.0

* Rename `bartz.BART` to `bartz.BART.gbart`.
* Expose submodule `bartz.jaxext` with auxiliary functions for jax.
* Shorter compilation time if no burnin or saved samples are drawn. This is useful when using the interface only to create the initial MCMC state, or when saving all samples to inspect the warm-up phase.
* 20x faster on GPU.
* 2x faster on CPU.
* Use less temporary memory to quantilize covariates, avoiding out-of-memory errors on GPU.

## 0.1.0

* Optimize the MCMC step to only traverse each tree once.
* Now `bartz` runs at the same speed as the R package `BART` (tested at $p=10$, $n=100\ldots 10000$).
* The MCMC functions are heavily changed, but the interface is the same.

## 0.0.1

* `BART` has attributes `maxdepth`, `sigest`.
* Fix errors with scaling of noise variance prior.
* Fix iteration report when `keepevery` is not 1.
* Lower required versions of dependencies to allow running on Colab.

## 0.0

First release.