(GitHub issue/PR number in parentheses)

Added Mans Magnusson as a coauthor.

New functions

`loo_subsample()`

and`loo_approximate_posterior()`

(and new vignette) for doing PSIS-LOO with large data. (#113)Added support for standard importance sampling and truncated importance sampling (functions

`sis()`

and`tis()`

). (#125)`compare()`

now throws a deprecation warning suggesting`loo_compare()`

. (#93)A smaller threshold is used when checking the uniqueness of tail values. (#124)

For WAIC, warnings are only thrown when running

`waic()`

and not when printing a`waic`

object. (#117, @mcol)Use markdown syntax in roxygen documentation wherever possible. (#108)

(GitHub issue/PR number in parentheses)

New function

`loo_compare()`

for model comparison that will eventually replace the existing`compare()`

function. (#93)New vignette on LOO for non-factorizable joint Gaussian models. (#75)

New vignette on “leave-future-out” cross-validation for time series models. (#90)

New glossary page (use

`help("loo-glossary")`

) with definitions of key terms. (#81)New

`se_diff`

column in model comparison results. (#78)Improved stability of

`psis()`

when`log_ratios`

are very small. (#74)Allow

`r_eff=NA`

to suppress warning when specifying`r_eff`

is not applicable (i.e., draws not from MCMC). (#72)Update effective sample size calculations to match RStan’s version. (#85)

Naming of k-fold helper functions now matches scikit-learn. (#96)

This is a major release with many changes. Whenever possible we have opted to deprecate rather than remove old functionality, but it is possible that old code that accesses elements inside loo objects by position rather than name may error.

New package documentation website http://mc-stan.org/loo/ with vignettes, function reference, news.

Updated existing vignette and added two new vignettes demonstrating how to use the package.

New function

`psis()`

replaces`psislw()`

(now deprecated). This version implements the improvements to the PSIS algorithm described in the latest version of https://arxiv.org/abs/1507.02646. Additional diagnostic information is now also provided, including PSIS effective sample sizes.New

`weights()`

method for extracting smoothed weights from a`psis`

object. Arguments`log`

and`normalize`

control whether the weights are returned on the log scale and whether they are normalized.Updated the interface for the

`loo()`

methods to integrate nicely with the new PSIS algorithm. Methods for log-likelihood arrays, matrices, and functions are provided. Several arguments have changed, particularly for the`loo.function`

method. The documentation at`help("loo")`

has been updated to describe the new behavior.The structure of the objects returned by the

`loo()`

function has also changed slightly, as described in the**Value**section at`help("loo", package = "loo")`

.New function

`loo_model_weights()`

computes weights for model averaging as described in https://arxiv.org/abs/1704.02030. Implemented methods include stacking of predictive distributions, pseudo-BMA weighting or pseudo-BMA+ weighting with the Bayesian bootstrap.Setting

`options(loo.cores=...)`

is now deprecated in favor of`options(mc.cores=...)`

. For now, if both the`loo.cores`

and`mc.cores`

options have been set, preference will be given to`loo.cores`

until it is removed in a future release. (thanks to @cfhammill)New functions

`example_loglik_array()`

and`example_loglik_matrix()`

that provide objects to use in examples and tests.When comparing more than two models with

`compare()`

, the first column of the output is now the`elpd`

difference from the model in the first row.New helper functions for splitting observations for K-fold CV:

`kfold_split_random()`

,`kfold_split_balanced()`

,`kfold_split_stratified()`

. Additional helper functions for implementing K-fold CV will be included in future releases.

- Introduce the
`E_loo`

function for computing weighted expectations (means, variances, quantiles).

`pareto_k_table`

and`pareto_k_ids`

convenience functions for quickly identifying problematic observations- pareto k values now grouped into
`(-Inf, 0.5]`

,`(0.5, 0.7]`

,`(0.7, 1]`

,`(1, Inf)`

(didn’t used to include 0.7) - warning messages are now issued by
`psislw`

instead of`print.loo`

`print.loo`

shows a table of pareto k estimates (if any k > 0.7)- Add argument to
`compare`

to allow loo objects to be provided in a list rather than in`'...'`

- Update references to point to published paper

- GitHub repository moved from @jgabry to @stan-dev
- Better error messages from
`extract_log_lik`

- Fix example code in vignette (thanks to GitHub user @krz)

- Add warnings if any p_waic estimates are greather than 0.4
- Improve line coverage of tests to 100%
- Update references in documentation
- Remove model weights from
`compare`

.

In previous versions of**loo**model weights were also reported by`compare`

. We have removed the weights because they were based only on the point estimate of the elpd values ignoring the uncertainty. We are currently working on something similar to these weights that also accounts for uncertainty, which will be included in future versions of**loo**.

This update makes it easier for other package authors using **loo** to write tests that involve running the `loo`

function. It also includes minor bug fixes and additional unit tests. Highlights:

- Don’t call functions from
**parallel**package if`cores=1`

. - Return entire vector/matrix of smoothed weights rather than a summary statistic when
`psislw`

function is called in an interactive session. - Test coverage > 80%

This update provides several important improvements, most notably an alternative method for specifying the pointwise log-likelihood that reduces memory usage and allows for **loo** to be used with larger datasets. This update also makes it easier to to incorporate **loo**’s functionality into other packages.

- Add Ben Goodrich as contributor
- S3 generics and
`matrix`

and`function`

methods for both`loo`

and`waic`

. The matrix method provide the same functionality as in previous versions of**loo**(taking a log-likelihood matrix as the input). The function method allows the user to provide a function for computing the log-likelihood from the data and posterior draws (which are also provided by the user). The function method is less memory intensive and should make it possible to use**loo**for models fit to larger amounts of data than before. - Separate
`plot`

and`print`

methods.`plot`

also provides`label_points`

argument, which, if`TRUE`

, will label any Pareto`k`

points greater than 1/2 by the index number of the corresponding observation. The plot method also now warns about`Inf`

/`NA`

/`NaN`

values of`k`

that are not shown in the plot. `compare`

now returns model weights and accepts more than two inputs.- Allow setting number of cores using
`options(loo.cores = NUMBER)`

.

- Updates names in package to reflect name changes in the accompanying paper.

- Better handling of special cases
- Deprecates
`loo_and_waic`

function in favor of separate functions`loo`

and`waic`

- Deprecates
`loo_and_waic_diff`

. Use`compare`

instead.

- Initial release