NEWS.md
New sample size specific diagnostic threshold for Pareto k. The pre-2022 version of the PSIS paper recommended diagnostic thresholds of k < 0.5 "good", 0.5 <= k < 0.7 "ok", 0.7 <= k < 1 "bad", k>=1 "very bad". The 2022 revision of the PSIS paper now recommends k < min(1 - 1/log10(S), 0.7) "good", min(1 - 1/log10(S), 0.7) <= k < 1 "bad", k > 1 "very bad", where S is the sample size. There is now one fewer diagnostic threshold ("ok" has been removed), and the most important threshold now depends on the sample size S. With sample sizes 100, 320, 1000, 2200, 10000 the sample size specific part 1 - 1/log10(S) corresponds to thresholds of 0.5, 0.6, 0.67, 0.7, 0.75. Even if the sample size grows, the bias in the PSIS estimate dominates if 0.7 <= k < 1, and thus the diagnostic threshold for good is capped at 0.7 (if k > 1, the mean does not exist and bias is not a valid measure). The new recommended thresholds are based on more careful bias-variance analysis of PSIS based on truncated Pareto sums theory. For those who use the Stan default 4000 posterior draws, the 0.7 threshold will be roughly the same, but there will be fewer warnings as there will be no diagnostic message for 0.5 <= k < 0.7. Those who use smaller sample sizes may see diagnostic messages with a threshold less than 0.7, and they can simply increase the sample size to about 2200 to get the threshold to 0.7.
No more warnings if the r_eff argument is not provided, and the default is now r_eff = 1. The summary print output showing MCSE and ESS now shows diagnostic information on the range of r_eff. The change was made to reduce unnecessary warnings. The use of r_eff does not change the expected value of elpd_loo, p_loo, and Pareto k, and is needed only to estimate MCSE and ESS. Thus it is better to show the diagnostic information about r_eff only when MCSE and ESS values are shown.
k Inf if it is NA by @topipa in #224E_loo() when type is variance by @jgabry in #226E_loo() now allows type="sd" by @jgabry in #226pointwise() convenience function for extracting pointwise estimates by @jgabry in #241k threshold by @avehtari in #235mcse_elpd using log-normal approximation by @avehtari in #246n_eff/ESS if k > k_threshold by @avehtari in #248E_loo() Pareto-k diagnostics by @avehtari in #247loo_subsample.R by @avehtari in #238New loo_predictive_metric() function for computing estimates of leave-one-out predictive metrics: mean absolute error, mean squared error and root mean squared error for continuous predictions, and accuracy and balanced accuracy for binary classification. (#202, @LeeviLindgren)
New functions crps(), scrps(), loo_crps(), and loo_scrps() for computing the (scaled) continuously ranked probability score. (#203, @LeeviLindgren)
New vignette “Mixture IS leave-one-out cross-validation for high-dimensional Bayesian models.” This is a demonstration of the mixture estimators proposed by Silva and Zanella (2022). (#210)
loo_model_weights() to make them consistent with loo_compare(). (#217)New Frequently Asked Questions page on the package website. (#143)
Speed improvement from simplifying the normalization when fitting the generalized Pareto distribution. (#187, @sethaxen)
Added parallel likelihood computation to speedup loo_subsample() when using posterior approximations. (#171, @kdubovikov)
Switch unit tests from Travis to GitHub Actions. (#164)
save_psis = TRUE (#166, @fweber144).Fixed a bug in relative_eff.function() that caused an error on Windows when using multiple cores. (#152)
Fixed a potential numerical issue in loo_moment_match() with split=TRUE. (#153)
Fixed potential integer overflow with loo_moment_match(). (#155, @ecmerkle)
Fixed relative_eff() when used with a posterior::draws_array. (#161, @rok-cesnovar)
elpd() (and methods for matrices and arrays) for computing expected log predictive density of new data or log predictive density of observed data. A new vignette demonstrates using this function when doing K-fold CV with rstan. (#159, @bnicenboim)loo_moment_match() that prevented ... arguments from being used correctly. (#149)Added Topi Paananen and Paul Bürkner as coauthors.
New function loo_moment_match() (and new vignette), which can be used to update a loo object when Pareto k estimates are large. (#130)
The log weights provided by the importance sampling functions psis(), tis(), and sis() no longer have the largest log ratio subtracted from them when returned to the user. This should be less confusing for anyone using the weights() method to make an importance sampler. (#112, #146)
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)
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.
E_loo() function for computing weighted expectations (means, variances, quantiles).pareto_k_table() and pareto_k_ids() convenience functions for quickly identifying problematic observations(-Inf, 0.5], (0.5, 0.7], (0.7, 1], (1, Inf) (didn’t used to include 0.7)psislw() instead of print.loo
print.loo() shows a table of pareto k estimates (if any k > 0.7)compare() to allow loo objects to be provided in a list rather than in '...'
extract_log_lik()
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:
cores=1.psislw function is called in an interactive session.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.
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.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.options(loo.cores = NUMBER).