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If you read this from a place other than <https://mc-stan.org/projpred/news/index.html>, please consider switching to that website since it features better formatting and cross-linking.
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# projpred 2.9.1.9000 (development)
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# projpred 2.10.0
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## Major changes
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* Added support for censored observations when using the latent projection (with response-scale analyses). This makes it possible, e.g., to use the latent projection for time-to-event models (also known as models for survival analysis). Due to this new feature, the function passed to argument `latent_ll_oscale` of `extend_family()` now needs to have an argument `cens`. See `?extend_family` (section "Latent projection") as well as the latent-projection vignette (section ["Censored observations (survival analysis)"](https://mc-stan.org/projpred/articles/latent.html#cens)) for more information and examples. Note that only the performance statistics `"elpd"`, `"mlpd"`, and `"gmpd"` take censoring into account (on response scale). (GitHub: #528)
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## Minor changes
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*`suggest_size()` returns `nterms_max` instead of NA when no model satisfies the criterion
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* Use `reformulas` package directly for formula processing functions. This package is already a dependency of `lme4` >= v1.1-38.
To make this vignette build faster, we use only 2 MCMC chains and 1000 iterations per chain (with half of them being discarded as warmup draws).
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In practice, 4 chains and 2000 iterations per chain are reasonable defaults.
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Furthermore, we make use of [**rstan**](https://mc-stan.org/rstan/)'s parallelization, which means to run each chain on a separate CPU core^[More generally, the number of chains is split up as evenly as possible among the number of CPU cores.].
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Furthermore, we make use of [**rstan**](https://mc-stan.org/)'s parallelization, which means to run each chain on a separate CPU core^[More generally, the number of chains is split up as evenly as possible among the number of CPU cores.].
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If you run the following code yourself, you can either rely on an automatic mechanism to detect the number of CPU cores (like the `parallel::detectCores()` function shown below) or adapt `ncores` manually to your system.
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