This release adds a new marginal model based on primarycensored which provides a more efficient approach for fitting delay distributions compared to the existing latent model. We've also improved data handling by adding support for aggregated data across all models, added comprehensive examples using real world data, and enhanced documentation based on user feedback. The package has also undergone significant internal improvements including generalised Stan reparameterisation and improved data transformation methods.
As part of this release we have moved from @athowes maintaining the package (who led the initial package development, implementation of the S3 infrastructure, implementation of the core models, and wrote the first versions of the getting started vignette, Ebola case study, FAQ section, and the approximate inference vignette) to @seabbs maintaining the package.
primarycensored. This can be specified using as_epidist_marginal_model(). This is currently limited to Weibull, log-normal, and gamma distributions with uniform primary censoring but this will be generalised in future releases. See #426.weight argument to as_epidist_marginal_model() to allow for weighted data (for example count data) to be used in the marginal model. See #509.epidist_aggregate_data method to as_epidist_marginal_model() to allow straightforward use of the marginal model with aggregated data. See #510.epidist_aggregate_data method to as_epidist_latent_model() to allow straightforward use of the latent model with aggregated data. See #510.epidist_aggregate_data method to as_epidist_naive_model() to allow straightforward use of the naive model with aggregated data. See #510.epidist(). See #473.enforce_presence argument to epidist_prior() to allow for priors to be
specified if they do not match existing parameters. See #474.merge argument to epidist_prior() to allow for not merging user and package priors. See #474.brms and then extracting the reparameterisation. See #474.transform_data S3 method to allow for data to be transformed for specific models. This is specifically useful for the marginal model at the moment as it allows reducing the data to its unique strata. See #474.epidist_aggregate_data class to handle pre-aggregated line list data. See #510.as_epidist_aggregate_data() method for epidist_linelist_data objects to allow for easy conversion to aggregate data. See #510.as_epidist_linelist_data() method for epidist_aggregate_data objects to allow for easy conversion to linelist data. See #510.sierra_leone_ebola_data to the package. See #510.epidist_transform_data() methods work for the marginal and naive models. See #510.epinowcast standards. See #467.primarycensored that doesn't make use of size(). This fixes some Mac compilation edge cases. See #524.This is the first minor release of epidist intended for early test users of the package.
As some features may change, the package is marked as experimental.
We expect to release a stable 1.0.0 version shortly.
The epidist package implements models for epidemiological delay distributions.
It uses brms to perform Bayesian inference.
One data format is currently available:
Two statistical models are currently available:
brmsThe package is readily extensible to additional models via an S3 class based system. In particular, model fitting with [epidist()] is possible using S3 classes for custom:
We provide functionality for post-processing.
Alternatively, users may directly use tidybayes for specific families.
Three vignettes are available. There is also a frequently asked questions section.