epidist 0.2.0

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.

Models

• Added a marginalised likelihood model based on 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.
• Added user settable primary event priors to the latent model. See #474.
• Added a marginalised likelihood to the latent model. See #474.
• Added a 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.
• Added a epidist_aggregate_data method to as_epidist_marginal_model() to allow straightforward use of the marginal model with aggregated data. See #510.
• Added a epidist_aggregate_data method to as_epidist_latent_model() to allow straightforward use of the latent model with aggregated data. See #510.
• Added a epidist_aggregate_data method to as_epidist_naive_model() to allow straightforward use of the naive model with aggregated data. See #510.
• Updated the naive model to internally transform the data to be optimally aggregated as for the marginal model. See #510.

Package

• Remove the default method for epidist(). See #473.
• Added enforce_presence argument to epidist_prior() to allow for priors to be
  specified if they do not match existing parameters. See #474.
• Added a merge argument to epidist_prior() to allow for not merging user and package priors. See #474.
• Generalised the Stan reparametrisation feature to work across all distributions without manual specification by generating Stan code with brms and then extracting the reparameterisation. See #474.
• Added a 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.
• Added new epidist_aggregate_data class to handle pre-aggregated line list data. See #510.
• Added a as_epidist_aggregate_data() method for epidist_linelist_data objects to allow for easy conversion to aggregate data. See #510.
• Added a as_epidist_linelist_data() method for epidist_aggregate_data objects to allow for easy conversion to linelist data. See #510.
• Added an example dataset sierra_leone_ebola_data to the package. See #510.
• Added examples to most functions to show usage of the package. See #510.
• Added improved documentation explaining how the epidist_transform_data() methods work for the marginal and naive models. See #510.

Documentation

• Brings the README into line with epinowcast standards. See #467.
• Switched over to using the marginal model as default in the documentation. See #426.
• Added a helper functions for new variables to avoid code duplication in vignettes. See #426.
• Improved the Ebola case study vignette to use truncated data and to reduce the focus on exploratory data analysis. See #510.

Bugs

• Switched to using a patched of primarycensored that doesn't make use of size(). This fixes some Mac compilation edge cases. See #524.

Pull requests

• Update README by @github-actions in #506
• Issue #507: Add weighting support to marginal model class by @seabbs in #509
• Update README by @github-actions in #511
• Issue #503: Fix windows CI by @seabbs in #517
• fix typo by @sbfnk in #518
• fix typo by @sbfnk in #519
• Add @sbfnk as contributor by @seabbs in #520
• Update README by @github-actions in #522
• Issue #504: Use patched primarycensored without size() by @seabbs in #524
• Issue #521: Change maintainer by @seabbs in #525
• Issue #508: Aggregate data class by @seabbs in #513

New Contributors

• @sbfnk made their first contribution in #518

Full Changelog: https://github.com/epinowcast/epidist/commits/v0.2.0

Temporary development release

Bump to dev version

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epidist 0.1.0

epidist 0.1.0

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:

1. The linelist data format

Two statistical models are currently available:

1. The naive model: which models the delay directly using brms
2. The latent model: which implements a latent variable model to correct for biases in the data

The 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:

1. Families
2. Formula
3. Prior distributions
4. Stan code

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 website section.