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Changelog

Source: NEWS.md

epidist 0.3.0

This release adds support for a wider range of distributions in the marginal model, improves documentation with new vignettes and FAQ sections, enhances the getting started guide with clearer examples of model comparison, and fixes several bugs related to parameter bounds and likelihood calculations.

Models

  • Added Stan-side support for fitting all distributions supported by primarycensored in the marginal model. See #540.
  • Added R-side analytical likelihood support for Lognormal, Gamma, and Weibull distributions. See #540.

Package

  • Remove caching of vignettes. See #533.

Documentation

  • Added a new vignette “Guide to the statistical models implemented in epidist”. See #514.
  • Added a new FAQ section showcasing how to use the posterior package with epidist models, particularly for working with random variables (rvars) to propagate uncertainty in calculations. See #547.
  • Added a new FAQ section on how to use the marginaleffects package with epidist models. See #547.
  • Reduced the focus on simulating data in the getting started vignette to make it more accessible. See #549.
  • Made the entry to the package friendlier with clearer examples and improved documentation. See #549.
  • Added a schematic to explain right truncation more clearly to the getting started vignette. See #549.
  • Added a comparison of fitting naive and marginal models in the getting started vignette to highlight the importance of accounting for biases. See #549.
  • Added examples showing how to extract estimated parameters and plot them against true values to evaluate model performance. See #549.

Bugs

  • Fixed a vector length issue for censoring that was causing problems in some likelihood calls. See #540.
  • Fixed a bug in the preprocessing of the Weibull family. See #540.
  • Fixed a bug where bounds were not set for mu parameters in custom families. See #549.
  • Fixed a bug in predict_delay_parameters() where it couldn’t detect brms families when used directly. See #549.

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.

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 section.