Skip to content

Latest commit

 

History

History
56 lines (38 loc) · 3.16 KB

NEWS.md

File metadata and controls

56 lines (38 loc) · 3.16 KB
Raw

epidist 0.1.0.1000

Development version of epidist.

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.

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.

Documentation

  • Brings the README into line with epinowcast standards. See #467.
  • Switched over to using the marginal model as default in documentation. See #426.
  • Added helper functions for new variables to avoid code duplication in vignettes. See #426.

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.