Provides functions for working with primary event censored distributions and ‘Stan’ implementations for use in Bayesian modeling. Primary event censored distributions are useful for modeling delayed reporting scenarios in epidemiology and other fields (Charniga et al. (2024) doi:10.48550/arXiv.2405.08841). It also provides support for arbitrary delay distributions, a range of common primary distributions, and allows for truncation and secondary event censoring to be accounted for (Park et al. (2024) doi:10.1101/2024.01.12.24301247). A subset of common distributions also have analytical solutions implemented, allowing for faster computation. In addition, it provides multiple methods for fitting primary event censored distributions to data via optional dependencies.
Installing the package
You can install the latest released version from CRAN using the standard
install.packages function:
install.packages("primarycensored")Alternatively, you can install the latest release from our r-universe repository:
install.packages("primarycensored", repos = "https://epinowcast.r-universe.dev")To install the development version from GitHub (warning! this version
may contain breaking changes and/or bugs), use the pak
package:
pak::pak("epinowcast/primarycensored")Similarly, you can install historical versions by specifying the release
tag (e.g.,
v0.2.0):
pak::pak("epinowcast/primarycensored@v0.2.0")Note: You can also use the above approach to install a specific commit if needed, for example, if you want to try out a specific unreleased feature, but not the absolute latest developmental version.
Installing CmdStan (optional for Stan functionality)
If you wish to use the Stan functions, you will need to install
CmdStan, which also
entails having a suitable C++ toolchain setup. We recommend using the
cmdstanr package. The Stan team
provides instructions in the Getting started with
cmdstanr
vignette, with other details and support at the package
site along with some key instructions
available in the Stan resources package
vignette,
but the brief version is:
# if you not yet installed `primarycensored`, or you installed it without
# `Suggests` dependencies
install.packages(
"cmdstanr",
repos = c("https://stan-dev.r-universe.dev", getOption("repos"))
)
# once `cmdstanr` is installed:
cmdstanr::install_cmdstan()Note: You can speed up CmdStan installation using the cores argument.
If you are installing a particular version of epinowcast, you may also
need to install a past version of CmdStan, which you can do with the
version argument.
We provide a range of other documentation, case studies, and community spaces to ask (and answer!) questions:
Package Website
The primarycensored website
includes a function reference, model outline, and case studies using the
package. The site mainly concerns the release version, but you can also
find documentation for the latest development
version.
Vignettes
We have created package vignettes to help you get started with primarycensored and to highlight other features with case studies.
Organisation Website
Our organisation website includes links to other resources, guest posts, and seminar schedule for both upcoming and past recordings.
Community Forum
Our community forum has areas for
question and answer
and considering new methods and
tools, among others. If
you are generally interested in real-time analysis of infectious
disease, you may find this useful even if you do not use
primarycensored.
We welcome contributions and new contributors! We particularly appreciate help on identifying and identified issues. Please check and add to the issues, and/or add a pull request and see our contributing guide for more information.
If you need a different underlying model for your work:
primarycensored provides a flexible framework for censored
distributions in both R and Stan. If you implement new distributions or
censoring mechanisms that expand the overall flexibility or improve the
defaults, please let us know either here or on the community
forum. We always like to hear about
new use-cases and extensions to the package.
Please briefly describe your problem and what output you expect in an issue. If you have a question, please don’t open an issue. Instead, ask on our Q and A page. See our contributing guide for more information.
Please note that the primarycensored project is released with a
Contributor Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
If making use of our methodology or the methodology on which ours is
based, please cite the relevant papers from our methods
outline.
If you use primarycensored in your work, please consider citing it
with citation("primarycensored").
All contributions to this project are gratefully acknowledged using the
allcontributors package
following the all-contributors
specification. Contributions of any kind are welcome!