Changelog

Source: NEWS.md

SeverityEstimate 0.1.0

• Added SeverityEstimateModel S4 class to represent a model and contain the metadata for building a model. This forms the foundation for the tidymodels-esque API, like so:

library(SeverityEstimate)
line_list <- data.frame(
  id = 1L:3L,
  week = c(1L, 1L, 2L),
  sex = c("M", "F", "M"),
  outcome = c("Asymptomatic", "Symptomatic", "Death"),
  detection = c("Active", "Active", "Passive")
)
population <- data.frame(
  sex = c("M", "F"),
  amount = c(123L, 456L)
)
model <- SeverityEstimateModel(line_list, population) |>
  set_active_prior(alpha = 1.0, beta = 1.0) |>
  set_passive_asymptomatic_prior(alpha = 1.0, beta = 3.0) |>
  set_passive_symptomatic_prior(alpha = 3.0, beta = 1.0) |>
  set_strata("sex") |>
  set_timesteps("week") |>
  set_detection(
    "detection",
    map = c("Active" = "active", "Passive" = "passive")
  ) |>
  set_outcome(
    "outcome",
    map = c(
      "Asymptomatic" = "asymptomatic",
      "Symptomatic" = "symptomatic",
      "Death" = "severe"
    )
  )
model
# Severity Estimate Model:
#
# Data:
#     dataset rows columns
#   line_list    3       5
#  population    2       2
#
# Detection Probability Priors:
#   active prior: beta(1.0, 1.0)
#   passive_asymptomatic prior: beta(1.0, 3.0)
#   passive_symptomatic prior: beta(3.0, 1.0)
#
# Timesteps:
#   week: 1 to 2 (2 timesteps)
#
# Detection:
#   column: detection
#     active: 2 cases (values: Active)
#     passive: 1 cases (values: Passive)
#
# Outcome:
#   column: outcome
#     asymptomatic: 1 cases (values: Asymptomatic)
#     symptomatic: 1 cases (values: Symptomatic)
#     severe: 1 cases (values: Death)
#
# Strata:
#   sex: 2 levels, df = 0 (F, M)

• Added default_model to quickly construct a model from well formated population and line list data.frames as a quick way to get started with the package. #114.
• Implemented fit() for SeverityEstimateModel, completing the S4 pipeline API. The model now supports an arbitrary number of ordered/unordered strata dimensions via additive fixed effects on the logit scale. Continuing from the example above:

fit_result <- model |>
  fit(
    chains = 2L,
    iter = 500L,
    seed = 42L,
    cores = 4L
  )
fit_result
# An object of class "SeverityEstimateFit"
# Slot "model_fit":
# Inference for Stan model: estimate_severity.stan.j2.
# 2 chains, each with iter=500; warmup=250; thin=1;
# post-warmup draws per chain=250, total post-warmup draws=500.
# ...

• Added a getting-started vignette demonstrating the default model, from synthetic data generation through model fitting and result extraction as well as a model-explainer vignette to do a deep dive into the model. Also added a mers-korea-2015 vignette to highlight how to use this package on a “real world” data set. #78, #107, #115.
• Setup pkgdown site hosted on GitHub pages at accidda.github.io/SeverityEstimate/. #28.
• Enhanced documentation elements, such as the README.md, CONTRIBUTING.md to add more helpful content to the package’s documentation site. #3, #53.
• Restructured package utilizing rstantools. #73.
• Added support for “mean”/“concentration” parameterization of beta prior distributions. #71.
• Added summary generic for SeverityEstimateFit objects. #30.
• Added an integration-style notebook under inst/notebooks/ for synthetic parameter-recovery checks, along with local render commands for heavier non-vignette R Markdown analyses. #116.
• Added calculate_hazard to get the underlying force of infection as a data.frame for diagnostics/additional analysis. #9.
• Removed the estimate_severity API. #88.

SeverityEstimate 0.0.1

• Initial version of the package with the main entry point being SeverityEstimate::estimate_severity that implements the basics of the model from doi:10.1093/aje/kwv452.