Package: EpiSoon 0.3.1

Sam Abbott

EpiSoon: Forecast Cases Using Reproduction Numbers

To forecast the time-varying reproduction number and use this to forecast reported case counts. Includes tools to evaluate a range of models across samples and time series using proper scoring rules.

Authors:Sam Abbott [aut, cre], Nikos Bosse [aut], Joel Hellewell [aut], Katharine Sherratt [aut], James Munday [aut], Robin Thompson [aut], Aurelien Chateigner [aut], Sylvain Mareschal [aut], Andrea Rau [aut], Nathalie Vialaneix [aut], Michael DeWitt [aut], Sebastian Funk [aut]

EpiSoon_0.3.1.tar.gz

EpiSoon_0.3.1.tar.gz(r-4.6-any)
EpiSoon_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
EpiSoon/json (API)

# Install 'EpiSoon' in R:

install.packages('EpiSoon', repos = c('https://epiforecasts.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/epiforecasts/episoon/issues

Datasets:

example_obs_cases - Example Observed Cases
example_obs_rts - Example Observed Rts
example_serial_interval - Example Serial Interval

On CRAN:

case-forecasts forecasts

4.79 score 7 stars 25 scripts 28 exports 58 dependencies

Last updated from:03a8b80001 (on v0.3.2). Checks:7 FAIL, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	FAIL	147
source / vignettes	OK	305
linux-release-x86_64	FAIL	183
macos-release-arm64	FAIL	79
macos-oldrel-arm64	FAIL	82
windows-devel	FAIL	124
windows-release	FAIL	108
windows-oldrel	FAIL	95
wasm-release	OK	226

Exports:%>%brms_model bsts_model compare_models compare_timeseries draw_from_si_prob evaluate_model fable_model forecast_cases forecast_cases_directly forecast_model forecast_rt forecastHybrid_model iterative_case_forecast iterative_direct_case_forecast iterative_rt_forecast lopensemble_model plot_compare_timeseries plot_forecast plot_forecast_evaluation plot_scores predict_cases predict_current_cases score_case_forecast score_forecast summarise_case_forecast summarise_forecast summarise_scores

Dependencies:backports checkmate cli cmdstanr codetools cowplot cpp11 data.table digest dplyr evaluate farver furrr future generics ggplot2 globals glue gtable HDInterval highr isoband knitr labeling lifecycle listenv lopensemble lubridate magrittr MASS parallelly pillar pkgconfig purrr R.methodsS3 R.oo R.utils R6 RColorBrewer Rcpp RcppArmadillo rlang S7 scales scoringRules scoringutils stringi stringr tibble tidyr tidyselect timechange utf8 vctrs viridisLite withr xfun yaml

Getting started

Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2023-03-01
Started: 2020-03-28

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
brms Model Wrapper	brms_model
bsts Model Wrapper	bsts_model
Compare forecasting models	compare_models
Compare timeseries and forecast models	compare_timeseries
Draw from the Serial Interval Probability Distribution	draw_from_si_prob
Evaluate a Model for Forecasting Rts	evaluate_model
Example Observed Cases	example_obs_cases
Example Observed Rts	example_obs_rts
Example Serial Interval	example_serial_interval
fable Model Wrapper	fable_model
Forecasts Cases for a Rt Forecasts	forecast_cases
Forecasts Cases Directly	forecast_cases_directly
forecast Model Wrapper	forecast_model
Fit and Forecast using a Model	forecast_rt
forecastHybrid Model Wrapper	forecastHybrid_model
Iteratively Forecast Cases Using an Iterative Rt Forecast	iterative_case_forecast
Iteratively forecast directly on cases	iterative_direct_case_forecast
Iteratively Forecast	iterative_rt_forecast
Stack Models According to CRPS	lopensemble_model
Summary plots to compare timeseries and forecast models	plot_compare_timeseries
Plot a Forecast	plot_forecast
Plot a Forecast	plot_forecast_evaluation
Plot forecast scores	plot_scores
Predict cases for a single Rt sample forecasts	predict_cases
Predict cases for Rts based on observed data	predict_current_cases
Score a case forecast	score_case_forecast
Score a Model Fit	score_forecast
Summarise Forecast Cases	summarise_case_forecast
Summarise Forecast Rts	summarise_forecast
Summarise model forecasting scores	summarise_scores