Package: sparsesurv 0.1.1

Alexandros Angelakis

sparsesurv: Forecasting and Early Outbreak Detection for Sparse Count Data

Functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics.

Authors:Alexandros Angelakis [aut, cre], Bryan Nyawanda [aut], Penelope Vounatsou [aut]

sparsesurv_0.1.1.tar.gz
sparsesurv_0.1.1.zip(r-4.7-any)sparsesurv_0.1.1.zip(r-4.6-any)sparsesurv_0.1.1.zip(r-4.5-any)
sparsesurv_0.1.1.tgz(r-4.6-any)sparsesurv_0.1.1.tgz(r-4.5-any)
sparsesurv_0.1.1.tar.gz(r-4.7-any)sparsesurv_0.1.1.tar.gz(r-4.6-any)
sparsesurv_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sparsesurv/json (API)

# Install 'sparsesurv' in R:
install.packages('sparsesurv', repos = c('https://alexangelakis-ang.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alexangelakis-ang/sparsesurv/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

2.70 score 344 downloads 9 exports 15 dependencies

Last updated from:3789f5930a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK118
source / vignettesOK186
linux-releaseOK131
macos-releaseOK192
macos-oldrelOK183
windows-develOK80
windows-releaseOK81
windows-oldrelOK80
wasm-releaseOK97

Exports:GARMA_NBGARMA_ZINBNBoutbreakHNBoutbreakNBoutbreakZINBSENBSEZINBZINB

Dependencies:abindbootclicodagluelatticelifecyclemagrittrR2jagsR2WinBUGSrjagsrlangstringistringrvctrs