Package: R0
Type: Package
Title: Estimation of R0 and Real-Time Reproduction Number from
Epidemics
Version: 1.2-6
Date: 2015-05-21
Author: Pierre-Yves Boelle, Thomas Obadia
Maintainer: Thomas Obadia <thomas.obadia@iplesp.upmc.fr>
Depends: R (>= 2.13.0), MASS
Description: Estimation of reproduction numbers for disease outbreak, based on
incidence data. The R0 package implements several documented methods. It is
therefore possible to compare estimations according to the methods used.
Depending on the methods requested by user, basic reproduction number
(commonly denoted as R0) or real-time reproduction number (referred to as
R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
will give different graphs depending on the methods requested : basic
reproductive number estimations will only show the epidemic curve
(collected data) and an adjusted model, whereas real-time methods will also
show the R(t) variations throughout the outbreak time period. Sensitivity
analysis tools are also provided, and allow for investigating effects of
varying Generation Time distribution or time window on estimates.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2015-05-21 15:45:29 UTC; thomas
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-05-21 19:34:12

Title: Compressive Sampling: Sparse signal recovery utilities
Description: Provides miminization routines and related utilities for
compressive sampling. l-1, l-2 and TV (1-D) minimization, DFT
basis matrix, sparse signal generator and examples.

basis matrix, sparse signal generator, mutual cumulative

Title: Compressive Sampling: Sparse Signal Recovery Utilities
email = "mehmet.suzen@physics.org", role = c("aut", "cre")))
Description: Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.

Authors@R: person("Nan", "Zhang", email = "awekevin@onethird-lab.com", role  = c("aut", "cre"))
Description: Since December 2019, Wuhan City, Hubei Province has continued to carry out surveillance of influenza and related diseases, and found multiple cases of viral pneumonia, all of which were diagnosed with viral pneumonia / pulmonary infection. On January 12, 2020, the World Health Organization officially named the new coronavirus causing the pneumonia epidemic in Wuhan as "2019 New Coronavirus (2019-nCoV)". The current epidemic situation is very serious, here we developed an R package for 2019-nCoV analysis(Real-time monitoring and Visualization) by querying real-time statistics of 2019-nCoV virus cases from <https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5&callback=1580373566110> and performing follow-up analysis.
Imports: jsonlite, ggplot2, dplyr, pinyin, maps