backimpute_I(), which takes a vector representing incidence
and estimates the number of unobserved infections prior to the first reported
case.estimate_R now accepts the backimputation_window parameter, which determines
the number of observations used to backimpute unobserved cases. If this is set
to 0 no backimputation will be performed. 0 is the default value guaranteeing
compatibility with previous versions of the package.vignettes/EpiEstim_backimputation.Rmd and tests/testthat/test-backimpute.R.master (#159, @Bisaloo).This release contains various spelling fixes for CRAN maintenance.
sample_posterior_R() samples values of R from the posterior distribution of
an estimate_R object (#70, @acori)NEWS.md file to track changes to the package. (#74, @zkamvar)EstimateR becomes estimate_R,
OverallInfectivity becomes oberall_infectivity, WT becomes
wallinga_teunis, and DiscrSI becomes discr_si. Names of arguments to
these functions have also changed to snake_case. Note that compatibility
functions have been added so that the old functions as written in EpiEstim
1.1-0 should still work but throw a warning pointing to the newest functions.incidence package: in the function estimate_R, the
first argument, i.e. the incidence from which the reproduction number is
calculated, can now be, either a vector of case counts (as in version 1.1-0) or
an incidence object (see R package incidence).estimate_R, the first
argument, i.e. the incidence from which the reproduction number can now
provide information about known imported cases: by specifying the first
argument as either a dataframe with columns "local" and "imported", or an
incidence object with two groups (local and imported, see R package
incidence). This new feature is described in Thompson et al. Epidemics 2019
(currently in review).estimate_R: in addition to
non_parametric_si, parametric_si and uncertain_si, which were already
available in EpiEstim 1.1-0, two new methods have been added: si_from_data or
si_from_sample. These allow feeding function estimate_R data on observed
serial intervals (method si_from_data) or posterior samples of serial
interval distributions obtained from such data (method si_from_sample). These
new features are described in Thompson et al. Epidemics 2019 (currently in
review).estimate_R: estimate_R now generates on
object of class estimate_R, which can be plotted separately by using the
new estimate_R_plots function, which also now allows to plot several R
estimates on a single plot.config for estimate_R function: this is meant to minimise
the number of arguments to function estimate_R; so arguments method,
t_start, t_end, n1, n2, mean_si, std_si, std_mean_si,
min_mean_si, max_mean_si, std_std_si, min_std_si, max_std_si,
si_distr, mean_prior, std_prior, and cv_posterior are now specified as
a group under this new config argument. Such a config argument must be of
class estimate_R_config and can be obtained as a results of the new
make_config function.make_config, which defines settings for function estimate_R,
and sets defaults where arguments are missing. In particular, if argument
incid is not NULL, by default config$t_start and config$t_end will be
set so that, when the configuration is used inside estimate_R function, the
reproduction number is estimated by default on sliding weekly windows (in
EpiEstim 1.1-0 there was no default for the time window of estimation of R).flu_2009_NYC_schoolmers_2014_15,MockRotavirusstats (to use the gamma distribution; it was already used in EpiEstim 1.1-0
but making the dependency explicit)coarseDataTools, fitdistrplus, coda (used for the new methods
si_from_data and si_from_sample in estimate_R function to estimate the
serial interval from data).incidence (so that estimate_R can take an incidence object as first
argument)graphics, reshape2, ggplot2, gridExtra, scales, grDevices (to
make new plots of outputs of estimate_R and wallinga_teunis functions)