AR2R0
: calculate the R0 corresponding to a give attack
rateR02AR
: calculate the attack rate corresponding to a
give R0R02herd_immunity_threshold
: calculate the herd immunity
threshold for a given R0sim_linelist
: simulates a simple linelist (with no epi
model implied) data.frame
clean_labels()
emperical_incubation_dist()
will estimate the empirical
incubation distribution if given a data frame with dates of onset and a
range of exposure dates (@ffinger, #13)fit_gamma_incubation_dist()
wraps
empirical_incubation_dist()
and
fit_disc_gamma()
to fit a discretized gamma distribution to
the empirical incubation distribution results (@ffinger, #13).clean_labels()
gains the protect
argument
to protect meaningful symbols in the data.hash_names()
now has the hashfun
option
that allows users to specify either a “fast” or “secure” hashing
function to use (@zkamvar, #21).dplyr
, purrr
,
rlang
, and tidyr
are now imported.clean_labels()
can now handle non-latin characters and
gains the trans_id
argument, which allows the user to
customise the transformations (see
https://github.com/reconhub/epitrix/issues/19 for details).digest
with sodium
in Importssodium::scrypt()
as a more cryptographically secure
hashing algorithm for hash_names()
. Thanks to @dirkschumacher for
this addition. For details, see
https://github.com/reconhub/epitrix/pull/7.clean_labels
which can be used to
standardise labels in variables, removing non-ascii characters,
standardising separators, and more; now used in
hash_names
added salting algorithm to hash_names
(issue
1)
fixed bug happening when using tibble
inputs in
hash_names
(issue 2)
fit_disc_gamma
now also returns the fitted discretised
gamma distribution as a distcrete
objectFirst release of the package! This includes the following features:
fit_disc_gamma
: fit discretised gamma
distribution
gamma_log_likelihood
: compute gamma log
likelihood
gamma_mucv2shapescale
/gamma_shapescale2mucv
:
convert between different parametrisation of gamma
distributions.
hash_names
: generate hashed (‘anonymised’) labels
from individual data.
r2R0
: compute R0 from r
lm2R0_sample
: genrate samples of R0 from a
log-linear regression