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.frameclean_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_namesadded 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