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
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