Depends: R (>= 4.1) to reflect use of the
native pipe in vignettes.@return documentation to
seqwrap_summarise(), simcounts(),
seqwrapResults, and swcontainer.seqwrap_check,
fit_fun, fit_fun_lme,
data_helper, seqwrap_mtf) with
@noRd so they no longer generate user-facing Rd pages.seqwrap:::simcounts with
seqwrap::simcounts in tests.cores = "max" and
cores = NULL in seqwrap(); invalid values now
error early.edgeR) are unavailable.nlme::lme and nlme::gls,
see the vignette for the use of additional_vars when
working with nlme::glstargetdata now supports a list of data frames.
Target-specific data frames are made available by their column
names.This update has focused on improving the workflow of using seqwrap. A
new function (seqwrap_compose) allows for the user to
collect all data needed to iterate over targets to fit models.
seqwrap can still be specified using the same arguments,
but can also use a swcontainer created with
seqwrap_compose. In seqwrap we only need to
specify e.g. the number of cores and return/save models.
fittin_fun argument in seqwrap has been replaced by
modelfun.seqwrap_compose let’s you collect all data elements
and arguments needed to run iterative modelling with
seqwrap without initializing it.
seqwrap:::simcounts was created as an internal
function used in testing. It creates a simulated data set of counts
based on variation across genes in a set of parameters.
A new set of classes and methods has been written using the S7 OOP system. This means that data is validated to prevent errors in setting up the models and data.
targetdata is now available as an argument in
seqwrap and seqwrap_compose making it possible
to supply target-wise values used in the arguments. E.g. setting the
dispersion parameter to a fixed value.
Using a swcontainer object as the first argument in
seqwrap followed by a named argument will lead to an update
of the swcontainer object before any modelling.
seqwrap_summarise efficiently combine data frames
from summary and evaluation functions.
and more…