Check why wisp() does not clear memory on exit when
fit_only is TRUE.
Autodetect fixed-effect type? E.g., fixed-effect columns of R type
“character” or “factor” are treated as two-level categorical factors,
and numeric columns are treated as discrete additive effects (e.g., like
a time series). Will need to update the RORB tutorial and any other
tutorials which discuss time-series modeling.
Ensure any columns flagged as a time series are also treated as
fixed-effects.
Add vignette on mixed-effects modeling and random effects (vs
normalization).
Finish vignettes on modeling celltypes, statistical analyses, and
the wisp plot.
Add a check during the fit to see if any transition points can be
replaced with a slope.
Add variable check to the loading of plot.settings in wisp().
Version 1.1
Added discrete time-series modeling functionality (the
timeseries variable option) and plotting (the function
plot.timeseries()).
Ensured code would robustly run for any data including at least
count and bin columns, without need
for context, species,
ran, or fixed-effect variables.
Added explicit fit_only option to wisp() to avoid
running any parameter estimation (MCMC or bootstrapping).
Version 1.0
Initial public release of wispack, as used in this preprint.
Defines the wisp() function for implementing wisps.
Introduces one-dimensional warped sigmoidal Poisson-process
mixed-effect modeling (one-dimensional wisps) for testing for functional
spatial effects in spatial transcriptomics data.