Release Notes

Planned changes for future releases

Experiment with extending the treatment knock-out (trtKO) functionality into a likelihood-ratio test for hypothesis testing. Update the tutorial on customizing statistical analyses, if needed.
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 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. Currently only runs check_list without doing a more substantive check. Compare with the more detailed checks of MCMC.settings.
Allow MCMC.prior to be a vector of different prior distributions for each parameter. When completed, update the tutorial on customizing statistical analyses.

Added model.settings argument trtKO to wisp which enables excluding treatments to run reduced models.
Replaced corrupted data file “corticallaminar_model.rds” with working version.

Release attached to NAR resubmission.
Redesigned plots.
Added attractor simulation functionality and benchmarking.
Includes first-draft versions of package tutorials.
Note that the package data file “corticallaminar_model.rds” was corrupted and should be pulled from the latest version or latest commit on the main branch.

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).

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.