Major refactoring of classes and models. This should help development moving forward.
Added several methods for access to CalmrExperiment contents, including c (to bind experiments) results, plot, graph, design and parameters.
Created CalmrDesign and CalmrResult classes.
Rewrote parsers to be less verbose and to rely less on the tidyverse suite and piping.
Substantially reduced the complexity of make_experiment function (previous make_model_args).
Introduced distinction between stimulus-specific and global parameters.
Parameters are now lists instead of data.frames.
Modified UI for calmr app to include a sidebar (to reduce clutter).
Simplified the app by removing some of the options.
Nearly duplicated the number of tests.
Added first version of the SOCR model (SM2007) as well as two vignettes explaining the math behind the implementation and some quick simulations. Warning: EXPERIMENTAL.
Added multiple models to package and app (RW1972, PKH1982, MAC1975).
Implementation of basic S4 classes for model, experiment, fit, and RSA comparison objects, as well as their methods.
Added genetic algorithms (via GA) for parameter estimation.
Added basic tools to perform representational similarity analysis.
heidi is now calmr: Canonical Associative Learning Models in R. The package now aims to maintain several associative learning models and implement tools for the their use.
Major overhaul of the training function (train_pav_model). All relevant calculations are now done as a function of all functional stimuli instead of just the US.
Support for the specification of expectation/correction steps within the trial via “>”. For example, the trial “A>(US)” will use only A to generate the expectation, but will learn about both stimuli during the correction step.
The previous plotting function for R-values has been revamped to allow both simple and complex versions. The complex version facets r-values on a predictor basis, and uses colour lines for each target.