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calmr 0.4.0

  • 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.

calmr 0.3.0

  • 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.
  • Documentation progress.

calmr 0.2.0

  • 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.
  • Documentation progress.

calmr 0.1.0

  • 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.
  • Bugfix related to stimulus saliencies.