calmr 0.6.1

• Added outputs argument to run_experiment(), parse(), and aggregate(), allowing the user to parse/aggregate only some model outputs.
• Documentation corrections for CRAN resubmission.

calmr 0.6.0

• Added dependency on data.table resulting in great speedups for large experiments.
• Replaced dependency on cowplot with dependency on patchwork.
• Removed dependencies on tibble, dplyr, tidyr, and other packages from the tidyverse.
• Removed shiny app from the package.
• The previous app is now distributed separately via the calmr.app package available on GitHub.
• Test coverage has reached 100%.
• The package is now ready for CRAN submission.

calmr 0.5.1

• Added parallelization and progress bars via future, future.apply, and progressr.
• Function calmr_verbosity can set the verbosity of the package.

calmr 0.5.0

• Implementation of ANCCR (Jeong et al., 2022), the first time-based model included in calmr.
• Added parameter distinction between trial-wise and period-wise parameters.
• Added internal augmentation of design/arguments depending on the model.
• All trial-based models do not use pre/post distinctions anymore. Using the “>” special character does not affect these models anymore.
• The “>” special character is used to specify periods within a trial. For example, “A>B>C” implies A is followed by B which is followed by C. See the using_time_models vignette for additional information.
• Named stimuli now support numbers trailing characters (e.g., “(US1)” is valid now.)

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_experiment).
• 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.
• 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.
• 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. The package now aims to maintain several associative learning models and implement tools for 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.