Skip to contents

calmr 0.6.3

  • Added set_calmr_palette() function to control the colour/fill scales used to plot results (#1).
  • Fixed bug in make_experiment() that was triggered by empty phases and no miniblocks.
  • Changed get_timings() to require a specific model name.
  • Added vignette for TD model.

calmr 0.6.2

  • Aggregation of ANCCR data now ignores time; time entries are averaged.
  • Added the Temporal Difference model under the name “TD”. The model is in an experimental state.
  • Experiments for time-based models now require a separate list to construct time-based experiences. See get_timings().
  • Added experiences<-, timings, timings<- methods for CalmrExperiment class.
  • Revamped plotting functions and parsing functions.
  • Revamped output names for all models to make them more intelligible.
  • Fixed a bug related to the aggregation of pools in HDI2020 and HD2022.
  • Consolidated some man pages.

calmr 0.6.1

CRAN release: 2024-03-14

  • 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 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 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.