Victor Navarro

Using the calmr app

If you are a beginner useR or simply want to simulate an experimental design to see what a model does, you might be interested in using the calmr application.

The calmr application offers a GUI that allows you to simulate experiments without writing any code. If you want to use the online app, you can find it at: Alternatively, if you have installed the calmr package, you can launch the app via calmr::calmr_app(). The rest of the tutorial assumes that you have the app open and ready to run. Let’s break down the GUI.


The calmr app GUI upon launch
The calmr app GUI upon launch

Design Table

The design table is where you specify the experimental design to run. Using the Group- and Group+, you can remove or add groups from the design. Using the Phase- and Phase+, you can remove or add phases from the design. The Parse Design button is used to parse the design, which is a required step to run the simulation. More on that later.

The P1 and P2 columns above specify the phases in the experiment and are simulated from left to right. Each entry in those columns specifies the trials given to the corresponding groups (G1 or G2, in this case). These entries must obey a special syntax (see calmr_basics for additional information). For now, it will suffice to say that:

Additionally, one can choose to randomize the trials within each phase by ticking the boxes in the R1 and R2 columns. It is important to note that whatever the user sets up here will interact with the “Create trial blocks” option in the Options tab in the sidebar (see ahead). Here’s the full breakdown of combinations and their behaviour:

Go ahead and parse the design. Some new things will appear on the GUI.


The calmr app GUI after parsing
The calmr app GUI after parsing

After parsing a valid design. The user can set up the parameters for the experiment (including all stimulus-related and global parameters). In this case, the Rescorla-Wagner model has 4 parameters per stimulus. The default values are fairly sensible, but you can modify each parameter by hand as you would in your favorite spreadsheet software.

The parametrization of each model in calmr can sometimes differ from what appears in the literature. The following table contains links to the documentation pages for each model.

Citation Name Model page
Rescorla and Wagner (1972) RW1972 Link
Mackintosh (1975) MAC1975 Link
Pearce, Kaye, and Hall (1982) PKH1982 Link
Stout and Miller (2007) SM2007 Link
Honey, Dwyer and Illiescu (2020) HDI2020 Link
Honey and Dwyer (2022) HD2022 Link


A new button appears after parsing an experiment. A final click on that button will run the model and populate the “Results” and “Association Graphs” portions of the app. Go ahead and run the experiment. After you do, a new button will appear, allowing you to download the results as a spreadsheet.

The calmr app GUI after running the model
The calmr app GUI after running the model

In the calmr app, the results are shown visually. Clicking the bar above the graph (the one containing “Blocking - Response Strength …” above) will show you all the plots available. The first portion of each plot’s name denotes the group’s name. In the above, the plot shows the strength of the associations among all stimuli in the experiment across trials (or blocks), faceted with phases as columns, and origin stimuli as rows. For example, the yellow lines above denote the strength that A and B have with the US. The top column corresponds to A (look at the label to the right) and the middle column corresponds to B.

Go ahead and explore all available plots. These are usually self explanatory, but you should consult the documentation of the package in case something is unclear (specially when using more obscure models).

Association Graphs

The bottom portion of the app shows network graphs depicting the strength of the associations in the model on a given trial, for all groups. Yellow denotes excitatory strength (i.e., positive values), gray denotes neutral strength (i.e., values close to zero), and purple (not shown above) shows inhibitory strength (i.e., negative values). Move the “Trial” slider to explore how the associations in the model change across the experiment.

Other bits

The sections above implement the bulk of the functionalities in the calmr app. The following sections describe additional options convenience functions that I found useful.


Here we set the number of iterations to run the experiment for (important if model behaviour is sensitive to trial order effects), and whether we want to create trial blocks. Here we also set Here we can choose to plot in a common scale for the y-axis (active by default).

A final message

Hope you enjoy the app! If you do find any bugs, have comments, or would like something implemented, feel free to post a message on the package’s github repository or drop me a line at navarrov [at]