Fits Psychometric Functions for Multiple Groups

Quickly fits and plots psychometric functions (normal, logistic, Weibull or any or any function defined by the user) for multiple groups.

quickpsy is an R package developed by Daniel Linares and Joan López-Moliner to quickly fit and plot psychometric functions for multiple conditions. It makes an extensive use of Hadley Wickham's packages ggplot2 and dplyr.

To understand the fundamentals of fitting psychometric functions in R, we recommend the book Modeling Psychophysical Data in R.

  • Fits and plots multiple conditions with minimal coding.

  • Exploits the computational speed of dplyr.

  • The user does not need to introduce initial parameters.

  • Calculates parametric and non-parametric bootstrap confidence intervals.

  • Compares parameters and thresholds for different conditions using bootstrap.

  • Guess and lapses can be fixed or free as parameters.

  • Fits cumulative normal, logistic, weibull functions or any function defined by the user.

  • Facilitates the reading of several data files.

  • Performs goodness-of-fit (deviance).

  • Computes AIC.

Download and install R (we also recommend Rstudio).

In R, install the following packages: boot, DEoptim, dplyr, ggplot2, tidyr and devtools.


Quickpsy can be installed from CRAN


To install the latest developed version, you can install quickpsy from github (which will also install dplyr and ggplot2)

library(MPDiR) # contains the Vernier data; use ?Venier for the reference
fit <- quickpsy(Vernier, Phaseshift, NumUpward, N, 
                grouping = .(Direction, WaveForm, TempFreq))

plotpar(fit) #plot the parameters


To obtain information and examples for specific functions use ?


For more examples and information visit


Reference manual

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0.1.4 by Linares Daniel, a year ago

Browse source code at

Authors: Linares Daniel [aut, cre], López-Moliner Joan [aut]

Documentation:   PDF Manual  

Task views: Psychometric Models and Methods

MIT + file LICENSE license

Imports MPDiR

Depends on DEoptim, dplyr, ggplot2

See at CRAN