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.

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.

Computes AIC.

Download and install R (we also recommend Rstudio).

In the console, you need to type

`install.packages('quickpsy')`

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

`fit %>% plotpar # or plotpar(fit)`

`fit %>% plotthresholds() # or plotthresholds(fit)`

To obtain information and examples for specific functions use *?*

```
?quickpsy
?plotthresholds
```

psyphy: among other things, it provides links functions to fit psychometric functions using an approach based on generalized linear models.

modelfree: fits psychometric functions using a non-parametric approach.