The following is a minimal example of a simple model fit.

```
# Load libraries
library(RColorBrewer)
library(ggplot2)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(reshape2)
library(latex2exp)
library(lddmm)
theme_set(theme_bw(base_size = 14))
cols = brewer.pal(9, "Set1")
```

```
# Load the data
data('data')
# Descriptive plots
plot_accuracy(data)
plot_RT(data)
# Run the model
hypers = NULL
hypers$s_sigma_mu = hypers$s_sigma_b = 0.1
# Change the number of iterations when running the model
# Here the number is small so that the code can run in less than 1 minute
Niter = 25
burnin = 15
thin = 1
samp_size = (Niter - burnin) / thin
set.seed(123)
fit = LDDMM(data = data,
hypers = hypers,
Niter = Niter,
burnin = burnin,
thin = thin)
# Plot the results
plot_post_pars(data, fit, par = 'drift')
plot_post_pars(data, fit, par = 'boundary')
# Compute the WAIC to compare models
compute_WAIC(fit)
```

To extract relevant posterior draws or posterior summaries instead of
simply plotting them, one can use the functions
`extract_post_mean`

or `extract_post_draws`

. The
following auxiliary functions are available by selecting the
corresponding argument in the `LDDMM()`

function:

`boundaries = "constant"`

: constant boundary parameters over time, \(b_{d,s}^{(i)}(t) = b_{d,s} + u_{d,s}^{(i)}\) using the article notation`boundaries = "fixed"`

: fixed boundaries across input predictors, \(b_{d,s}^{(i)}(t) = b_{d}(t) + u^{(i)}_{d}(t)\) using the article notation`boundaries = "fixed-constant"`

: fixed*and*constant boundaries, \(b_{d,s}^{(i)}(t) = b_{d} + u_{d}^{(i)}\) using the article notation