An R package for optimal item calibaration in computerized achievement tests.

With this `optical`

package, a set of items that require
calibration can be optimally allocated to a group of examinees. The
optimization process utilizes the restricted optimal design method,
which has been described in detail by Ul Hassan and Miller in their
works published in **2019**
and **2021**.
To use the method, preliminary item characteristics must be provided as
input. These characteristics can either be expert guesses or based on
previous calibration with a small number of examinees. The item
characteristics should be described in the form of parameters for an
Item Response Theory (IRT) model. These models can include the Rasch
model, the 2-parameter logistic model, the 3-parameter logistic model,
or a mixture of these models. The output consists of a set of rules for
each item that determine which examinees should be assigned to each
item. The efficiency or gain achieved through the optimal design is
quantified by comparing it to a random allocation. This comparison
allows for an assessment of how much improvement or advantage is gained
by using the optimal design approach.

The easiest way to install the **optical**
package from CRAN using:

`install.packages("optical")`

You can install the development version of **optical**
from GitHub with the following
code:

```
# if not installed already on your computer, install devtools
install.packages("devtools")
# Install the package
::install_github("scenic555/optical")
devtools
# Load the optical package
library(optical)
```

This is a basic example which shows you how to solve a common problem:

```
library(optical)
# 2PL-models with difficulty and common discrimination parameters
<- cbind(c(1.6, 1.6), c(-1, 1))
ip
<- optical(ip)
yyy #> -----> Outer iteration = 1
#> ++++++++++++++++++
#> -----> Adapt grid; outer iteration = 2
#> ++
# Table of interval boundaries for optimal design with items and probabilities
$ht
yyy#> Lower Upper Item Probability
#> 1 -Inf -0.73445 1 0.2313373
#> 2 -0.73445 0.00005 2 0.2686827
#> 3 0.00005 0.73445 1 0.2686428
#> 4 0.73445 Inf 2 0.2313373
# Graph for (optimal) design
drawdesign(yyy=yyy, ip=ip, ylowl=-1000, refline=0.002, layout=1)
```

This package is free and open source software, licensed under GPL (>= 3).

This work was supported by the Swedish Research Council (Vetenskapsrådet) Grant 2019-02706.

Ul Hassan and Miller (2019). Optimal item calibration for computerized achievement tests. Psychometrika, 84, 1101-1128.

Ul Hassan and Miller (2021). An exchange algorithm for optimal calibration of items in computerized achievement tests. Computational Statistics and Data Analysis, 157: 107177.

Bjermo, Fackle-Fornius, and Miller (2021). Optimizing Calibration Designs with Uncertaintyin Abilities. Manuscript.