`powerly`

is an `R`

package that implements the
method by Constantin et al.
(2021) for conducting sample size analysis for cross-sectional
network models. The method implemented is implemented in the main
function `powerly`

()`. The implementation takes the form of a
three-step recursive algorithm designed to find an optimal sample size
value given a model specification and an outcome measure of interest. It
starts with a Monte Carlo simulation step for computing the outcome at
various sample sizes. Then, it continues with a monotone curve-fitting
step for interpolating the outcome. The final step employs stratified
bootstrapping to quantify the uncertainty around the fitted curve.

```
Check out the documentation and tutorials at
<h3>
<a href="https://powerly.dev">powerly.dev</a>
</h3>
```

- to install from CRAN run
`install.packages("powerly")`

- to install the latest version from GitHub run
`remotes::install_github("mihaiconstantin/powerly")`

The code block below illustrates the main function in the package.
For more information, see the documentation `?powerly`

, or
check out the tutorials at powerly.dev.

```
# Suppose we want to find the sample size for observing a sensitivity of `0.6`
# with a probability of `0.8`, for a GGM true model consisting of `10` nodes
# with a density of `0.4`.
# We can run the method for an arbitrarily generated true model that matches
# those characteristics (i.e., number of nodes and density).
<- powerly(
results range_lower = 300,
range_upper = 1000,
samples = 30,
replications = 20,
measure = "sen",
statistic = "power",
measure_value = .6,
statistic_value = .8,
model = "ggm",
nodes = 10,
density = .4,
cores = 2,
verbose = TRUE
)
# Or we omit the `nodes` and `density` arguments and specify directly the edge
# weights matrix via the `model_matrix` argument.
# To get a matrix of edge weights we can use the `generate_model()` function.
<- generate_model(type = "ggm", nodes = 10, density = .4)
true_model
# Then, supply the true model to the algorithm directly.
<- powerly(
results range_lower = 300,
range_upper = 1000,
samples = 30,
replications = 20,
measure = "sen",
statistic = "power",
measure_value = .6,
statistic_value = .8,
model = "ggm",
model_matrix = true_model, # Note the change.
cores = 2,
verbose = TRUE
)
```

To validate the results of the analysis, we can use the
`validate()`

method. For more information, see the
documentation `?validate`

.

```
# Validate the recommendation obtained during the analysis.
<- validate(results) validation
```

To visualize the results, we can use the `plot`

function
and indicate the step that should be plotted.

```
# Step 1.
plot(results, step = 1)
```

```
# Step 2.
plot(results, step = 2)
```

```
# Step 3.
plot(results, step = 3)
```

```
# Validation.
plot(validation)
```

*To support*a new model, performance measure, or statistic, please open a pull request on GitHub.*To request*a new model, performance measure, or statistic, please open an issue on GitHub. If possible, also include references discussing the topics you are requesting.

The code in this repository is licensed under the MIT license.

To use `powerly`

please cite: - Constantin, M. A.,
Schuurman, N. K., & Vermunt, J. (2021). A General Monte Carlo Method
for Sample Size Analysis in the Context of Network Models. PsyArXiv. https://doi.org/10.31234/osf.io/j5v7u