You can install pypcurve with pip:

```
pip install pypcurve
```

First and foremost, read the user guide to the p-curve. It is crucial that users understand what p-curve can and cannot do, that they know which statistical results to select, and that they properly prepare the disclosure table.

pypcurve only requires a list of statistical results, stored in a list (or an array). Similar to the p-curve app, pypcurve accepts the following formats of statistical tests:

- F(1, 302)=3.273
- t(103)=4.23
- r(76)=.42
- z=1.98
- chi2(1)=7.1

In addition, pypcurve will accept raw p-values:

- p = .0023

This is not recommended though: p-values are often weirdly rounded, so enter the statistical result instead if it is reported in the paper.

For this example, I will assume that your tests have been properly formatted, and stored in a column called “Tests” of a .csv file.

```
from pypcurve import PCurve
import pandas as pd
df = pd.read_csv("mydata.csv")
pc = PCurve(df.Tests)
```

If all your tests are properly formatted, there will be no error, and pcurve will be initialized properly.

Next, you can print the summary of the p-curve, as you would see it using the web-app:

```
pc.summary()
```

This will output the p-curve plot, as well as the table summarizing the binomial and Stouffer tests of the
p-curve analysis. You can get the plot alone, or the table alone, using the methods `pc.plot_pcurve()`

and
`pc.pcurve_analysis_summary()`

.

You can use pycurve to estimate the power of the design that generated the statistical tests:

`pc.estimate_power()`

will return the power estimate, and the (lower, upper) bounds of 90% confidence interval.`pc.plot_power_estimate()`

will plot the power estimate (as the webapp does).

You can directly access the results of the p-curve analysis using three methods:

`pc.get_stouffer_tests()`

will recover the Z and p-values of the Stouffer tests`pc.get_binomial_tests()`

will recover the p-values of the binomial tests`pc.get_results_entered()`

will recover the statistical results entered in the p-curve, and the pp-values and z scores associated with the different alternatives to which they are compared.

You can also directly check if the p-curve passes the cutoff for evidential value, and the cutoff for
inadequate evidence (as defined in
Better P-Curve),
using the properties `pc.has_evidential_value`

and `pc.has_inadequate_evidence`

The app is still in beta, so please take care when interpreting the results. I have tested pypcurve against the p-curve app using multiple examples: There are occasional minor deviations between the two, because of the way R (vs. Python) compute the non-central F distribution.

First beta release.