# frscore

## Overview

Functions for automatically performing a reanalysis series on a data
set using `cna::cna()`

, and for calculating the
fit-robustness of the resulting models, as described in Parkkinen and
Baumgartner (2021):
https://journals.sagepub.com/doi/full/10.1177/0049124120986200.

In the most common use case, one wants to obtain a set of models and
their respective fit-robustness scores given a range of consistency and
coverage values that determine a reanalysis series of the data set of
interest. The function `frscored_cna()`

runs the reanalysis
series on a data set and calculates the fit-robustness scores of the
recovered models in one go. If one only wishes to repeatedly analyze a
data set with different consistency and coverage thresholds in a given
range, `rean_cna()`

automates this. If one wishes to
calculate the fit-robustness scores for an existing set of models, or
simply count (causal) sub- and supermodel relations in a set of models
for any reason, `frscore()`

does this.
`causal_submodel()`

is a generalization of
`cna::is.submodel()`

that checks whether all causal relevance
ascriptions, rather than only ascriptions of direct causation, made by
one model are contained in another model. `causal_submodel()`

is used by default in `frscored_cna()`

and
`frscore()`

to calculate fr-scores, but the user can change
this to `cna::is.submodel()`

to obtain a moderate speed
improvement if needed.

Have a look at the NEWS
for information about recent changes and developments.

## Installation

```
# latest version on CRAN
install.packages("frscore")
```

## Usage

```
library(frscore)
frsc <- frscored_cna(selectCases("A+B+F*g<->R"))
frsc
rean_cna(ct2df(selectCases("A+B+F*g<->R")), attempt = seq(1, 0.7, -0.1))
res <- rean_cna(selectCases("A+B+F*g<->R"), attempt = seq(1, 0.7, -0.1))
res <- do.call(rbind, res)
fr <- frscore(res[,2])
fr
target <- "(A+B<->C)*(C+D<->E)"
candidate <- "A+B<->E"
causal_submodel(candidate, target)
```