pcalg

Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

Last 30 days

This package has been downloaded 1,880 times in the last 30 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 23 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 69,328 times in the last 365 days. The kind of number that gets mentioned in a keynote speech. Well done! The day with the most downloads was Dec 10, 2024 with 2,059 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.