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CausalLoopAnalytics 1.0.0

Data-driven causal-loop and feedback network analysis in R.

CausalLoopAnalytics provides a compact workflow for signed causal models: construct a causal-loop diagram (CLD), discover temporal edges with a Granger-style test, detect and classify reinforcing/balancing feedback loops, quantify loop strength, assess bootstrap stability, calculate network centrality and leverage scores, compare models, and explore structural interventions.

What makes it different

The package is intentionally focused on evidence and interpretation of feedback structure, rather than network enumeration or stochastic differential-equation simulation. Causal discovery output retains polarity, effect weight, p-value and method metadata.

Quick start

library(CausalLoopAnalytics)

m <- create_cld(c("Antibiotic use", "AMR", "Treatment failure"))
m <- add_link(m, "Antibiotic use", "AMR", "+", 0.8)
m <- add_link(m, "AMR", "Treatment failure", "+", 0.7)
m <- add_link(m, "Treatment failure", "Antibiotic use", "+", 0.6)

find_feedback_loops(m)
classify_loops(m)
loop_strength(m)
loop_stability(m, B = 100, seed = 1)
network_summary(m)
identify_leverage_points(m)
plot_cld(m)
plot_loops(m)

Data-driven discovery

model <- infer_causal_network(my_time_series, lag = 2, alpha = 0.05)
summary_causal_loop(model)

The Granger-style routine identifies temporal predictive relationships. It does not establish mechanistic causation and should be interpreted alongside study design and domain knowledge.

Installation from source

install.packages("CausalLoopAnalytics_1.0.0.tar.gz", repos = NULL, type = "source")

Scope

The methods are domain-agnostic and can be used for human medicine, veterinary medicine, agriculture, epidemiology, ecology, public health and One Health systems.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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