{"package":"loo","ecosystem":"cran","latest_version":"2.9.0","description":"Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models. Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing im","license":"GPL (>= 3)","license_risk":"unknown","commercial_use_notes":"verify manually — license not parseable / not declared.","homepage":"https://mc-stan.org/loo/","repository":"https://CRAN.R-project.org/package=loo","downloads_weekly":74302,"health":{"score":52,"risk":"high","breakdown":{"maintenance":15,"popularity":10,"security":25,"maturity":0,"community":2},"deprecated":false,"max_score":100},"vulnerabilities":{"count":0,"critical":0,"high":0,"medium":0,"low":0,"details":[]},"versions":{"latest":"2.9.0","total_count":0,"recent":[]},"metadata":{"deprecated":false,"deprecated_message":null,"maintainers_count":1,"first_published":"2025-12-23 09:53:12","last_published":"2025-12-23T06:50:02+00:00","dependencies_count":5,"dependencies":["checkmate","matrixStats","parallel","posterior","stats"]},"github_stats":null,"bundle":null,"typescript":null,"known_issues":{"bugs_count":0,"bugs_severity":{},"status_breakdown":{},"link":null,"scope":"none"},"historical_compromise":null,"recommendation":{"action":"safe_to_use","issues":[],"use_version":"2.9.0","version_hint":null,"summary":"loo@2.9.0 is safe to use (health: 52/100)"},"version_scoped":null,"requested_version":null,"_cache":"hit","_response_ms":0,"_powered_by":"depscope.dev — free package intelligence for AI agents","typosquat":{"is_suspected":false},"maintainer_trust":{"available":false},"malicious":{"is_malicious":false},"scorecard":{"available":false},"quality":{"available":false},"co_used_with":[{"package":"BioMoR","occurrences":3},{"package":"monitoring-client","occurrences":3}]}