{"package":"evinf","ecosystem":"cran","latest_version":"0.8.10","description":"Inference with Extreme Value Inflated Count Data. Allows users to model and draw inferences from extreme value inflated count data, and to evaluate these models and compare to non extreme-value inflated counterparts. The package is built to be compatible with standard presentation tools such as 'broom', 'tidy', and 'modelsummary'.","license":"MIT + file LICENSE","license_risk":"unknown","commercial_use_notes":"verify manually — license not parseable / not declared.","homepage":"https://github.com/Doktorandahl/evinf","repository":"https://github.com/Doktorandahl/evinf","downloads_weekly":72,"health":{"score":38,"risk":"critical","breakdown":{"maintenance":5,"popularity":0,"security":25,"maturity":6,"community":2},"deprecated":false,"max_score":100},"vulnerabilities":{"count":0,"critical":0,"high":0,"medium":0,"low":0,"details":[]},"versions":{"latest":"0.8.10","total_count":0,"recent":[]},"metadata":{"deprecated":false,"deprecated_message":null,"maintainers_count":1,"first_published":"2024-05-18 01:41:13","last_published":"2024-05-17T22:40:17+00:00","dependencies_count":23,"dependencies":["generics","dplyr","Rcpp","RcppArmadillo","foreach","doParallel","magrittr","doRNG","tibble","mistr","tidyr","purrr","MASS","pscl","MLmetrics","Rdpack","stringi","stringr","rlang","methods","stats","utils","parallel"]},"bundle":null,"typescript":null,"known_issues":{"bugs_count":0,"bugs_severity":{},"status_breakdown":{},"link":null,"scope":"none"},"historical_compromise":null,"recommendation":{"action":"use_with_caution","issues":["Low health score (38/100)"],"use_version":"0.8.10","version_hint":null,"summary":"evinf@0.8.10 low health (38/100) — consider alternatives"},"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}}