{"package":"r-inaparc","ecosystem":"conda","latest_version":"1.2.1","description":"Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting to partition the data sets. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the data elements, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'.","license":"GPL-2.0-or-later","license_risk":"unknown","commercial_use_notes":"verify manually — license not parseable / not declared.","homepage":"https://CRAN.R-project.org/package=inaparc","repository":"","downloads_weekly":182,"health":{"score":49,"risk":"high","breakdown":{"maintenance":10,"popularity":3,"security":25,"maturity":9,"community":2},"deprecated":false,"max_score":100},"vulnerabilities":{"count":0,"critical":0,"high":0,"medium":0,"low":0,"details":[]},"versions":{"latest":"1.2.1","total_count":4,"recent":["0.2.0","1.1.0","1.2.0","1.2.1"]},"metadata":{"deprecated":false,"deprecated_message":null,"maintainers_count":1,"first_published":"2021-05-24 21:45:57.656000+00:00","last_published":"2025-09-18 02:08:26.460000+00:00","dependencies_count":0,"dependencies":[]},"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":"use_with_caution","issues":["Moderate health score (49/100) — verify manually"],"use_version":"1.2.1","version_hint":null,"summary":"r-inaparc@1.2.1 low health (49/100) — consider alternatives"},"version_scoped":null,"_meta":{"endpoint":"check","tier":"full","philosophy":"DepScope is free. Use the cheapest endpoint that answers your real question.","cheaper_alternatives":[{"endpoint":"/api/exists/conda/r-inaparc","tokens_estimated":12,"use_when":"you only need to know if the package exists (hallucination guard)"},{"endpoint":"/api/health/conda/r-inaparc","tokens_estimated":80,"use_when":"you only need a 0-100 score for go/no-go (>=70 = safe)"},{"endpoint":"/api/prompt/conda/r-inaparc","tokens_estimated":280,"use_when":"you want a plain-text LLM-friendly brief instead of JSON"},{"endpoint":"POST /api/check_bulk","tokens_estimated":60,"use_when":"you have 5+ packages to check; sends one round-trip instead of N"}],"docs":"https://depscope.dev/integrate","hint_bulk":"You've called /api/check 18 times in 60s. Save bandwidth + tokens with POST /api/check_bulk (1 round-trip for N pkgs)."},"requested_version":null,"_cache":"miss","_response_ms":290,"_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},"version_history_summary":{"total_versions":4,"first_release_age_days":1804,"last_release_days_ago":227,"avg_days_between_releases":601,"release_velocity":"moderate"}}