{"package":"r-graddescent","ecosystem":"conda","latest_version":"3.0","description":"An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. The variants of gradient descent algorithm are : Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load. Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically. Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average. Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning. Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning. Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning. Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning. RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability. Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning. Stochastic Variance Reduce Gradient (SVRG), which is an optimization SGD-based algorithm to accelerates the process toward converging by reducing the gradient. Semi Stochastic Gradient Descent (SSGD),which is a SGD-based algorithm that combine GD and SGD to accelerates the process toward converging by choosing one of the gradients at a time. Stochastic Recursive Gradient Algorithm (SARAH), which is an optimization algorithm similarly SVRG to accelerates the process toward converging by accumulated stochastic information. Stochastic Recursive Gradient Algorithm+ (SARAHPlus), which is a SARAH practical variant algorithm to accelerates the process toward converging provides a possibility of earlier termination.","license":"GPL-2.0-or-later","license_risk":"unknown","commercial_use_notes":"verify manually — license not parseable / not declared.","homepage":"https://github.com/drizzersilverberg/gradDescentR","repository":"","downloads_weekly":71,"health":{"score":46,"risk":"high","breakdown":{"maintenance":10,"popularity":0,"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":"3.0","total_count":1,"recent":["3.0"]},"metadata":{"deprecated":false,"deprecated_message":null,"maintainers_count":1,"first_published":"2021-11-06 20:35:53.271000+00:00","last_published":"2025-09-24 19:37:23.090000+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":"safe_to_use","issues":[],"use_version":"3.0","version_hint":null,"summary":"r-graddescent@3.0 is safe to use (health: 46/100)"},"version_scoped":null,"requested_version":null,"_cache":"miss","_response_ms":307,"_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":1,"first_release_age_days":1637,"last_release_days_ago":219,"avg_days_between_releases":null,"release_velocity":"moderate"}}