<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> AutoQuant is an R-based automation framework designed to streamline machine learning workflows, particularly in forecasting, model evaluation, and interpretation. It targets data scientists and engineers seeking to accelerate model development and operationalization through a comprehensive suite of tools and an artifact-first design philosophy. The primary benefit is increased efficiency, reproducibility, and robustness across diverse ML tasks.

How It Works

AutoQuant employs an "artifact-first" design philosophy, emphasizing structured outputs for downstream consumption. It integrates popular ML engines like CatBoost, LightGBM, XGBoost, and H2O, supporting GPU acceleration for some. The framework offers extensive capabilities in supervised learning (regression, binary, multi-class classification) and a wide array of time-series forecasting methods, including single-series, panel, hierarchical, intermittent-demand, funnel, multi-target, and cross-target approaches. A related component, Rodeo, handles temporal transformation replay. Its vNext architecture focuses on typed artifacts and modularity for future extensibility.

Quick Start & Requirements

• Installation: Primarily via devtools::install_github('AdrianAntico/AutoQuant').
• Prerequisites: R, devtools, and core R packages like data.table, collapse, lubridate. Specific ML libraries (e.g., catboost, lightgbm, xgboost, h2o) must be installed separately based on usage. GPU support requires compatible hardware and drivers for CatBoost, LightGBM, and XGBoost.
• Documentation: Extensive examples and function documentation are available within the R package's help files and the README. Links to detailed architecture documents are provided.

Highlighted Details

• Comprehensive Forecasting: Supports single-series, panel, hierarchical, intermittent-demand, funnel, multi-target, and cross-target forecasting strategies.
• Artifact-First Design: Emphasizes structured, typed artifacts for reproducible ML pipelines and integration with other tools (e.g., AnalyticsShinyApp).
• Multi-Engine Support: Integrates with CatBoost, LightGBM, XGBoost, and H2O, offering flexibility in model selection.
• GPU Acceleration: Available for CatBoost, LightGBM, and XGBoost models.
• Forecasting Capability Planning: Includes tools to discover and plan forecasting strategies before execution.

Maintenance & Community

Information regarding maintainers, community channels (e.g., Slack, Discord), or active development signals is not present in the provided text.

Licensing & Compatibility

The repository's license and compatibility notes for commercial use or closed-source linking are not specified in the provided text.

Limitations & Caveats

The "vNext" components represent a significant redesign, with some documentation focusing on design rather than implemented APIs. While legacy APIs are maintained, the vNext path is emphasized for new development. The primary focus is on R.