QuantregForest
R package - Quantile Regression Forests, a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006).
Install / Use
/learn @lorismichel/QuantregForestREADME
quantregForest
Overview
Quantile Regression Forests is a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006). It is particularly well suited for high-dimensional data. Predictor variables of mixed classes can be handled. The package is dependent on the package 'randomForest', written by Andy Liaw.
Installation
you can install the official version from CRAN using the command:
install.packages("quantregForest")
if you want to install the development version on github use:
install.packages("devtools")
devtools::install_github("lorismichel/quantregForest")
Issues
To report an issue, please use the issue tracker on github.com.
Related Skills
YC-Killer
2.7kA library of enterprise-grade AI agents designed to democratize artificial intelligence and provide free, open-source alternatives to overvalued Y Combinator startups. If you are excited about democratizing AI access & AI agents, please star ⭐️ this repository and use the link in the readme to join our open source AI research team.
groundhog
398Groundhog's primary purpose is to teach people how Cursor and all these other coding agents work under the hood. If you understand how these coding assistants work from first principles, then you can drive these tools harder (or perhaps make your own!).
sec-edgar-agentkit
10AI agent toolkit for accessing and analyzing SEC EDGAR filing data. Build intelligent agents with LangChain, MCP-use, Gradio, Dify, and smolagents to analyze financial statements, insider trading, and company filings.
Kiln
4.7kBuild, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.
