Lean Analytics and Robust Exploration Sidekick

Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.

lares

Lean Analytics and Robust Exploration Sidekick

lares is an R package designed to automate, improve, and accelerate everyday analytics and machine learning tasks. It offers a wide variety of functions grouped in families for:

• Machine Learning: Streamlined model training and evaluation, including friendly AutoML pipelines.
• Data Cleaning & Processing: Functions to quickly prepare your data for modeling or analyzes.
• Exploratory Data Analysis (EDA): Instantly visualize and summarize your data.
• Reporting: Easily generate comprehensive reports to share insights for MMM and ML models.
• Visualization: Out-of-the-box plotting for classification and regression models, timelines, and more.
• API Integrations & Scrapers: Simplify data collection from various sources.
• Time Series & Portfolio Analysis: Specialized utilities for financial and temporal data.
• Credentials & Secrets Management: Securely handle sensitive information in your analytics pipelines.
• NLP & Text Analytics: Tools to analyze and process text data.

Tip: See all available functions and documentation here or type ?lares:: in RStudio to explore interactively.

Installation

# CRAN VERSION
install.packages("lares")

# DEV VERSION (latest updates)
# If you don't have remotes installed yet, run: install.packages('remotes')
remotes::install_github("laresbernardo/lares")
# For a full installation with recommended dependencies:
remotes::install_github("laresbernardo/lares", dependencies = TRUE)

Windows users: You may need to install RTools to build the dev version.

Read about lares in action!

• AutoML Quickstart: Introduction to AutoML using lares
• Model Results Visualization: Classification | Regression
• Marketing Mix Model Selection: Select the right MMM candidate
• Cross-Correlations: Find Insights with Ranked Cross-Correlations
• Secure Credentials: Manage Secrets in R
• Portfolio Analysis: Performance & Reporting
• Fun games: Wordle, Scrabble, Sudoku, Mazes, etc.
• More Examples: Read other posts

Popular Functions

• h2o_automl(), mplot_full() – Automated machine learning pipeline with optimal model selection and visualizations.
• freqs(), distr(), corr_var(), corr_cross() – Instantly summarize, visualize, and uncover relationships in your data.
• ohse() – Efficient and smart one-hot encoding for categorical variables.
• cache_* – Speed up workflows by caching expensive computations.
• robyn_* – Additional functions to support Robyn inputs and outputs.
• fb_* – Interact with Meta's Marketing API
• gpt_* – Structured prompts builder and interact with OpenAI's API
• read_encrypted(), write_encrypted() – Interact with encrypted files to keep secrets safe
• ...and many more!

Getting Started & Help

• Browse all functions in the online reference.
• Use ?lares::function_name in RStudio for detailed help on any function.
• Found a bug or have a feature request? Open an issue.
• For questions or suggestions, reach out to laresbernardo.