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lares

Lean Analyticsand Robust Exploration Sidekick

lares is an R package designed to automate, improve,and accelerate everydayanalytics and machine learningtasks. It offers a wide variety of functions grouped in familiesfor:

• Machine Learning: Streamlined model training andevaluation, including friendly AutoML pipelines.
• Data Cleaning & Processing: Functions toquickly prepare your data for modeling or analyzes.
• Exploratory Data Analysis (EDA): Instantlyvisualize and summarize your data.
• Reporting: Easily generate comprehensive reports toshare insights for MMM and ML models.
• Visualization: Out-of-the-box plotting forclassification and regression models, timelines, and more.
• API Integrations & Scrapers: Simplify datacollection from various sources.
• Time Series & Portfolio Analysis: Specializedutilities for financial and temporal data.
• Credentials & Secrets Management: Securelyhandle sensitive information in your analytics pipelines.
• NLP & Text Analytics: Tools to analyze andprocess text data.

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

Installation

# CRAN VERSIONinstall.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 installRTools tobuild the dev version.

Read aboutlares inaction!

• AutoML Quickstart:Introductionto AutoML usinglares
• Model Results Visualization:Classification|Regression
• Marketing Mix Model Selection:Selectthe right MMM candidate
• Cross-Correlations:FindInsights with Ranked Cross-Correlations
• Secure Credentials:ManageSecrets in R
• Portfolio Analysis:Performance& Reporting
• Fun games:Wordle,Scrabble,Sudoku,Mazes,etc.
• More Examples:Read otherposts

Popular Functions

• h2o_automl(),plot_model_results() –Automated machine learning pipeline with optimal model selection andvisualizations.
• freqs(),distr(),corr_var(),corr_cross() – Instantly summarize, visualize, and uncoverrelationships in your data.
• ohse() – Efficient and smart one-hot encoding forcategorical variables.
• cache_* – Speed up workflows by caching expensivecomputations.
• robyn_* – Additional functions to supportRobyn inputsand outputs.
• fb_* – Interact with Meta’s Marketing API
• gpt_* – Structured prompts builder and interact withOpenAI’s API
• read_encrypted(),write_encrypted() –Interact with encrypted files to keep secrets safe
• …andmanymore!

Getting Started & Help

• Browse all functions in theonlinereference.
• Use?lares::function_name in RStudio for detailed helpon any function.
• Found a bug or have a feature request?Open anissue.
• For questions or suggestions, reach out tolaresbernardo.