Author: Bernardo Lares
Date: 2026-04-23

# Robyn Enhancers

#### Bernardo Lares

#### 2026-04-23

## Introduction

The `lares` package provides a suite of auxiliary functions to enhance your experience with Robyn, a Marketing Mix Modeling (MMM) package developed by Meta’s Marketing Science Team. These functions help with hyperparameter building, model selection, performance analysis, and cross-model budget allocation.

## Hyperparameter Builder

The function `robyn_hypsbuilder()` automates the creation of hyperparameter lists for Robyn. It’s particularly useful when you have many media channels and want to set reasonable default ranges.

    # Define your input variables
    paid_media_spends <- c("tv_spend", "digital_spend")
    organic_vars <- c("newsletter")

    # Generate hyperparameters
    hyperparameters <- robyn_hypsbuilder(
        channels = c(paid_media_spends, organic_vars),
        media_type = "default",
        adstock = "weibull_pdf", # or "weibull_cdf", "weibull_pdf"
        date_type = "weekly",
        lagged = FALSE
    )

    # The output is a list ready to be passed to robyn_inputs()
    print(hyperparameters)
    #> $tv_spend_alphas
    #> [1] 0.01 3.00
    #>
    #> $tv_spend_gammas
    #> [1] 0.3 1.0
    #>
    #> $tv_spend_shapes
    #> [1]  0 10
    #>
    #> $tv_spend_scales
    #> [1] 0.0 0.2
    #>
    #> $digital_spend_alphas
    #> [1] 0.01 3.00
    #>
    #> $digital_spend_gammas
    #> [1] 0.3 1.0
    #>
    #> $digital_spend_shapes
    #> [1]  0 10
    #>
    #> $digital_spend_scales
    #> [1] 0.0 0.2
    #>
    #> $newsletter_alphas
    #> [1] 0.01 3.00
    #>
    #> $newsletter_gammas
    #> [1] 0.3 1.0
    #>
    #> $newsletter_shapes
    #> [1]  0 10
    #>
    #> $newsletter_scales
    #> [1] 0.0 0.2
    #>
    #> $train_size
    #> [1] 0.5 0.8

## Model Selection

The function `robyn_modelselector()` provides a visual and metric-based approach to select the best models from a Robyn run. It ranks models based on their performance metrics and helps you identify the best candidates to evaluate. For a deeper dive into this topic, check out this Medium post.

    # Assuming you have a Robyn OutputCollect object
    # OutputCollect <- robyn_run(...)

    # Select best models
    best_models <- robyn_modelselector(
        InputCollect = InputCollect,
        OutputCollect = OutputCollect,
        metrics = c(
            "rsq_train", "performance", "potential_improvement", "non_zeroes",
            "incluster_models", "cluster_sd", "certainty", "baseline_dist"
        ),
        wt = c(2, 0.1, 0, 1, 0.1, 0, 1.5, 0)
    )

    # View the results
    print(best_models)

    # Plot the clusters
    plot(best_models)

## Model Performance

The function `robyn_performance()` calculates detailed performance metrics for a specific model or a set of models. It breaks down contribution, ROAS or CPA for each channel, etc.

    # Get performance for a specific model and date range
    performance <- robyn_performance(
        InputCollect = InputCollect,
        OutputCollect = OutputCollect,
        select_model = "1_123_4",
        # Calculate for specific date range
        start_date = NULL, end_date = NULL,
        non_promo = TRUE, # Add non-promo decomposition as well
        marginals = TRUE, # Add mROAS or mCPA for spend levels
        carryovers = FALSE # Add mean percentage of carryover
    )

    # View performance table
    print(performance)

    # Plot performance
    plot(performance)

## Marginal Performance (Experimental)

The function `robyn_marginal()` allows you to explore the marginal returns of your media channels (mROAS or mCPA).

    # Calculate marginal response
    marginal <- robyn_marginal(
        InputCollect = InputCollect,
        OutputCollect = OutputCollect,
        select_model = "1_123_4",
        metric_name = "tv_spend",
        metric_value = 100000
    )

## Cross-Model Budget Allocation (Experimental)

If you have multiple Robyn models (e.g., for different countries or brands), `robyn_xmodels()` and `robyn_xchannels()` help you allocate budget across all of them simultaneously.

    # Load multiple models
    model_paths <- c("model_us.json", "model_uk.json", "model_de.json")
    models <- lapply(model_paths, Robyn::robyn_recreate)
    names(models) <- c("US", "UK", "DE")

    # Allocate budget across models
    cross_allocation <- robyn_xmodels(
        models = models,
        total_budget = 5000000,
        scenario = "max_response"
    )

    # View results
    print(cross_allocation)

## Conclusion

These functions streamline the post-modeling analysis in Robyn, making it easier to interpret results, select the best models, and make data-driven budget allocation decisions.

## Additional Reading

For more information on Robyn and MMM, check out the following resources:

- Robyn GitHub Repository
- Robyn: Analyst’s Guide to MMM
- Select the right MMM candidate based on your specific criteria and business knowledge
- Stop building MMMs without business-backed hypotheses
- Hitting ROAS Target Using Robyn’s Budget Allocator
- The Convergence of Marginal ROAS in the Budget Allocation in Robyn