Changelog

Source: NEWS.md

mediamix (development version)

Bug fixes

• Subsetting an mm_paths object so that part of a journey is removed now recomputes touch_rank and touch_n. Previously the stale ranks made credit_first(), credit_last() and credit_position() give some converting journeys no credit at all after, for example, filtering out one channel.

• tune_carryover()’s metric now means what it is called. Per-split metrics were averaged, so with the default one-period assessment sets the reported “rmse” was a mean absolute error. The default is now aggregate = "pooled", which evaluates the metric once over every out-of-sample forecast; aggregate = "mean" keeps the old behaviour.

• The documentation and the getting-started vignette recommended computing marginal ROI on spend as the saturation slope times the kernel’s first weight. That counts only the period of spend and understated slow channels’ marginal return several-fold (six-fold for mm_weekly’s television), reversing a reallocation conclusion. See marginal_roi().

• build_paths() now reports when journeys hold several conversion events (possible under split_on = "none" or "gap"), since each is credited as one conversion.

• diagnose_media() now computes the implied-CPM median and outlier rule within each series when by is supplied. Pooled across geographies, a region that buys media at a different price was flagged as a stream of join errors. The column formerly called period, which held a row number, is now row, and a group column is added with by.

New features

• marginal_roi() computes average and marginal return by re-running carryover and saturation on a slightly larger budget, following Jin et al. (2017), with optional planning windows and carryover run-out.
• markov_removal() implements order-1 Markov removal-effect attribution (Anderl et al., 2016) exactly, in base R. attribute() includes it as the "markov" rule.
• adstock_delayed() and adstock_weights_delayed() add the delayed-peak kernel of Jin et al. (2017); media_transform() accepts kernel = "delayed".
• tune_carryover() reports std_err and a best_1se choice using a paired one-standard-error rule, and gains controls (entered into the model and kept aligned with every split) and warm_start.
• tune_carryover_joint() tunes every channel’s carryover inside one model with controls, by coordinate descent, reporting each channel’s profile.
• block_bootstrap(): moving block bootstrap (Kunsch 1989) with percentile intervals for ROI, marginal ROI or any statistic.
• adstock_steady_state() seeds a filter at steady state, removing the start-up bias of long-carryover channels.
• diagnose_media(decay =) measures collinearity on adstocked media.
• plot() methods for carryover profiles, joint tuning, bootstrap intervals, contributions (over time or in total), response curves (with the extrapolation region shaded) and attribution shares, in one colour-vision-checked style. mm_palette() exports the colours. contributions(), response_curve() and attribute() now return subclassed data frames so the methods dispatch.
• inst/CITATION added.

Performance

• assisted_conversions() is about 16x faster and top_paths() / as_channel_paths() about 2x faster on large journey tables, via data.table. response_curve() computes marginals in one vectorised pass.

Documentation

• A pkgdown site with an end-to-end budget walkthrough, a methods and references article and a comparison with other tools, deployed to GitHub Pages by a new workflow.
• mm_weekly spend is rescaled so that average ROIs are between about 1 and 4 rather than 0.07; revenue, decays and shapes are unchanged.
• README and vignette claims about other packages corrected: the README no longer says Robyn is not a CRAN package or that no other package builds journeys, and the normalised adstock kernel is now credited as the Jin et al. (2017) convention rather than a departure from the literature. The spine vignette no longer suggests an MMM half-life is an attribution lookback.
• The README no longer tells users to install.packages("mediamix"); the package is not on CRAN.

mediamix 0.4.0

First release.

Media transforms

• adstock_geometric(), adstock_weibull(), adstock_filter() and adstock_state() apply carryover transforms with explicit, inspectable filter state, grouping via by=, and an explicit normalise argument whose consequence for coefficient interpretation is documented.
• adstock_weights() and adstock_weights_weibull() expose the kernels directly.
• saturate_hill(), saturate_exponential(), saturate_michaelis_menten(), saturate_power() and the saturate() dispatcher provide four diminishing-returns curves.
• media_transform() composes carryover and saturation in the conventional order and warns rather than silently reversing it.
• decay_from_half_life(), half_life() and effective_window() give both halves of the package one vocabulary.

Carryover selection

• tune_carryover() selects (max_lag, decay) by cross-validation against an actual KPI using a user-supplied model, with forward-only resampling. It warns when the selected parameters sit on the edge of the search grid.
• fit_ols(), rmse() and mae() provide working defaults.

Attribution

• build_paths() constructs customer journeys from a raw event log, handling journey splitting, lookback windows, non-converting paths, direct and missing channel labels, consecutive duplicates, deterministic tie-breaking, and the accounting for conversions left unattributable by filtering.
• credit_linear(), credit_first(), credit_last(), credit_position(), credit_time_decay() and credit_custom() assign credit; attribute() runs several rules at once and attribution_spread() reports how much the answer depends on the choice.
• path_summary(), path_lengths(), channel_positions(), assisted_conversions(), top_paths(), conversion_lag() and path_diagnostics() describe the journey table.
• as_channel_paths() exports journeys in ChannelAttribution’s format.

Recipe steps

• step_adstock() and step_saturation() carry filter state across the train/test boundary without changing row count or order, classifying new data as contiguous, overlapping, gapped or unseen and warm-starting only when that is correct.
• tunable() methods and the carryover_decay(), carryover_max_lag(), saturation_half_max() and saturation_shape() parameter objects support joint tuning with model hyperparameters.

Reporting and diagnostics

• contributions(), roi(), mroi(), response_curve() and spend_for() turn a fitted model into a marketing mix deliverable.
• diagnose_media() reports collinearity, insufficient variation, flighting and implied CPM inconsistency.

Data

• mm_weekly, a synthetic weekly panel generated from known parameters.
• mm_events, a synthetic touchpoint log containing the awkward cases on purpose.