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docs: CRAN 3.11.0 (#1001)
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R/DESCRIPTION

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Package: Robyn
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Type: Package
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Title: Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science
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Version: 3.10.7.9001
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Version: 3.11.0
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Authors@R: c(
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person("Gufeng", "Zhou", , "[email protected]", c("cre","aut")),
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person("Bernardo", "Lares", , "[email protected]", c("aut")),
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person("Gufeng", "Zhou", , "[email protected]", c("aut")),
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person("Bernardo", "Lares", , "[email protected]", c("cre","aut")),
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person("Leonel", "Sentana", , "[email protected]", c("aut")),
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person("Igor", "Skokan", , "[email protected]", c("aut")),
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person("Meta Platforms, Inc.", role = c("cph", "fnd")))
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Maintainer: Gufeng Zhou <gufeng@meta.com>
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Maintainer: Bernardo Lares <laresbernardo@gmail.com>
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Description: Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.
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Depends:
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R (>= 4.0.0)
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reticulate,
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stringr,
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tidyr
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Suggests:
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shiny
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Config/reticulate:
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list(
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packages = list(

R/R/inputs.R

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#' needs to be specified in \code{paid_media_spends} specifically. Run \code{hyper_names()}
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#' to get correct hyperparameter names. All names in hyperparameters must
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#' equal names from \code{hyper_names()}, case sensitive.
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#' \item{Get guidance for setting hyperparameter bounds:
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#' \item Get guidance for setting hyperparameter bounds:
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#' For geometric adstock, use theta, alpha & gamma. For both weibull adstock options,
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#' use shape, scale, alpha, gamma.}
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#' use shape, scale, alpha, gamma.
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#' \itemize{
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#' \item{Theta: }{In geometric adstock, theta is decay rate. guideline for usual media genre:
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#' TV c(0.3, 0.8), OOH/Print/Radio c(0.1, 0.4), digital c(0, 0.3)}
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#' \item{Shape: }{In weibull adstock, shape controls the decay shape. Recommended c(0.0001, 2).
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#' \item Theta: In geometric adstock, theta is decay rate. guideline for usual media genre:
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#' TV c(0.3, 0.8), OOH/Print/Radio c(0.1, 0.4), digital c(0, 0.3)
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#' \item Shape: In weibull adstock, shape controls the decay shape. Recommended c(0.0001, 2).
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#' The larger, the more S-shape. The smaller, the more L-shape. Channel-type specific
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#' values still to be investigated}
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#' \item{Scale: }{In weibull adstock, scale controls the decay inflexion point. Very conservative
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#' values still to be investigated
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#' \item Scale: In weibull adstock, scale controls the decay inflexion point. Very conservative
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#' recommended bounce c(0, 0.1), because scale can increase adstocking half-life greatly.
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#' Channel-type specific values still to be investigated}
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#' \item{Gamma: }{In s-curve transformation with hill function, gamma controls the inflexion point.
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#' Channel-type specific values still to be investigated
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#' \item Gamma: In s-curve transformation with hill function, gamma controls the inflexion point.
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#' Recommended bounce c(0.3, 1). The larger the gamma, the later the inflection point
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#' in the response curve}
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#' in the response curve
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#' }
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#' \item{Set each hyperparameter bounds. They either contains two values e.g. c(0, 0.5),
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#' or only one value (in which case you've "fixed" that hyperparameter)}
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#' \item Set each hyperparameter bounds. They either contains two values e.g. c(0, 0.5),
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#' or only one value (in which case you've "fixed" that hyperparameter)
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#' }
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#'
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#' @section Helper plots:

R/R/plots.R

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sign = as.factor(ifelse(.data$xDecompPerc >= 0, "Positive", "Negative"))
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)
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p2 <- suppressWarnings(
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ggplot(plotWaterfallLoop, aes(x = .data$id, fill = .data$sign)) +
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geom_rect(aes(
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x = .data$rn, xmin = .data$id - 0.45, xmax = .data$id + 0.45,
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ymin = .data$end, ymax = .data$start
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), stat = "identity") +
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scale_x_discrete("", breaks = levels(plotWaterfallLoop$rn), labels = plotWaterfallLoop$rn) +
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scale_y_percent() +
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scale_fill_manual(values = c("Positive" = "#59B3D2", "Negative" = "#E5586E")) +
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theme_lares(background = "white", legend = "top") +
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geom_text(mapping = aes(
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label = paste0(
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formatNum(.data$xDecompAgg, abbr = TRUE),
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"\n", round(.data$xDecompPerc * 100, 1), "%"
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),
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y = rowSums(cbind(.data$end, .data$xDecompPerc / 2))
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), fontface = "bold", lineheight = .7) +
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coord_flip() +
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labs(
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title = "Response Decomposition Waterfall by Predictor",
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x = NULL, y = NULL, fill = "Sign"
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)
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)
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p2 <- ggplot(plotWaterfallLoop, aes(x = .data$id, fill = .data$sign)) +
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geom_rect(aes(
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xmin = .data$id - 0.45, xmax = .data$id + 0.45,
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ymin = .data$end, ymax = .data$start
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), stat = "identity") +
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scale_x_discrete("", breaks = levels(plotWaterfallLoop$rn), labels = plotWaterfallLoop$rn) +
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scale_y_percent() +
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scale_fill_manual(values = c("Positive" = "#59B3D2", "Negative" = "#E5586E")) +
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theme_lares(background = "white", legend = "top") +
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geom_text(mapping = aes(
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label = paste0(
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formatNum(.data$xDecompAgg, abbr = TRUE),
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"\n", round(.data$xDecompPerc * 100, 1), "%"
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),
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y = rowSums(cbind(.data$end, .data$xDecompPerc / 2))
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), fontface = "bold", lineheight = .7) +
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coord_flip() +
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labs(
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title = "Response Decomposition Waterfall by Predictor",
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x = NULL, y = NULL, fill = "Sign"
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)
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## 3. Adstock rate
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if (InputCollect$adstock == "geometric") {

R/README.md

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- Terms of Use - https://opensource.facebook.com/legal/terms
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- Privacy Policy - https://opensource.facebook.com/legal/privacy
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## Contact
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* [email protected], Gufeng Zhou, Marketing Science Partner
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* [email protected], Leonel Sentana, Marketing Science Partner
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* [email protected], Igor Skokan, Marketing Science Partner
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* [email protected], Bernardo Lares, Marketing Science Partner

R/man/hyper_names.Rd

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demo/demo.R

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# LICENSE file in the root directory of this source tree.
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#############################################################################################
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#################### Meta MMM Open Source: Robyn 3.10.7 #######################
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#################### Meta MMM Open Source: Robyn 3.11.0 #######################
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#################### Quick demo guide #######################
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#############################################################################################
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InputCollect = InputCollect,
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OutputCollect = OutputCollect,
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select_model = select_model,
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# date_range = NULL, # Default last month as initial period
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# date_range = NULL, # Default: "all" available dates
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scenario = "target_efficiency",
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# target_value = 2, # Customize target ROAS or CPA value
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export = create_files

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