Decisions from a fitted posterior: future response paths, paired plan comparisons and concave, constant-weekly-budget allocation.
Every function takes the built model and a posterior (from inference/fit
or io/read!) and evaluates future plans after the model's training
history, including the adstock carryover of past spend.
Decisions from a fitted posterior: future response paths, paired plan comparisons and concave, constant-weekly-budget allocation. Every function takes the built model and a posterior (from `inference/fit` or `io/read!`) and evaluates future plans after the model's training history, including the adstock carryover of past spend.
Geo-specific intercept/noise and non-centered log media amplitudes. Adstock, saturation shape and control effects are shared across groups.
Geo-specific intercept/noise and non-centered log media amplitudes. Adstock, saturation shape and control effects are shared across groups.
NUTS inference returning portable chains, and convergence diagnostics.
fit samples a built model with Foerster's NUTS and returns a plain-data
posterior (see io for persistence); diagnostics-summary checks it.
NUTS inference returning portable chains, and convergence diagnostics. `fit` samples a built model with Foerster's NUTS and returns a plain-data posterior (see `io` for persistence); `diagnostics-summary` checks it.
Versioned EDN and lossless tagged JSON posterior persistence. Tagged JSON preserves keyword identifiers and nonfinite diagnostics explicitly.
Versioned EDN and lossless tagged JSON posterior persistence. Tagged JSON preserves keyword identifiers and nonfinite diagnostics explicitly.
An additive MMM with finite geometric adstock and selectable media response.
A model owns its fixed channel layout, scaling and compiled numerical block. Observations and experimental contrasts are runtime inputs. Default priors: alpha ~ Beta(2,2), beta ~ HalfNormal(1), lambda ~ Gamma(3,rate=1), intercept ~ Normal(0,1), sigma ~ HalfNormal(0.5), controls ~ Normal(0,1).
An additive MMM with finite geometric adstock and selectable media response. A model owns its fixed channel layout, scaling and compiled numerical block. Observations and experimental contrasts are runtime inputs. Default priors: alpha ~ Beta(2,2), beta ~ HalfNormal(1), lambda ~ Gamma(3,rate=1), intercept ~ Normal(0,1), sigma ~ HalfNormal(0.5), controls ~ Normal(0,1).
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