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Getting Started
Guide
Example Gallery
How We Compare
API
Contributing
About PyMC Labs
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bass
model
F
create_bass_model
f
BassPriors
causal_utils
same_markov_equivalence_class_CPdag
clv
distributions
BetaGeoBetaBinom
BetaGeoNBD
ModifiedBetaGeoNBD
ParetoNBD
ShiftedBetaGeometric
models
basic
CLVModel
beta_geo
BetaGeoModel
beta_geo_beta_binom
BetaGeoBetaBinomModel
gamma_gamma
BaseGammaGammaModel
GammaGammaModel
GammaGammaModelIndividual
modified_beta_geo
ModifiedBetaGeoModel
pareto_nbd
ParetoNBDModel
shifted_beta_geo
ShiftedBetaGeoModel
ShiftedBetaGeoModelIndividual
plotting
plot_customer_exposure
plot_expected_purchases_over_time
plot_expected_purchases_ppc
plot_frequency_recency_matrix
plot_probability_alive_matrix
utils
customer_lifetime_value
rfm_segments
rfm_summary
rfm_train_test_split
to_xarray
customer_choice
mixed_logit
MixedLogit
mnl_logit
MNLogit
mv_its
plot_product
MVITS
nested_logit
NestedLogit
synthetic_data
generate_saturated_data
generate_unsaturated_data
data
fivetran
process_fivetran_ad_reporting
process_fivetran_shopify_unique_orders
FivetranAccessor
idata
mmm_wrapper
MMMIDataWrapper
schema
InferenceDataGroupSchema
MMMIdataSchema
VariableSchema
utils
aggregate_idata_dims
aggregate_idata_time
filter_idata_by_dates
filter_idata_by_dims
subsample_draws
deserialize
hsgp_kwargs
HSGPKwargs
metrics
crps
nmae
nrmse
per_observation_crps
mlflow
autolog
create_log_callback
load_mmm
log_arviz_summary
log_error
log_inference_data
log_likelihood_type
log_metadata
log_mmm
log_mmm_configuration
log_mmm_evaluation_metrics
log_model_derived_info
log_model_graph
log_sample_diagnostics
log_types_of_parameters
log_versions
MMMWrapper
mmm
additive_effect
safe_to_datetime
EventAdditiveEffect
FourierEffect
LinearTrendEffect
Model
MuEffect
base
BaseValidateMMM
MMMModelBuilder
budget_optimizer
optimizer_xarray_builder
BudgetOptimizer
BuildMergedModel
CustomModelWrapper
OptimizerCompatibleModelWrapper
builders
factories
build
locate
resolve
yaml
build_mmm_from_yaml
causal
BuildModelFromDAG
CausalGraphModel
TBFPC
TestResult
components
adstock
adstock_from_dict
AdstockTransformation
BinomialAdstock
DelayedAdstock
GeometricAdstock
NoAdstock
WeibullCDFAdstock
WeibullPDFAdstock
base
create_registration_meta
index_variable
Transformation
saturation
saturation_from_dict
HillSaturation
HillSaturationSigmoid
InverseScaledLogisticSaturation
LogisticSaturation
MichaelisMentenSaturation
NoSaturation
RootSaturation
SaturationTransformation
TanhSaturation
TanhSaturationBaselined
constraints
build_default_sum_constraint
compile_constraints_for_scipy
Constraint
evaluation
calculate_metric_distributions
compute_summary_metrics
summarize_metric_distributions
events
basis_from_dict
days_from_reference
AsymmetricGaussianBasis
Basis
EventEffect
GaussianBasis
HalfGaussianBasis
fourier
generate_fourier_modes
FourierBase
MonthlyFourier
WeeklyFourier
YearlyFourier
hsgp
approx_hsgp_hyperparams
create_complexity_penalizing_prior
create_constrained_inverse_gamma_prior
create_eta_prior
create_m_and_L_recommendations
hsgp_from_dict
CovFunc
HSGP
HSGPBase
HSGPPeriodic
PeriodicCovFunc
SoftPlusHSGP
incrementality
Incrementality
lift_test
add_cost_per_target_potentials
add_lift_measurements_to_likelihood_from_saturation
add_saturation_observations
assert_is_subset
assert_monotonic
create_time_varying_saturation
create_variable_indexer
exact_row_indices
scale_channel_lift_measurements
scale_lift_measurements
scale_target_for_lift_measurements
linear_regression
FancyLinearRegression
linear_trend
LinearTrend
media_transformation
MediaConfig
MediaConfigList
MediaTransformation
mmm
BaseMMM
MMM
multidimensional
create_sample_kwargs
MMM
MultiDimensionalBudgetOptimizerWrapper
plot
MMMPlotSuite
preprocessing
preprocessing_method_X
preprocessing_method_y
MaxAbsScaleChannels
MaxAbsScaleTarget
StandardizeControls
scaling
Scaling
VariableScaling
sensitivity_analysis
SensitivityAnalysis
summary
MMMSummaryFactory
time_slice_cross_validation
TimeSliceCrossValidationResult
TimeSliceCrossValidator
transformers
batched_convolution
binomial_adstock
delayed_adstock
geometric_adstock
hill_function
hill_saturation_sigmoid
inverse_scaled_logistic_saturation
logistic_saturation
michaelis_menten
root_saturation
tanh_saturation
tanh_saturation_baselined
weibull_adstock
ConvMode
TanhSaturationBaselinedParameters
TanhSaturationParameters
WeibullType
tvp
create_hsgp_from_config
create_time_varying_gp_multiplier
infer_time_index
is_hsgp_kwargs_format
time_varying_prior
types
MMMBuilder
utility
adjusted_value_at_risk_score
average_response
conditional_value_at_risk
diversification_ratio
mean_tightness_score
portfolio_entropy
raroc
sharpe_ratio
tail_distance
value_at_risk
utils
add_noise_to_channel_allocation
apply_sklearn_transformer_across_dim
build_contributions
create_index
create_new_spend_data
create_zero_dataset
transform_1d_array
validating
validation_method_X
validation_method_y
ValidateChannelColumns
ValidateControlColumns
ValidateDateColumn
ValidateTargetColumn
model_builder
create_idata_accessor
create_sample_kwargs
requires_model
ModelBuilder
ModelIO
RegressionModelBuilder
model_config
parse_model_config
model_graph
deterministics_to_flat
paths
create_data_url
URLPath
plot
create_legend_handles
drop_scalar_coords
generate_colors
get_plot_coords
get_total_coord_size
plot_curve
plot_hdi
plot_samples
random_samples
selections
set_subplot_kwargs_defaults
prior
deserialize_alternative_prior
is_alternative_prior
pytensor_utils
extract_response_distribution
merge_models
validate_unique_value_vars
ModelSamplerEstimator
special_priors
LaplacePrior
LogNormalPrior
MaskedPrior
SpecialPrior
utils
from_netcdf
API
mmm
incrementality
Incrementality
Incrementality.__init__
Incrementality.__init__
#
Incrementality.
__init__
(
model
,
idata
=
None
,
data
=
None
)
[source]
#
On this page
Incrementality.__init__()
Edit on GitHub
Consulting and training
