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Getting Started
Guide
Example Gallery
How We Compare
API
Contributing
About PyMC Labs
Section Navigation
bass
data
to_bass_dataset
model
F
create_bass_model
f
BassModel
BassPriors
plotting
plot_adoption_curve
plot_cumulative
plot_decomposition
plot_peak
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
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
bayesian_blp
BayesianBLP
consideration_set_logit
ConsiderationInstruments
ConsiderationSetMixedLogit
maxdiff
prepare_maxdiff_data
MaxDiffArrays
MaxDiffMixedLogit
mixed_logit
MixedLogit
mnl_logit
MNLogit
mv_its
plot_product
MVITS
nested_logit
NestedLogit
synthetic_data
generate_blp_panel
generate_maxdiff_conjoint_data
generate_maxdiff_data
generate_saturated_data
generate_unsaturated_data
taste_profiles
brand_buyer_nu
buyer_nu_posterior
consumer_taste_grid
demand_concentration_gini
plot_brand_buyer_heatmap
plot_buyer_profile_heatmap
plot_demand_concentration
plot_taste_profile_stacked
taste_type_demand_share
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
get_posterior_predictive
get_prior
get_prior_predictive
idata_from_zarr
idata_to_zarr
subsample_draws
hsgp_kwargs
CovFunc
HSGPKwargs
metrics
crps
nmae
nrmse
per_observation_crps
mlflow
autolog
create_log_callback
load_mmm
log_arviz_summary
log_bass_configuration
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
ControlMuEffect
DataVarMuEffect
EventAdditiveEffect
FourierEffect
IncrementalitySpec
LinearTrendEffect
MediaMuEffect
Model
MuEffect
budget_optimizer
merge_inference_data
merge_models_and_idata
optimizer_xarray_builder
BudgetOptimizationResult
BudgetOptimizer
BuildMergedModel
ConstraintIterationInfo
CustomModelWrapper
OptimizationIterationInfo
OptimizerCompatibleModel
builders
factories
build
locate
naming
resolve
schema
suggest_typo_fix
BuildSpec
CalibrationStep
DataConfig
MMMYamlConfig
yaml
build_mmm_from_yaml
causal
BuildModelFromDAG
CausalGraphModel
TBFPC
TestResult
components
adstock
AdstockTransformation
BinomialAdstock
DelayedAdstock
GeometricAdstock
NoAdstock
WeibullCDFAdstock
WeibullPDFAdstock
base
Transformation
saturation
HillSaturation
HillSaturationSigmoid
InverseScaledLogisticSaturation
LogSaturation
LogisticSaturation
MichaelisMentenSaturation
NoSaturation
RootSaturation
SaturationTransformation
TanhSaturation
TanhSaturationBaselined
constraints
build_default_sum_constraint
compile_constraints_for_scipy
Constraint
counterfactual
find_named_node
CounterfactualEvaluator
CounterfactualScenarios
DateIndexedInput
EvaluationWindows
PeriodWindow
data_conversion
to_mmm_dataset
decomposition
identity_counterfactual_component
log_counterfactual_remove_component
original_scale_prediction_from_mu
safe_proportional_share
dims
evaluation
calculate_metric_distributions
compute_summary_metrics
summarize_metric_distributions
events
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
HSGPBase
HSGPPeriodic
PeriodicCovFunc
SoftPlusHSGP
incrementality
IdentityLinkReducer
IncrementalReducer
Incrementality
LogLinkReducer
lift_test
add_cost_per_target_observations
add_cost_per_target_potentials
add_lift_measurements_to_likelihood_from_saturation
add_saturation_observations
assert_is_subset
assert_monotonic
create_time_varying_saturation
exact_row_indices
scale_channel_lift_measurements
scale_lift_measurements
scale_target_for_lift_measurements
linear_regression
FancyLinearRegression
linear_trend
LinearTrend
link
get_link_spec
IdentityLinkSpec
LinkFunction
LinkSpec
LogLinkSpec
media_transformation
MediaConfig
MediaConfigList
MediaTransformation
mmm
BudgetOptimizerWrapper
MMM
multidimensional
optimization_variables
align_to_model_coords
LeverVariable
MediaVariable
OptimizationVariable
OptimizationVariables
plot
MMMPlotSuite
plot_interactive
MMMPlotlyFactory
plotting
budget
BudgetPlots
cv
MMMCVPlotSuite
decomposition
DecompositionPlots
diagnostics
DiagnosticsPlots
sensitivity
SensitivityPlots
suite
MMMPlotSuiteFacade
transformations
TransformationPlots
preprocessing
preprocessing_method_X
preprocessing_method_y
MaxAbsScaleChannels
MaxAbsScaleTarget
StandardizeControls
scaling
deserialize_variable_scaling
panel_channel_fixed_scaling_remaining_dims
validate_fixed_scaling_keys
DataDerivedScaling
FixedScaling
Scaling
VariableScaling
sensitivity_analysis
SensitivityAnalysis
spend_reach
linear_predictor
resolve_channel_dependent_effects
ChannelDependentEffect
SpendProbe
SpendReach
TemporalReach
summary
budget
prepare_allocation_roas
prepare_contribution_over_time
BudgetSummaryFactory
cv
MMMCVSummaryFactory
factory
MMMSummaryFactory
helpers
compute_channel_shares
compute_residuals
compute_summary_stats_with_hdi
compute_waterfall_components
convert_output
dataframe_to_json_records
get_channel_x_data
get_prior_for_plot
prepare_sensitivity_data
StatsHelper
sensitivity
sensitivity_analysis
sensitivity_marginal
sensitivity_uplift
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
cdf
create_index
create_new_spend_data
create_zero_dataset
density
transform_1d_array
validating
validation_method_X
validation_method_y
ValidateChannelColumns
ValidateControlColumns
ValidateDateColumn
ValidateTargetColumn
xarray_utils
model_builder
create_idata_accessor
create_sample_kwargs
requires_model
ModelBuilder
ModelFitter
ModelIO
RegressionModelBuilder
model_config
parse_model_config
model_graph
deterministics_to_flat
get_symbolic_rv_shape
paths
create_data_url
URLPath
pie
model
PIEModel
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
pytensor_utils
extract_response_distribution
merge_models
validate_unique_value_vars
ModelSamplerEstimator
r2d2
R2D2
R2D2Sigma
R2D2Split
serialization
DeferredFactory
DeserializationContext
Serializable
SerializableBaseModel
TypeRegistry
serialization_migration
main
migrate_idata
special_priors
deserialize_alternative_prior
is_alternative_prior
LaplacePrior
LogNormalPrior
MaskedPrior
SpecialPrior
utils
from_netcdf
API
mmm
additive_effect
IncrementalitySpec
IncrementalitySpec.parse_obj
IncrementalitySpec.parse_obj
#
classmethod
IncrementalitySpec.
parse_obj
(
obj
)
[source]
#
On this page
IncrementalitySpec.parse_obj()
Edit on GitHub
Consulting and training
