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Getting Started
Guide
Example Gallery
How We Compare
API
Contributing
About PyMC Labs

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bass
- data
  - to_bass_dataset
- model
  - F
  - create_bass_model
  - f
  - BassModel
    
    default_model_config
    
    default_sampler_config
    
    fit_result
    
    id
    
    output_var
    
    posterior
    
    posterior_predictive
    
    predictions
    
    prior
    
    prior_predictive
    
    version
    
    idata
    
    sampler_config
    
    model_config
    
    model
    
    is_fitted_
  - BassPriors
    
    m
    
    p
    
    q
    
    likelihood
- plotting
causal_utils
- same_markov_equivalence_class_CPdag
clv
customer_choice
data
- fivetran
- idata
hsgp_kwargs
- CovFunc
- HSGPKwargs
  - model_config
  - m
  - L
  - eta_lam
  - ls_mu
  - ls_sigma
  - cov_func
metrics
- crps
- nmae
- nrmse
- per_observation_crps
mlflow
mmm
model_builder
model_config
- parse_model_config
model_graph
- deterministics_to_flat
paths
- create_data_url
- URLPath
  - url
pie
- model
  - PIEModel
    
    default_model_config
    
    default_sampler_config
    
    fit_result
    
    id
    
    output_var
    
    posterior
    
    posterior_predictive
    
    predictions
    
    prior
    
    prior_predictive
    
    version
    
    idata
    
    sampler_config
    
    model_config
    
    model
    
    is_fitted_
plot
pytensor_utils
r2d2
- R2D2
  - built
  - centered
  - error_sigma
  - splits
  - r2
  - total_sigma
  - dims
- R2D2Sigma
  - dims
  - decomposition
- R2D2Split
serialization
serialization_migration
- main
- migrate_idata
special_priors
terms
- build_param
- collect_coords
- collect_terms
- get_coords
- register_data
- set_data
- Dot
  - name
  - var_name
  - prior
- Intercept
  - name
  - prior
- ModelTerm
- Parameter
  - name
  - prior
- Product
  - left
  - right
- Sum
  - terms
- Transform
  - inner
  - func
utils
- from_netcdf

API
mmm
components
adstock
BinomialAdstock
BinomialAdstock.from_dict

BinomialAdstock.from_dict#

classmethod BinomialAdstock.from_dict(data)[source]#: Reconstruct a transformation from a dict.

On this page

BinomialAdstock.from_dict()

Consulting and training

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