geostep.analyzer.CRTAnalyzer

class geostep.analyzer.CRTAnalyzer(config=None, bootstrap_reps=None, bootstrap_seed=None)

Period-adjusted OLS with geo-clustered t(G-1) inference.

This approximation is not a mixed-effects model or a guarantee of adequate small-cluster inference. Assignment to rollout sequences must be randomised.

Methods

__init__([config, bootstrap_reps, ...])	Initialize analyzer with configuration.
analyze(df, **kwargs)	Validate, prepare and analyse; failures cannot become successful results.
enable_monitoring([enabled])	Enable or disable performance monitoring.
get_metrics()	Get performance and execution metrics.
monitor_operation(operation_name[, ...])	Context manager for monitoring operations.
post_process_result(result)	Post-process analysis results.
prepare_data(df, **kwargs)	Prepare data for analysis.
set_metrics_collector(collector)	Set the metrics collector for this instance.
validate_inputs(df, **kwargs)	Validate input data and parameters.

Attributes

__init__(config=None, bootstrap_reps=None, bootstrap_seed=None)

Initialize analyzer with configuration.

validate_inputs(df, **kwargs)

Validate input data and parameters.

Override this method to add specific validation logic.

Parameters:

• df (pd.DataFrame) – Input data to validate

• **kwargs (Any) – Additional parameters to validate

Raises:

ValidationError – If validation fails

prepare_data(df, **kwargs)

Prepare data for analysis.

Override this method to add specific data preparation logic.

Parameters:

• df (pd.DataFrame) – Input data to prepare

• **kwargs (Any) – Additional parameters

Returns:

Prepared data

Return type:

pd.DataFrame