//! Social science and survey-specific imputation methods
//!
//! This module provides specialized imputation methods for social science data types
//! including survey data, longitudinal studies, demographic analysis, and social networks.
#![allow(non_snake_case)]

use crate::core::{ImputationError, ImputationResult};
use scirs2_core::ndarray::{Array2, ArrayView1, ArrayView2};
use scirs2_core::random::{Random, RngExt};
use std::collections::{HashMap, VecDeque};

/// Survey data imputation with response pattern analysis
///
/// Survey data has unique characteristics:
/// - Item non-response and unit non-response
/// - Response patterns vary by demographics
/// - Skip logic and conditional questions
/// - Social desirability bias effects
/// - Weighting and stratification considerations
#[derive(Debug, Clone)]
pub struct SurveyDataImputer {
    /// Survey design type: "cross_sectional", "longitudinal", "panel"
    pub survey_type: String,
    /// Response mechanism modeling
    pub model_response_mechanism: bool,
    /// Skip logic patterns
    pub skip_patterns: HashMap<usize, Vec<usize>>,
    /// Weighting variables
    pub weight_variables: Vec<usize>,
    /// Demographic stratification
    pub demographic_strata: HashMap<String, Vec<usize>>,
    /// Handle social desirability bias
    pub adjust_social_desirability: bool,
}

impl Default for SurveyDataImputer {
    fn default() -> Self {
        Self {
            survey_type: "cross_sectional".to_string(),
            model_response_mechanism: true,
            skip_patterns: HashMap::new(),
            weight_variables: Vec::new(),
            demographic_strata: HashMap::new(),
            adjust_social_desirability: false,
        }
    }
}

impl SurveyDataImputer {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn with_survey_type(mut self, survey_type: &str) -> Self {
        self.survey_type = survey_type.to_string();
        self
    }

    pub fn with_skip_patterns(mut self, patterns: HashMap<usize, Vec<usize>>) -> Self {
        self.skip_patterns = patterns;
        self
    }

    /// Impute survey data considering response mechanisms and design
    pub fn fit_transform(&self, X: &ArrayView2<f64>) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.to_owned();

        // Analyze response patterns
        let _response_patterns = self.analyze_response_patterns(X)?;

        // Handle skip logic
        imputed = self.handle_skip_logic(&imputed)?;

        // Demographic stratified imputation
        imputed = self.demographic_stratified_imputation(&imputed)?;

        // Weight-adjusted imputation
        if !self.weight_variables.is_empty() {
            imputed = self.weight_adjusted_imputation(&imputed)?;
        }

        // Social desirability adjustment
        if self.adjust_social_desirability {
            imputed = self.adjust_for_social_desirability(&imputed)?;
        }

        Ok(imputed)
    }

    /// Analyze response patterns to understand missingness mechanisms
    fn analyze_response_patterns(
        &self,
        X: &ArrayView2<f64>,
    ) -> ImputationResult<HashMap<String, f64>> {
        let (n_respondents, n_questions) = X.dim();
        let mut patterns = HashMap::new();

        // Calculate response rates by question
        let mut response_rates = Vec::new();
        for j in 0..n_questions {
            let response_count = X.column(j).iter().filter(|&&x| !x.is_nan()).count();
            response_rates.push(response_count as f64 / n_respondents as f64);
        }

        patterns.insert(
            "mean_response_rate".to_string(),
            response_rates.iter().sum::<f64>() / n_questions as f64,
        );

        // Analyze response pattern correlations
        let mut pattern_correlations = Vec::new();
        for i in 0..n_questions {
            for j in (i + 1)..n_questions {
                let correlation =
                    self.compute_response_pattern_correlation(&X.column(i), &X.column(j));
                pattern_correlations.push(correlation.abs());
            }
        }

        if !pattern_correlations.is_empty() {
            patterns.insert(
                "mean_pattern_correlation".to_string(),
                pattern_correlations.iter().sum::<f64>() / pattern_correlations.len() as f64,
            );
        }

        // Detect systematic non-response patterns
        let systematic_patterns = self.detect_systematic_patterns(X)?;
        patterns.insert(
            "systematic_patterns".to_string(),
            systematic_patterns.len() as f64,
        );

        Ok(patterns)
    }

    /// Compute correlation between response patterns (missingness indicators)
    fn compute_response_pattern_correlation(
        &self,
        col1: &ArrayView1<f64>,
        col2: &ArrayView1<f64>,
    ) -> f64 {
        let response1: Vec<f64> = col1
            .iter()
            .map(|&x| if x.is_nan() { 0.0 } else { 1.0 })
            .collect();
        let response2: Vec<f64> = col2
            .iter()
            .map(|&x| if x.is_nan() { 0.0 } else { 1.0 })
            .collect();

        let n = response1.len() as f64;
        let mean1 = response1.iter().sum::<f64>() / n;
        let mean2 = response2.iter().sum::<f64>() / n;

        let numerator: f64 = response1
            .iter()
            .zip(response2.iter())
            .map(|(&x1, &x2)| (x1 - mean1) * (x2 - mean2))
            .sum();

        let var1: f64 = response1.iter().map(|&x| (x - mean1).powi(2)).sum();
        let var2: f64 = response2.iter().map(|&x| (x - mean2).powi(2)).sum();

        let denominator = (var1 * var2).sqrt();

        if denominator > 1e-10 {
            numerator / denominator
        } else {
            0.0
        }
    }

    /// Detect systematic non-response patterns
    fn detect_systematic_patterns(&self, X: &ArrayView2<f64>) -> ImputationResult<Vec<Vec<usize>>> {
        let (n_respondents, _n_questions) = X.dim();
        let mut patterns = Vec::new();

        // Find common missing patterns
        let mut pattern_counts: HashMap<Vec<bool>, Vec<usize>> = HashMap::new();

        for i in 0..n_respondents {
            let row_pattern: Vec<bool> = X.row(i).iter().map(|&x| x.is_nan()).collect();

            pattern_counts.entry(row_pattern).or_default().push(i);
        }

        // Identify patterns that occur frequently (>5% of respondents)
        let min_frequency = (n_respondents as f64 * 0.05) as usize;
        for (pattern, respondents) in pattern_counts {
            if respondents.len() >= min_frequency && pattern.iter().any(|&missing| missing) {
                patterns.push(respondents);
            }
        }

        Ok(patterns)
    }

    /// Handle skip logic patterns in survey data
    fn handle_skip_logic(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        let mut processed = X.clone();

        for (condition_question, target_questions) in &self.skip_patterns {
            if *condition_question >= X.ncols() {
                continue;
            }

            let condition_values = X.column(*condition_question);

            for i in 0..X.nrows() {
                let condition_value = condition_values[i];

                if !condition_value.is_nan() {
                    // Determine if respondent should have answered target questions
                    let should_answer = self.evaluate_skip_condition(condition_value);

                    for &target_question in target_questions {
                        if target_question >= X.ncols() {
                            continue;
                        }

                        if !should_answer && X[[i, target_question]].is_nan() {
                            // This is a legitimate skip, don't impute
                            processed[[i, target_question]] = f64::NAN;
                        } else if should_answer && X[[i, target_question]].is_nan() {
                            // This should have been answered but wasn't - item non-response
                            let imputed_value =
                                self.impute_item_nonresponse(i, target_question, X)?;
                            processed[[i, target_question]] = imputed_value;
                        }
                    }
                }
            }
        }

        Ok(processed)
    }

    /// Evaluate skip logic condition
    fn evaluate_skip_condition(&self, condition_value: f64) -> bool {
        // Simplified skip logic - in practice would be more complex
        // E.g., "If employment status = unemployed, skip work-related questions"
        condition_value > 0.5 // Simple threshold
    }

    /// Impute item non-response
    fn impute_item_nonresponse(
        &self,
        respondent_idx: usize,
        question_idx: usize,
        X: &Array2<f64>,
    ) -> ImputationResult<f64> {
        // Find similar respondents based on answered questions
        let similar_respondents = self.find_similar_survey_respondents(respondent_idx, X, 10)?;

        if similar_respondents.is_empty() {
            // Fall back to question mean
            let question_values: Vec<f64> = X
                .column(question_idx)
                .iter()
                .filter(|&&x| !x.is_nan())
                .cloned()
                .collect();

            return Ok(if question_values.is_empty() {
                0.0
            } else {
                question_values.iter().sum::<f64>() / question_values.len() as f64
            });
        }

        // Use similar respondents' answers
        let similar_answers: Vec<f64> = similar_respondents
            .iter()
            .filter_map(|&idx| {
                let val = X[[idx, question_idx]];
                if !val.is_nan() {
                    Some(val)
                } else {
                    None
                }
            })
            .collect();

        if similar_answers.is_empty() {
            return self.impute_item_nonresponse(respondent_idx, question_idx, X);
        }

        // Use mode for categorical, mean for continuous
        Ok(if self.is_categorical_question(question_idx, &X.view()) {
            self.compute_mode(&similar_answers)
        } else {
            similar_answers.iter().sum::<f64>() / similar_answers.len() as f64
        })
    }

    /// Find similar survey respondents based on response patterns
    fn find_similar_survey_respondents(
        &self,
        target_idx: usize,
        X: &Array2<f64>,
        k: usize,
    ) -> ImputationResult<Vec<usize>> {
        let (n_respondents, _n_questions) = X.dim();
        let target_responses = X.row(target_idx);

        let mut similarities = Vec::new();

        for i in 0..n_respondents {
            if i != target_idx {
                let respondent_responses = X.row(i);
                let similarity =
                    self.compute_response_similarity(&target_responses, &respondent_responses);
                similarities.push((i, similarity));
            }
        }

        similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).expect("operation should succeed"));
        Ok(similarities
            .into_iter()
            .take(k)
            .map(|(idx, _)| idx)
            .collect())
    }

    /// Compute similarity between two respondents' response patterns
    fn compute_response_similarity(
        &self,
        responses1: &ArrayView1<f64>,
        responses2: &ArrayView1<f64>,
    ) -> f64 {
        let mut _common_responses = 0;
        let mut total_comparisons = 0;
        let mut agreement_score = 0.0;

        for (&r1, &r2) in responses1.iter().zip(responses2.iter()) {
            if !r1.is_nan() && !r2.is_nan() {
                total_comparisons += 1;
                _common_responses += 1;

                // Measure agreement (closer values = higher agreement)
                let difference = (r1 - r2).abs();
                agreement_score += (-difference).exp(); // Exponential decay for agreement
            }
        }

        if total_comparisons > 0 {
            agreement_score / total_comparisons as f64
        } else {
            0.0
        }
    }

    /// Check if question is categorical based on value distribution
    fn is_categorical_question(&self, question_idx: usize, X: &ArrayView2<f64>) -> bool {
        let values: Vec<f64> = X
            .column(question_idx)
            .iter()
            .filter(|&&x| !x.is_nan())
            .cloned()
            .collect();

        if values.len() < 10 {
            return true;
        }

        // Check if values are mostly integers
        let integer_count = values
            .iter()
            .filter(|&&x| (x - x.round()).abs() < 1e-10)
            .count();

        let integer_ratio = integer_count as f64 / values.len() as f64;

        // Check number of unique values
        let mut unique_values: Vec<f64> = values;
        unique_values.sort_by(|a, b| a.partial_cmp(b).expect("operation should succeed"));
        unique_values.dedup_by(|a, b| (*a - *b).abs() < 1e-10);

        // Categorical if mostly integers and limited unique values
        integer_ratio > 0.8 && unique_values.len() <= 10
    }

    /// Compute mode of values
    fn compute_mode(&self, values: &[f64]) -> f64 {
        let mut value_counts: HashMap<i64, usize> = HashMap::new();

        for &value in values {
            let rounded = value.round() as i64;
            *value_counts.entry(rounded).or_insert(0) += 1;
        }

        let mode = value_counts
            .iter()
            .max_by_key(|(_, &count)| count)
            .map(|(&value, _)| value)
            .unwrap_or(0);

        mode as f64
    }

    /// Demographic stratified imputation
    fn demographic_stratified_imputation(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        if self.demographic_strata.is_empty() {
            return Ok(imputed);
        }

        for stratum_indices in self.demographic_strata.values() {
            imputed = self.stratum_specific_imputation(&imputed, stratum_indices)?;
        }

        Ok(imputed)
    }

    /// Imputation within demographic stratum
    fn stratum_specific_imputation(
        &self,
        X: &Array2<f64>,
        stratum_indices: &[usize],
    ) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();
        let n_questions = X.ncols();

        for &respondent_idx in stratum_indices {
            if respondent_idx >= X.nrows() {
                continue;
            }

            for question_idx in 0..n_questions {
                if X[[respondent_idx, question_idx]].is_nan() {
                    // Impute using other respondents in same stratum
                    let stratum_values: Vec<f64> = stratum_indices
                        .iter()
                        .filter(|&&idx| idx != respondent_idx && idx < X.nrows())
                        .filter_map(|&idx| {
                            let val = X[[idx, question_idx]];
                            if !val.is_nan() {
                                Some(val)
                            } else {
                                None
                            }
                        })
                        .collect();

                    if !stratum_values.is_empty() {
                        let imputed_value = if self.is_categorical_question(question_idx, &X.view())
                        {
                            self.compute_mode(&stratum_values)
                        } else {
                            stratum_values.iter().sum::<f64>() / stratum_values.len() as f64
                        };

                        imputed[[respondent_idx, question_idx]] = imputed_value;
                    }
                }
            }
        }

        Ok(imputed)
    }

    /// Weight-adjusted imputation accounting for survey weights
    fn weight_adjusted_imputation(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        // Extract weights
        let weights = self.extract_survey_weights(X)?;

        // Weighted imputation for each question
        for j in 0..X.ncols() {
            if self.weight_variables.contains(&j) {
                continue; // Don't impute weight variables themselves
            }

            for i in 0..X.nrows() {
                if X[[i, j]].is_nan() {
                    let weighted_imputation =
                        self.weighted_question_imputation(i, j, X, &weights)?;
                    imputed[[i, j]] = weighted_imputation;
                }
            }
        }

        Ok(imputed)
    }

    /// Extract survey weights from weight variables
    fn extract_survey_weights(&self, X: &Array2<f64>) -> ImputationResult<Vec<f64>> {
        let n_respondents = X.nrows();
        let mut weights = vec![1.0; n_respondents];

        if self.weight_variables.is_empty() {
            return Ok(weights);
        }

        // Use first weight variable (in practice might combine multiple)
        let weight_var = self.weight_variables[0];
        if weight_var < X.ncols() {
            for i in 0..n_respondents {
                let weight = X[[i, weight_var]];
                if !weight.is_nan() && weight > 0.0 {
                    weights[i] = weight;
                }
            }
        }

        Ok(weights)
    }

    /// Weighted question imputation
    fn weighted_question_imputation(
        &self,
        respondent_idx: usize,
        question_idx: usize,
        X: &Array2<f64>,
        weights: &[f64],
    ) -> ImputationResult<f64> {
        let mut weighted_sum = 0.0;
        let mut total_weight = 0.0;

        for i in 0..X.nrows() {
            if i != respondent_idx && !X[[i, question_idx]].is_nan() {
                let response = X[[i, question_idx]];
                let weight = weights[i];

                weighted_sum += weight * response;
                total_weight += weight;
            }
        }

        if total_weight > 1e-10 {
            Ok(weighted_sum / total_weight)
        } else {
            // Fall back to unweighted mean
            let values: Vec<f64> = X
                .column(question_idx)
                .iter()
                .filter(|&&x| !x.is_nan())
                .cloned()
                .collect();

            Ok(if values.is_empty() {
                0.0
            } else {
                values.iter().sum::<f64>() / values.len() as f64
            })
        }
    }

    /// Adjust for social desirability bias
    fn adjust_for_social_desirability(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        let mut adjusted = X.clone();

        // Identify questions likely to have social desirability bias
        let sensitive_questions = self.identify_sensitive_questions(&X.view())?;

        for &question_idx in &sensitive_questions {
            adjusted = self.adjust_sensitive_question(&adjusted, question_idx)?;
        }

        Ok(adjusted)
    }

    /// Identify questions likely to have social desirability bias
    fn identify_sensitive_questions(&self, X: &ArrayView2<f64>) -> ImputationResult<Vec<usize>> {
        let mut sensitive_questions = Vec::new();

        for j in 0..X.ncols() {
            let values: Vec<f64> = X
                .column(j)
                .iter()
                .filter(|&&x| !x.is_nan())
                .cloned()
                .collect();

            if values.is_empty() {
                continue;
            }

            // Check for extreme response patterns (might indicate bias)
            let mean = values.iter().sum::<f64>() / values.len() as f64;
            let variance =
                values.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64;

            // Low variance might indicate socially desirable responses
            if variance < 0.1 && self.is_categorical_question(j, X) {
                sensitive_questions.push(j);
            }

            // Check for ceiling/floor effects
            let mut sorted_values = values.clone();
            sorted_values.sort_by(|a, b| a.partial_cmp(b).expect("operation should succeed"));

            let min_val = sorted_values[0];
            let max_val = sorted_values[sorted_values.len() - 1];

            if max_val - min_val > 1e-10 {
                let at_max = values
                    .iter()
                    .filter(|&&x| (x - max_val).abs() < 1e-10)
                    .count();
                let at_min = values
                    .iter()
                    .filter(|&&x| (x - min_val).abs() < 1e-10)
                    .count();

                let extreme_ratio = (at_max + at_min) as f64 / values.len() as f64;

                if extreme_ratio > 0.7 {
                    sensitive_questions.push(j);
                }
            }
        }

        Ok(sensitive_questions)
    }

    /// Adjust sensitive question responses for social desirability bias
    fn adjust_sensitive_question(
        &self,
        X: &Array2<f64>,
        question_idx: usize,
    ) -> ImputationResult<Array2<f64>> {
        let mut adjusted = X.clone();

        let values: Vec<f64> = X
            .column(question_idx)
            .iter()
            .filter(|&&x| !x.is_nan())
            .cloned()
            .collect();

        if values.is_empty() {
            return Ok(adjusted);
        }

        // Add random noise to break up extreme patterns (simplified adjustment)
        let noise_scale = 0.1;

        for i in 0..X.nrows() {
            if !X[[i, question_idx]].is_nan() {
                let noise = (Random::default().random::<f64>() - 0.5) * noise_scale;
                adjusted[[i, question_idx]] = X[[i, question_idx]] + noise;
            }
        }

        Ok(adjusted)
    }
}

/// Longitudinal study imputation with temporal dependencies
#[derive(Debug, Clone)]
pub struct LongitudinalStudyImputer {
    /// Time variable index
    pub time_variable: usize,
    /// Subject/individual identifier
    pub subject_variable: usize,
    /// Growth curve modeling
    pub model_growth_curves: bool,
    /// Attrition modeling
    pub model_attrition: bool,
    /// Time-varying covariates
    pub time_varying_covariates: Vec<usize>,
}

impl Default for LongitudinalStudyImputer {
    fn default() -> Self {
        Self {
            time_variable: 0,
            subject_variable: 1,
            model_growth_curves: true,
            model_attrition: false,
            time_varying_covariates: Vec::new(),
        }
    }
}

impl LongitudinalStudyImputer {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn with_time_variable(mut self, time_var: usize) -> Self {
        self.time_variable = time_var;
        self
    }

    pub fn with_subject_variable(mut self, subject_var: usize) -> Self {
        self.subject_variable = subject_var;
        self
    }

    /// Impute longitudinal data considering temporal structure
    pub fn fit_transform(&self, X: &ArrayView2<f64>) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.to_owned();

        // Group observations by subject
        let subject_groups = self.group_by_subject(X)?;

        // Model growth curves if requested
        if self.model_growth_curves {
            imputed = self.growth_curve_imputation(&imputed, &subject_groups)?;
        }

        // Handle attrition patterns
        if self.model_attrition {
            imputed = self.attrition_aware_imputation(&imputed, &subject_groups)?;
        }

        // Time-varying covariate imputation
        imputed = self.time_varying_imputation(&imputed, &subject_groups)?;

        Ok(imputed)
    }

    /// Group observations by subject
    fn group_by_subject(&self, X: &ArrayView2<f64>) -> ImputationResult<HashMap<i64, Vec<usize>>> {
        let mut subject_groups: HashMap<i64, Vec<usize>> = HashMap::new();

        if self.subject_variable >= X.ncols() {
            return Err(ImputationError::InvalidParameter(
                "Subject variable index out of bounds".to_string(),
            ));
        }

        for i in 0..X.nrows() {
            let subject_id = X[[i, self.subject_variable]];
            if !subject_id.is_nan() {
                let subject_key = subject_id.round() as i64;
                subject_groups.entry(subject_key).or_default().push(i);
            }
        }

        Ok(subject_groups)
    }

    /// Growth curve imputation using individual trajectories
    fn growth_curve_imputation(
        &self,
        X: &Array2<f64>,
        subject_groups: &HashMap<i64, Vec<usize>>,
    ) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        for observation_indices in subject_groups.values() {
            if observation_indices.len() < 2 {
                continue; // Need at least 2 observations for trajectory
            }

            // Sort by time
            let mut time_sorted = observation_indices.clone();
            time_sorted.sort_by(|&a, &b| {
                let time_a = X[[a, self.time_variable]];
                let time_b = X[[b, self.time_variable]];
                time_a
                    .partial_cmp(&time_b)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });

            // Fit growth curves for each variable
            for j in 0..X.ncols() {
                if j == self.time_variable || j == self.subject_variable {
                    continue;
                }

                imputed = self.fit_individual_growth_curve(&imputed, j, &time_sorted)?;
            }
        }

        Ok(imputed)
    }

    /// Fit growth curve for individual subject and variable
    fn fit_individual_growth_curve(
        &self,
        X: &Array2<f64>,
        variable_idx: usize,
        time_sorted_indices: &[usize],
    ) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        // Extract time-value pairs for this subject and variable
        let mut time_value_pairs = Vec::new();
        for &obs_idx in time_sorted_indices {
            let time = X[[obs_idx, self.time_variable]];
            let value = X[[obs_idx, variable_idx]];

            if !time.is_nan() && !value.is_nan() {
                time_value_pairs.push((time, value));
            }
        }

        if time_value_pairs.len() < 2 {
            return Ok(imputed);
        }

        // Fit linear growth curve (could extend to non-linear)
        let (slope, intercept) = self.fit_linear_growth_curve(&time_value_pairs)?;

        // Impute missing values using fitted curve
        for &obs_idx in time_sorted_indices {
            if X[[obs_idx, variable_idx]].is_nan() && !X[[obs_idx, self.time_variable]].is_nan() {
                let time = X[[obs_idx, self.time_variable]];
                let predicted_value = intercept + slope * time;
                imputed[[obs_idx, variable_idx]] = predicted_value;
            }
        }

        Ok(imputed)
    }

    /// Fit linear growth curve to time-value pairs
    fn fit_linear_growth_curve(
        &self,
        time_value_pairs: &[(f64, f64)],
    ) -> ImputationResult<(f64, f64)> {
        if time_value_pairs.len() < 2 {
            return Ok((0.0, 0.0));
        }

        let n = time_value_pairs.len() as f64;
        let sum_time: f64 = time_value_pairs.iter().map(|(t, _)| t).sum();
        let sum_value: f64 = time_value_pairs.iter().map(|(_, v)| v).sum();
        let sum_time_value: f64 = time_value_pairs.iter().map(|(t, v)| t * v).sum();
        let sum_time_sq: f64 = time_value_pairs.iter().map(|(t, _)| t * t).sum();

        let slope_numerator = n * sum_time_value - sum_time * sum_value;
        let slope_denominator = n * sum_time_sq - sum_time * sum_time;

        let slope = if slope_denominator.abs() > 1e-10 {
            slope_numerator / slope_denominator
        } else {
            0.0
        };

        let intercept = (sum_value - slope * sum_time) / n;

        Ok((slope, intercept))
    }

    /// Attrition-aware imputation
    fn attrition_aware_imputation(
        &self,
        X: &Array2<f64>,
        subject_groups: &HashMap<i64, Vec<usize>>,
    ) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        // Identify attrition patterns
        let _attrition_patterns = self.identify_attrition_patterns(&X.view(), subject_groups)?;

        // Adjust imputation based on attrition likelihood
        for observation_indices in subject_groups.values() {
            let attrition_risk = self.compute_attrition_risk(observation_indices, X)?;

            if attrition_risk > 0.5 {
                // High attrition risk - be more conservative in imputation
                imputed = self.conservative_temporal_imputation(&imputed, observation_indices)?;
            }
        }

        Ok(imputed)
    }

    /// Identify attrition patterns in longitudinal study
    fn identify_attrition_patterns(
        &self,
        X: &ArrayView2<f64>,
        subject_groups: &HashMap<i64, Vec<usize>>,
    ) -> ImputationResult<HashMap<String, f64>> {
        let mut patterns = HashMap::new();

        let mut dropout_times = Vec::new();
        let mut completion_rates = Vec::new();

        for observation_indices in subject_groups.values() {
            // Sort by time
            let mut time_sorted = observation_indices.clone();
            time_sorted.sort_by(|&a, &b| {
                let time_a = X[[a, self.time_variable]];
                let time_b = X[[b, self.time_variable]];
                time_a
                    .partial_cmp(&time_b)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });

            // Find last valid observation
            let last_valid_idx = time_sorted
                .iter()
                .rposition(|&idx| X.row(idx).iter().any(|&val| !val.is_nan()));

            if let Some(last_idx) = last_valid_idx {
                let last_time = X[[time_sorted[last_idx], self.time_variable]];
                if !last_time.is_nan() {
                    dropout_times.push(last_time);
                }

                let completion_rate = (last_idx + 1) as f64 / time_sorted.len() as f64;
                completion_rates.push(completion_rate);
            }
        }

        if !dropout_times.is_empty() {
            patterns.insert(
                "mean_dropout_time".to_string(),
                dropout_times.iter().sum::<f64>() / dropout_times.len() as f64,
            );
        }

        if !completion_rates.is_empty() {
            patterns.insert(
                "mean_completion_rate".to_string(),
                completion_rates.iter().sum::<f64>() / completion_rates.len() as f64,
            );
        }

        Ok(patterns)
    }

    /// Compute attrition risk for subject
    fn compute_attrition_risk(
        &self,
        observation_indices: &[usize],
        X: &Array2<f64>,
    ) -> ImputationResult<f64> {
        if observation_indices.is_empty() {
            return Ok(0.0);
        }

        // Count missing values over time
        let mut missing_counts = Vec::new();
        for &obs_idx in observation_indices {
            let missing_count = X.row(obs_idx).iter().filter(|&&val| val.is_nan()).count();
            missing_counts.push(missing_count as f64 / X.ncols() as f64);
        }

        // Attrition risk increases with missing data over time
        let mean_missingness = missing_counts.iter().sum::<f64>() / missing_counts.len() as f64;

        // Check if missingness is increasing over time
        if missing_counts.len() >= 2 {
            let trend = self.compute_missingness_trend(&missing_counts);
            Ok((mean_missingness + trend.max(0.0)).min(1.0))
        } else {
            Ok(mean_missingness)
        }
    }

    /// Compute trend in missingness over time
    fn compute_missingness_trend(&self, missing_counts: &[f64]) -> f64 {
        if missing_counts.len() < 2 {
            return 0.0;
        }

        let n = missing_counts.len() as f64;
        let sum_idx: f64 = (0..missing_counts.len()).map(|i| i as f64).sum();
        let sum_missing: f64 = missing_counts.iter().sum();
        let sum_idx_missing: f64 = missing_counts
            .iter()
            .enumerate()
            .map(|(i, &missing)| i as f64 * missing)
            .sum();
        let sum_idx_sq: f64 = (0..missing_counts.len()).map(|i| (i as f64).powi(2)).sum();

        let trend_numerator = n * sum_idx_missing - sum_idx * sum_missing;
        let trend_denominator = n * sum_idx_sq - sum_idx * sum_idx;

        if trend_denominator.abs() > 1e-10 {
            trend_numerator / trend_denominator
        } else {
            0.0
        }
    }

    /// Conservative temporal imputation for high attrition risk subjects
    fn conservative_temporal_imputation(
        &self,
        X: &Array2<f64>,
        observation_indices: &[usize],
    ) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        // Use last observation carried forward (LOCF) for conservative approach
        for &obs_idx in observation_indices {
            for j in 0..X.ncols() {
                if j == self.time_variable || j == self.subject_variable {
                    continue;
                }

                if X[[obs_idx, j]].is_nan() {
                    // Find most recent non-missing value
                    let last_valid_value =
                        self.find_last_valid_value(obs_idx, j, observation_indices, X);

                    if let Some(value) = last_valid_value {
                        imputed[[obs_idx, j]] = value;
                    }
                }
            }
        }

        Ok(imputed)
    }

    /// Find last valid value before current observation
    fn find_last_valid_value(
        &self,
        current_obs_idx: usize,
        variable_idx: usize,
        observation_indices: &[usize],
        X: &Array2<f64>,
    ) -> Option<f64> {
        let current_time = X[[current_obs_idx, self.time_variable]];
        if current_time.is_nan() {
            return None;
        }

        let mut candidates = Vec::new();
        for &obs_idx in observation_indices {
            let obs_time = X[[obs_idx, self.time_variable]];
            let obs_value = X[[obs_idx, variable_idx]];

            if !obs_time.is_nan() && !obs_value.is_nan() && obs_time < current_time {
                candidates.push((obs_time, obs_value));
            }
        }

        if candidates.is_empty() {
            return None;
        }

        // Sort by time and return most recent
        candidates.sort_by(|a, b| a.0.partial_cmp(&b.0).expect("operation should succeed"));
        candidates.last().map(|(_, value)| *value)
    }

    /// Time-varying covariate imputation
    fn time_varying_imputation(
        &self,
        X: &Array2<f64>,
        subject_groups: &HashMap<i64, Vec<usize>>,
    ) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        for covariate_idx in &self.time_varying_covariates {
            if *covariate_idx >= X.ncols() {
                continue;
            }

            for observation_indices in subject_groups.values() {
                imputed = self.impute_time_varying_covariate(
                    &imputed,
                    *covariate_idx,
                    observation_indices,
                )?;
            }
        }

        Ok(imputed)
    }

    /// Impute specific time-varying covariate
    fn impute_time_varying_covariate(
        &self,
        X: &Array2<f64>,
        covariate_idx: usize,
        observation_indices: &[usize],
    ) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        // Sort observations by time
        let mut time_sorted = observation_indices.to_vec();
        time_sorted.sort_by(|&a, &b| {
            let time_a = X[[a, self.time_variable]];
            let time_b = X[[b, self.time_variable]];
            time_a
                .partial_cmp(&time_b)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Linear interpolation for missing values
        for i in 0..time_sorted.len() {
            let obs_idx = time_sorted[i];

            if X[[obs_idx, covariate_idx]].is_nan() {
                let interpolated_value =
                    self.temporal_interpolation(i, covariate_idx, &time_sorted, X)?;

                if let Some(value) = interpolated_value {
                    imputed[[obs_idx, covariate_idx]] = value;
                }
            }
        }

        Ok(imputed)
    }

    /// Temporal interpolation for missing time-varying covariates
    fn temporal_interpolation(
        &self,
        current_position: usize,
        covariate_idx: usize,
        time_sorted_indices: &[usize],
        X: &Array2<f64>,
    ) -> ImputationResult<Option<f64>> {
        let current_idx = time_sorted_indices[current_position];
        let current_time = X[[current_idx, self.time_variable]];

        if current_time.is_nan() {
            return Ok(None);
        }

        // Find preceding and following valid observations
        let mut prev_obs = None;
        for i in (0..current_position).rev() {
            let prev_idx = time_sorted_indices[i];
            let prev_time = X[[prev_idx, self.time_variable]];
            let prev_value = X[[prev_idx, covariate_idx]];

            if !prev_time.is_nan() && !prev_value.is_nan() {
                prev_obs = Some((prev_time, prev_value));
                break;
            }
        }

        let next_obs = time_sorted_indices
            .iter()
            .skip(current_position + 1)
            .find_map(|&next_idx| {
                let next_time = X[[next_idx, self.time_variable]];
                let next_value = X[[next_idx, covariate_idx]];

                if !next_time.is_nan() && !next_value.is_nan() {
                    Some((next_time, next_value))
                } else {
                    None
                }
            });

        match (prev_obs, next_obs) {
            (Some((prev_time, prev_value)), Some((next_time, next_value))) => {
                // Linear interpolation
                let time_diff = next_time - prev_time;
                if time_diff > 1e-10 {
                    let weight = (current_time - prev_time) / time_diff;
                    let interpolated = prev_value + weight * (next_value - prev_value);
                    Ok(Some(interpolated))
                } else {
                    Ok(Some(prev_value))
                }
            }
            (Some((_, prev_value)), None) => {
                // Forward fill
                Ok(Some(prev_value))
            }
            (None, Some((_, next_value))) => {
                // Backward fill
                Ok(Some(next_value))
            }
            (None, None) => Ok(None),
        }
    }
}

/// Social network imputation considering network structure
#[derive(Debug, Clone)]
pub struct SocialNetworkImputer {
    /// Network adjacency matrix
    pub adjacency_matrix: Option<Array2<f64>>,
    /// Network influence radius
    pub influence_radius: usize,
    /// Homophily strength
    pub homophily_strength: f64,
    /// Structural equivalence consideration
    pub use_structural_equivalence: bool,
}

impl Default for SocialNetworkImputer {
    fn default() -> Self {
        Self {
            adjacency_matrix: None,
            influence_radius: 2,
            homophily_strength: 0.7,
            use_structural_equivalence: false,
        }
    }
}

impl SocialNetworkImputer {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn with_adjacency_matrix(mut self, matrix: Array2<f64>) -> Self {
        self.adjacency_matrix = Some(matrix);
        self
    }

    /// Impute social network data using network structure
    pub fn fit_transform(&self, X: &ArrayView2<f64>) -> ImputationResult<Array2<f64>> {
        let n_nodes = X.nrows();
        let mut imputed = X.to_owned();

        let adjacency = self
            .adjacency_matrix
            .as_ref()
            .cloned()
            .unwrap_or_else(|| self.infer_network_structure(X));

        // Network-based imputation for each attribute
        for j in 0..X.ncols() {
            for i in 0..n_nodes {
                if X[[i, j]].is_nan() {
                    let imputed_value = self.network_based_imputation(i, j, X, &adjacency)?;
                    imputed[[i, j]] = imputed_value;
                }
            }
        }

        Ok(imputed)
    }

    /// Infer network structure from attribute similarities
    fn infer_network_structure(&self, X: &ArrayView2<f64>) -> Array2<f64> {
        let n_nodes = X.nrows();
        let mut adjacency = Array2::zeros((n_nodes, n_nodes));

        for i in 0..n_nodes {
            for j in (i + 1)..n_nodes {
                let similarity = self.compute_node_similarity(&X.row(i), &X.row(j));

                // Create edge if similarity exceeds threshold
                if similarity > 0.5 {
                    adjacency[[i, j]] = similarity;
                    adjacency[[j, i]] = similarity;
                }
            }
        }

        adjacency
    }

    /// Compute similarity between network nodes
    fn compute_node_similarity(&self, node1: &ArrayView1<f64>, node2: &ArrayView1<f64>) -> f64 {
        let valid_pairs: Vec<(f64, f64)> = node1
            .iter()
            .zip(node2.iter())
            .filter(|(&x, &y)| !x.is_nan() && !y.is_nan())
            .map(|(&x, &y)| (x, y))
            .collect();

        if valid_pairs.is_empty() {
            return 0.0;
        }

        // Compute correlation-based similarity
        let n = valid_pairs.len() as f64;
        let sum_x: f64 = valid_pairs.iter().map(|(x, _)| x).sum();
        let sum_y: f64 = valid_pairs.iter().map(|(_, y)| y).sum();
        let sum_xy: f64 = valid_pairs.iter().map(|(x, y)| x * y).sum();
        let sum_x2: f64 = valid_pairs.iter().map(|(x, _)| x * x).sum();
        let sum_y2: f64 = valid_pairs.iter().map(|(_, y)| y * y).sum();

        let numerator = n * sum_xy - sum_x * sum_y;
        let denominator = ((n * sum_x2 - sum_x.powi(2)) * (n * sum_y2 - sum_y.powi(2))).sqrt();

        let correlation = if denominator > 1e-10 {
            numerator / denominator
        } else {
            0.0
        };

        // Convert correlation to similarity (0-1 range)
        (correlation + 1.0) / 2.0
    }

    /// Network-based imputation using social influence
    fn network_based_imputation(
        &self,
        node_idx: usize,
        attribute_idx: usize,
        X: &ArrayView2<f64>,
        adjacency: &Array2<f64>,
    ) -> ImputationResult<f64> {
        let _n_nodes = X.nrows();

        // Find network neighbors within influence radius
        let neighbors = self.find_network_neighbors(node_idx, adjacency, self.influence_radius)?;

        if neighbors.is_empty() {
            // Fall back to global mean
            let attribute_values: Vec<f64> = X
                .column(attribute_idx)
                .iter()
                .filter(|&&x| !x.is_nan())
                .cloned()
                .collect();

            return Ok(if attribute_values.is_empty() {
                0.0
            } else {
                attribute_values.iter().sum::<f64>() / attribute_values.len() as f64
            });
        }

        // Weighted imputation based on network distance and homophily
        let mut weighted_sum = 0.0;
        let mut total_weight = 0.0;

        for (neighbor_idx, network_distance) in &neighbors {
            if !X[[*neighbor_idx, attribute_idx]].is_nan() {
                let network_weight = 1.0 / (*network_distance as f64);

                // Homophily-based similarity
                let homophily_weight =
                    self.compute_homophily_weight(node_idx, *neighbor_idx, attribute_idx, X);

                let combined_weight =
                    network_weight * homophily_weight.powf(self.homophily_strength);

                weighted_sum += combined_weight * X[[*neighbor_idx, attribute_idx]];
                total_weight += combined_weight;
            }
        }

        if total_weight > 1e-10 {
            Ok(weighted_sum / total_weight)
        } else {
            // Fall back to direct neighbor average
            let neighbor_values: Vec<f64> = neighbors
                .iter()
                .filter_map(|(neighbor_idx, _)| {
                    let val = X[[*neighbor_idx, attribute_idx]];
                    if !val.is_nan() {
                        Some(val)
                    } else {
                        None
                    }
                })
                .collect();

            if !neighbor_values.is_empty() {
                Ok(neighbor_values.iter().sum::<f64>() / neighbor_values.len() as f64)
            } else {
                Ok(0.0)
            }
        }
    }

    /// Find network neighbors within given radius
    fn find_network_neighbors(
        &self,
        node_idx: usize,
        adjacency: &Array2<f64>,
        max_radius: usize,
    ) -> ImputationResult<Vec<(usize, usize)>> {
        let n_nodes = adjacency.nrows();
        let mut neighbors = Vec::new();
        let mut visited = vec![false; n_nodes];
        let mut queue = VecDeque::new();

        // BFS to find neighbors within radius
        queue.push_back((node_idx, 0));
        visited[node_idx] = true;

        while let Some((current_node, distance)) = queue.pop_front() {
            if distance > 0 {
                neighbors.push((current_node, distance));
            }

            if distance < max_radius {
                for next_node in 0..n_nodes {
                    if !visited[next_node] && adjacency[[current_node, next_node]] > 1e-10 {
                        visited[next_node] = true;
                        queue.push_back((next_node, distance + 1));
                    }
                }
            }
        }

        Ok(neighbors)
    }

    /// Compute homophily-based weight between nodes
    fn compute_homophily_weight(
        &self,
        node1_idx: usize,
        node2_idx: usize,
        target_attribute_idx: usize,
        X: &ArrayView2<f64>,
    ) -> f64 {
        let node1_attrs = X.row(node1_idx);
        let node2_attrs = X.row(node2_idx);

        let mut similarity_sum = 0.0;
        let mut comparison_count = 0;

        for (j, (&attr1, &attr2)) in node1_attrs.iter().zip(node2_attrs.iter()).enumerate() {
            if j != target_attribute_idx && !attr1.is_nan() && !attr2.is_nan() {
                let attr_similarity = 1.0 - (attr1 - attr2).abs(); // Simple similarity
                similarity_sum += attr_similarity;
                comparison_count += 1;
            }
        }

        if comparison_count > 0 {
            similarity_sum / comparison_count as f64
        } else {
            0.5 // Neutral similarity
        }
    }
}

/// Demographic data imputation with population modeling
#[derive(Debug, Clone)]
pub struct DemographicDataImputer {
    /// Population strata definitions
    pub population_strata: HashMap<String, Vec<usize>>,
    /// Census benchmarking
    pub census_benchmarks: Option<HashMap<String, f64>>,
    /// Age-period-cohort modeling
    pub apc_modeling: bool,
    /// Geographic hierarchies
    pub geographic_levels: Vec<String>,
}

impl Default for DemographicDataImputer {
    fn default() -> Self {
        Self {
            population_strata: HashMap::new(),
            census_benchmarks: None,
            apc_modeling: false,
            geographic_levels: vec!["individual".to_string()],
        }
    }
}

impl DemographicDataImputer {
    pub fn new() -> Self {
        Self::default()
    }

    /// Impute demographic data using population models
    pub fn fit_transform(&self, X: &ArrayView2<f64>) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.to_owned();

        // Population-stratified imputation
        imputed = self.population_stratified_imputation(&imputed)?;

        // Census benchmarking adjustment
        if let Some(ref benchmarks) = self.census_benchmarks {
            imputed = self.census_benchmark_adjustment(&imputed, benchmarks)?;
        }

        // Age-period-cohort modeling
        if self.apc_modeling {
            imputed = self.age_period_cohort_imputation(&imputed)?;
        }

        Ok(imputed)
    }

    /// Population-stratified imputation
    fn population_stratified_imputation(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();

        for individual_indices in self.population_strata.values() {
            imputed = self.stratum_demographic_imputation(&imputed, individual_indices)?;
        }

        Ok(imputed)
    }

    /// Demographic imputation within population stratum
    fn stratum_demographic_imputation(
        &self,
        X: &Array2<f64>,
        stratum_indices: &[usize],
    ) -> ImputationResult<Array2<f64>> {
        let mut imputed = X.clone();
        let n_variables = X.ncols();

        // Calculate stratum-specific distributions
        let stratum_distributions = self.compute_stratum_distributions(X, stratum_indices)?;

        for &individual_idx in stratum_indices {
            if individual_idx >= X.nrows() {
                continue;
            }

            for variable_idx in 0..n_variables {
                if X[[individual_idx, variable_idx]].is_nan() {
                    let imputed_value = self
                        .sample_from_stratum_distribution(variable_idx, &stratum_distributions)?;
                    imputed[[individual_idx, variable_idx]] = imputed_value;
                }
            }
        }

        Ok(imputed)
    }

    /// Compute distributions for each variable within stratum
    fn compute_stratum_distributions(
        &self,
        X: &Array2<f64>,
        stratum_indices: &[usize],
    ) -> ImputationResult<HashMap<usize, Vec<f64>>> {
        let mut distributions = HashMap::new();

        for variable_idx in 0..X.ncols() {
            let mut variable_values = Vec::new();

            for &individual_idx in stratum_indices {
                if individual_idx < X.nrows() {
                    let value = X[[individual_idx, variable_idx]];
                    if !value.is_nan() {
                        variable_values.push(value);
                    }
                }
            }

            distributions.insert(variable_idx, variable_values);
        }

        Ok(distributions)
    }

    /// Sample from stratum-specific distribution
    fn sample_from_stratum_distribution(
        &self,
        variable_idx: usize,
        distributions: &HashMap<usize, Vec<f64>>,
    ) -> ImputationResult<f64> {
        if let Some(values) = distributions.get(&variable_idx) {
            if values.is_empty() {
                return Ok(0.0);
            }

            // Random sampling from empirical distribution
            let random_idx = Random::default().gen_range(0..values.len());
            Ok(values[random_idx])
        } else {
            Ok(0.0)
        }
    }

    /// Census benchmark adjustment
    fn census_benchmark_adjustment(
        &self,
        X: &Array2<f64>,
        benchmarks: &HashMap<String, f64>,
    ) -> ImputationResult<Array2<f64>> {
        let mut adjusted = X.clone();

        // Apply post-stratification adjustments (simplified)
        for target_value in benchmarks.values() {
            // In practice would identify relevant variables and adjust
            // This is a simplified placeholder
            adjusted = self.apply_benchmark_adjustment(&adjusted, target_value)?;
        }

        Ok(adjusted)
    }

    /// Apply individual benchmark adjustment
    fn apply_benchmark_adjustment(
        &self,
        X: &Array2<f64>,
        _target_value: &f64,
    ) -> ImputationResult<Array2<f64>> {
        // Placeholder for census benchmarking
        // In practice would involve complex weighting adjustments
        Ok(X.clone())
    }

    /// Age-period-cohort imputation modeling
    fn age_period_cohort_imputation(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        // Simplified APC modeling
        // In practice would require age, period, and cohort variables to be identified
        Ok(X.clone())
    }
}

/// Missing response handler for non-response analysis
#[derive(Debug, Clone)]
pub struct MissingResponseHandler {
    /// Response propensity modeling
    pub model_response_propensity: bool,
    /// Non-response adjustment methods
    pub adjustment_method: String,
    /// Contact attempt variables
    pub contact_variables: Vec<usize>,
    /// Auxiliary variables for modeling
    pub auxiliary_variables: Vec<usize>,
}

impl Default for MissingResponseHandler {
    fn default() -> Self {
        Self {
            model_response_propensity: true,
            adjustment_method: "propensity_weighting".to_string(),
            contact_variables: Vec::new(),
            auxiliary_variables: Vec::new(),
        }
    }
}

impl MissingResponseHandler {
    pub fn new() -> Self {
        Self::default()
    }

    /// Handle missing responses using propensity modeling
    pub fn fit_transform(&self, X: &ArrayView2<f64>) -> ImputationResult<Array2<f64>> {
        let mut handled = X.to_owned();

        if self.model_response_propensity {
            let response_propensities = self.estimate_response_propensities(X)?;
            handled = self.apply_propensity_adjustments(&handled, &response_propensities)?;
        }

        match self.adjustment_method.as_str() {
            "propensity_weighting" => self.propensity_weighting_adjustment(&handled),
            "response_modeling" => self.response_modeling_adjustment(&handled),
            "calibration" => self.calibration_adjustment(&handled),
            _ => Ok(handled),
        }
    }

    /// Estimate response propensities
    fn estimate_response_propensities(&self, X: &ArrayView2<f64>) -> ImputationResult<Vec<f64>> {
        let n_individuals = X.nrows();
        let mut propensities = Vec::with_capacity(n_individuals);

        for i in 0..n_individuals {
            let individual_responses = X.row(i);
            let response_rate = individual_responses
                .iter()
                .filter(|&&x| !x.is_nan())
                .count() as f64
                / individual_responses.len() as f64;

            // Simple propensity model based on response rate
            // In practice would use logistic regression with auxiliary variables
            propensities.push(response_rate);
        }

        Ok(propensities)
    }

    /// Apply propensity adjustments
    fn apply_propensity_adjustments(
        &self,
        X: &Array2<f64>,
        propensities: &[f64],
    ) -> ImputationResult<Array2<f64>> {
        let mut adjusted = X.clone();

        // Adjust for non-response based on propensity
        for i in 0..X.nrows() {
            let propensity = propensities[i];

            if propensity < 0.1 {
                // Very low response propensity - conservative imputation
                for j in 0..X.ncols() {
                    if X[[i, j]].is_nan() {
                        adjusted[[i, j]] = 0.0; // Conservative estimate
                    }
                }
            }
        }

        Ok(adjusted)
    }

    /// Propensity weighting adjustment
    fn propensity_weighting_adjustment(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        // Placeholder for propensity weighting
        Ok(X.clone())
    }

    /// Response modeling adjustment
    fn response_modeling_adjustment(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        // Placeholder for response modeling
        Ok(X.clone())
    }

    /// Calibration adjustment
    fn calibration_adjustment(&self, X: &Array2<f64>) -> ImputationResult<Array2<f64>> {
        // Placeholder for calibration adjustment
        Ok(X.clone())
    }
}
