# Create larger synthetic dataset for performance testing
create_large_panel <- function(n_units = 50, n_periods = 30, n_outcomes = 3) {
  
  units <- c("treated", paste0("control_", 1:(n_units - 1)))
  periods <- 1990:(1990 + n_periods - 1)
  
  panel <- expand.grid(unit = units, year = periods, stringsAsFactors = FALSE)
  setDT(panel)
  
  # Multiple outcomes
  for (i in 1:n_outcomes) {
    outcome_name <- paste0("outcome_", i)
    panel[, (outcome_name) := 100 + (year - 1990) * runif(1, 0.5, 2) + rnorm(.N, 0, 5)]
    
    # Treatment effects
    panel[unit == "treated" & year >= 2005, 
          (outcome_name) := get(outcome_name) + rnorm(1, -10, 2)]
  }
  
  # Multiple covariates
  for (i in 1:8) {
    covar_name <- paste0("covar_", i)
    panel[, (covar_name) := rnorm(.N, 50, 10)]
  }
  
  return(panel)
}

# Create performance test dataset
large_panel <- create_large_panel(n_units = 30, n_periods = 25, n_outcomes = 2)
cat(sprintf("Created dataset with %d observations\n", nrow(large_panel)))

# Detect available cores and configure parallel processing
n_cores <- parallel::detectCores()
optimal_cores <- min(n_cores - 1, 8)  # Leave one core free, cap at 8

cat(sprintf("System has %d cores, using %d for analysis\n", n_cores, optimal_cores))

# Configure parallel backend
if (require("doParallel", quietly = TRUE)) {
  cl <- makeCluster(optimal_cores)
  registerDoParallel(cl)
  
  # Ensure proper cleanup
  on.exit(stopCluster(cl), add = TRUE)
}

# Memory-aware specification processing
process_large_spec_curve <- function(dataset, max_specs_per_batch = 50) {
  
  # Create specification combinations
  covariate_specs <- list(
    basic = list(
      label = "basic",
      operations = list(
        list(var = "covar_1", compute = "mean"),
        list(var = "covar_2", compute = "mean")
      )
    ),
    extended = list(
      label = "extended",
      operations = list(
        list(var = "covar_1", compute = "mean"),
        list(var = "covar_2", compute = "mean"),
        list(var = "covar_3", compute = "mean"),
        list(var = "covar_4", compute = "mean")
      )
    ),
    behavioral = list(
      label = "behavioral",
      operations = list(
        list(var = "covar_5", compute = "mean"),
        list(var = "covar_6", compute = "mean")
      )
    )
  )
  
  # Use memory management for large specification spaces
  storage_manager <- SpecResultsManager$new(
    max_memory_mb = 500,  # Limit memory usage
    compression_level = 9  # High compression for storage
  )
  
  analysis_params <- list(
    outcomes = c("outcome_1", "outcome_2"),
    covagg = covariate_specs,
    col_name_unit_name = "unit",
    name_treated_unit = "treated",
    treated_period = 2005,
    min_period = 1990,
    end_period = 2014,
    col_name_period = "year",
    feature_weights = c("uniform"),
    outcome_models = c("none", "ridge"),
    donor_sample = "all",
    constants = FALSE,
    constraints = list(list(name = "simplex")),
    inference_type = "placebo",
    inference_config = list(verbose = TRUE)
  )

  # Process analysis through the wrapper
  results <- run_spec_curve_analysis(
    dataset = dataset,
    params = analysis_params,
    cores = optimal_cores
  )
  
  return(results)
}

# Define custom constraint function
create_custom_constraint <- function(constraint_type = "custom", parameters = list()) {
  
  if (constraint_type == "elastic_net") {
    # Elastic net combines L1 and L2 penalties
    return(list(
      name = "L1-L2",
      Q1 = parameters$alpha * parameters$lambda,      # L1 penalty
      Q2 = (1 - parameters$alpha) * parameters$lambda  # L2 penalty
    ))
  }
  
  if (constraint_type == "group_sparsity") {
    # Encourage sparsity in groups of similar units
    return(list(
      name = "custom_group",
      penalty_matrix = parameters$group_penalty_matrix,
      lambda = parameters$lambda
    ))
  }
  
  if (constraint_type == "bounded_simplex") {
    # Simplex with additional bounds on individual weights
    return(list(
      name = "bounded_simplex",
      w_min = parameters$min_weight,
      w_max = parameters$max_weight
    ))
  }
  
  stop(paste("Unknown constraint type:", constraint_type))
}

# Example custom constraints
custom_constraints <- list(
  
  # Elastic net with different mixing parameters
  create_custom_constraint("elastic_net", 
                          list(alpha = 0.3, lambda = 0.1)),
  
  create_custom_constraint("elastic_net",
                          list(alpha = 0.7, lambda = 0.05)),
  
  # Bounded simplex (prevent any single unit from dominating)
  create_custom_constraint("bounded_simplex",
                          list(min_weight = 0.0, max_weight = 0.4))
)

print("Custom constraint specifications:")
str(custom_constraints)

# Intelligent solver selection based on problem characteristics
configure_optimal_solver <- function(n_donors, constraint_type, problem_size) {
  
  available_solvers <- CVXR::installed_solvers()
  
  # Commercial solvers (if available) for large problems
  if (problem_size > 1000 && "GUROBI" %in% available_solvers) {
    return(list(
      primary = "GUROBI",
      settings = list(TimeLimit = 300, MIPGap = 1e-4)
    ))
  }
  
  # Specialized solvers for different constraint types
  if (constraint_type == "lasso" && "OSQP" %in% available_solvers) {
    return(list(
      primary = "OSQP", 
      settings = list(eps_abs = 1e-5, eps_rel = 1e-5)
    ))
  }
  
  if (constraint_type == "simplex" && n_donors <= 50 && "ECOS" %in% available_solvers) {
    return(list(
      primary = "ECOS",
      settings = list(abstol = 1e-6, reltol = 1e-6)
    ))
  }
  
  # Default fallback
  return(list(
    primary = available_solvers[1],
    settings = list()
  ))
}

# Apply solver configuration
solver_config <- configure_optimal_solver(
  n_donors = 29,
  constraint_type = "simplex", 
  problem_size = nrow(large_panel)
)

cat("Recommended solver configuration:\n")
str(solver_config)

# Initialize advanced storage manager
storage_manager <- SpecResultsManager$new(
  storage_dir = tempfile("scm_results"),
  max_memory_mb = 1000,
  compression_level = 6
)

# Demonstrate storage and retrieval
demo_spec_results <- list(
  results = data.table(
    spec_id = paste0("spec_", 1:100),
    treatment_effect = rnorm(100, -5, 2),
    rmse = runif(100, 0.5, 2.0)
  ),
  abadie_inference = list(
    p_values = data.table(spec_id = paste0("spec_", 1:100), p_value = runif(100))
  )
)

# Store results
storage_id <- "demo_analysis_2023"
storage_manager$store_results(demo_spec_results, storage_id)

# Retrieve specific subsets without loading all data
filtered_results <- storage_manager$get_filtered_results(
  storage_id,
  filter_function = function(dt) dt[treatment_effect < -3],
  select_cols = c("spec_id", "treatment_effect", "rmse")
)

print("Filtered results sample:")
print(head(filtered_results))

# Get storage statistics
storage_stats <- storage_manager$get_storage_summary()
print("Storage summary:")
str(storage_stats)

# Process specifications in memory-efficient batches
process_specifications_in_batches <- function(all_specifications, batch_size = 100) {
  
  n_specs <- length(all_specifications)
  n_batches <- ceiling(n_specs / batch_size)
  
  cat(sprintf("Processing %d specifications in %d batches\n", n_specs, n_batches))
  
  results <- vector("list", n_batches)
  
  for (batch_i in 1:n_batches) {
    
    start_idx <- (batch_i - 1) * batch_size + 1
    end_idx <- min(batch_i * batch_size, n_specs)
    
    batch_specs <- all_specifications[start_idx:end_idx]
    
    cat(sprintf("Processing batch %d/%d (%d specifications)...\n", 
                batch_i, n_batches, length(batch_specs)))
    
    # Process batch with error handling
    batch_result <- tryCatch({
      
      # Your specification processing logic here
      process_specification_batch(batch_specs)
      
    }, error = function(e) {
      warning(sprintf("Batch %d failed: %s", batch_i, e$message))
      return(NULL)
    })
    
    results[[batch_i]] <- batch_result
    
    # Periodic cleanup
    if (batch_i %% 5 == 0) {
      gc()  # Force garbage collection
    }
  }
  
  # Combine successful batches
  successful_results <- results[!sapply(results, is.null)]
  return(do.call(rbind, successful_results))
}

# Create highly customized specification curve visualization
create_advanced_spec_plot <- function(spec_results, 
                                     highlight_specs = NULL,
                                     color_by = "shap_values",
                                     facet_by = NULL) {
  
  # Extract plotting data
  plot_data <- spec_results$results[unit_type == "treated" & post_period == TRUE]
  
  # Base plot
  p <- ggplot(plot_data, aes(x = reorder(full_spec_id, tau), y = tau))
  
  # Color scheme based on user choice
  if (color_by == "shap_values" && "shap_feature_importance" %in% names(plot_data)) {
    p <- p + geom_point(aes(color = shap_feature_importance), alpha = 0.7, size = 1.5)
    p <- p + scale_color_gradient2(
      low = "blue", mid = "white", high = "red",
      name = "SHAP\nImportance"
    )
  } else if (color_by == "rmse") {
    p <- p + geom_point(aes(color = rmse), alpha = 0.7, size = 1.5)
    p <- p + scale_color_gradient(low = "darkgreen", high = "darkred", name = "RMSE")
  } else {
    p <- p + geom_point(alpha = 0.6, size = 1.2, color = "steelblue")
  }
  
  # Highlight specific specifications
  if (!is.null(highlight_specs)) {
    highlight_data <- plot_data[full_spec_id %in% highlight_specs]
    p <- p + geom_point(data = highlight_data, color = "red", size = 3, shape = 1, stroke = 2)
  }
  
  # Add reference lines
  p <- p + geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5)
  p <- p + geom_hline(yintercept = median(plot_data$tau, na.rm = TRUE), 
                      color = "darkred", linetype = "solid", alpha = 0.7)
  
  # Faceting if requested
  if (!is.null(facet_by) && facet_by %in% names(plot_data)) {
    p <- p + facet_wrap(as.formula(paste("~", facet_by)), scales = "free_x")
  }
  
  # Styling
  p <- p + theme_minimal() +
    theme(
      axis.text.x = element_blank(),
      axis.ticks.x = element_blank(),
      panel.grid.minor = element_blank(),
      plot.title = element_text(size = 14, hjust = 0.5),
      plot.subtitle = element_text(size = 12, hjust = 0.5, color = "gray50")
    ) +
    labs(
      x = "Specifications (ordered by effect size)",
      y = "Treatment Effect Estimate",
      title = "Advanced Specification Curve Analysis",
      subtitle = paste("Distribution of treatment effects across", 
                      nrow(plot_data), "specifications")
    )
  
  return(p)
}

# Example usage (with mock data)
mock_results <- list(
  results = data.table(
    full_spec_id = paste0("spec_", 1:200),
    unit_type = "treated",
    post_period = TRUE,
    tau = rnorm(200, -8, 3),
    rmse = runif(200, 0.8, 2.5),
    outcome_model = sample(c("none", "ridge", "lasso"), 200, replace = TRUE),
    const = sample(c("simplex", "lasso", "ridge"), 200, replace = TRUE)
  )
)

# Create custom plot
advanced_plot <- create_advanced_spec_plot(
  mock_results,
  color_by = "rmse",
  facet_by = "outcome_model"
)

print(advanced_plot)

# Advanced SHAP analysis functions
analyze_shap_patterns <- function(shap_data, specification_data) {
  
  if (is.null(shap_data) || nrow(shap_data) == 0) {
    warning("No SHAP data available for analysis")
    return(NULL)
  }
  
  # Merge SHAP values with specification results
  merged_data <- merge(shap_data, specification_data, by = "full_spec_id")
  
  # Calculate feature importance statistics
  feature_importance <- merged_data[, .(
    mean_abs_shap = mean(abs(shap_value), na.rm = TRUE),
    sd_shap = sd(shap_value, na.rm = TRUE),
    positive_proportion = mean(shap_value > 0, na.rm = TRUE),
    correlation_with_effect = cor(shap_value, tau, use = "complete.obs")
  ), by = feature_name][order(-mean_abs_shap)]
  
  return(list(
    feature_importance = feature_importance,
    merged_data = merged_data
  ))
}

# Create SHAP interaction plots
plot_shap_interactions <- function(shap_analysis_result) {
  
  if (is.null(shap_analysis_result)) return(NULL)
  
  merged_data <- shap_analysis_result$merged_data
  
  # Create interaction heatmap
  interaction_plot <- ggplot(merged_data, 
                            aes(x = feature_name, y = reorder(full_spec_id, tau))) +
    geom_tile(aes(fill = shap_value)) +
    scale_fill_gradient2(low = "blue", mid = "white", high = "red",
                        name = "SHAP\nValue") +
    theme_minimal() +
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(x = "Features", y = "Specifications (ordered by effect size)",
         title = "SHAP Value Heatmap Across Specifications")
  
  return(interaction_plot)
}

# Implement custom inference procedure
custom_bootstrap_inference <- function(scdata_obj, n_bootstrap = 1000, 
                                      confidence_level = 0.95) {
  
  # Extract data components
  Y_treated_pre <- scdata_obj$Y.pre
  Y_donors_pre <- scdata_obj$Y.donors
  
  n_pre_periods <- length(Y_treated_pre)
  n_donors <- ncol(Y_donors_pre)
  
  # Bootstrap procedure
  bootstrap_effects <- replicate(n_bootstrap, {
    
    # Resample time periods with replacement
    bootstrap_periods <- sample(1:n_pre_periods, n_pre_periods, replace = TRUE)
    
    # Create bootstrap data
    Y_boot_treated <- Y_treated_pre[bootstrap_periods]
    Y_boot_donors <- Y_donors_pre[bootstrap_periods, , drop = FALSE]
    
    # Estimate synthetic control on bootstrap sample
    bootstrap_scdata <- scdata_obj
    bootstrap_scdata$Y.pre <- Y_boot_treated
    bootstrap_scdata$Y.donors <- Y_boot_donors
    
    # Estimate effect (simplified for demonstration)
    tryCatch({
      boot_result <- scest(bootstrap_scdata, w.constr = list(name = "simplex"))
      boot_effect <- mean(boot_result$est.results$Y.post.fit - scdata_obj$Y.post)
      return(boot_effect)
    }, error = function(e) {
      return(NA)
    })
  })
  
  # Calculate confidence intervals
  bootstrap_effects <- bootstrap_effects[!is.na(bootstrap_effects)]
  
  alpha <- 1 - confidence_level
  ci_lower <- quantile(bootstrap_effects, alpha/2, na.rm = TRUE)
  ci_upper <- quantile(bootstrap_effects, 1 - alpha/2, na.rm = TRUE)
  
  return(list(
    bootstrap_distribution = bootstrap_effects,
    confidence_interval = c(ci_lower, ci_upper),
    p_value = 2 * min(mean(bootstrap_effects >= 0, na.rm = TRUE),
                     mean(bootstrap_effects <= 0, na.rm = TRUE))
  ))
}

# Advanced feature engineering for synthetic control
engineer_advanced_features <- function(data, outcome_var, time_var, id_var,
                                      feature_specs = list()) {
  
  setDT(data)
  
  # Create polynomial time trends
  if ("polynomial_trends" %in% names(feature_specs)) {
    degree <- feature_specs$polynomial_trends$degree
    data[, paste0("time_poly_", 1:degree) := lapply(1:degree, function(d) {
      (get(time_var) - min(get(time_var)))^d
    })]
  }
  
  # Create interaction terms between covariates
  if ("interactions" %in% names(feature_specs)) {
    interaction_pairs <- feature_specs$interactions$pairs
    for (pair in interaction_pairs) {
      new_var <- paste(pair, collapse = "_x_")
      data[, (new_var) := get(pair[1]) * get(pair[2])]
    }
  }
  
  # Create rolling averages of outcomes
  if ("rolling_outcomes" %in% names(feature_specs)) {
    window_sizes <- feature_specs$rolling_outcomes$windows
    for (window in window_sizes) {
      new_var <- paste0(outcome_var, "_roll_", window)
      data[, (new_var) := frollmean(get(outcome_var), window), by = get(id_var)]
    }
  }
  
  # Create volatility measures
  if ("volatility" %in% names(feature_specs)) {
    window <- feature_specs$volatility$window
    vol_var <- paste0(outcome_var, "_volatility")
    data[, (vol_var) := frollapply(get(outcome_var), window, sd, na.rm = TRUE), 
         by = get(id_var)]
  }
  
  return(data)
}

# Example feature engineering specification
advanced_features <- list(
  polynomial_trends = list(degree = 3),
  interactions = list(pairs = list(c("income", "price"), c("age", "education"))),
  rolling_outcomes = list(windows = c(3, 5)),
  volatility = list(window = 5)
)

# Apply feature engineering (example with mock data)
# engineered_data <- engineer_advanced_features(
#   large_panel, "outcome_1", "year", "unit", advanced_features
# )

# Advanced SHAP analysis with XGBoost
run_advanced_shap_analysis <- function(spec_results, xgboost_config = NULL) {

  if (is.null(xgboost_config)) {
    xgboost_config <- create_xgboost_config(
      dataset_name = "analysis",
      treated_unit_name = "treated"
    )
  }

  # Run XGBoost SHAP analysis on long-format specification results
  shap_results <- run_xgboost_shap_analysis(
    long_data = spec_results$results,
    config = xgboost_config
  )

  return(shap_results)
}

# Integration with database backends for very large datasets
setup_database_backend <- function(connection_string, table_name) {
  
  require(DBI)
  require(RSQLite)  # or appropriate database driver
  
  # Establish connection
  con <- dbConnect(RSQLite::SQLite(), connection_string)
  
  # Create optimized table structure
  dbExecute(con, sprintf("
    CREATE TABLE IF NOT EXISTS %s (
      unit_id TEXT,
      time_period INTEGER,
      outcome_value REAL,
      covariate_1 REAL,
      covariate_2 REAL,
      /* ... additional columns ... */
      INDEX(unit_id, time_period)
    )
  ", table_name))
  
  return(con)
}

# Query data efficiently for specification curve analysis
query_specification_data <- function(db_connection, specification_params) {
  
  # Build optimized query based on specification
  query <- sprintf("
    SELECT unit_id, time_period, outcome_value, %s
    FROM panel_data
    WHERE time_period BETWEEN %d AND %d
      AND unit_id IN (%s)
  ", 
    paste(specification_params$covariates, collapse = ", "),
    specification_params$min_period,
    specification_params$max_period,
    paste0("'", specification_params$units, "'", collapse = ", ")
  )
  
  # Execute query and return data.table
  result <- dbGetQuery(db_connection, query)
  return(as.data.table(result))
}

# Monitor memory usage during large analyses
monitor_memory_usage <- function(analysis_function, ...) {
  
  # Baseline memory
  baseline_memory <- pryr::mem_used()
  
  cat(sprintf("Baseline memory usage: %s\n", 
              format(baseline_memory, units = "auto")))
  
  # Run analysis with memory monitoring
  start_time <- Sys.time()
  
  result <- tryCatch({
    analysis_function(...)
  }, error = function(e) {
    cat(sprintf("Analysis failed: %s\n", e$message))
    return(NULL)
  })
  
  end_time <- Sys.time()
  peak_memory <- pryr::mem_used()
  
  # Report results
  cat(sprintf("Analysis completed in: %s\n", 
              format(end_time - start_time)))
  cat(sprintf("Peak memory usage: %s\n", 
              format(peak_memory, units = "auto")))
  cat(sprintf("Additional memory used: %s\n", 
              format(peak_memory - baseline_memory, units = "auto")))
  
  # Cleanup
  gc()
  
  return(result)
}

# Provide optimization recommendations based on data characteristics
recommend_optimizations <- function(n_units, n_periods, n_specifications, 
                                   available_memory_gb = 8) {
  
  # Estimate memory requirements
  estimated_memory_gb <- (n_units * n_periods * n_specifications * 8) / (1024^3)
  
  recommendations <- list()
  
  # Memory recommendations
  if (estimated_memory_gb > available_memory_gb) {
    recommendations$memory <- c(
      "Consider batch processing",
      "Use disk-based storage manager",
      "Increase compression level",
      "Reduce specification space if possible"
    )
  }
  
  # Computational recommendations
  if (n_specifications > 500) {
    recommendations$computation <- c(
      "Enable parallel processing",
      "Consider distributed computing",
      "Use faster solvers if available"
    )
  }
  
  # Data structure recommendations
  if (n_units > 100) {
    recommendations$data_structure <- c(
      "Use data.table for all operations",
      "Consider database backend",
      "Implement sparse matrix representations where possible"
    )
  }
  
  return(recommendations)
}

# Example usage
opts <- recommend_optimizations(
  n_units = 200,
  n_periods = 40, 
  n_specifications = 1000,
  available_memory_gb = 16
)

cat("Optimization recommendations:\n")
str(opts)