fix schema

Author: soodoku

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "rmcp"
-version = "0.4.1"
+version = "0.4.2"
 description = "Comprehensive Model Context Protocol server with 44 statistical analysis tools, flexible configuration, and HTTP transport"
 authors = ["Gaurav Sood <gsood07@gmail.com>"]
 license = "MIT"

rmcp/r_assets/scripts/descriptive/frequency_table.R

@@ -55,10 +55,10 @@ result <- list(
             Missing_Values = ifelse(is.null(ft$n_missing), 0, ft$n_missing)
           )
         }))
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           freq_summary,
           format = "markdown", digits = 4
-        ))
+        )), collapse = "\n")
       },
       error = function(e) {
         "Frequency tables created successfully"

rmcp/r_assets/scripts/descriptive/outlier_detection.R

@@ -55,7 +55,7 @@ result <- list(
           Total_Observations = length(values_clean),
           Outlier_Percentage = round(length(outliers) / length(values_clean) * 100, 2)
         )
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           outlier_df,
           format = "markdown", digits = 4
         ))

rmcp/r_assets/scripts/descriptive/summary_stats.R

@@ -70,10 +70,10 @@ if (is.null(group_by)) {
           s <- stats_list[[var]]
           data.frame(variable = var, n = s$n, mean = s$mean, sd = s$sd, min = s$min, max = s$max)
         }))
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           stats_df,
           format = "markdown", digits = 4
-        ))
+        )), collapse = "\n")
       },
       error = function(e) {
         "Summary statistics computed successfully"
@@ -108,10 +108,10 @@ if (is.null(group_by)) {
       {
         # Create summary table for grouped stats
         group_summary <- paste0("Grouped statistics by ", group_by, " (", length(groups), " groups)")
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           data.frame(Summary = group_summary),
           format = "markdown"
-        ))
+        )), collapse = "\n")
       },
       error = function(e) {
         "Grouped summary statistics computed successfully"

rmcp/r_assets/scripts/econometrics/instrumental_variables.R

@@ -67,7 +67,7 @@ result <- list(
       {
         # Try to tidy the IV model
         tidy_model <- broom::tidy(iv_model)
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           tidy_model,
           format = "markdown", digits = 4
         ))
@@ -80,7 +80,7 @@ result <- list(
           Observations = nobs(iv_model),
           Robust_SE = robust
         )
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           iv_summary,
           format = "markdown", digits = 4
         ))

rmcp/r_assets/scripts/econometrics/panel_regression.R

@@ -65,7 +65,7 @@ result <- list(
       {
         # Try to tidy the panel model
         tidy_model <- broom::tidy(model)
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           tidy_model,
           format = "markdown", digits = 4
         ))
@@ -78,7 +78,7 @@ result <- list(
           Time_Periods = pdim(model)$nT$T,
           R_Squared = round(summary(model)$r.squared[1], 4)
         )
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           panel_summary,
           format = "markdown", digits = 4
         ))

rmcp/r_assets/scripts/econometrics/var_model.R

@@ -60,7 +60,7 @@ result <- list(
           AIC = round(AIC(var_model), 2),
           BIC = round(BIC(var_model), 2)
         )
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           var_summary_df,
           format = "markdown", digits = 4
         ))

rmcp/r_assets/scripts/machine_learning/decision_tree.R

@@ -69,7 +69,7 @@ result <- list(
       {
         # Try to tidy the tree model
         tidy_tree <- broom::tidy(tree_model)
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           tidy_tree,
           format = "markdown", digits = 4
         ))
@@ -82,7 +82,7 @@ result <- list(
           Complexity = round(tree_model$cptable[nrow(tree_model$cptable), "CP"], 6),
           Observations = nrow(data)
         )
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           tree_summary,
           format = "markdown", digits = 4
         ))

rmcp/r_assets/scripts/machine_learning/kmeans_clustering.R

@@ -67,7 +67,7 @@ result <- list(
           Variance_Explained = round(between_ss / total_ss * 100, 2),
           Silhouette_Score = round(silhouette_score, 3)
         )
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           cluster_summary,
           format = "markdown", digits = 4
         ))

rmcp/r_assets/scripts/machine_learning/random_forest.R

@@ -113,7 +113,7 @@ result <- list(
           OOB_Error = if (is.null(oob_error_val)) NA else round(oob_error_val, 4),
           Observations = nrow(data)
         )
-        as.character(knitr::kable(
+        paste(as.character(knitr::kable(
           rf_summary,
           format = "markdown", digits = 4
         ))