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diff --git a/R Sample Tools.tbx b/R Sample Tools.tbx
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diff --git a/SemiparametricRegression.aprx b/SemiparametricRegression.aprx
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diff --git a/scripts/semi_par_regression.R b/scripts/semi_par_regression.R
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+
+make_knots <-function (x1, x2, num.knots)
+{
+  if (missing(num.knots)) 
+  num.knots <- max(10, min(50, round(length(x1)/4)))
+  X <- cbind(x1, x2)
+  dup.inds <- (1:nrow(X))[dup.matrix(X) == T]
+  if (length(dup.inds) > 0) 
+      X <- X[-dup.inds, ]
+  knots <- cluster::clara(X, num.knots)$medoids
+  return(knots)
+}
+
+tool_exec <- function(in_params, out_params)
+{
+  #### Load Library for Analysis ####
+  if (!requireNamespace("SemiPar", quietly = TRUE))
+    install.packages("SemiPar")
+  require(SemiPar)
+
+  #### Get Input Parameters ####
+  input_features <- in_params[[1]]
+  input_predictions <- in_params[[2]]
+  dep_variable <- in_params[[3]]
+  lin_variables <- in_params[[4]]
+  nonlin_variables <- in_params[[5]]
+  input_knots <- in_params[[6]]
+  output_features <- out_params[[1]]
+  output_graph_pdf <- out_params[[2]]
+
+
+  #### Import Dataset to Dataframe ####
+  fc <- arc.open(input_features)
+  df <- arc.select(fc, c(dep_variable, nonlin_variables, lin_variables))
+  df['x'] <- arc.shape(df)$x
+  df['y'] <- arc.shape(df)$y
+  #### Import Knots to DataFrame ####
+  if (is.null(input_knots))
+  {
+    message("Creating default knots")
+    knots.est <- make_knots(df$x, df$y)
+  }
+  else
+  {
+    knots_df <- arc.select(arc.open(input_knots))
+    knots.est <- make_knots(arc.shape(knots_df)$x, arc.shape(knots_df)$y)
+  }
+
+  #### Create Spatial Effect ####
+  fxy <- "f(x,y,knots=knots.est)"
+
+  #### Create Formula and Fit SemiPar ####
+  e <- as.list(df)
+  e$knots.est <- knots.est
+  all_params <- paste0(dep_variable, "~",  fxy)
+
+  #### Create Non-Linear Params ####
+  if (!is.null(lin_variables))
+  {
+    nonlin_params <- paste(paste0("f(", nonlin_variables, ")"), collapse = "+")
+    all_params <- paste0(all_params, "+", nonlin_params)
+  }
+  #### Create Linear Params ####
+  if (!is.null(nonlin_variables ))
+    all_params <- paste0(all_params, "+", paste(lin_variables , collapse = "+"))
+
+  message(paste0("formula = ", all_params))
+  tryCatch(
+  {
+    attach(e)
+    form <- as.formula(all_params)
+    fit <- spm(form, family="binomial")
+  },finally = detach(e))
+
+  summary(fit)
+  #### Prediction ####
+  pred_fc <- arc.open(input_predictions)
+  oid_field <- pred_fc@fields
+  oid_field <- names(oid_field[oid_field == 'OID'])[[1]]
+  pred_df = arc.select(pred_fc, c(oid_field, nonlin_variables, lin_variables))
+  
+  if (!is.null(output_graph_pdf))
+  {
+   tryCatch(
+     {
+       pdf(output_graph_pdf)
+       #nn <- (length(lin_variables) + length(nonlin_variables) + 2)/2
+       #par(mfrow=c(ceiling(nn), 2))
+       suppressWarnings(
+          plot(fit, bdry=default.bdry(bdry = knots.est))
+       )
+     }, finally = { dev.off() })
+  }
+
+  pred_df_xy <- data.frame(
+                    x = arc.shape(pred_df)$x,
+                    y = arc.shape(pred_df)$y,
+                    pred_df)
+  pred = with(pred_df_xy, predict.spm(fit, newdata = pred_df_xy, se = TRUE))
+
+  #### Write Output ####
+  pred_df['prediction'] = 1 / (1+exp(-pred$fit))
+  #pred_df['std_error'] = pred$se
+  pred_df['LCL_95'] = 1 / (1+exp(-pred$fit+1.96*pred$se))
+  pred_df['UCL_95'] = 1/(1+exp(-pred$fit-1.96*pred$se))
+
+  arc.write(output_features, pred_df)
+
+  return(out_params)
+}
+
diff --git a/symbology/predictions_feature.lyr b/symbology/predictions_feature.lyr
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