Add an extra vignette

Author: Lei Song

NEWS.md

@@ -1,3 +1,11 @@
+# itsdm 0.2.0
+
+- Convert Shapley values-based functions to usable by external models (as described in issue # 3), and add examples in function documentation and vignettes to show users how to use these functions.
+- Add a function `detect_envi_change` to use Shapley values technique to analyze the potential impacts of changing environmental variables in space.
+- Modify function `isotree_po` to take presence-absence dataset as well (as described in issue #7). To make this happen smoothly, another function `format_observation` is created to help the users to convert their data to fit into `itsdm` workflow.
+- Reorganized reference section of the online documentation to make it user-friendly.
+- Fix a few bugs in the functions.
+
 # itsdm 0.1.3
 
 - Fix a bug in function `print.VariableAnalysis` mentioned in issue #2: if any value is negative then it would fail. 

R/variable_contrib.R

@@ -112,7 +112,7 @@ variable_contrib <- function(model,
   out <- explain(model, X = var_occ,
                  nsim = shap_nsim,
                  newdata = var_occ_analysis,
-                 pred_wrapper = .pfun_shap)
+                 pred_wrapper = pfun)
   out <- list(shapley_values = out,
               feature_values = var_occ_analysis)
   class(out) <- append("VariableContribution", class(out))

README.md

@@ -13,11 +13,13 @@
 - A few functions to download environmental variables.
 - Outlier tree-based suspicious environmental outliers detection.
 - Isolation forest-based environmental suitability modeling.
-- Response curves of environmental variable.
+- Non-spatial response curves of environmental variables.
+- Spatial response maps of environmental variables.
 - Variable importance analysis.
 - Presence-only model evaluation.
 - Method to convert predicted suitability to presence-absence map.
 - Variable contribution analysis for the target observations.
+- Method to analyze the spatial impacts of changing environment.
 
 ## Installation
 
@@ -122,7 +124,7 @@ pfun <- function(X.model, newdata) {
 }
 
 # Use a fixed value
-climate_changes <- detect_climate_change(
+climate_changes <- detect_envi_change(
   model = mod_rf,
   var_occ = model_data %>% select(-occ),
   variables = env_vars,