fix: fix lpdf_vec to return 0 if data is missing

inst/include/distributions/functors/multinomial_lpmf.hpp

@@ -28,7 +28,7 @@ namespace fims_distributions
     {
         Type lpdf = 0.0; /**< total negative log-likelihood contribution of the distribution */
         fims::Vector<size_t> dims; /**< Dimensions of the number of rows and columns of the multivariate dataset */
-       
+
         // data_indicator<tmbutils::vector<Type> , Type> keep;  /**< Indicator used in TMB one-step-ahead residual calculations */
 
         /** @brief Constructor.
@@ -49,16 +49,16 @@ namespace fims_distributions
             // set dims using observed_values if no user input
             if(dims.size() != 2){
                 dims.resize(2);
-                dims[0] = this->observed_values->get_imax(); 
+                dims[0] = this->observed_values->get_imax();
                 dims[1] = this->observed_values->get_jmax();
             }
 
 
             // setup vector for recording the log probability density function values
             Type lpdf = 0.0; /**< total log probability mass contribution of the distribution */
             this->lpdf_vec.resize(dims[0]);
-            std::fill(this->lpdf_vec.begin(), this->lpdf_vec.end(), 0); 
-                    
+            std::fill(this->lpdf_vec.begin(), this->lpdf_vec.end(), 0);
+
             if (dims[0]*dims[1] != this->expected_values.size()) {
             ERROR_LOG << "Error: observed age comp is of size " << dims[0]*dims[1]
                 << " and expected is of size " << this->expected_values.size()
@@ -67,15 +67,15 @@ namespace fims_distributions
             } else {
                 for (size_t i = 0; i < dims[0]; i++)
                 {
-                    // for each row, create new x and prob vectors 
+                    // for each row, create new x and prob vectors
                     fims::Vector<Type> x_vector;
                     fims::Vector<Type> prob_vector;
                     x_vector.resize(dims[1]);
                     prob_vector.resize(dims[1]);
-                    
+
                     bool containsNA =
                         false; /**< skips the entire row if any values are NA */
-                    
+
                     #ifdef TMB_MODEL
                     for (size_t j = 0; j < dims[1]; j++){
                         if(this->input_type == "data"){
@@ -91,14 +91,18 @@ namespace fims_distributions
                                 prob_vector[j] = this->expected_values[idx];
                             }
                         } else {
-                            // if not data (i.e. prior or process), use x vector instead of observed_values  
+                            // if not data (i.e. prior or process), use x vector instead of observed_values
                             size_t idx = (i * dims[1]) + j;
                             x_vector[j] = this->x[idx];
                             prob_vector[j] = this->expected_values[idx];
                         }
                     }
-
-                    this->lpdf_vec[i] = dmultinom((vector<Type>)x_vector, (vector<Type>) prob_vector, true);
+
+                    if(!containsNA){
+                      this->lpdf_vec[i] = dmultinom((vector<Type>)x_vector, (vector<Type>) prob_vector, true);
+                    } else {
+                      this->lpdf_vec[i] = 0;
+                    }
                     lpdf += this->lpdf_vec[i];
                     /*
                     if (this->simulate_flag)