Updating single-header

epiworld.hpp

@@ -1467,6 +1467,13 @@ inline LFMCMCProposalFun<TData> make_proposal_norm_reflective(
 
         }
 
+        #ifdef EPI_DEBUG
+        for (auto & p : params_now)
+            if (p < lb || p > ub)
+                throw std::range_error("The parameter is out of bounds.");
+        #endif
+
+
         return;
 
     };
@@ -8749,21 +8756,19 @@ inline void Model<TSeq>::reset() {
         }
         #endif
 
-    } 
-    else
-    {
-        for (auto & p : population)
-            p.reset();
+    }
 
-        #ifdef EPI_DEBUG
-        for (auto & a: population)
-        {
-            if (a.get_state() != 0u)
-                throw std::logic_error("Model::reset population doesn't match."
-                    "Some agents are not in the baseline state.");
-        }
-        #endif
+    for (auto & p : population)
+        p.reset();
+
+    #ifdef EPI_DEBUG
+    for (auto & a: population)
+    {
+        if (a.get_state() != 0u)
+            throw std::logic_error("Model::reset population doesn't match."
+                "Some agents are not in the baseline state.");
     }
+    #endif
 
     if (entities_backup.size() != 0)
     {
@@ -18405,6 +18410,34 @@ inline ModelSEIRDCONN<TSeq> & ModelSEIRDCONN<TSeq>::initial_states(
 #ifndef EPIWORLD_MODELS_SIRLOGIT_HPP 
 #define EPIWORLD_MODELS_SIRLOGIT_HPP
 
+
+/**
+ * @brief Template for a Susceptible-Infected-Removed (SIR) model
+ * 
+ * @details
+ * In this model, infection and recoveru probabilities are computed
+ * using a logit model. Particularly, the probability of infection
+ * is computed as:
+ * 
+ * \f[
+ * \frac{1}{1 + \exp\left(-\left(\beta_0 E_i + \sum_{i=1}^{n} \beta_i x_i\right)\right)}
+ * \f]
+ * 
+ * where \f$\beta_0\f$ is the exposure coefficient and \f$E_i\f$ is the exposure
+ * number, \f$\beta_i\f$ are the
+ * coefficients for the features \f$x_i\f$ of the agents, and \f$n\f$ is the
+ * number of features. The probability of recovery is computed as:
+ * 
+ * \f[
+ * \frac{1}{1 + \exp\left(-\left(\sum_{i=1}^{n} \beta_i x_i\right)\right)}
+ * \f]
+ * 
+ * where \f$\beta_i\f$ are the coefficients for the features \f$x_i\f$ of the agents,
+ * and \f$n\f$ is the number of features.
+ * 
+ * @param TSeq Type of the sequence (e.g. std::vector, std::deque)
+
+*/
 template<typename TSeq = EPI_DEFAULT_TSEQ>
 class ModelSIRLogit : public epiworld::Model<TSeq>
 {