update readme

README.md

@@ -137,6 +137,20 @@ The `fitModelParallel()` function in **HTRfit** provides a powerful way to fit m
 
 Furthermore, it's worth noting that the output object generated by fitModelParallel can be substantial in terms of memory (RAM) usage. In simulations involving 6,000 genes and 2,000 experimental conditions (equivalent to 8,000 samples), the output object can occupy a significant amount of memory, reaching approximately 10 GB. Therefore, users need to ensure that their computing environment has enough available RAM to handle these large output objects.
 
+### Diagnostic metrics
+
+The `metrics_plot()` function allows to plot a diagnostic plot of AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), logLik (log-likelihood), deviance, df.resid (residual degrees of freedom), and dispersion. These metrics provide insights into how well the model fits the data and help in comparing different models. By examining these metrics, users can quickly identify any anomalies or potential issues in the fitting process
+
+```
+## -- plot all metrics
+p <- metrics_plot(list_tmb = l_tmb)
+```
+
+<div id="bg"  align="center">
+  <img src="./vignettes/figs/diagnostic_plot.png" width="600" height="360">
+</div> 
+
+
 ### Evalutation
 
 ```
@@ -146,4 +160,10 @@ resSimu <- simulationReport(mock_data,
                             coeff_threshold = 0.27, 
                             alt_hypothesis = "greater")
 
-```
\ No newline at end of file
+```
+
+
+
+<div id="bg"  align="center">
+  <img src="./vignettes/figs/evaluation.png" width="340" height="200">
+</div> 
\ No newline at end of file