fix path

vignettes/03-rnaseq_analysis.Rmd

@@ -96,7 +96,7 @@ l_tmb <- fitModelParallel(
 
 ## Use mixed effect in your model
 
-**HTRfit** uses the **glmmTMB** functions for model fitting algorithms. This choice allows for the utilization of random effects within your formula design. For further details on how to specify your model, please refer to the [mixed model documentation](https://rdrr.io/cran/glmmTMB/man/glmmTMBControl.html).
+HTRfit uses the **glmmTMB** functions for model fitting algorithms. This choice allows for the utilization of random effects within your formula design. For further details on how to specify your model, please refer to the [mixed model documentation](https://rdrr.io/cran/glmmTMB/man/glmmTMBControl.html).
 
 ```{r example-fitModelParallel_mixed, warning = FALSE, message = FALSE}
 l_tmb <- fitModelParallel(

vignettes/04-htrfit_vs_deseq2.Rmd

@@ -22,7 +22,7 @@ library(DESeq2)
 ```
 
 
-**HTRfit** offers a wrapper for **DESeq2** outputs. This functionality allows users to seamlessly integrate the results obtained from **DESeq2** into the **HTRfit** evaluation pipeline. By doing so, you can readily compare the performance of **HTRfit** with **DESeq2** on your RNAseq data. This comparative analysis aids in determining which tool performs better for your specific research goals and dataset
+HTRfit offers a wrapper for DESeq2 outputs. This functionality allows users to seamlessly integrate the results obtained from DESeq2 into the HTRfit evaluation pipeline. By doing so, you can readily compare the performance of HTRfit with DESeq2 on your RNAseq data. This comparative analysis aids in determining which tool performs better for your specific research goals and dataset
 
 
 ## Simulation

vignettes/05-list_var_object.Rmd

@@ -21,7 +21,7 @@ library(HTRfit)
 
 The `init_variable()` function is used for defining the variables in your experimental design. Sizes of effects for each variable and interaction term can be defined in two different ways: 1) The user can manually sets values of all levels of a variable, in which case the effects are necessarily considered fixed in the model; 2) The effects can be randomly picked in a normal distribution with mean and standard deviation defined by the user, in which case the user can decide whether the effects are considered fixed or random in the model.
 
-It is crucial to highlight that the choice of these parameters plays a critical role in the simulation process. For optimal results, we recommend basing these decisions on real data, as outlined in the [Tutorial](articles/tutorial.html)
+It is crucial to highlight that the choice of these parameters plays a critical role in the simulation process. For optimal results, we recommend basing these decisions on real data, as outlined in the [Simulation tutorial](articles/02-tutorial.html)
 
 ## Manually init a single variable
 
@@ -100,7 +100,7 @@ mock_data <- mock_rnaseq(list_var,
                          max_replicates = 4)
 ```
 
-Observe the  0.99 correlation in the `mock_obj`: 
+Observe the  -0.99 correlation in the `mock_obj`: 
 
 ```{r simu_corr_var}
 plot(mock_data$groundTruth$effects$varA, 
@@ -111,7 +111,7 @@ plot(mock_data$groundTruth$effects$varA,
 
 ## Structure of list_var object 
 
-The list_var object collected all the information needed to generate a [`mock_rnaseq` object](mock_rnaseq_object.html).
+The list_var object collected all the information needed to generate a [`mock_rnaseq` object](06-mock_rnaseq_object.html).
 
 ```{r example-str_obj_init, warning = FALSE, message = FALSE}
 str(list_var)

vignettes/06-mock_rnaseq_object.Rmd

 ---
-title: "Mock rnaseq object"
+title: "Object - Mock rnaseq"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Mock rnaseq object}
+  %\VignetteIndexEntry{Object - Mock rnaseq }
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 library(HTRfit)
 ```
 
-In this section, you will explore how to generate RNAseq data based on the [`list var`](list_var_object.html) object. The `mock_rnaseq()` function enables you to manage parameters in your RNAseq design, including number of genes, minimum and maximum number of replicates within your experimental setup, sequencing depth, basal expression of each gene, and dispersion of gene expression used for simulating counts.
+In this section, you will explore how to generate RNAseq data based on the [`list var`](05-list_var_object.html) object. The `mock_rnaseq()` function enables you to manage parameters in your RNAseq design, including number of genes, minimum and maximum number of replicates within your experimental setup, sequencing depth, basal expression of each gene, and dispersion of gene expression used for simulating counts.
 
 ## Minimal example