Closes #1247 Improve text "What is NCA?"

.gitignore

@@ -15,4 +15,7 @@ inst/shiny/log/*
 .DS_Store
 # {shinytest2}: Ignore new debug snapshots for `$expect_values()`
 *_.new.png
-desktop.ini
+desktop.ini
+/inst/suggestions_*
+/inst/suggestions.html
+/.quarto/

inst/suggestions.md

@@ -0,0 +1,58 @@
+---
+title: "suggestions.md to improve background.md "
+format: html
+---
+
+as of 25 APR 2026
+
+ DRAFT 
+
+## Vignettes 
+
+* background.Rmd (https://github.com/pharmaverse/aNCA/blob/main/vignettes/background.Rmd)
+* manual.Rmd (https://github.com/pharmaverse/aNCA/blob/main/vignettes/manual.Rmd)
+
+
+## Contents
+
+* Objective
+* Audience?
+* Set goals
+
+## OBJECTIVE
+The goal is improve the document "What is NCA?" (background.md).   Other than a few minor suggestions, I think document is already very good.
+
+However, there is a fairly large gap between "What is NCA?" and the next
+vignette, "Manual" .  So I want to suggest supplementing "What is NCA?" or
+adding a new document before manual.Rmd
+
+In particular, I would include:
+
+* few videos, to introducing NCA and introduce the implementation with PKNCA and aNCA packages
+   * Appilson, 5/29/25,   (https://www.youtube.com/watch?v=hvVS80I6s_U) (reduce playback speed to 0.8 if accents are new to you)
+   * Basic pharmakinetics 1 Paths of drug through body (https://www.youtube.com/watch?v=WCmFrheYtcc ) 
+   * Basic pharmakinetics 2 - How choice of route affects NCA parameters https://www.youtube.com/watch?v=WCmFrheYtcc 
+* a diagram or two to illustrate the flow of data  
+  * input -> create objects -> run calculations
+  * video (2016) - Bill P introduces PKNCA package & process  (https://www.youtube.com/watch?v=WCmFrheYtcc)
+  * PKNCA architecture https://github.com/humanpred/pknca/blob/ee1b433a0efcf102f4ec5260a1e3b39803ba26b2/design/01-architecture.md
+
+* introduce some of subleties NCA must accommodate :  
+  * possible time measurements, 
+  * possible NCA parmaters, exclusions...
+* where to find all the allowable variables and meaning (CDISC document)
+
+## AUDIENCE?
+
+Is this document for:
+
+* a new developer?   
+* a new user of the package **aNCA** ?   
+
+What assumptions should we make about the reader?
+
+* Knows R?
+* Knows NCA?
+* Knows CDISC?
+
+I would like to write so useful to someone like me.   Familar with R, but new to pharmacokinetics (PK) , CDISC standards and the functions of aNCA package.  This person wants  to contribute to the codebase, ie a new developer.

vignettes/background.Rmd

@@ -12,11 +12,10 @@ vignette: >
 
 # Background: Understanding Non-Compartmental Analysis (NCA) and the Role of aNCA
 This document will help you understand the fundamentals of Non-Compartmental Analysis (NCA) in pharmacokinetics (PK) and the challenges analysts face when performing NCA. It also explains why the aNCA application was developed and how it aims to streamline and enhance the NCA workflow. For a video-content referring to all these concepts you can also visit the [R Pharma aNCA video](https://www.youtube.com/watch?v=RW1zbnDkTCM).
-This document will help you understand the fundamentals of Non-Compartmental Analysis (NCA) in pharmacokinetics (PK) and the challenges analysts face when performing NCA. It also explains why the aNCA application was developed and how it aims to streamline and enhance the NCA workflow.
 
 ## What is Pharmacokinetics and Why Does NCA Matter?
 
-Drug development is not just about studying how drugs act on the body (*pharmacodynamics*); it’s equally important to understand how the body interacts with drugs. This is what we call **pharmacokinetics (PK)**—the study of how drugs are absorbed, distributed, metabolized, and excreted over time.
+Drug development is not just studying how drugs act on the body (*pharmacodynamics*); it’s equally important to understand how the body interacts with drugs. This is what we call **pharmacokinetics (PK)**—the study of how drugs are absorbed, distributed, metabolized, and excreted over time.
 
 To visualize this, we use PK curves: graphs showing drug concentration plotted against time after dosing. These curves tell us how long a drug stays in the patient's system, helping us determine the optimal dose that balances efficacy and safety. The sweet spot—where drug levels are high enough to have an effect but not so high as to cause toxicity—is called the **therapeutic window**. 
 
@@ -28,7 +27,7 @@ To visualize this, we use PK curves: graphs showing drug concentration plotted a
 
 When it comes to evaluating PK data, analysis approaches can range from simple to highly complex. On one end, you have **compartmental models**, where drug movement between body compartments (like blood, liver, or muscle) is described mathematically using differential equations. On the other end, you have **Non-Compartmental Analysis (NCA)**, which simplifies the process by treating the body as a "black box."
 
-In NCA, instead of modeling how a drug moves between compartments, we focus only on the *macroparameters*—high-level PK metrics that describe overall drug behavior. These include:
+In NCA, instead of modeling how a drug moves between compartments, we focus only on the *macroparameters*—high-level PK metrics that describe overall drug behavior. These include: \
 - **AUC (Area Under the Curve)**: A measure of total drug exposure over time.  
 - **Cmax**: The peak concentration of the drug in the blood.  
 - **Tmax**: The time it takes to reach peak concentration.  
@@ -82,14 +81,14 @@ Despite its mathematical simplicity, **performing NCA can be operationally compl
 
 ## Why Do We Need aNCA?
 
-Despite its simplicity, NCA workflows often become a bottleneck in drug development due to inefficiencies and heavy manual interventions:
-- Fragmented workflows require coordination between multiple roles (data collectors, programmers, analysts).
-- Many NCA tools are proprietary, expensive, and lack flexibility for custom needs.
+Despite its simplicity, NCA workflows often become a bottleneck in drug development due to inefficiencies and heavy manual interventions: \
+- Fragmented workflows require coordination between multiple roles (data collectors, programmers, analysts). \
+- Many NCA tools are proprietary, expensive, and lack flexibility for custom needs. \
 - Errors introduced during manual data handling lead to reproducibility challenges and delays.
 
-This is why we created **aNCA**:
-- To simplify and unify the entire NCA process, from data preparation to generating CDISC-compliant outputs.
-- To empower analysts, even those without extensive programming experience, by offering a **user-friendly interface** built on **PKNCA**, an open-source R package for NCA in pharmacokinetics.
+This is why we created **aNCA**: \
+- To simplify and unify the entire NCA process, from data preparation to generating CDISC-compliant outputs.\
+- To empower analysts, even those without extensive programming experience, by offering a **user-friendly interface** built on **PKNCA**, an open-source R package for NCA in pharmacokinetics.\
 - To increase automation and reduce errors, ensuring a faster and reproducible workflow.
 
 ## Do you want to know more?