You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A survey of tools for the analysis of quantitative PCR (qPCR) data 
Chapter Title:  R-based tools 
1. Abstract of Cited Paper (BIB001):  Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data. 
2. Abstract of Cited Paper (BIB002):  Since R was first launched, it has managed to gain the support of an ever-increasing percentage of academic and professional statisticians. However, the spread of its use among novice and occasional users of statistics have not progressed at the same pace, which can be attributed partially to the lack of a graphical user interface (GUI). Nevertheless, this situation has changed in the last years and there is currently several projects that have added GUIs to R. This article discusses briefly the history of GUIs for data analysis and then introduces the papers submitted to an special issue of the Journal of Statistical Software on GUIs for R. 
3. Abstract of Cited Paper (BIB003):  R is a free open-source implementation of the S statistical computing language and programming environment. The current status of R is a command line driven interface with no advanced cross-platform graphical user interface (GUI), but it includes tools for building such. Over the past years, proprietary and non-proprietary GUI solutions have emerged, based on internal or external tool kits, with different scopes and technological concepts. For example, Rgui.exe and Rgui.app have become the de facto GUI on the Microsoft Windows and Mac OS X platforms, respectively, for most users. In this paper we discuss RKWard which aims to be both a comprehensive GUI and an integrated development environment for R. RKWard is based on the KDE software libraries. Statistical procedures and plots are implemented using an extendable plugin architecture based on ECMAScript (JavaScript), R, and XML. RKWard provides an excellent tool to manage different types of data objects; even allowing for seamless editing of certain types. The objective of RKWard is to provide a portable and extensible R interface for both basic and advanced statistical and graphical analysis, while not compromising on flexibility and modularity of the R programming environment itself.

Literature Review:
R is a free, open source cross-platform (e.g., UNIX platforms, Windows, and MacOS) software environment for statistical computing, visualization, and report generation. This scripting language is considered to be the lingua franca in the academic sector and in business applications. R is distributed with many statistical routines, but most of the functionality is provided by "R packages". R packages are collections of functions, data, documentation, and occasionally compiled code in a structured format. Most of the packages can be installed from the Comprehensive R Archive Network (CRAN) or Bioconductor BIB001 . By default R is command-line driven but tools have been published to provide a graphical user interface (GUI) or Integrated Development Environment (IDE) on top of the underlying framework BIB003 BIB004 . ddCT is an R package that collects, analyzes, and visualizes qPCR data using an improved ddCT  method. It takes predetermined Cq and optional efficiency values to perform relative quantification using (multiple) reference genes. Results are returned in text format and the package offers methods to generate quantification results as bar plots. The methods can be either invoked by a script or through a provided application programming interface (API). The package is well documented and sample R-code is provided. The R package dpcR is the first open source package for the statistical analysis of digital PCR experiments. It is a comprehensive collection of functions for dPCR data analysis and simulation based on Poisson statistics. The package can be used for chamber based real-time digital PCR systems as well as droplet based digital PCR systems and contains methods derived from peer-review publications for quantification and zygosity determination. In addition, novel uncertainty calculations have been introduced for the analysis of dPCR experiments. Absolute quantification is performed by counting the number of positive compartments and relating it to the total number of compartments by means of Poisson statistics. Furthermore, dpcR is able to perform analysis of CNVs and rare mutations. The package includes many published statistical approaches, summary functions, and data sets for dPCR. In addition, it supports the generation of bar plot for quantification results, amplitude plots and density plots. dpcR uses the shiny  R packages to provide an interactive web or off-line graphical user interface application, which can be used by both R novices and experts. The recently released R package EasyqpcR is based on the qBase algorithms BIB002 and calculates relative quantities using Cq values. It is able to determine amplification efficiencies using dilution series, and calculates normalization factors based on multiple reference genes. In general, the provided functionality is very similar to qBase. A graphical user interface is built upon R GUI Studio and the gWidgets package. FPK-PCR is an R-based tool for the analysis of raw fluorescence data (before baseline subtraction). It calculates reaction efficiencies and Cq values by reconstructing the entire chain of cycle efficiencies. The R-code is released with a short documentation illustrating a general example. HTqPCR is a newly published R package designed for the analysis of Cq values. It accepts tab-delimited text files and performs quality assessment, normalization (quantile normalization, rankinvariant, scale rank-invariant, housekeeping -deltaCq, geometric mean), and testing for statistical significance. Individual samples can be flagged as undetermined or unreliable, which is used throughout the analysis. The package supports principal component analysis (PCA) and clustering of analysis results. It offers multiple methods for data visualization, such as bar/box plots of quantification results, or Cq values as heatmap/scatter plot. HTqPCR is released with an extensive documentation illustrating several use cases. The R package NormqPCR provides methods for the normalization and quantification of qPCR data. Its documentation contains several examples helping novice R-users to get used with the package. It supports technical replicate handling and offers methods for selecting the best genes for normalization. Furthermore, it uses the R-package ReadqPCR for data import and supports normalization using multiple reference genes. Relative quantification can be performed by assuming perfect amplification or by including predetermined reaction efficiencies. The R-based package qpcrNorm includes three (quantile normalization, rank-invariant, housekeeping -deltaCq) algorithms for qPCR data normalization. It uses Cq values as input but can also deal with any measure of transcript abundance. The included normalization methods are especially applicable to any high-throughput qPCR setup or experiments where the stability of reference genes has not been validated. In addition to textual output, the package provides functionality to generate diagnostic plots (e.g., scatter plot for comparing the effects of normalization methods). Calculated quantification results can be displayed as box plots. Documentation explains the basic functionality but does not include sample code. The qpcR library is an R-based package that assists researchers in the modeling and analysis of quantitative real-time PCR data. It includes many published methods to perform a variety of qPCR data analysis steps including different methods for replicate handling, Cq value calculation, normalization, and relative quantification. qpcR is able to perform melting curve analysis and allows visualization of the raw fluorescence curve (before as well as after baseline subtraction). Furthermore, it features customizable import functionality and plotting quantification results as bar plots, box plots, or Cq heatmaps. The package is well documented but does not provide much sample code for novice R-users. To provide a graphical user interface for the qpcR library, a plug-in for the RKWard BIB004  system has been created. RKWard is a GUI and IDE for statistical analysis with R. The RKWard qPCR plugin uses core features (e.g., Cq value calculation) of the qpcR package while other functionality for preprocessing (e.g., removal of missing values, data transformation) and melting curve analysis is derived from the chipPCR  and the MBmca  packages. The chipPCR package contains functions to perform background subtraction, to simulate qPCRs, and to detect the start of an amplification reaction. Furthermore, it includes a Cq value quantification method based on the first and second derivative maximum method and supports calculation of the amplification efficiency of dilution experiments.