R Page

R is an open-source software environment and programming language that is widely used in statistical computing and data analysis. Developed by Ross Ihaka and Robert Gentleman at the University of Auckland in the early 1990s, it has gained popularity both within academia and industry for its powerful capabilities in the analysis and visualization of data. The software is FREE and can be downloaded from http://www.r-project.org/. With R, you can perform a wide range of data analysis tasks, such as data cleaning and visualization, mathematical modeling, advanced statistical modeling, and machine learning. It has been applied in fields such as mathematics, statistics, data science, biology, economics, social sciences, mathematical ecology, mathematical epidemiology, and many others. In addition, R software is not only used for coding but also for creating manuscripts and presentations. R markdown will help in this case. The major yardstick of software is R packages. R packages are collections of functions, data, and compiled code in a well-defined format, and the library is the directory in which it is stored.

The majority of my research work has been conducted using this software. In this R page, first I show the derivation procedure, and distribution of analytical-based ISRP proposed by Bhowmick et al. (2014) by using simulation studies and then also show that how the ISRP profile changes if we consider parameter variation in simulation studies. I also provide applications of analytical-based ISRP in the detection of parameter variation in different types of real data sets. 

I also show the derivation procedure and distribution of the computational-based ISRP proposed by Karim and Bhowmick (2023+) by using both the simulation study and the real data set. Also, if there are any discrepancies, please do not hesitate to contact me. The contact details are provided on my home page.

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