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EMpeaksR  

Conducting the Peak Fitting Based on the EM Algorithm
View on CRAN: Click here


Download and install EMpeaksR package within the R console
Install from CRAN:
install.packages("EMpeaksR")

Install from Github:
library("remotes")
install_github("cran/EMpeaksR")

Install by package version:
library("remotes")
install_version("EMpeaksR", "0.3.1")



Attach the package and use:
library("EMpeaksR")
Maintained by
Tarojiro Matsumura
[Scholar Profile | Author Map]
First Published: 2021-04-20
Latest Update: 2023-03-29
Description:
The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) , . The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.
How to cite:
Tarojiro Matsumura (2021). EMpeaksR: Conducting the Peak Fitting Based on the EM Algorithm. R package version 0.3.1, https://cran.r-project.org/web/packages/EMpeaksR. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.1.0 (2021-04-20 15:50), 0.2.0 (2021-12-20 15:02), 0.3.0 (2023-03-07 08:30)
Other packages that cited EMpeaksR R package
View EMpeaksR citation profile
Other R packages that EMpeaksR depends, imports, suggests or enhances
Complete documentation for EMpeaksR
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