Journal of Computational and Graphical Statistics

Aims & Scope

The Journal of Computational and Graphical Statistics ( JCGS ) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis.

Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception.

Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.8
2024	1.40

Journal Rank

Year	Value
2024	3364

Journal Citation Indicator

Year	Value
2024	784

SJR (SCImago Journal Rank)

Year	Value
2024	1.241

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	108

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Decision Sciences and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• R: A Language for Data Analysis and Graphics

  Citation: 8624

  Authors: Ross, Robert

• General Methods for Monitoring Convergence of Iterative Simulations

  Citation: 5227

  Authors: Stephen P., Andrew

• Unbiased Recursive Partitioning: A Conditional Inference Framework

  Citation: 2974

  Authors: Torsten, Kurt, Achim

• Sparse Principal Component Analysis

  Citation: 2150

  Authors: Hui, Trevor, Robert

• Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models

  Citation: 1592

  Authors: Genshiro

• Markov Chain Sampling Methods for Dirichlet Process Mixture Models

  Citation: 1398

  Authors: Radford M.

• Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

  Citation: 1226

  Authors: Alex, Adam, Justin, Emil

• A Sparse-Group Lasso

  Citation: 1046

  Authors: Noah, Jerome, Trevor, Robert

• Randomized Quantile Residuals

  Citation: 916

  Authors: Peter K., Gordon K.

• Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model

  Citation: 868

  Authors: José C., Douglas M.

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