James Balamuta

431 total citations
13 papers, 214 citations indexed

About

James Balamuta is a scholar working on Artificial Intelligence, Computer Networks and Communications and Management Science and Operations Research. According to data from OpenAlex, James Balamuta has authored 13 papers receiving a total of 214 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 3 papers in Management Science and Operations Research. Recurrent topics in James Balamuta's work include Statistical Methods and Bayesian Inference (3 papers), Inertial Sensor and Navigation (3 papers) and Target Tracking and Data Fusion in Sensor Networks (3 papers). James Balamuta is often cited by papers focused on Statistical Methods and Bayesian Inference (3 papers), Inertial Sensor and Navigation (3 papers) and Target Tracking and Data Fusion in Sensor Networks (3 papers). James Balamuta collaborates with scholars based in United States and Switzerland. James Balamuta's co-authors include Dirk Eddelbuettel, Steven Andrew Culpepper, Ryan R. Curtin, Suryoday Basak, Conrad Sanderson, Roberto Molinari, Stéphane Guerrier, Jan Škaloud, Yuming Zhang and Jeffrey A. Douglas and has published in prestigious journals such as Psychometrika, The American Statistician and IEEE Sensors Journal.

In The Last Decade

James Balamuta

13 papers receiving 209 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
James Balamuta United States	7	59	42	24	22	21	13	214
C. Weber United States	6	56 0.9×	31 0.7×	13 0.5×	13 0.6×	24 1.1×	15	272
Sophie Donnet France	11	91 1.5×	110 2.6×	19 0.8×	15 0.7×	42 2.0×	28	410
Yihui Xie China	6	45 0.8×	21 0.5×	9 0.4×	21 1.0×	56 2.7×	14	293
David Smith Australia	5	36 0.6×	49 1.2×	21 0.9×	34 1.5×	40 1.9×	8	323
Robert M. Carroll United States	5	37 0.6×	53 1.3×	28 1.2×	20 0.9×	8 0.4×	14	415
Dale S. Borowiak United States	6	49 0.8×	129 3.1×	32 1.3×	6 0.3×	14 0.7×	10	362
Robert J. MacG. Dawson Canada	9	45 0.8×	23 0.5×	14 0.6×	21 1.0×	5 0.2×	51	387
Andrew Greenfield Australia	7	29 0.5×	20 0.5×	42 1.8×	107 4.9×	21 1.0×	10	329
Beatriz Pateiro‐López Spain	11	66 1.1×	84 2.0×	10 0.4×	55 2.5×	22 1.0×	24	331
Ana M. Pires Portugal	14	58 1.0×	80 1.9×	13 0.5×	30 1.4×	33 1.6×	32	382

Countries citing papers authored by James Balamuta

Since Specialization

Citations

This map shows the geographic impact of James Balamuta's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by James Balamuta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Balamuta more than expected).

Fields of papers citing papers by James Balamuta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James Balamuta. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by James Balamuta. The network helps show where James Balamuta may publish in the future.

Co-authorship network of co-authors of James Balamuta

This figure shows the co-authorship network connecting the top 25 collaborators of James Balamuta. A scholar is included among the top collaborators of James Balamuta based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with James Balamuta. James Balamuta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

Curtin, Ryan R., et al.. (2023). mlpack 4: a fast, header-only C++ machine learninglibrary. The Journal of Open Source Software. 8(82). 5026–5026. 12 indexed citations

Balamuta, James, et al.. (2023). A sequential exploratory diagnostic model using a Pólya‐gamma data augmentation strategy. British Journal of Mathematical and Statistical Psychology. 76(3). 513–538. 5 indexed citations

Balamuta, James & Steven Andrew Culpepper. (2022). Exploratory Restricted Latent Class Models with Monotonicity Requirements under Pòlya—gamma Data Augmentation. Psychometrika. 87(3). 903–945. 13 indexed citations

Culpepper, Steven Andrew & James Balamuta. (2021). Inferring Latent Structure in Polytomous Data with a Higher-Order Diagnostic Model. Multivariate Behavioral Research. 58(2). 368–386. 8 indexed citations

Balamuta, James, et al.. (2021). Data Sets for Psychometric Modeling [R package edmdata version 1.1.0]. 1 indexed citations

Balamuta, James, Steven Andrew Culpepper, & Jeffrey A. Douglas. (2020). Bayesian Estimation of an Exploratory Deterministic Input, Noisy and Gate Model [R package edina version 0.1.1]. 1 indexed citations

Guerrier, Stéphane, et al.. (2019). Time Series Analysis Tools [R package simts version 0.1.1]. 1 indexed citations

Eddelbuettel, Dirk & James Balamuta. (2017). Extending R with C++ : A Brief Introduction to Rcpp. 42 indexed citations

Eddelbuettel, Dirk & James Balamuta. (2017). Extending R with C++: A Brief Introduction to Rcpp. The American Statistician. 72(1). 28–36. 105 indexed citations

10.

Balamuta, James, et al.. (2017). A Computationally Efficient Framework for Automatic Inertial Sensor Calibration. IEEE Sensors Journal. 18(4). 1636–1646. 14 indexed citations

11.

Škaloud, Jan, et al.. (2017). An overview of a new sensor calibration platform. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 45. 364–368. 3 indexed citations

12.

Balamuta, James, et al.. (2016). Discussion on Maximum Likelihood-Based Methods for Inertial Sensor Calibration. IEEE Sensors Journal. 16(14). 5522–5523. 6 indexed citations

13.

Culpepper, Steven Andrew & James Balamuta. (2015). A Hierarchical Model for Accuracy and Choice on Standardized Tests. Psychometrika. 82(3). 820–845. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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