Ben Goodrich
- Statistics and Probability top 0.5%
- Statistical Methods and Bayesian Inference 4
- Statistical Methods and Inference 4
- Advanced Causal Inference Techniques 2
- General Decision Sciences top 2%
- Ecological Modeling top 2%
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- Bayesian Modeling and Causal Inference 2
- Neural Networks and Applications 2
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- Robot Manipulation and Learning 2
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- Blind Source Separation Techniques 2
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- Global trade and economics 2
- Co-authors
- Andrew GelmanMarcus A. BrubakerPeter LiJiqiang GuoDaniel C. LeeMatthew D. HoffmanMichael BetancourtAllen Riddell
- Journals
- Journal of Statistical Software (2 papers)The American Statistician (1 paper)International Organization (1 paper)
- Partner nations
- United StatesCanadaFinland
In The Last Decade
Ben Goodrich
24 papers receiving 5.6k citations
Hit Papers
Peers
Comparison fields: 5 of 223
- Statistics and Probability 747
- General Decision Sciences 124
- Ecological Modeling 236
- Nature and Landscape Conservation 525
- Experimental and Cognitive Psychology 469
Countries citing papers authored by Ben Goodrich
This map shows the geographic impact of Ben Goodrich'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 Ben Goodrich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben Goodrich more than expected).
Fields of papers citing papers by Ben Goodrich
This network shows the impact of papers produced by Ben Goodrich. 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 Ben Goodrich. The network helps show where Ben Goodrich may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ben Goodrich, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2021 | 43 | |
| 4 | Bayesian Applied Regression Modeling via Stan [R package rstanarm version 2.21.1] | 2020 | 65 |
| 5 | 2019 | 92 | |
| 6 | Imitation Learning from Visual Data with Multiple Intentions | 2018 | 2 |
| 7 | R-squared for Bayesian Regression Modelsbreakdown → | 2018 | 563 |
| 8 | Stan: A Probabilistic Programming Languagebreakdown → | 2017 | 496 |
| 9 | 2017 | 23 | |
| 10 | Stan: A Probabilistic Programming Languagebreakdown → | 2017 | 4257 |
| 11 | Missing Data Imputation and Model Checking | 2015 | 11 |
| 12 | 2014 | 12 | |
| 13 | 2014 | 55 | |
| 14 | 2014 | 3 | |
| 15 | 2012 | 11 | |
| 16 | 2010 | 1 | |
| 17 | 2006 | 4 | |
| 18 | More Pain, More Gain: Politics and Economics of Eliminating Tariffs | 2003 | 1 |
| 19 | Next Move in Steel: Revocation or Retaliation? | 2003 | 4 |
| 20 | Time for a Grand Bargain in Steel | 2002 | 6 |
About Ben Goodrich
Ben Goodrich is a scholar working on Statistics and Probability, General Social Sciences, Public Administration, Computer Vision and Pattern Recognition and General Economics, Econometrics and Finance, having authored 26 papers that have together received 5.7k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (4 papers), Statistical Methods and Inference (4 papers), Bayesian Modeling and Causal Inference (2 papers), Robot Manipulation and Learning (2 papers), Blind Source Separation Techniques (2 papers), Global trade and economics (2 papers), Neural Networks and Applications (2 papers) and Advanced Causal Inference Techniques (2 papers). The work is most often cited by research in Statistics and Probability (747 citations), General Decision Sciences (124 citations), Ecological Modeling (236 citations), Nature and Landscape Conservation (525 citations) and Experimental and Cognitive Psychology (469 citations). Ben Goodrich has collaborated with scholars based in United States, Canada and Finland. Frequent co-authors include Andrew Gelman, Marcus A. Brubaker, Peter Li, Jiqiang Guo, Daniel C. Lee, Matthew D. Hoffman, Michael Betancourt, Allen Riddell, Bob Carpenter and Jonah Gabry. Their work appears in journals such as Journal of Statistical Software, The American Statistician, International Organization, Journal of Systems and Software and Anesthesia & Analgesia.
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.