David B. Dahl

• D. B. Dahl, D. J. Johnson, R. J. Andros (2023+), Comparison and Bayesian Estimation of Feature Allocations, Journal of Computational and Graphical Statistics, accepted. Paper. R package.
• D. B. Dahl, J. Andros, J. B. Carter (2023), Cluster analysis via random partition distributions, Statistical Analysis and Data Mining: ASA Data Science Journal, 16 (2023), 135-148. Paper. R package.
• D. B. Dahl, D. J. Johnson, P. Mueller (2022), Search Algorithms and Loss Functions for Bayesian Clustering, Journal of Computational and Graphical Statistics, 31(4), 1189-1201. Paper. R package.
• D. B. Dahl, S. Newcomb (2022), Sequentially-Allocated Merge-Split Samplers for Conjugate Bayesian Nonparametric Models, Journal of Computational Statistics and Simulation, 92(7), 1487-1511. Paper. R package.
• R. L. Warr, D. B. Dahl, Jeremy M. Meyer, Arthur Lui (2022), The Attraction Indian Buffet Distribution, Bayesian Analysis, 17 (3), 931-967. Paper. R package.
• G. L. Page, F. A. Quintana, D. B. Dahl (2022), Dependent Modeling of Temporal Sequences of Random Partitions, Journal of Computational and Graphical Statistics, 31(2), 614-627. Paper.
• D. B. Dahl, R. L. Warr, T. P. Jensen (2021), Invited Discussion of Paganin, et al.'s "Centered Partition Processes: Informative Priors for Clustering (with Discussion)," Bayesian Analysis, 16 (1), 301-370. Paper, discussion, and rejoinder.
• D. B. Dahl (2020), Integration of R and Scala Using rscala, Journal of Statistical Software, 92:4, 1-18. Paper, R package.
• F. R. Gilbert, D. B. Dahl (2018), jsr223: A Java Platform Integration for R with Programming Languages Groovy, JavaScript, JRuby, Jython, and Kotlin, R Journal, 10:2, 440-454. Paper.
• D. B. Dahl, R. Day, J. Tsai (2017), Random Partition Distribution Indexed by Pairwise Information, Journal of the American Statistical Association, 112, 721-732. Paper, R package.
• Q. Li, D. B. Dahl, M. Vannucci, H. Joo, J. W. Tsai (2016), KScons: A Bayesian Approach for Protein Residue Contact Prediction using the Knob-socket Model of Protein Tertiary Structure, Bioinformatics, 32(24): 3774-3781. Paper.
• Q. Li, D. B. Dahl, M. Vannucci, H. Joo, J. W. Tsai (2014), Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction, PLOS ONE, 9(10), e109832. Paper, R package, Web app.
• R. Day, H. Joo, A. Chavan, K. P. Lennox, Y. A. Chen, D. B. Dahl, M. Vannucci, J. Tsai (2013), Understanding the General Packing Rearrangements Required for Successful Template Based Modeling of Protein Structure from a CASP Experiment, Computational Biology and Chemistry, 42, 40-48. Paper.
• S. Schwartz, I. Friedberg, I. V. Ivanov, L. A. Davidson, J. S. Goldsby, D. B. Dahl, D. Herman, M. Wang, S. M. Donovan, R. S. Chapkin (2012), A Metagenomic Study of Diet-Dependent Interaction Between Gut Microbiota and Host in Infants Reveals Differences in Immune Response, Genome Biology, 2012, 13:r32. Paper.
• A. G. Chavan, H. Joo, R. Day, K. P. Lennox, P. Sukhanov, D. B. Dahl, M. Vannucci, and J. W. Tsai (2011), Near-Native Protein Loop Modeling using Nonparametric Density Estimation Accommodating Sparcity. PLoS Computational Biology, 7(10): e1002234. Paper.
• R. Day, K. P. Lennox, D. B. Dahl, M. Vannucci, J. W. Tsai (2010), Characterizing the regularity of tetrahedral packing motifs in protein tertiary structure. Bioinformatics, 26, 3059-3066. Paper.
• K. P. Lennox, D. B. Dahl, M. Vannucci, R. Day, J. W. Tsai (2010), A Dirichlet Process Mixture of Hidden Markov Models for Protein Structure Prediction, Annals of Applied Statistics, 4, 916-962. Paper. Publisher's Link. Website.
• S. Kim, D. B. Dahl, M. Vannucci (2009), Spike and Slab Dirichlet Process Prior for Multiple Hypothesis Testing in Random Effects Models, Bayesian Analysis, 4, 707-732. Paper.
• L. Tao, D. B. Dahl, L. M. Perez, D. H. Russell (2009), The Contributions of Molecular Framework to IMS Collision Cross-sections of Gas-phase Peptide Ions, Journal of the American Society for Mass Spectrometry, 20, 1593-1602. Paper.
• D. B. Dahl (2009), Bayesian Methods for Protein Structure Prediction: Annoted Bibliography, ISBA Bulletin, 16(2), 6-8. Bulletin.
• K. P. Lennox, D. B. Dahl, M. Vannucci, J. W. Tsai (2009), Density Estimation for Protein Conformation Angles Using a Bivariate von Mises Distribution and Bayesian Nonparametrics, Journal of the American Statistical Association, 104, 586-596. Paper with correction and supplement.
• D. B. Dahl (2009), Modal Clustering in a Class of Product Partition Models, Bayesian Analysis, 4, 243-264. Paper. Website.
• D. B. Dahl, S. Crawford (2009), RinRuby: Accessing the R Interpreter from Pure Ruby, Journal of Statistical Software, 29(4), 1-18. Paper. Software and website.
• D. B. Dahl (2008), Distance-Based Probability Distribution for Set Partitions With Applications to Bayesian Nonparametrics, in JSM Proceedings, Section on Bayesian Statistical Science, Alexandria, VA: American Statistical Association. Paper.
• D. B. Dahl, Z. Bohannan, Q. Mo, M. Vannucci, J. W. Tsai (2008), Assessing Side-Chain Perturbations of the Protein Backbone: A Knowledge Based Classification of Residue Ramachandran Space, Journal of Molecular Biology, 378, 749-758. Personal version, Publisher's version.
• D. B. Dahl, Q. Mo, M. Vannucci (2008), Simultaneous Inference for Multiple Testing and Clustering via a Dirichlet Process Mixture Model, Statistical Modelling, 8, 23-39. Paper, slides of talk, video of talk (ogg/theora) (from Third Erich L. Lehmann Symposium).
• D. B. Dahl, M. A. Newton (2007), Multiple Hypothesis Testing by Clustering Treatment Effects, Journal of the American Statistical Association, 102, 517-526. Paper, see also the slides and video for paper above.
• D. B. Dahl (2007), Invited Discussion of Jain and Neal's "Splitting and Merging Components of a Nonconjugate Dirichlet Process Mixture Model," Bayesian Analysis, 2, 473-478. Paper, discussion, and rejoinder.
• D. B. Dahl (2006), Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model, in Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Müller, Marina Vannucci (Eds.), Cambridge University Press. Paper. Software implementing least-squares clustering.
• D. Kwon, S. Kim, D. B. Dahl, M. Swartz, M. Tadesse, M. Vannucci (2006), Identification of DNA Regulatory Motifs and Regulators by Integrating Gene Expression and Sequence Data, in Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Müller, Marina Vannucci (Eds.), Cambridge University Press.
• S. Sengupta, J. A. den Boon, I.-H. Chen, M. A. Newton, D. B. Dahl, M. Chen, Y.-J. Cheng, W. H. Westra, C.-J. Chen, A. Hildesheim, B. Sugden, P. Ahlquist (2006), Genome-Wide Expression Profiling Reveals EBV-Associated Inhibition of MHC Class I Expression in Nasopharyngeal Carcinoma, Cancer Research, 66, 7999-8006.
• M. A. Croft, A. Glasser, G. Heatley, J. McDonald, T. Ebbert, D. B. Dahl, N. V. Nadkarni, P. L. Kaufman (2006), Accommodative Ciliary Body and Lens Function in Rhesus Monkeys, I: Normal Lens, Zonule and Ciliary Process Configuration in the Iridectomized Eye, Investigative Ophthalmology and Visual Science, 47, 1076-1086.
• J. A. Mattison, M. A. Croft, D. B. Dahl, G. S. Roth, M. A. Lane, D. K. Ingram, P. L. Kaufman (2005), Accommodative Function in Rhesus Monkeys: Effects of Aging and Calorie Restriction, Journal of the American Aging Association, 27, 59-67.
• S. Wamsley, B. T. Gabelt, D. B. Dahl, G. L. Case, R. W. Sherwood, C. A. May, M. R. Hernandez, P. L. Kaufman (2005), Vitreous Glutamate Concentration and Axon Loss in Monkeys with Experimental Glaucoma, Archives of Ophthalmology, 123, 64-70.
• N. Binkley, D. B. Dahl, T. Kawahara-Baccus, D. Krueger, R. J. Colman (2003), Bone Loss Assessment in Ovariectomized Rats Utilizing Two Dual-Energy X-Ray Absorptiometers, Journal of Bone and Mineral Research, 18, 370-375.
• N. T. Taylor, G. M. Burlingame, K. B. Kristensen, A. Fuhriman, J. Johansen, D. B. Dahl (2001), A Survey of Mental Health Care Provider's and Managed Care Organization Attitudes Toward, Familiarity with, and Use of Group Interventions, International Journal of Group Psychotherapy, 51(2), 243-263.
• M. Lopez, N. L. Van Zeeland, D. B. Dahl, R. Weindruch, J. M. Aiken (2000), Cellular Phenotypes of Age-Associated Skeletal Muscle Mitochondrial Abnormalities in Rhesus Monkeys, Mutation Research, 452, 123-138.

• salso: Implementation of methods in: D. B. Dahl, D. J. Johnson, P. Mueller (2022+), Search Algorithms and Loss Functions for Bayesian Clustering, Journal of Computational and Graphical Statistics, accepted. Paper. R package.
• rscala: Software described in: D. B. Dahl (2020), rscala: Integration of R and Scala, Journal of Statistical Software, 92:4, 1-18. Paper, R package.
• xtable: An R package to export tabular data into LaTeX and HTML tables. R package.
• shallot: Implementation of distributions described in: D. B. Dahl, R. Day, J. Tsai (2017), Random Partition Distribution Indexed by Pairwise Information, Journal of the American Statistical Association, 112, 721-732. Paper, R package.
• bamboo: Software implementation of the methods described in: Q. Li, D. B. Dahl, M. Vannucci, H. Joo, J. W. Tsai (2014), Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction, PLOS ONE, 9(10), e109832. Paper, R package.
• cortorgles: Software implementation of the methods described in: K. P. Lennox, D. B. Dahl, M. Vannucci, R. Day, J. W. Tsai (2010), A Dirichlet Process Mixture of Hidden Markov Models for Protein Structure Prediction, Annals of Applied Statistics, 4, 916-962. Paper. Website.
• modalclust: Software implementation of the methods described in: D. B. Dahl (2009), Modal Clustering in a Class of Product Partition Models, Bayesian Analysis, 4, 243-264. Paper. Website.
• RinRuby: Software described in: D. B. Dahl, S. Crawford (2009), RinRuby: Accessing the R Interpreter from Pure Ruby, Journal of Statistical Software, 29(4), 1-18. Paper. Website.
• dbdexam: LaTeX package and helper program to generate many versions of a multiple choice exam with solutions and answer keys.

• STAT 121: Principles of Statistics
• STAT 123: Introduction to R Programming
• STAT 223: Applied R Programming
• STAT 230: Analysis of Variance
• STAT 340: Inference
• STAT 624: Statistical Computation
• STAT 651: Bayesian Methods

David B. Dahl is Professor and Chair in the Department of Statistics at Brigham Young University. He started his academic career at Texas A&M University as an Assistant Professor in 2004, was promoted to an Associate Professor in 2010, and moved to BYU in 2012. He was promoted to Professor at BYU in 2015. He did doctoral work at the University of Wisconsin -- Madison in the Department of Statistics and the Department of Biostatistics and Medical Informatics under the direction of Michael Newton, receiving his Ph.D. in 2004. He received B.S. and M.S. degrees from Brigham Young University in 1997 and 1998, working on his masters with Scott Grimshaw in the Department of Statistics. During 2007-2012, Dr. Dahl was an adjunct faculty member in the Division of Quantitative Sciences at the University of Texas, M.D. Anderson Cancer Center.

David B. Dahl's research interests are Bayesian nonparametrics, model-based clustering, random partition models, statistical computing, and bioinformatics. His work has appeared in journals including the Journal of the American Statistical Association, Annals of Applied Statistics, Journal of Computational and Graphical Statistics, and Bayesian Analysis. He teaches both undergraduate and graduate classes, including Bayesian statistics, statistical methods, and statistical computing.