| The basis function approach for modeling autocorrelation in ecological data TJ Hefley, KM Broms, BM Brost, FE Buderman, SL Kay, HR Scharf, ... Ecology 98 (3), 632-646, 2017 | 119 | 2017 |
| Dynamic social networks based on movement HR Scharf, MB Hooten, BK Fosdick, DS Johnson, JM London, JW Durban | 46 | 2016 |
| Running on empty: recharge dynamics from animal movement data MB Hooten, HR Scharf, JM Morales Ecology letters 22 (2), 377-389, 2019 | 37 | 2019 |
| Imputation approaches for animal movement modeling H Scharf, MB Hooten, DS Johnson Journal of Agricultural, Biological and Environmental Statistics 22, 335-352, 2017 | 36 | 2017 |
| Process convolution approaches for modeling interacting trajectories HR Scharf, MB Hooten, DS Johnson, JW Durban Environmetrics 29 (3), e2487, 2018 | 23 | 2018 |
| Animal movement models for migratory individuals and groups MB Hooten, HR Scharf, TJ Hefley, AT Pearse, MD Weegman Methods in Ecology and Evolution 9 (7), 1692-1705, 2018 | 22 | 2018 |
| Accounting for phenology in the analysis of animal movement HR Scharf, MB Hooten, RR Wilson, GM Durner, TC Atwood Biometrics 75 (3), 810-820, 2019 | 15 | 2019 |
| Animal movement models for multiple individuals HR Scharf, FE Buderman Wiley Interdisciplinary Reviews: Computational Statistics 12 (6), e1506, 2020 | 13 | 2020 |
| Searching for refuge: A framework for identifying site factors conferring resistance to climate‐driven vegetation change AM Raiho, HR Scharf, CA Roland, DK Swanson, SE Stehn, MB Hooten Diversity and Distributions 28 (4), 793-809, 2022 | 10 | 2022 |
| Noise can create or erase long transient dynamics JR Reimer, J Arroyo-Esquivel, J Jiang, HR Scharf, EM Wolkovich, K Zhu, ... Theoretical Ecology 14 (4), 685-695, 2021 | 8 | 2021 |
| Multivariate Bayesian clustering using covariate-informed components with application to boreal vegetation sensitivity HR Scharf, AM Raiho, S Pugh, CA Roland, DK Swanson, SE Stehn, ... Biometrics 78 (4), 1427-1440, 2022 | 7 | 2022 |
| Constructing flexible, identifiable and interpretable statistical models for binary data HR Scharf, X Lu, PJ Williams, MB Hooten International Statistical Review 90 (2), 328-345, 2022 | 3 | 2022 |
| Detecting changes in dynamic social networks using multiply-labeled movement data ZL Boulil, JW Durban, H Fearnbach, TW Joyce, SGM Leander, HR Scharf Journal of Agricultural, Biological and Environmental Statistics 28 (2), 243-259, 2023 | 2 | 2023 |
| Hierarchical approaches for flexible and interpretable binary regression models HR Scharf, X Lu, PJ Williams, MB Hooten arXiv preprint arXiv:1905.05242, 2019 | 1 | 2019 |
| Local Indicators of Spatial Association (LISA) H Scharf Wiley StatsRef: Statistics Reference Online, 1-9, 2014 | 1 | 2014 |
| Predicting fine-scale taxonomic variation in landscape vegetation using large satellite imagery data sets H Scharf, J Schierbaum, H Matsumoto, T Assal arXiv preprint arXiv:2309.10325, 2023 | | 2023 |
| Embracing asymmetry in nature: How to account for skewness in ecological data PJ Williams, X Lu, HR Scharf, MB Hooten Ecological Informatics 75, 102085, 2023 | | 2023 |
| BIOMETRIC METHODOLOGY W Zhang, MV Bravington, RM Fewster, Q Mai, X Zhang, A Derkach, ... Biometrics 75 (3), 2019 | | 2019 |
| Statistical Models for Dependent Trajectories with Application to Animal Movement HR Scharf Colorado State University, 2017 | | 2017 |
| Prioritized Data Compression using Wavelets H Scharf, R Elmore, K Gruchalla arXiv preprint arXiv:1407.2954, 2014 | | 2014 |
Mevin HootenProfessor, The University of Texas at AustinVerified email at austin.utexas.edu
