| ggResidpanel: panels and interactive versions of diagnostic plots using ‘ggplot2’ K Goode, K Rey R package version 0.3. 0. Online< https://cran. r-project. org/web/packages …, 2019 | 48 | 2019 |
| ggResidpanel: Panels and interactive versions of diagnostic plots using'ggplot2.'R Package Version 0.3. 0 K Goode, K Rey | 35 | 2019 |
| Visual diagnostics of an explainer model: Tools for the assessment of LIME explanations K Goode, H Hofmann Statistical Analysis and Data Mining: The ASA Data Science Journal 14 (2 …, 2021 | 12 | 2021 |
| Effects of Transport Duration and Water Quality on Age‐0 Walleye Stress and Survival EE Ball, KJ Goode, MJ Weber North American Journal of Aquaculture 82 (1), 33-42, 2020 | 11 | 2020 |
| redres: Residuals and diagnostic plots for mixed models K Goode, K McClernon, J Zhao, Y Zhang, Y Huo R package version 0.0. 0.9. https://github. com/goodekat/redres. git, 2021 | 6 | 2021 |
| A probabilistic inverse prediction method for predicting plutonium processing conditions MA Ausdemore, A McCombs, D Ries, A Zhang, K Shuler, JD Tucker, ... Frontiers in Nuclear Engineering 1, 1083164, 2022 | 5 | 2022 |
| Using feature importance as exploratory data analysis tool on earth system models D Ries, K Goode, K McClernon, B Hillman Geoscientific Model Development Discussions 2024, 1-35, 2024 | 3 | 2024 |
| Characterizing climate pathways using feature importance on echo state networks K Goode, D Ries, K McClernon Statistical Analysis and Data Mining: The ASA Data Science Journal 17 (4 …, 2024 | 3 | 2024 |
| A comparison of model validation approaches for echo state networks using climate model replicates K McClernon, K Goode, D Ries Spatial Statistics, 100813, 2024 | 3 | 2024 |
| Explaining neural network predictions for functional data using principal component analysis and feature importance K Goode, D Ries, J Zollweg arXiv preprint arXiv:2010.12063, 2020 | 3 | 2020 |
| Profile likelihood confidence intervals for ECx P Dixon, K Goode, C Lay | 3 | 2020 |
| Characterizing climate pathways using feature importance on echo state networks K Goode, D Ries, K McClernon arXiv preprint arXiv:2310.08495, 2023 | 2 | 2023 |
| Visual diagnostics for explaining machine learning models KJ Goode Iowa State University, 2021 | 2 | 2021 |
| Explaining Neural Networks with Functional DataUsing PCA and Feature Importance. KJ Goode, D Ries, JD Zollweg Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020 | 2 | 2020 |
| Evaluation of a random forest model to identify invasive carp eggs based on morphometric features K Goode, MJ Weber, A Matthews, CL Pierce North American Journal of Fisheries Management 43 (1), 46-60, 2023 | 1 | 2023 |
| Semi-supervised Bayesian Low-shot Learning J Adams, K Goode, J Michalenko, P Lewis, D Ries, J Zollweg Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | 1 | 2021 |
| Comparison of self-efficacy for reducing sedentary time to self-efficacy for increasing physical activity J Lansing, L Ellingson, K Goode, JD Meyer ANNALS OF BEHAVIORAL MEDICINE 55, S113-S113, 2021 | 1 | 2021 |
| FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests KJ Goode, JD Tucker Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2024 | | 2024 |
| Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data AL McCombs, MA Stricklin, K Goode, JG Huerta, K Shuler, JD Tucker, ... Frontiers in Nuclear Engineering 3, 1331349, 2024 | | 2024 |
| VEESA R package J Tucker, K Goode Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2023 | | 2023 |
