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 ### 2024
 
-Bertolacci, M., Zammit-Mangion, A., Schuh, A., Bukosa, B., Fisher, J. A., Cao, Y., Kaushik, A., and Cressie, N. (2024). Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework. *Annals of Applied Statistics*, **18**, 303–327 [(doi:10.1214/23-AOAS1790)](https://doi.org/10.1214/23-AOAS1790).
+Bertolacci, M., Zammit-Mangion, A., Schuh, A., Bukosa, B., Fisher, J. A., Cao, Y., Kaushik, A., and Cressie, N. (2024). Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework. *Annals of Applied Statistics*, **18**, 303–327 <span class="ref-link">[(doi:10.1214/23-AOAS1790)](https://doi.org/10.1214/23-AOAS1790).</span>
 
-Bonas, M., C.K. Wikle and Castruccio, S. (2024). Calibrated forecasts of quasi-periodic climate processes with deep echo state networks and penalized quantile regression. *Environmetrics*, **35** [(doi:10.1002/env.2833)](https://doi.org/10.1002/env.2833).
+Bonas, M., Wikle, C.K., and Castruccio, S. (2024). Calibrated forecasts of quasi-periodic climate processes with deep echo state networks and penalized quantile regression. *Environmetrics*, **35**, e2833 <span class="ref-link">[(doi:10.1002/env.2833)](https://doi.org/10.1002/env.2833).</span>
 
-Grieshop, N. and Wikle, C.K. (2024). Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics. *Spatial Statistics*, **59** [(doi:10.1016/j.spasta.2023.100794)](https://doi.org/10.1016/j.spasta.2023.100794).
+Grieshop, N. and Wikle, C.K. (2024). Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics. *Spatial Statistics*, **59**, 100794 <span class="ref-link">[(doi:10.1016/j.spasta.2023.100794)](https://doi.org/10.1016/j.spasta.2023.100794).</span>
 
-Pearse, A. R., Cressie, N., and Gunawan, D. (2024). Optimal prediction of positive-valued spatial processes: Asymmetric power-divergence loss.  *Spatial Statistics*, **60**, 100829 [(doi:10.1016/j.spasta.2024.100829)](https://doi.org/10.1016/j.spasta.2024.100829).
+North, J.S., Wikle, C.K., and Schliep, E.M. (2024). A Bayesian approach for spatio-temporal data-driven dynamic equation discovery. *Bayesian Analysis* <span class="ref-link">[(doi:10.1214/23-BA1406)](https://doi.org/10.1214/23-BA1406).</span>
 
-Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2024). Modeling big, heterogeneous, non-Gaussian spatial and spatio-temporal data using FRK. *Journal of Statistical Software*, **108** (10), 1- 39 [(doi:10.18637/jss.v108.i10)](https://doi.org/10.18637/jss.v108.i10).
+Pearse, A. R., Cressie, N., and Gunawan, D. (2024). Optimal prediction of positive-valued spatial processes: Asymmetric power-divergence loss.  *Spatial Statistics*, **60**, 100829 <span class="ref-link">[(doi:10.1016/j.spasta.2024.100829)](https://doi.org/10.1016/j.spasta.2024.100829).</span>
 
-Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2024). Likelihood-free parameter estimation with neural bayes estimators. *The American Statistician*, **78**, 1-14 [(doi:10.1080/00031305.2023.2249522)](https://doi.org/10.1080/00031305.2023.2249522).
+Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2024). Modeling big, heterogeneous, non-Gaussian spatial and spatio-temporal data using FRK. *Journal of Statistical Software*, **108(10)**, 1- 39 <span class="ref-link">[(doi:10.18637/jss.v108.i10)](https://doi.org/10.18637/jss.v108.i10).</span>
 
-Yoo, M. and Wikle, C.K. (2024). A Bayesian spatio-temporal level set dynamical model and application to fire front propagation. *Annals of Applied Statistics*, **18**, 404-423 [(https://arxiv.org/abs/2210.14978)](https://arxiv.org/abs/2210.14978).
+Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2024). Likelihood-free parameter estimation with neural Bayes estimators. *The American Statistician*, **78**, 1-14 <span class="ref-link">[(doi:10.1080/00031305.2023.2249522)](https://doi.org/10.1080/00031305.2023.2249522).</span>
 
-Zammit-Mangion, A., Kaminski, M. D., Tran, B-H., Filippone, M., and Cresise, N. (2024). Spatial Bayesian neural networks. *Spatial Statistics*, **60**, 100825 [(doi:10.1016/j.spasta.2024.100825)](https://doi.org/10.1016/j.spasta.2024.100825).
+Yoo, M. and Wikle, C.K. (2024). A Bayesian spatio-temporal level set dynamical model and application to fire front propagation. *Annals of Applied Statistics*, **18**, 404-423 <span class="ref-link">[(doi:10.48550/arXiv.2210.14978)](https://doi.org/10.48550/arXiv.2210.14978).</span>
+
+Zammit-Mangion, A., Kaminski, M. D., Tran, B-H., Filippone, M., and Cressie, N. (2024). Spatial Bayesian neural networks. *Spatial Statistics*, **60**, 100825 <span class="ref-link">[(doi:10.1016/j.spasta.2024.100825)](https://doi.org/10.1016/j.spasta.2024.100825).</span>
 
 <br/>
 
 ### 2023
 
-Berliner, L.M., Herbei, R., Wikle, C.K., and  Milliff, R.F. (2023). Excursions in the Bayesian treatment of model error. *PLoS ONE*, **18**(6), e0286624 [(doi:10.1371/journal.pone.0286624)](https://doi.org/10.1371/journal.pone.0286624).
-
-Byrne, B. et al. (with 60 co-authors including Cressie, N. and Zammit-Mangion, A.). (2023). National CO~2~ budgets (2015-2020) inferred from atmospheric CO~2~ observations in support of the global stocktake. *Earth System Science Data*, **15**, 963-1004 [(doi:10.5194/essd-15-963-2023)](https://doi.org/10.5194/essd-15-963-2023).
-
-Cressie, N. (2023). Adapting statistical science for a fast-changing climate. *CHANCE*, **36.1**,  9-13 [(doi:10.1080/09332480.2023.2179263)](https://doi.org/10.1080/09332480.2023.2179263).
+Berliner, L.M., Herbei, R., Wikle, C.K., and  Milliff, R.F. (2023). Excursions in the Bayesian treatment of model error. *PLoS ONE*, **18**(6), e0286624 <span class="ref-link">[(doi:10.1371/journal.pone.0286624)](https://doi.org/10.1371/journal.pone.0286624).</span>
 
-Cressie, N. (2023). Decisions, decisions, decisions in an uncertain environment. *Environmetrics*, **34**, e2767 [(doi:10.1002/env.2767)](https://doi.org/10.1002/env.2767).
+Byrne, B. et al. (with 60 co-authors including Cressie, N. and Zammit-Mangion, A.). (2023). National CO<sub>2</sub> budgets (2015-2020) inferred from atmospheric CO<sub>2</sub> observations in support of the global stocktake. *Earth System Science Data*, **15**, 963-1004 <span class="ref-link">[(doi:10.5194/essd-15-963-2023)](https://doi.org/10.5194/essd-15-963-2023).</span>
 
-Cressie, N. and Moores, M. T. (2023). Spatial statistics, in *Encyclopedia of Mathematical Geosciences*, eds B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg. Springer, Cham, CH, pp.1362-1373 [(doi:10.1007/978-3-030-26050-7_31-2)](https://doi.org/10.1007/978-3-030-26050-7_31-2).
+Cressie, N. (2023). Adapting statistical science for a fast-changing climate. *CHANCE*, **36.1**,  9-13 <span class="ref-link">[(doi:10.1080/09332480.2023.2179263)](https://doi.org/10.1080/09332480.2023.2179263).</span>
 
-Cressie, N., Zammit-Mangion, A., Jacobson, J., and Bertolacci, M. (2023). Earth’s CO~2~ battle: a view from space. *Significance*, **20**, February 2023 issue, 14-19 [(doi:10.1093/jrssig/qmad003)](https://doi.org/10.1093/jrssig/qmad003).
+Cressie, N. (2023). Decisions, decisions, decisions in an uncertain environment. *Environmetrics*, **34**, e2767 <span class="ref-link">[(doi:10.1002/env.2767)](https://doi.org/10.1002/env.2767).</span>
 
-Daw, R. and  Wikle, C.K. (2023). REDS: Random ensemble deep spatial prediction. *Environmetrics*, **34** [(doi:110.1002/env.2780)](https://doi.org/110.1002/env.2780).
+Cressie, N. and Moores, M. T. (2023). Spatial statistics, in *Encyclopedia of Mathematical Geosciences*, eds B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg. Springer, Cham, CH, pp.1362-1373 <span class="ref-link">[(doi:10.1007/978-3-030-26050-7_31-2)](https://doi.org/10.1007/978-3-030-26050-7_31-2).</span>
 
-Jacobson, J., Cressie, N., and Zammit-Mangion, A. (2023). Spatial statistical prediction of solar-induced chlorophyll fluorescence (SIF) from multivariate OCO-2 data. *Remote Sensing*, **15**, 4038 [(doi:10.3390/rs15164038)](https://doi.org/10.3390/rs15164038).
+Cressie, N., Zammit-Mangion, A., Jacobson, J., and Bertolacci, M. (2023). Earth’s CO<sub>2</sub> battle: a view from space. *Significance*, **20**, February 2023 issue, pp.14-19 <span class="ref-link">[(doi:10.1093/jrssig/qmad003)](https://doi.org/10.1093/jrssig/qmad003).</span>
 
-Ng, T.L.J.  and Zammit-Mangion, A. (2023). Non-homogeneous Poisson process intensity modelling and estimation using measure transport. *Bernoulli*, **29**, 815-838 [(doi:10.3150/22-BEJ1480)](https://doi.org/10.3150/22-BEJ1480).
+Daw, R. and  Wikle, C.K. (2023). REDS: Random ensemble deep spatial prediction. *Environmetrics*, **34**, e2780 <span class="ref-link">[(doi:110.1002/env.2780)](https://doi.org/110.1002/env.2780).</span>
 
-North, J.S., Wikle, C.K., and Schliep, E.M. (2023). A Bayesian approach for spatio-temporal data-driven dynamic equation discovery. *Bayesian Analysis* [(doi:10.1214/23-BA1406)](https://doi.org/10.1214/23-BA1406).
+Jacobson, J., Cressie, N., and Zammit-Mangion, A. (2023). Spatial statistical prediction of solar-induced chlorophyll fluorescence (SIF) from multivariate OCO-2 data. *Remote Sensing*, **15**, 4038 <span class="ref-link">[(doi:10.3390/rs15164038)](https://doi.org/10.3390/rs15164038).</span>
 
-North, J.S., Wikle, C.K., and Schliep, E.M. (2023). A review of data-driven discovery for dynamic systems. *International Statistical Review*, **91**, 464-492 [(doi:10.1111/insr.12554)](https://doi.org/10.1111/insr.12554).
+Ng, T.L.J.  and Zammit-Mangion, A. (2023). Non-homogeneous Poisson process intensity modelling and estimation using measure transport. *Bernoulli*, **29**, 815-838 <span class="ref-link">[(doi:10.3150/22-BEJ1480)](https://doi.org/10.3150/22-BEJ1480).</span>
 
-Schliep, E., Wikle, C.K., and Daw, R. (2023). Correcting for informative sampling in spatial covariance estimation and kriging predictions. *Journal of Geographical Systems*, **25**, 587-613. [(doi:10.1007/s10109-023-00426-9)](https://doi.org/10.1007/s10109-023-00426-9).
+North, J.S., Wikle, C.K., and Schliep, E.M. (2023). A review of data-driven discovery for dynamic systems. *International Statistical Review*, **91**, 464-492 <span class="ref-link">[(doi:10.1111/insr.12554)](https://doi.org/10.1111/insr.12554).</span>
 
-Simpson, M., Holan, S.H., Wikle C.K., and Bradley, J.R. (2023). Interpolating population distributions using public-use data: An application to income segregation using American Community Survey data. *Journal of the American Statistical Association*, **118**, 84-96 [(doi:10.1080/01621459.2022.2126779)](https://doi.org/10.1080/01621459.2022.2126779).
+Schliep, E., Wikle, C.K., and Daw, R. (2023). Correcting for informative sampling in spatial covariance estimation and kriging predictions. *Journal of Geographical Systems*, **25**, 587-613. <span class="ref-link">[(doi:10.1007/s10109-023-00426-9)](https://doi.org/10.1007/s10109-023-00426-9).</span>
 
-Vu., Q., Zammit-Mangion, A., and Chuter S. (2023). Constructing large nonstationary spatio-temporal covariances via compositional warpings. *Spatial Statistics*, **54**, 100742 [(doi:10.1016/j.spasta.2023.100742)](https://doi.org/10.1016/j.spasta.2023.100742).
+Simpson, M., Holan, S.H., Wikle, C.K., and Bradley, J.R. (2023). Interpolating population distributions using public-use data: An application to income segregation using American Community Survey data. *Journal of the American Statistical Association*, **118**, 84-96 <span class="ref-link">[(doi:10.1080/01621459.2022.2126779)](https://doi.org/10.1080/01621459.2022.2126779).</span>
 
-Wikle, C.K. and Zammit-Mangion, A. (2023). Statistical deep learning for spatial and spatio-temporal data. *Annual Review of Statistics and Its Application*, **10**, 247-270 [(doi:10.1146/annurev-statistics-033021-112628)](https://doi.org/10.1146/annurev-statistics-033021-112628).
+Vu., Q., Zammit-Mangion, A., and Chuter, S. (2023). Constructing large nonstationary spatio-temporal covariances via compositional warpings. *Spatial Statistics*, **54**, 100742 <span class="ref-link">[(doi:10.1016/j.spasta.2023.100742)](https://doi.org/10.1016/j.spasta.2023.100742).</span>
 
-Wikle, C.K. and Zammit-Mangion, A. (2023). Statistical deep learning for spatial and spatiotemporal data. *Annual Review of Statistics and its Application*, **10**, 247-270 [(doi:10.1146/annurev-statistics-033021-112628)](https://doi.org/10.1146/annurev-statistics-033021-112628).
+Wikle, C.K. and Zammit-Mangion, A. (2023). Statistical deep learning for spatial and spatio-temporal data. *Annual Review of Statistics and Its Application*, **10**, 247-270 <span class="ref-link">[(doi:10.1146/annurev-statistics-033021-112628)](https://doi.org/10.1146/annurev-statistics-033021-112628).</span>
 
-Wikle, C.K., Mateu, J., and Zammit-Mangion, A. (2023). Deep learning and spatial statistics. *Spatial Statistics*, **57**, 100774 [(doi:10.1016/j.spasta.2023.100774)](https://doi.org/10.1016/j.spasta.2023.100774).
+Wikle, C.K., Mateu, J., and Zammit-Mangion, A. (2023). Deep learning and spatial statistics. *Spatial Statistics*, **57**, 100774 <span class="ref-link">[(doi:10.1016/j.spasta.2023.100774)](https://doi.org/10.1016/j.spasta.2023.100774).</span>
 
-Yoo, M. and Wikle, C.K. (2023). Using echo state networks to inform physical models for fire front propagation. *Spatial Statistics*, **54** [(doi:10.1016/j.spasta.2023.100732)](https://doi.org/10.1016/j.spasta.2023.100732).
+Yoo, M. and Wikle, C.K. (2023). Using echo state networks to inform physical models for fire front propagation. *Spatial Statistics*, **54** <span class="ref-link">[(doi:10.1016/j.spasta.2023.100732)](https://doi.org/10.1016/j.spasta.2023.100732).</span>
 
 <br/>
 
 ### 2022
 
-Cressie, N., Pearse, A., and Gunawan, D. (2022). Optimal spatial prediction for non-negative spatial processes using a phi-divergence loss function, in *Trends in Mathematical, Information, and Data Sciences*, eds N. Balakrishnan, M. A. Gil, N. Martin, D. Morales, and M. Pardo. Springer, Cham, CH, pp. 181-197 [(doi:10.1007/978-3-031-04137-2_17)](https://doi.org/10.1007/978-3-031-04137-2_17).
+Cressie, N., Pearse, A., and Gunawan, D. (2022). Optimal spatial prediction for non-negative spatial processes using a phi-divergence loss function, in *Trends in Mathematical, Information, and Data Sciences*, eds N. Balakrishnan, M. A. Gil, N. Martin, D. Morales, and M. Pardo. Springer, Cham, CH, pp. 181-197 <span class="ref-link">[(doi:10.1007/978-3-031-04137-2_17)](https://doi.org/10.1007/978-3-031-04137-2_17).</span>
 
-Cressie, N., Sainsbury-Dale, M., and Zammit-Mangion, A. (2022). Basis-function models in spatial statistics. *Annual Review of Statistics and its Application*, **9**, 373-400 [(doi:10.1146/annurev-statistics-040120-020733)](https://doi.org/10.1146/annurev-statistics-040120-020733).
+Cressie, N., Sainsbury-Dale, M., and Zammit-Mangion, A. (2022). Basis-function models in spatial statistics. *Annual Review of Statistics and its Application*, **9**, 373-400 <span class="ref-link">[(doi:10.1146/annurev-statistics-040120-020733)](https://doi.org/10.1146/annurev-statistics-040120-020733).</span>
 
-Daw, R.  and Wikle, C.K. (2022). Supervised spatial regionalization using the Karhunen-Loève expansion and minimum spanning trees. *Journal of Data Science*, **20**, 566–584 [(doi:110.6339/22-JDS1077)](https://doi.org/110.6339/22-JDS1077).
+Daw, R.  and Wikle, C.K. (2022). Supervised spatial regionalization using the Karhunen-Loève expansion and minimum spanning trees. *Journal of Data Science*, **20**, 566–584 <span class="ref-link">[(doi:110.6339/22-JDS1077)](https://doi.org/110.6339/22-JDS1077).</span>
 
-Gopalan, G. and Wikle, C.K. (2022). A multi-surrogate higher-order singular value decomposition tensor emulator for spatio-temporal simulators. *Journal of Agricultural, Biological, and Environmental Statistics*, **27**, 22–45 [(doi:10.1007/s13253-021-00459-x)](https://doi.org/10.1007/s13253-021-00459-x).
+Gopalan, G. and Wikle, C.K. (2022). A multi-surrogate higher-order singular value decomposition tensor emulator for spatio-temporal simulators. *Journal of Agricultural, Biological and Environmental Statistics*, **27**, 22–45 <span class="ref-link">[(doi:10.1007/s13253-021-00459-x)](https://doi.org/10.1007/s13253-021-00459-x).</span>
 
-North, J.S., Wikle, C.K., and Schliep, E.M. (2022). A Bayesian approach for data-driven dynamic equation discovery. *Journal of Agricultural, Biological, and Environmental Statistics*, **27**, 728–747 [(doi:10.1007/s13253-022-00514-1)](https://doi.org/10.1007/s13253-022-00514-1).
+North, J.S., Wikle, C.K., and Schliep, E.M. (2022). A Bayesian approach for data-driven dynamic equation discovery. *Journal of Agricultural, Biological and Environmental Statistics*, **27**, 728–747 <span class="ref-link">[(doi:10.1007/s13253-022-00514-1)](https://doi.org/10.1007/s13253-022-00514-1).</span>
 
-Schafer, T.L.J., Wikle, C.K., and Hooten, M.B. (2022) Bayesian inverse reinforcement learning for collective animal movement. *Annals of Applied Statistics*, **16**, 999-1013 [(doi:10.1214/21-AOAS1529)](https://doi.org/10.1214/21-AOAS1529).
+Schafer, T.L.J., Wikle, C.K., and Hooten, M.B. (2022) Bayesian inverse reinforcement learning for collective animal movement. *Annals of Applied Statistics*, **16**, 999-1013 <span class="ref-link">[(doi:10.1214/21-AOAS1529)](https://doi.org/10.1214/21-AOAS1529).</span>
 
-Vu, Q., Zammit-Mangion, A., and Cressie, N. (2022). Modeling nonstationary and asymmetric multivariate spatial covariances via deformations. *Statistica Sinica*, **32**, 2071-2093 [(doi:10.5705/ss.202020.0156)](https://doi.org/10.5705/ss.202020.0156).
+Vu, Q., Zammit-Mangion, A., and Cressie, N. (2022). Modeling nonstationary and asymmetric multivariate spatial covariances via deformations. *Statistica Sinica*, **32**, 2071-2093 <span class="ref-link">[(doi:10.5705/ss.202020.0156)](https://doi.org/10.5705/ss.202020.0156).</span>
 
-Wikle, C.K., Datta, A., Hari, B.V., Boone, E.L., Sahoo, I., Kavila, I., Castruccio, S., Simmons, S.J., Burr, W.S., and Chang, W. (2022). An illustration of model agnostic explainability methods applied to environmental data. *Environmetrics*, **34**, e2772 [(doi:110.1002/env.2772)](https://doi.org/110.1002/env.2772).
+Wikle, C.K., Datta, A., Hari, B.V., Boone, E.L., Sahoo, I., Kavila, I., Castruccio, S., Simmons, S.J., Burr, W.S., and Chang, W. (2022). An illustration of model agnostic explainability methods applied to environmental data. *Environmetrics*, **34**, e2772 <span class="ref-link">[(doi:110.1002/env.2772)](https://doi.org/110.1002/env.2772).</span>
 
-Zammit-Mangion, A., Bertolacci, M., Fisher, J., Stavert, A., Rigby, M., Cao, Y., and Cressie, N. (2022). WOMBAT v1.0: a fully Bayesian global flux-inversion framework. *Geoscientific Model Development*, **15**, 45-73 [(doi:10.5194/gmd-15-45-2022)](https://doi.org/10.5194/gmd-15-45-2022).
+Zammit-Mangion, A., Bertolacci, M., Fisher, J., Stavert, A., Rigby, M., Cao, Y., and Cressie, N. (2022). WOMBAT v1.0: a fully Bayesian global flux-inversion framework. *Geoscientific Model Development*, **15**, 45-73 <span class="ref-link">[(doi:10.5194/gmd-15-45-2022)](https://doi.org/10.5194/gmd-15-45-2022).</span>
 
-Zammit-Mangion, A., Ng, T.L.J., Vu, Q., and Filippone, M. (2022). Deep compositional spatial models. *Journal of the American Statistical Association*, **117**, 1787-1808 [(doi:10.1080/01621459.2021.1887741)](https://doi.org/10.1080/01621459.2021.1887741).
+Zammit-Mangion, A., Ng, T.L.J., Vu, Q., and Filippone, M. (2022). Deep compositional spatial models. *Journal of the American Statistical Association*, **117**, 1787-1808 <span class="ref-link">[(doi:10.1080/01621459.2021.1887741)](https://doi.org/10.1080/01621459.2021.1887741).</span>
 
-Zhang, B., Li, F., Sang, H., and Cressie, N. (2022). Inferring changes in Arctic sea ice through a spatio-temporal logistic autoregression fitted to remote-sensing data. *Remote Sensing*, **14**, 5995 [(doi:10.3390/rs14235995)](https://doi.org/10.3390/rs14235995).
+Zhang, B., Li, F., Sang, H., and Cressie, N. (2022). Inferring changes in Arctic sea ice through a spatio-temporal logistic autoregression fitted to remote-sensing data. *Remote Sensing*, **14**, 5995 <span class="ref-link">[(doi:10.3390/rs14235995)](https://doi.org/10.3390/rs14235995).</span>
 
 <br/>
 
 ### 2021
 
-Cressie, N. (2021). A few statistical principles for data science. *Australian & New Zealand Journal of Statistics*, **63**, 182-200 [(doi:10.1111/anzs.12324)](https://doi.org/10.1111/anzs.12324). 
+Cressie, N. (2021). A few statistical principles for data science. *Australian & New Zealand Journal of Statistics*, **63**, 182-200 <span class="ref-link">[(doi:10.1111/anzs.12324)](https://doi.org/10.1111/anzs.12324). </span>
 
-Cressie, N. and Wikle, C. K. (2021). Modeling dependence in spatio-temporal econometrics, in *Advances in Contemporary Statistics and Econometrics*, eds A. Daouia and A. Ruiz-Gazen. Springer, Cham, CH, pp. 363-383 [(doi:10.1007/978-3-030-73249-3_19)](https://doi.org/10.1007/978-3-030-73249-3_19).
+Cressie, N. and Wikle, C. K. (2021). Modeling dependence in spatio-temporal econometrics, in *Advances in Contemporary Statistics and Econometrics*, eds A. Daouia and A. Ruiz-Gazen. Springer, Cham, CH, pp. 363-383 <span class="ref-link">[(doi:10.1007/978-3-030-73249-3_19)](https://doi.org/10.1007/978-3-030-73249-3_19).</span>
 
-Huang, H.-C., Cressie, N., Zammit-Mangion, A., and Huang, G. (2021). False discovery rates to detect signals from incomplete spatially aggregated data. *Journal of Computational and Graphical Statistics*, **30**, 1081-1094 [(doi:10.1080/10618600.2021.1873144)](https://doi.org/10.1080/10618600.2021.1873144).
+Huang, H.-C., Cressie, N., Zammit-Mangion, A., and Huang, G. (2021). False discovery rates to detect signals from incomplete spatially aggregated data. *Journal of Computational and Graphical Statistics*, **30**, 1081-1094 <span class="ref-link">[(doi:10.1080/10618600.2021.1873144)](https://doi.org/10.1080/10618600.2021.1873144).</span>
 
-Lucchesi, L.R., Kuhnert, P.M., and Wikle, C.K. (2021). Vizumap: an R package for visualising uncertainty in spatial data. *Journal of Open Source Software*, **6**, 2409. [(doi:10.21105/joss.02409)](https://doi.org/10.21105/joss.02409).
+Lucchesi, L.R., Kuhnert, P.M., and Wikle, C.K. (2021). Vizumap: an R package for visualising uncertainty in spatial data. *Journal of Open Source Software*, **6**, 2409 <span class="ref-link">[(doi:10.21105/joss.02409)](https://doi.org/10.21105/joss.02409).</span>
 
-North, J.S., Schliep, E.M., and Wikle, C.K. (2021) On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi-annual harmonic, *Environmetrics*, **32**(6) [(doi:10.1002/env.266)](https://doi.org/10.1002/env.266).
+North, J.S., Schliep, E.M., and Wikle, C.K. (2021) On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi-annual harmonics, *Environmetrics*, **32**, e2665 <span class="ref-link">[(doi:10.1002/env.266)](https://doi.org/10.1002/env.266).</span>
 
-Raim, A.R., Holan, S.H., Bradley, J.R., and Wikle, C.K. (2021). An R package for spatio-temporal change of support. *Computational Statistics*, **36**, 749–780. [(doi:10.1007/s00180-020-01029-4)](https://doi.org/10.1007/s00180-020-01029-4).
+Raim, A.R., Holan, S.H., Bradley, J.R., and Wikle, C.K. (2021). An R package for spatio-temporal change of support. *Computational Statistics*, **36**, 749–780 <span class="ref-link">[(doi:10.1007/s00180-020-01029-4)](https://doi.org/10.1007/s00180-020-01029-4).</span>
 
-Zammit-Mangion, A. and Cressie, N. (2021). FRK: An R package for spatial and spatio-temporal prediction with large datasets. *Journal of Statistical Software*, **98(4)**, 1-42 [(doi:10.18637/jss.v098.i04)](https://doi.org/10.18637/jss.v098.i04).
+Zammit-Mangion, A. and Cressie, N. (2021). FRK: An R package for spatial and spatio-temporal prediction with large datasets. *Journal of Statistical Software*, **98(4)**, 1-42 <span class="ref-link">[(doi:10.18637/jss.v098.i04)](https://doi.org/10.18637/jss.v098.i04).</span>
 
 <br/>
 
 ### 2020
 
-Bradley, J.R., Holan, S.H., and Wikle, C.K. (2020). Bayesian hierarchical models with conjugate full-conditional distributions for dependent data from the natural exponential family. *Journal of the American Statistical Association*, **115**, 2037–2052. [(doi:10.1080/01621459.2019.1677471)](https://doi.org/10.1080/01621459.2019.1677471).
+Bradley, J.R., Holan, S.H., and Wikle, C.K. (2020). Bayesian hierarchical models with conjugate full-conditional distributions for dependent data from the natural exponential family. *Journal of the American Statistical Association*, **115**, 2037–2052 <span class="ref-link">[(doi:10.1080/01621459.2019.1677471)](https://doi.org/10.1080/01621459.2019.1677471).</span>
 
-Bradley, J.R., Wikle, C.K., and Holan, S.H. (2020). Hierarchical models for spatial data with errors that are correlated with the latent process. *Statistica Sinica*, **30**, 81-109. [(doi:10.5705/SS.202016.0230)](https://doi.org/10.5705/SS.202016.0230).
+Bradley, J.R., Wikle, C.K., and Holan, S.H. (2020). Hierarchical models for spatial data with errors that are correlated with the latent process. *Statistica Sinica*, **30**, 81-109 <span class="ref-link">[(doi:10.5705/SS.202016.0230)](https://doi.org/10.5705/SS.202016.0230).</span>
 
-Cressie, N. and Suesse, T. (2020). Great expectations and even greater exceedances from spatially referenced data. *Spatial Statistics*, **37**, 100420 [(doi:10.1016/j.spasta.2020.100420)](https://doi.org/10.1016/j.spasta.2020.100420).
+Cressie, N. and Suesse, T. (2020). Great expectations and even greater exceedances from spatially referenced data. *Spatial Statistics*, **37**, 100420 <span class="ref-link">[(doi:10.1016/j.spasta.2020.100420)](https://doi.org/10.1016/j.spasta.2020.100420).</span>
 
-Cressie, N. and Wikle, C.K. (2020). Measuring, mapping, and uncertainty quantification in the space-time cube. *Revista Matemática Complutense*, **33**, 643-660 [(doi:10.1007/s13163-020-00359-7)](https://doi.org/10.1007/s13163-020-00359-7).
+Cressie, N. and Wikle, C.K. (2020). Measuring, mapping, and uncertainty quantification in the space-time cube. *Revista Matemática Complutense*, **33**, 643-660 <span class="ref-link">[(doi:10.1007/s13163-020-00359-7)](https://doi.org/10.1007/s13163-020-00359-7).</span>
 
-Hooten, M.B., Wikle, C.K., and M.R. Schwob, M.R. (2020). Statistical implementations of agent-based demographic models. *International Statistical Review*, **88**, 441–461. [(doi:10.1111/insr.12399)](https://doi.org/10.1111/insr.12399).
+Hooten, M.B., Wikle, C.K., and Schwob, M.R. (2020). Statistical implementations of agent-based demographic models. *International Statistical Review*, **88**, 441–461 <span class="ref-link">[(doi:10.1111/insr.12399)](https://doi.org/10.1111/insr.12399).</span>
 
-Katzfuss, M., Stroud, J.R., and Wikle, C.K. (2020). Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models. *Journal of the American Statistical Association*, **115**, 866-885 [(doi:10.1080/01621459.2019.1592753)](https://doi.org/10.1080/01621459.2019.1592753).
+Katzfuss, M., Stroud, J.R., and Wikle, C.K. (2020). Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models. *Journal of the American Statistical Association*, **115**, 866-885 <span class="ref-link">[(doi:10.1080/01621459.2019.1592753)](https://doi.org/10.1080/01621459.2019.1592753).</span>
 
-Stough, T., Cressie, N., Kang, E. L., Michalak, A. M., and Sahr, K. (2020). Spatial analysis and visualization of global data on multi-resolution hexagonal grids. *Japanese Journal of Statistics and Data Science*, **3**, 107-128 [(doi:10.1007/s42081-020-00077-w)](https://doi.org/10.1007/s42081-020-00077-w).
+Stough, T., Cressie, N., Kang, E. L., Michalak, A. M., and Sahr, K. (2020). Spatial analysis and visualization of global data on multi-resolution hexagonal grids. *Japanese Journal of Statistics and Data Science*, **3**, 107-128 <span class="ref-link">[(doi:10.1007/s42081-020-00077-w)](https://doi.org/10.1007/s42081-020-00077-w).</span>
 
-Zammit-Mangion, A. and Rougier, J. (2020). Multi-scale process modelling and distributed computation for spatial data. *Statistics and Computing*, **30**, 1609-1627. [(doi:10.1007/s11222-020-09962-6)](https://doi.org/10.1007/s11222-020-09962-6).
+Zammit-Mangion, A. and Rougier, J. (2020). Multi-scale process modelling and distributed computation for spatial data. *Statistics and Computing*, **30**, 1609-1627 <span class="ref-link">[(doi:10.1007/s11222-020-09962-6)](https://doi.org/10.1007/s11222-020-09962-6).</span>
 
-Zammit-Mangion, A. and Wikle, C.K. (2020). Deep integro-difference equation models for spatio-temporal forecasting. *Spatial Statistics*, **37**, 100408. [doi:10.1016/j.spasta.2020.100408](https://doi.org/10.1016/j.spasta.2020.100408).
+Zammit-Mangion, A. and Wikle, C.K. (2020). Deep integro-difference equation models for spatio-temporal forecasting. *Spatial Statistics*, **37**, 100408 <span class="ref-link">[(doi:10.1016/j.spasta.2020.100408)](https://doi.org/10.1016/j.spasta.2020.100408).</span>
 
-Zhang, B. and Cressie, N. (2020). Bayesian inference of spatio-temporal changes of Arctic sea ice. *Bayesian Analysis*, **15**, 605-631 [(doi:10.1214/20-BA1209)](https://doi.org/10.1214/20-BA1209).
+Zhang, B. and Cressie, N. (2020). Bayesian inference of spatio-temporal changes of Arctic sea ice. *Bayesian Analysis*, **15**, 605-631 <span class="ref-link">[(doi:10.1214/20-BA1209)](https://doi.org/10.1214/20-BA1209).</span>
 
 <br/>
 
 ### 2019
 
-Bradley, J.R., Wikle, C.K., and Holan, S.H. (2019). Spatio-temporal models for big multinomial data using the conditional multivariate logit-beta distribution. *Journal of Time Series Analysis*, **40**, 363-382 [(doi:10.1111/jtsa.12468)](https://doi.org/10.1111/jtsa.12468).
+Bradley, J.R., Wikle, C.K., and Holan, S.H. (2019). Spatio-temporal models for big multinomial data using the conditional multivariate logit-beta distribution. *Journal of Time Series Analysis*, **40**, 363-382 <span class="ref-link">[(doi:10.1111/jtsa.12468)](https://doi.org/10.1111/jtsa.12468).</span>
 
-Cressie, N. and Hardouin, C. (2019). A diagonally weighted matrix norm between two covariance matrices. *Spatial Statistics*, **29**, 316-328 [(doi:10.1016/j.spasta.2019.01.001)](https://doi.org/10.1016/j.spasta.2019.01.001).
+Cressie, N. and Hardouin, C. (2019). A diagonally weighted matrix norm between two covariance matrices. *Spatial Statistics*, **29**, 316-328 <span class="ref-link">[(doi:10.1016/j.spasta.2019.01.001)](https://doi.org/10.1016/j.spasta.2019.01.001).</span>
 
-Gopalan, G., Hrafnkelsson, B., Wikle, C.K., Rue, H., Aoalgeirsdottir, G., Jarosch, A., and Paisson, F. (2019). A hierarchical spatio-temporal statistical model motivated by glaciology. *Journal of Agricultural, Biological and Environmental Statistics*, **24**, 669-692 [(doi:10.1007/s13253-019-00367-1)](https://doi.org/10.1007/s13253-019-00367-1).
+Gopalan, G., Hrafnkelsson, B., Wikle, C.K., Rue, H., Aðalgeirsdóttir, G., Jarosch, A., and Pálsson, F. (2019). A hierarchical spatiotemporal statistical model motivated by glaciology. *Journal of Agricultural, Biological and Environmental Statistics*, **24**, 669-692 <span class="ref-link">[(doi:10.1007/s13253-019-00367-1)](https://doi.org/10.1007/s13253-019-00367-1).</span>
 
-Heaton, M. J., Datta, A., Finley, A. O., Furrer, R., Guinness, J., Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., Lindgren, F., Nychka, D. W., Sun, F., and Zammit-Mangion, A. (2019). A case study competition among methods for analyzing large spatial data. *Journal of Agricultural, Biological and Environmental Statistics*, **24**, 398–425 [(doi:10.1007/s13253-018-00348-w)](https://doi.org/10.1007/s13253-018-00348-w).
+Heaton, M. J., Datta, A., Finley, A. O., Furrer, R., Guinness, J., Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., Lindgren, F., Nychka, D. W., Sun, F., and Zammit-Mangion, A. (2019). A case study competition among methods for analyzing large spatial data. *Journal of Agricultural, Biological and Environmental Statistics*, **24**, 398–425 <span class="ref-link">[(doi:10.1007/s13253-018-00348-w)](https://doi.org/10.1007/s13253-018-00348-w).</span>
 
-McDermott, P.L. and Wikle, C.K. (2019). Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatio-temporal data. *Entropy*, **21,184** [(doi:10.3390/e21020184)](https://doi.org/10.3390/e21020184).
+McDermott, P.L. and Wikle, C.K. (2019). Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatio-temporal data. *Entropy*, **21**, 184 <span class="ref-link">[(doi:10.3390/e21020184)](https://doi.org/10.3390/e21020184).</span>
 
-McDermott, P.L. and Wikle, C.K. (2019). Deep echo state networks with uncertainty quantification for spatio-temporal forecasting. *Environmetrics*, **30**, e2553 [(doi:10.1002/env.2553)](https://doi.org/10.1002/env.2553).  
+McDermott, P.L. and Wikle, C.K. (2019). Deep echo state networks with uncertainty quantification for spatio-temporal forecasting. *Environmetrics*, **30**, e2553 <span class="ref-link">[(doi:10.1002/env.2553)](https://doi.org/10.1002/env.2553).</span>
 
-Wikle, C.K. (2019). Comparison of deep neural networks and deep hierarchical models for spatio-temporal data. *Journal of Agricultural, Biological and Environmental Statistics*, **24**, 175–203. [(doi:10.1007/s13253-019-00361-7)](https://doi.org/10.1007/s13253-019-00361-7).
+Wikle, C.K. (2019). Comparison of deep neural networks and deep hierarchical models for spatio-temporal data. *Journal of Agricultural, Biological and Environmental Statistics*, **24**, 175–203 <span class="ref-link">[(doi:10.1007/s13253-019-00361-7)](https://doi.org/10.1007/s13253-019-00361-7).</span>
 
-Zhang, B. and Cressie, N. (2019). Estimating spatial changes over time of Arctic sea ice using hidden 2 x 2 tables. *Journal of Time Series Analysis*, **40**, 288-311 [(doi:10.1111/jtsa.12425)](https://doi.org/10.1111/jtsa.12425).
+Zhang, B. and Cressie, N. (2019). Estimating spatial changes over time of Arctic sea ice using hidden 2 x 2 tables. *Journal of Time Series Analysis*, **40**, 288-311 <span class="ref-link">[(doi:10.1111/jtsa.12425)](https://doi.org/10.1111/jtsa.12425).</span>