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2008.
(<a href="http://dx.doi.org/10.1016/j.csda.2008.02.033">doi:10.1016/j.csda.2008.02.033</a>)</dd>

<!-- Authors: Anderson Sean C and Ward Eric J and English Philina A and Barnett
Lewis A K and Thorson James T -->
<dt><a name="andersonSdmTMB2022a">[Anderson et al.,
2022]</a></dt><dd>Sean C. Anderson, Eric J.
Ward, Philina A. English, Lewis A. K. Barnett,
and James T. Thorson.
sdmTMB: An R package for fast, flexible, and user-friendly generalized
linear mixed effects models with spatial and spatiotemporal random fields,
March 2022.
(<a href="http://dx.doi.org/10.1101/2022.03.24.485545">doi:10.1101/2022.03.24.485545</a>)</dd>

<!-- Authors: Angrist Joshua D and Pischke Jörn Steffen -->
<dt><a name="angrist_mostly_2009">[Angrist and Pischke,
2009]</a></dt><dd>Joshua D. Angrist and
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<cite>Statistics and Computing</cite>, 21(2):173–183, April 2011.
(<a href="http://dx.doi.org/10.1007/s11222-009-9157-4">doi:10.1007/s11222-009-9157-4</a>)</dd>

<!-- Authors: Bellio Ruggero and Ghosh Swarnadip and Owen Art B and Varin
Cristiano -->
<!-- Keywords: Statistics - Computation, Statistics - Methodology -->
<dt><a name="bellioScalable2023">[Bellio et al.,
2023]</a></dt><dd>Ruggero Bellio, Swarnadip
Ghosh, Art B. Owen, and Cristiano Varin.
<a href="http://arxiv.org/abs/2308.15681">Scalable Estimation of Probit
Models with Crossed Random Effects</a>, August 2023.
arXiv:2308.15681 [stat].
(<a href="http://dx.doi.org/10.48550/arXiv.2308.15681">doi:10.48550/arXiv.2308.15681</a>)</dd>

<!-- Authors: Belshe E F and Schuur E A G and Bolker B M -->
<!-- Keywords: Carbon flux, climate change, tundra -->
<dt><a name="belshe_tundra_2013">[Belshe et al.,
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loglinear mixed models</a>.
<cite>Statistical Modelling</cite>, 3(3):179–191, 2003.</dd>

<!-- Authors: Breiman Leo -->
<dt><a name="breimanStatistical2001">[Breiman,
2001]</a></dt><dd>Leo Breiman.
<a href="http://www.jstor.org/stable/2676681">Statistical Modeling: The
Two Cultures</a>.
<cite>Statistical Science</cite>, 16(3):199–215, August 2001.
There are two cultures in the use of statistical modeling to reach conclusions
from data. One assumes that the data are generated by a given stochastic data
model. The other uses algorithmic models and treats the data mechanism as
unknown. The statistical community has been committed to the almost exclusive
use of data models. This commitment has led to irrelevant theory,
questionable conclusions, and has kept statisticians from working on a large
range of interesting current problems. Algorithmic modeling, both in theory
and practice, has developed rapidly in fields outside statistics. It can be
used both on large complex data sets and as a more accurate and informative
alternative to data modeling on smaller data sets. If our goal as a field is
to use data to solve problems, then we need to move away from exclusive
dependence on data models and adopt a more diverse set of tools.</dd>

<!-- Authors: N E Breslow -->
<dt><a name="breslow_extrapoisson_1984">[Breslow,
1984]</a></dt><dd>N. E. Breslow.
Expand All @@ -308,6 +349,21 @@
<cite>Proceedings of the second Seattle symposium in biostatistics:
Analysis of correlated data</cite>, pages 1–22. Springer, 2004.</dd>

<!-- Authors: Bridge Helen and Morgan Katy E and Frost Chris -->
<!-- Keywords: boundary problems, improper solutions, mixed models,
non-positive semidefinite covariance matrices, random slopes, singular fit
-->
<dt><a name="bridgeNegative2024">[Bridge
et al.]</a></dt><dd>Helen Bridge, Katy E.
Morgan, and Chris Frost.
<a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.10070">Negative
variance components and intercept-slope correlations greater than one in
magnitude: How do such “non-regular” random intercept and slope models
arise, and what should be done when they do?</a>.
<cite>Statistics in Medicine</cite>, n/a(n/a).
_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.10070.
(<a href="http://dx.doi.org/10.1002/sim.10070">doi:10.1002/sim.10070</a>)</dd>

<!-- Authors: Mollie E Brooks and Kasper Kristensen and Koen J van Benthem and
Arni Magnusson and Casper W Berg and Anders Nielsen and Hans J Skaug and
Martin Mächler and Benjamin M Bolker -->
Expand Down Expand Up @@ -367,6 +423,25 @@
<a href="http://andrewgelman.com/2017/11/28/computational-statistical-issues-uniform-interval-priors/">Computational
and statistical issues with uniform interval priors</a>, November 2017.</dd>

<!-- Authors: Carvalho Carlos M and Polson Nicholas G and Scott James G -->
<dt><a name="carvalhoHandling2009">[Carvalho et al.,
2009]</a></dt><dd>Carlos M. Carvalho,
Nicholas G. Polson, and James G. Scott.
<a href="https://proceedings.mlr.press/v5/carvalho09a.html">Handling Sparsity
via the Horseshoe</a>.
In <cite>Proceedings of the Twelth International Conference on
Artificial Intelligence and Statistics</cite>, pages 73–80. PMLR,
April 2009.
ISSN: 1938-7228.</dd>

<!-- Authors: Chambers John M and Hastie Trevor J -->
<dt><a name="chambersStatistical1992a">[Chambers and Hastie,
1992]</a></dt><dd>John M. Chambers and Trevor J.
Hastie.
Statistical Models.
In <cite>Statistical Models in S</cite>. Routledge, 1992.
Num Pages: 32.</dd>

<!-- Authors: Chung Yeojin and Rabe Hesketh Sophia and Dorie Vincent and Gelman
Andrew and Liu Jingchen -->
<!-- Keywords: Assessment, Testing and Evaluation, Bayes modal estimation,
Expand Down Expand Up @@ -531,6 +606,17 @@
Regressor and random-effects dependencies in multilevel models.
<cite>Statistica Neerlandica</cite>, 58(2):161–178, 2004.</dd>

<!-- Authors: Efron B -->
<!-- Keywords: Fisherian inference, Frequentist theory, Neyman-Pearson-Wald,
Objectivity -->
<dt><a name="efronWhy1986">[Efron, 1986]</a></dt><dd>B. Efron.
<a href="https://www.tandfonline.com/doi/abs/10.1080/00031305.1986.10475342">Why
isn't everyone a Bayesian?</a>.
<cite>The American Statistician</cite>, 40(1):1–5, February 1986.
Publisher: Taylor &amp; Francis _eprint:
https://www.tandfonline.com/doi/pdf/10.1080/00031305.1986.10475342.
(<a href="http://dx.doi.org/10.1080/00031305.1986.10475342">doi:10.1080/00031305.1986.10475342</a>)</dd>

<!-- Authors: D A Elston and R Moss and T Boulinier and C Arrowsmith and X
Lambin -->
<dt><a name="elston_analysis_2001">[Elston et al.,
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<cite>Statistics &amp; Probability Letters</cite>, 76(8):764–772, April 2006.
(<a href="http://dx.doi.org/10.1016/j.spl.2005.10.036">doi:10.1016/j.spl.2005.10.036</a>)</dd>

<!-- Authors: Freitas Carla and Olsen Esben M and Knutsen Halvor and Albretsen
Jon and Moland Even -->
<!-- Keywords: acoustic telemetry,location filtering,marine
reserve,Norway,resource selection functions,vemco positioning system,VPS -->
<dt><a name="freitasTemperatureassociated2016">[Freitas et al.,
2016]</a></dt><dd>Carla Freitas, Esben M.
Olsen, Halvor Knutsen, Jon Albretsen, and
Even Moland.
Temperature-associated habitat selection in a cold-water marine fish.
<cite>Journal of Animal Ecology</cite>, 85(3):628–637, 2016.
(<a href="http://dx.doi.org/10.1111/1365-2656.12458">doi:10.1111/1365-2656.12458</a>)</dd>

<!-- Authors: Friendly Michael and Kwan Ernest -->
<dt><a name="friendly_effect_2003">[Friendly and Kwan,
2003]</a></dt><dd>Michael Friendly and Ernest
Expand All @@ -630,6 +728,34 @@
2003.
(<a href="http://dx.doi.org/10.1016/S0167-9473(02)00290-6">doi:10.1016/S0167-9473(02)00290-6</a>)</dd>

<!-- Authors: Gao K and Owen A B -->
<!-- Keywords: Statistics - Methodology, Statistics - Applications, Statistics
- Computation -->
<dt><a name="gaoEstimation2017">[Gao and Owen,
2017]</a></dt><dd>K. Gao and A. B. Owen.
<a href="http://arxiv.org/abs/1610.08088">Estimation and Inference for Very
Large Linear Mixed Effects Models</a>.
<cite>arXiv:1610.08088 [stat]</cite>, May 2017.
arXiv: 1610.08088.</dd>

<!-- Authors: Gao Katelyn and Owen Art -->
<!-- Keywords: variance components, big data, Crossed random effects -->
<dt><a name="gaoEfficient2017">[Gao and Owen,
2017]</a></dt><dd>Katelyn Gao and Art Owen.
<a href="https://projecteuclid.org/euclid.ejs/1492135234">Efficient moment
calculations for variance components in large unbalanced crossed random
effects models</a>.
<cite>Electronic Journal of Statistics</cite>, 11(1):1235–1296, 2017.
(<a href="http://dx.doi.org/10.1214/17-EJS1236">doi:10.1214/17-EJS1236</a>)</dd>

<!-- Authors: Gao Katelyn and Owen Art B -->
<dt><a name="gaoEstimation2020a">[Gao and Owen,
2020]</a></dt><dd>Katelyn Gao and Art B. Owen.
<a href="https://www.jstor.org/stable/26969394">Estimation and Inference for
Very Large Linear Mixed Effects Models</a>.
<cite>Statistica Sinica</cite>, 30(4):1741–1771, 2020.
Publisher: Institute of Statistical Science, Academia Sinica.</dd>

<!-- Authors: Gelman Andrew and Hill Jennifer -->
<!-- Keywords: uploaded -->
<dt><a name="gelman_data_2006">[Gelman and Hill,
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<cite>Statistics in Medicine</cite>, 27(15):2865–2873, July 2008.
(<a href="http://dx.doi.org/10.1002/sim.3107">doi:10.1002/sim.3107</a>)</dd>

<!-- Authors: Gelman Andrew -->
<dt><a name="gelmanObjections2008">[Gelman,
2008]</a></dt><dd>Andrew Gelman.
<a href="http://ba.stat.cmu.edu/journal/2008/vol03/issue03/gelman.pdf">Objections
to Bayesian statistics</a>.
<cite>Bayesian Analysis</cite>, 3:445–450, 2008.
(<a href="http://dx.doi.org/10.1214/08-BA318">doi:10.1214/08-BA318</a>)</dd>

<!-- Authors: Ghandwani Disha and Ghosh Swarnadip and Hastie Trevor and Owen
Art B -->
<!-- Keywords: Statistics - Methodology -->
<dt><a name="ghandwaniScalable2023">[Ghandwani et al.,
2023]</a></dt><dd>Disha Ghandwani, Swarnadip
Ghosh, Trevor Hastie, and Art B. Owen.
<a href="http://arxiv.org/abs/2307.12378">Scalable solution to crossed random
effects model with random slopes</a>, September 2023.
arXiv:2307.12378 [stat].
(<a href="http://dx.doi.org/10.48550/arXiv.2307.12378">doi:10.48550/arXiv.2307.12378</a>)</dd>

<!-- Authors: Ghosh Swarnadip and Hastie Trevor and Owen Art B -->
<!-- Keywords: backfitting, generalized linear mixed models, quasi-likelihood,
Schall’s algorithm -->
<dt><a name="ghoshScalable2022">[Ghosh et al.,
2022]</a></dt><dd>Swarnadip Ghosh, Trevor
Hastie, and Art B. Owen.
<a href="https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-2/Scalable-logistic-regression-with-crossed-random-effects/10.1214/22-EJS2047.full">Scalable
logistic regression with crossed random effects</a>.
<cite>Electronic Journal of Statistics</cite>, 16(2):4604–4635, January 2022.
Publisher: Institute of Mathematical Statistics and Bernoulli Society.
(<a href="http://dx.doi.org/10.1214/22-EJS2047">doi:10.1214/22-EJS2047</a>)</dd>

<!-- Authors: Ghosh Swarnadip and Hastie Trevor and Owen Art B -->
<!-- Keywords: 62J05, 65C60, collapsed Gibbs sampler, generalized least
squares, mixed effect models -->
<dt><a name="ghoshBackfitting2022">[Ghosh et al.,
2022]</a></dt><dd>Swarnadip Ghosh, Trevor
Hastie, and Art B. Owen.
<a href="https://projecteuclid.org/journals/annals-of-statistics/volume-50/issue-1/Backfitting-for-large-scale-crossed-random-effects-regressions/10.1214/21-AOS2121.full">Backfitting
for large scale crossed random effects regressions</a>.
<cite>The Annals of Statistics</cite>, 50(1):560–583, February 2022.
Publisher: Institute of Mathematical Statistics.
(<a href="http://dx.doi.org/10.1214/21-AOS2121">doi:10.1214/21-AOS2121</a>)</dd>

<!-- Authors: Goldman Nick and Whelan Simon -->
<dt><a name="GoldmanWhelan2000">[Goldman and Whelan,
2000]</a></dt><dd>Nick Goldman and Simon
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Problems in Inference for Additive and Linear Mixed Models</cite></a>.
Cuvillier Verlag, Göttingen, Germany, 2008.</dd>

<!-- Authors: Griewank Andreas and Walther Andrea -->
<dt><a name="griewankIntroduction2003">[Griewank and Walther,
2003]</a></dt><dd>Andreas Griewank and Andrea
Walther.
Introduction to Automatic Differentiation.
<cite>Proc. Appl. Math. Mech</cite>, 2(1):45–49, 2003.
(<a href="http://dx.doi.org/10.1002/pamm.200310012">doi:10.1002/pamm.200310012</a>)</dd>

<!-- Authors: Jarrod D Hadfield -->
<dt><a name="Hadfield:2009:JSSOBK:v33i02">[Hadfield,
2010]</a></dt><dd>Jarrod D. Hadfield.
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Models</cite></a>, 2018.
R package version 0.2.0.</dd>

<!-- Authors: He Hua and Zhang Hui and Ye Peng and Tang Wan -->
<dt><a name="heTest2019">[He et al., 2019]</a></dt><dd>Hua He,
Hui Zhang, Peng Ye, and Wan
Tang.
A test of inflated zeros for Poisson regression models.
<cite>Statistical methods in medical research</cite>, 28(4):1157–1169, April
2019.
(<a href="http://dx.doi.org/10.1177/0962280217749991">doi:10.1177/0962280217749991</a>)</dd>

<!-- Authors: Hedeker Donald and Toit Stephen H C du and Demirtas Hakan and
Gibbons Robert D -->
<!-- Keywords: Clustered data, Longitudinal data, Multilevel models,
Expand All @@ -827,6 +1013,29 @@
<cite>Biometrics</cite>, 74(1):354–361, 2018.
(<a href="http://dx.doi.org/10.1111/biom.12707">doi:10.1111/biom.12707</a>)</dd>

<!-- Authors: Heiling Hillary M and Rashid Naim U and Li Quefeng and Peng
Xianlu L and Yeh Jen Jen and Ibrahim Joseph G -->
<dt><a name="heilingEfficient2024">[Heiling et al.,
2024]</a></dt><dd>Hillary M Heiling, Naim U
Rashid, Quefeng Li, Xianlu L Peng,
Jen Jen Yeh, and Joseph G Ibrahim.
<a href="https://doi.org/10.1093/biomtc/ujae016">Efficient computation of
high-dimensional penalized generalized linear mixed models by latent factor
modeling of the random effects</a>.
<cite>Biometrics</cite>, 80(1):ujae016, March 2024.
(<a href="http://dx.doi.org/10.1093/biomtc/ujae016">doi:10.1093/biomtc/ujae016</a>)</dd>

<!-- Authors: Heiling Hillary M and Rashid Naim U and Li Quefeng and Ibrahim
Joseph G -->
<!-- Keywords: Statistics - Computation, Statistics - Methodology -->
<dt><a name="heilingglmmPen2024">[Heiling et al.,
2024]</a></dt><dd>Hillary M. Heiling, Naim U.
Rashid, Quefeng Li, and Joseph G. Ibrahim.
<a href="http://arxiv.org/abs/2305.08204">glmmPen: High Dimensional
Penalized Generalized Linear Mixed Models</a>, April 2024.
arXiv:2305.08204 [stat].
(<a href="http://dx.doi.org/10.48550/arXiv.2305.08204">doi:10.48550/arXiv.2305.08204</a>)</dd>

<!-- Authors: Heisterkamp Simon H and van Willigen Engelbertus and Diderichsen
Paul Matthias and Maringwa John -->
<dt><a name="heisterkamp_update_2017">[Heisterkamp et al.,
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ecology</a>.
<cite>Ecology</cite>, 95(11):3173–3180, 2014.</dd>

<!-- Authors: Madar Vered -->
<dt><a name="madarDirect2015a">[Madar, 2015]</a></dt><dd>Vered
Madar.
Direct formulation to Cholesky decomposition of a general nonsingular
correlation matrix.
<cite>Statistics &amp; Probability Letters</cite>, 103:142–147, August 2015.
(<a href="http://dx.doi.org/10.1016/j.spl.2015.03.014">doi:10.1016/j.spl.2015.03.014</a>)</dd>

<!-- Authors: Madsen Henrik and Thyregod Poul -->
<!-- Keywords: Mathematics / Probability &amp; Statistics / General, Business &amp;
Economics / Statistics -->
Expand Down Expand Up @@ -1366,6 +1583,18 @@
<cite>The American Statistician</cite>, 61(1):22–27, 2007.
(<a href="http://dx.doi.org/10.1198/000313007X171322">doi:10.1198/000313007X171322</a>)</dd>

<!-- Authors: Moritz Max A and Batllori Enric and Bolker Benjamin M -->
<!-- Keywords: fire, biogeography, global biodiversity, latitudinal species
richness, net primary productivity -->
<dt><a name="moritzrole2023a">[Moritz et al.,
2023]</a></dt><dd>Max A. Moritz, Enric
Batllori, and Benjamin M. Bolker.
<a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.14177">The role of
fire in terrestrial vertebrate richness patterns</a>.
<cite>Ecology Letters</cite>, 26(4):563–574, 2023.
_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.14177.
(<a href="http://dx.doi.org/10.1111/ele.14177">doi:10.1111/ele.14177</a>)</dd>

<!-- Authors: Muff Stefanie and Held Leonhard and Keller Lukas F and
Matthiopoulos Jason -->
<!-- Keywords: attenuation, Berkson measurement error, conditional model,
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mixed-effects models</a>, November 2017.
original-date: 2017-08-07T19:06:10Z.</dd>

<!-- Authors: Tanaka Emi and Hui Francis K C -->
<!-- Keywords: Fixed effects,Hierarchical model,Model API,Model formulae,Model
specification,Multi-level model,Random effects -->
<dt><a name="tanakaSymbolic2019">[Tanaka and Hui,
2019]</a></dt><dd>Emi Tanaka and Francis K. C.
Hui.
Symbolic Formulae for Linear Mixed Models.
In Hien Nguyen, editor, <cite>Statistics and Data
Science</cite>, Communications in Computer and Information Science,
pages 3–21, Singapore, 2019. Springer.
(<a href="http://dx.doi.org/10.1007/978-981-15-1960-4_1">doi:10.1007/978-981-15-1960-4_1</a>)</dd>

<!-- Authors: Tiwari Manjula and Bjorndal Karen A and Bolten Alan B and Bolker
Benjamin M -->
<dt><a name="Tiwari+2006">[Tiwari et al.,
Expand All @@ -1906,6 +2147,13 @@
mydas</em> hatchling production at Tortuguero, Costa Rica.
<cite>Marine Ecology Progress Series</cite>, 326:283–293, 2006.</dd>

<!-- Authors: Uriarte Maria and Yackulic Charles B -->
<dt><a name="uriartePreaching2009a">[Uriarte and Yackulic,
2009]</a></dt><dd>Maria Uriarte and Charles B
Yackulic.
Preaching to the unconverted.
<cite>Ecological Applications</cite>, 19(3):592–596, 2009.</dd>

<!-- Authors: Florin Vaida and Suzette Blanchard -->
<dt><a name="vaida_conditional_2005">[Vaida and Blanchard,
2005]</a></dt><dd>Florin Vaida and Suzette
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data analysis</cite></a>.
Springer New York, 2009.</dd>

<!-- Authors: Wicklin Rick -->
<dt><a name="wicklinFast2018">[Wicklin, 2018]</a></dt><dd>Rick
Wicklin.
<a href="https://blogs.sas.com/content/iml/2018/10/03/ar1-cholesky-root-simulation.html">Fast
simulation of multivariate normal data with an AR(1) correlation
structure</a>, October 2018.</dd>

<!-- Authors: G N Wilkinson and C E Rogers -->
<dt><a name="WilkinsonRogers1973">[Wilkinson and Rogers,
1973]</a></dt><dd>G. N. Wilkinson and C. E.
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