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@Article{lasso1996,
author = {Robert Tibshirani},
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
number = {1},
pages = {267--288},
title = {Regression Shrinkage and Selection via the {Lasso}},
volume = {58},
year = {1996},
url = {http://www.jstor.org/stable/2346178},
}
@article{lar2004,
author = {Bradley Efron and Trevor Hastie and Iain Johnstone and Robert Tibshirani},
title = {{Least angle regression}},
volume = {32},
journal = {The Annals of Statistics},
number = {2},
publisher = {Institute of Mathematical Statistics},
pages = {407--499},
keywords = {boosting, coefficient paths, Lasso, Linear regression, Variable selection},
year = {2004},
doi = {10.1214/009053604000000067},
}
@article{scad2008,
author = {Yongdai Kim and Hosik Choi and Hee-Seok Oh},
title = {Smoothly Clipped Absolute Deviation on High Dimensions},
journal = {Journal of the American Statistical Association},
volume = {103},
number = {484},
pages = {1665-1673},
year = {2008},
doi = {10.1198/016214508000001066},
}
@article{mcp2010,
author = {Cun-Hui Zhang},
title = {Nearly unbiased variable selection under minimax concave penalty},
volume = {38},
journal = {The Annals of Statistics},
number = {2},
publisher = {Institute of Mathematical Statistics},
pages = {894 -- 942},
year = {2010},
doi = {10.1214/09-AOS729},
}
@article{Tsagris2018,
author = {Tsagris M, Papadakis M.},
title = {Taking {R} to its limits: 70+ tips},
volume = {6},
journal = {PeerJ Preprints},
number = {e26605v1},
publisher = {Institute of Mathematical Statistics},
year = {2018},
doi = {10.7287/peerj.preprints.26605v1},
}
@article{Tsagris2022,
author = {Michail Tsagris and Manos Papadakis},
title = {Forward Regression in {R}: From The Extreme Slow to the Extreme Fast},
journal = {Journal of Data Science},
volume = {16},
number = {4},
year = {2022},
pages = {771--780},
doi = {10.6339/JDS.201810_16(4).00006},
issn = {1680-743X},
publisher = {School of Statistics, Renmin University of China}
}
@Book{xie2015,
title = {Dynamic Documents with {R} and knitr},
author = {Yihui Xie},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {2nd},
note = {ISBN 978-1498716963},
url = {https://yihui.org/knitr/},
}
@Article{Wei2009,
title = {《红楼梦》前80回与后40回某些文风差异的统计分析（两个独立二项总体等价性检验的一个应用）},
author = {韦博成},
journal = {应用概率统计},
year = {2009},
volume = {25},
number = {4},
pages = {441-448},
doi = {10.3969/j.issn.1001-4268.2009.04.012},
}
@Article{Brunson2020,
title = {{ggalluvial}: Layered Grammar for Alluvial Plots},
author = {Jason Cory Brunson},
year = {2020},
journal = {Journal of Open Source Software},
volume = {5},
number = {49},
pages = {2017},
doi = {10.21105/joss.02017},
}
@Article{Song2011,
title = {两个二项总体成功概率的比较},
author = {宋泽熙},
journal = {中国校外教育（理论）},
year = {2011},
volume = {z1},
pages = {81},
doi = {10.3969/j.issn.1004-8502-B.2011.z1.0919},
}
@article{Rigby2005,
doi = {10.1111/j.1467-9876.2005.00510.x},
year = {2005},
month = jun,
publisher = {Wiley},
volume = {54},
number = {3},
pages = {507--554},
author = {R. A. Rigby and D. M. Stasinopoulos},
title = {Generalized additive models for location, scale and shape (with discussion)},
journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)}
}
@article{Bickel1975,
author = {P. J. Bickel and E. A. Hammel and J. W. O'Connell},
title = {Sex Bias in Graduate Admissions: Data from Berkeley},
journal = {Science},
volume = {187},
number = {4175},
pages = {398-404},
year = {1975},
doi = {10.1126/science.187.4175.398},
abstract = {Examination of aggregate data on graduate admissions to the University of California, Berkeley, for fall 1973 shows a clear but misleading pattern of bias against female applicants. Examination of the disaggregated data reveals few decision-making units that show statistically significant departures from expected frequencies of female admissions, and about as many units appear to favor women as to favor men. If the data are properly pooled, taking into account the autonomy of departmental decision making, thus correcting for the tendency of women to apply to graduate departments that are more difficult for applicants of either sex to enter, there is a small but statistically significant bias in favor of women. The graduate departments that are easier to enter tend to be those that require more mathematics in the undergraduate preparatory curriculum. The bias in the aggregated data stems not from any pattern of discrimination on the part of admissions committees, which seem quite fair on the whole, but apparently from prior screening at earlier levels of the educational system. Women are shunted by their socialization and education toward fields of graduate study that are generally more crowded, less productive of completed degrees, and less well funded, and that frequently offer poorer professional employment prospects.}
}
@Article{Meyer2006,
title = {The Strucplot Framework: Visualizing Multi-Way Contingency Tables with {vcd}},
author = {David Meyer and Achim Zeileis and Kurt Hornik},
journal = {Journal of Statistical Software},
year = {2006},
volume = {17},
number = {3},
pages = {1--48},
doi = {10.18637/jss.v017.i03},
}
@Article{Zeileis2007,
title = {Residual-based Shadings for Visualizing (Conditional) Independence},
author = {Achim Zeileis and David Meyer and Kurt Hornik},
journal = {Journal of Computational and Graphical Statistics},
year = {2007},
volume = {16},
number = {3},
pages = {507--525},
doi = {10.1198/106186007X237856},
}
@Book{Hadley2023,
title = {{R} for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
author = {Hadley Wickham and Mine Çetinkaya-Rundel and Garrett Grolemund},
publisher = {O'Reilly Media, Inc.},
address = {Sebastopol, California},
year = {2023},
edition = {2nd},
note = {ISBN 978-1492097402},
url = {https://r4ds.hadley.nz/},
}
@Book{Kabacoff2022,
title = {{R} in Action: Data Analysis and graphics with {R} and {Tidyverse}},
author = {Robert I. Kabacoff},
publisher = {Manning Publications Co.},
address = {Shelter Island, NY},
year = {2022},
edition = {3rd},
note = {ISBN 978-1617296055},
}
@Book{Pinheiro2000,
title = {Mixed-Effects Models in {S} and {S-PLUS}},
author = {Pinheiro, Jos{\'e}C. and Bates, Douglas M.},
year = {2000},
publisher = {Springer-Verlag},
address = {New York, NY},
}
@Book{Demidenko2013,
title = {Mixed Models: Theory and Applications with {R}},
author = {Eugene Demidenko},
publisher = {John Wiley \& Sons},
address = {Hoboken, New Jersey},
year = {2013},
edition = {2nd},
note = {ISBN 978-1-118-09157-9},
doi = {10.1002/9781118651537},
}
@Book{Andrzej2013,
doi = {10.1007/978-1-4614-3900-4},
note = {ISBN 978-1-4614-3899-1},
year = {2013},
publisher = {Springer New York},
address = {New York, NY},
edition = {1st},
author = {Andrzej Gałecki and Tomasz Burzykowski},
title = {Linear Mixed-Effects Models Using {R}: A Step-by-Step Approach}
}
@Book{Jiang2021,
doi = {10.1007/978-1-0716-1282-8},
note = {ISBN 978-1-0716-1282-8},
year = {2021},
publisher = {Springer New York},
address = {New York, NY},
edition = {2nd},
author = {Jiming Jiang and Thuan Nguyen},
title = {Linear and Generalized Linear Mixed Models and Their Applications}
}
@Book{msg2021,
title = {现代统计图形},
author = {赵鹏 and 谢益辉 and 黄湘云},
publisher = {人民邮电出版社},
address = {北京},
year = {2021},
note = {ISBN 978-7-115-56690-4},
url = {https://bookdown.org/xiangyun/msg},
}
@Book{wen2020,
title = {最优化：建模、算法与理论},
author = {刘浩洋 and 户将 and 李勇锋 and 文再文},
publisher = {高等教育出版社},
address = {北京},
year = {2020},
note = {ISBN 978-7-040-55035-1},
url = {http://faculty.bicmr.pku.edu.cn/~wenzw/optbook.html},
}
@Book{Friendly2016,
title = {Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data},
author = {Michael Friendly and David Meyer},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2016},
edition = {1st},
note = {ISBN 978-1498725835},
}
@Book{Agresti2007,
title = {An Introduction to Categorical Data Analysis},
author = {Alan Agresti},
publisher = {John Wiley \& Sons, Inc.},
address = {Hoboken, New Jersey},
year = {2007},
edition = {2nd},
note = {ISBN 978-0-471-22618-5},
}
@Book{Agresti2013,
title = {Categorical Data Analysis},
author = {Alan Agresti},
publisher = {John Wiley \& Sons, Inc.},
address = {Hoboken, New Jersey},
year = {2013},
edition = {3rd},
note = {ISBN 978-0-470-46363-5},
}
@Book{Morten2017,
title = {Statistical Analysis of Contingency Tables},
author = {Morten W. Fagerland, Stian Lydersen, Petter Laake},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2017},
edition = {1st},
note = {ISBN 978-1466588172},
doi = {https://contingencytables.com/},
}
@Book{Dobson1983,
title = {An Introduction to Statistical Modelling},
author = {Annette J. Dobson},
publisher = {Chapman and Hall/CRC},
address = {London},
year = {1983},
edition = {1st},
note = {ISBN 978-0412248603},
doi = {10.1007/978-1-4899-3174-0},
}
@Book{Barry2015,
title = {Pareto Distributions},
author = {Barry C. Arnold},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2015},
edition = {2nd},
note = {ISBN 978-1-4665-8485-3},
}
@Book{Marron2022,
title = {Object Oriented Data Analysis},
author = {J. S. Marron and Ian L. Dryden},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2022},
edition = {1st},
note = {ISBN 978-0-8153-9282-8},
}
@Book{Richard2022,
title = {Computational Statistics with R},
author = {Niels Richard Hansen},
year = {2022},
url = {https://cswr.nrhstat.org/},
}
@Book{Chacon2018,
title = {Multivariate Kernel Smoothing and Its Applications},
author = {Jos{\'e}. Chac{\'o}n, Tarn Duong},
year = {2018},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
isbn = {9781498763011},
url = {https://www.mvstat.net/mvksa/},
}
@Book{KernSmooth1995,
title = {Kernel Smoothing},
author = {M. P. Wand and M. C. Jones},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {1995},
edition = {1st},
note = {ISBN 978-0412552700},
url = {http://matt-wand.utsacademics.info/webWJbook/},
}
@Book{Coene2021,
title = {JavaScript for R},
author = {John Coene},
publisher = {Chapman and Hall/CRC},
address = {Boca Raton, Florida},
year = {2021},
note = {ISBN 978-0367680633},
url = {https://book.javascript-for-r.com/},
}
@Book{Cressie1993,
title = {Statistics for Spatial Data},
author = {Noel A. C. Cressiee},
publisher = {John Wiley \& Sons, Inc},
year = {1993},
edition = {Revised Edition},
note = {ISBN 978-0471-00255-0},
}
@Book{Diggle2013,
title = {Statistical Analysis of Spatial and Spatio-Temporal Point Patterns},
year = {2013},
author = {Peter J. Diggle},
edition = {3rd},
address = {Boca Raton, Florida},
publisher = {Chapman and Hall/CRC},
}
@Article{Rue2009,
author = {H{\aa}vard Rue and Sara Martino and Nicholas Chopin},
title = {Approximate {Bayesian} Inference for Latent {Gaussian} Models Using Integrated Nested {Laplace} Approximations (with discussion)},
journal = {Journal of the Royal Statistical Society, Series B},
year = {2009},
volume = {71},
number = {2},
pages = {319--392},
}
@Article{Lindgren2015,
title = {Bayesian Spatial Modelling with {R-INLA}},
author = {Finn Lindgren and H{\aa}vard Rue},
journal = {Journal of Statistical Software},
year = {2015},
volume = {63},
number = {19},
pages = {1--25},
}
@Article{Virgilio2018,
author = {G{\'o}mez-Rubio, Virgilio and Rue, H{\aa}vard},
title = {Markov chain Monte Carlo with the Integrated Nested Laplace Approximation},
journal = {Statistics and Computing},
year = {2018},
volume = {28},
number = {5},
pages = {1033--1051},
}
@Article{Anderson2022,
title = {{sdmTMB}: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields},
author = {{Anderson} and Sean C. and {Ward} and Eric J. and {English} and Philina A. and {Barnett} and Lewis A.K.},
journal = {bioRxiv},
year = {2022},
volume = {2022.03.24.485545},
doi = {10.1101/2022.03.24.485545},
url = {https://doi.org/10.1101/2022.03.24.485545},
}
@Book{Diggle2007,
title = {Model-based Geostatistics},
author = {Peter J. Diggle and Paulo J. {Ribeiro Jr.}},
year = {2007},
publisher = {Springer-Verlag},
address = {New York, NY},
doi = {10.1007%2F978-0-387-48536-2},
}
@Book{Bivand2013,
author = {Roger S. Bivand and Edzer Pebesma and Virgilio Gomez-Rubio},
title = {Applied spatial data analysis with {R}},
year = {2013},
edition = {2nd},
publisher = {Springer, NY},
url = {https://asdar-book.org/},
}
@Book{Baddeley2015,
title = {Spatial Point Patterns: Methodology and Applications with {R}},
author = {Adrian Baddeley and Ege Rubak and Rolf Turner},
year = {2015},
publisher = {Chapman and Hall/CRC Press},
address = {London},
url = {https://www.routledge.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/9781482210200/},
}
@Article{Pebesma2018,
author = {Edzer Pebesma},
title = {Simple Features for {R}: Standardized Support for Spatial Vector Data},
year = {2018},
journal = {{The R Journal}},
doi = {10.32614/RJ-2018-009},
url = {https://doi.org/10.32614/RJ-2018-009},
pages = {439--446},
volume = {10},
number = {1},
}
@Article{Pebesma2005,
author = {Edzer J. Pebesma and Roger S. Bivand},
title = {Classes and methods for spatial data in {R}},
journal = {R News},
year = {2005},
volume = {5},
number = {2},
pages = {9--13},
month = {November},
url = {https://CRAN.R-project.org/doc/Rnews/},
}
@Article{Pebesma2004,
title = {Multivariable geostatistics in {S}: the {gstat} package},
author = {Edzer J. Pebesma},
journal = {Computers & Geosciences},
year = {2004},
volume = {30},
pages = {683-691},
}
@Article{Benedikt2016,
title = {Spatio-Temporal Interpolation using {gstat}},
author = {Benedikt Gräler and Edzer Pebesma and Gerard Heuvelink},
year = {2016},
journal = {The R Journal},
volume = {8},
issue = {1},
pages = {204-218},
url = {https://journal.r-project.org/archive/2016/RJ-2016-014/index.html},
}
@Article{Christensen2002,
title = {{geoRglm}: A package for generalised linear spatial models},
author = {O.F. Christensen and P.J. {Ribeiro Jr.}},
journal = {R News},
year = {2002},
volume = {2},
number = {2},
pages = {26-28},
}
@Article{PrevMap2017,
title = {{PrevMap}: An {R} Package for Prevalence Mapping},
author = {Emanuele Giorgi and Peter J. Diggle},
journal = {Journal of Statistical Software},
year = {2017},
volume = {78},
number = {8},
pages = {1--29},
}
@Article{Baddeley2005,
title = {{spatstat}: An {R} Package for Analyzing Spatial Point Patterns},
author = {Adrian Baddeley and Rolf Turner},
journal = {Journal of Statistical Software},
year = {2005},
volume = {12},
number = {6},
pages = {1--42},
doi = {10.18637/jss.v012.i06},
}
@article{glmmfields2018,
doi = {10.1002/ecy.2403},
year = {2018},
month = nov,
publisher = {Wiley},
volume = {100},
number = {1},
author = {Sean C. Anderson and Eric J. Ward},
title = {Black swans in space: modeling spatio-temporal processes with extremes}
}
@Article{Ronnegard2010,
title = {{hglm}: A Package for Fitting Hierarchical Generalized Linear Models},
author = {Lars Ronnegard and Xia Shen and Moudud Alam},
journal = {The R Journal},
year = {2010},
volume = {2},
number = {2},
pages = {20-28},
url = {https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Roennegaard~et~al.pdf},
}
@Article{Alam2015,
title = {Fitting Conditional and Simultaneous Autoregressive Spatial Models in {hglm}},
author = {Moudud Alam and Lars Ronnegard and Xia Shen},
journal = {The R Journal},
year = {2015},
volume = {7},
number = {2},
pages = {5-18},
url = {https://journal.r-project.org/archive/2015/RJ-2015-017/RJ-2015-017.pdf},
}
@Article{spacetime2012,
title = {{spacetime}: Spatio-Temporal Data in {R}},
author = {Edzer Pebesma},
journal = {Journal of Statistical Software},
year = {2012},
volume = {51},
number = {7},
pages = {1--30},
url = {https://www.jstatsoft.org/v51/i07/},
}
@Article{Ripley1987,
title = {Problems with likelihood estimation of covariance functions of spatial gaussian processes},
author = {J. J. Warnes and B. D. Ripley},
journal = {Biometrika},
year = {1987},
volume = {74},
number = {3},
pages = {640-642},
}
@article{Diggle1998,
author = {Diggle, P. J. and Tawn, J. A. and Moyeed, R. A.},
title = {Model-based geostatistics},
journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
year = {1998},
volume = {47},
number = {3},
pages = {299-350},
keywords = {Generalized linear mixed model, Geostatistics, Kriging, Markov chain Monte Carlo method, Spatial prediction},
doi = {10.1111/1467-9876.00113},
abstract = {Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S(x) based on observations Yi = S(xi) + Zi at sampling locations xi, where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly inappropriate. The first concerns the assessment of residual contamination from nuclear weapons testing on a South Pacific island, in which the sampling method generates spatially indexed Poisson counts conditional on an unobserved spatially varying intensity of radioactivity; we conclude that a conventional geostatistical analysis oversmooths the data and underestimates the spatial extremes of the intensity. The second application provides a description of spatial variation in the risk of campylobacter infections relative to other enteric infections in part of north Lancashire and south Cumbria. For this application, we treat the data as binomial counts at unit postcode locations, conditionally on an unobserved relative risk surface which we estimate. The theoretical framework for our extension of geostatistical methods is that, conditionally on the unobserved process S(x), observations at sample locations xi form a generalized linear model with the corresponding values of S(xi) appearing as an offset term in the linear predictor. We use a Bayesian inferential framework, implemented via the Markov chain Monte Carlo method, to solve the prediction problem for non-linear functionals of S(x), making a proper allowance for the uncertainty in the estimation of any model parameters.},
}
@article{Christensen2004,
author = {Ole F Christensen},
title = {Monte Carlo Maximum Likelihood in Model-Based Geostatistics},
journal = {Journal of Computational and Graphical Statistics},
volume = {13},
number = {3},
pages = {702-718},
year = {2004},
doi = {10.1198/106186004X2525},
}
@article{Christensen2006,
author = {Ole F Christensen and Gareth O. Roberts and Martin Sk{\"o}ld},
title = {Robust Markov Chain Monte Carlo Methods for Spatial Generalized Linear Mixed Models},
journal = {Journal of Computational and Graphical Statistics},
abstract = {Using Markov chain Monte Carlo methods for statistical inference is often troublesome in practice, because performance of the algorithm may hugely depend on the observed data, and what works well for one dataset may fail miserably for another. In this article, for spatial generalized linear mixed models (GLMMs), we discuss problems with algorithms previously used, and we construct an algorithm with robust mixing and convergence characteristics, independent of the data. The strategy we have used for this construction is not model specific and could be applied in a much wider context.},
volume = {15},
number = {1},
pages = {1-17},
year = {2006},
url = {http://www.jstor.org/stable/27594162},
}
@article{Wagner2015,
author = {Wagner Hugo Bonat and Paulo Justiniano Ribeiro Jr},
title = {Practical likelihood analysis for spatial generalized linear mixed models},
journal = {Electronic Journal of Statistics},
volume = {10},
number = {2},
pages = {3986-4009},
year = {2015},
doi = {10.1002/env.2375},
}
@article{Erlis2016,
author = {Erlis Ruli and Nicola Sartori and Laura Ventura},
title = {Improved Laplace approximation for marginal likelihoods},
journal = {Electronic Journal of Statistics},
volume = {10},
number = {2},
pages = {3986-4009},
year = {2016},
doi = {10.1214/16-EJS1218},
}
@Article{misc3d2008,
title = {Computing and Displaying Isosurfaces in {R}},
author = {Dai Feng and Luke Tierney},
journal = {Journal of Statistical Software},
year = {2008},
volume = {28},
number = {1},
doi = {10.18637/jss.v028.i01},
}
@Article{Anscombe1973,
doi = {10.2307/2682899},
year = {1973},
volume = {27},
number = {1},
pages = {17},
author = {F. J. Anscombe},
title = {Graphs in Statistical Analysis},
journal = {The American Statistician}
}
@Article{Galton1886,
year = {1886},
volume = {15},
pages = {246-263},
author = {Galton, F.},
title = {Regression Towards Mediocrity in Hereditary Stature},
journal = {Journal of the Anthropological Institute},
}
@Article{Hanley2004,
year = {2004},
volume = {58},
number = {3},
pages = {237-243},
month = aug,
author = {James A. Hanley},
title = {'Transmuting' women into men: Galton's family data on human stature},
journal = {The American Statistician},
}
@Unpublished{Hadley2011,
year = {2011},
month = Nov,
author = {Hadley Wickham and Lisa Stryjewski},
title = {40 years of boxplots},
url = {https://vita.had.co.nz/papers/boxplots.pdf},
}
@Article{Tukey1978,
URL = {https://www.jstor.org/stable/2683468},
author = {McGill, R., Tukey, J. W. and Larsen, W. A.},
journal = {The American Statistician},
number = {1},
pages = {12-16},
title = {Variations of box plots},
volume = {32},
year = {1978},
}
@Article{ggsignif2021,
title = {{ggsignif}: R Package for Displaying Significance Brackets for {ggplot2}},
author = {Ahlmann-Eltze Constantin and Indrajeet Patil},
year = {2021},
journal = {PsyArxiv},
url = {https://psyarxiv.com/7awm6},
doi = {10.31234/osf.io/7awm6},
}
@Article{Indrajeet2021,
doi = {10.21105/joss.03167},
url = {https://doi.org/10.21105/joss.03167},
year = {2021},
publisher = {{The Open Journal}},
volume = {6},
number = {61},
pages = {3167},
author = {Indrajeet Patil},
title = {{Visualizations with statistical details: The {ggstatsplot} approach}},
journal = {{Journal of Open Source Software}},
}
@Article{Bai2009,
author = {H. Bai and L. Wang and W. Pan and M. Frey},
journal = {Journal of Instructional Psychology},
number = {3},
pages = {185-193},
title = {Measuring mathematics anxiety: Psychometric analysis of a bidimensional affective scale},
volume = {36},
year = {2009},
}
@Article{Blyth1960,
URL = {https://www.jstor.org/stable/2333308},
author = {Colin R. Blyth and David W. Hutchinson},
journal = {Biometrika},
number = {3/4},
pages = {381--391},
title = {Table of Neyman-Shortest Unbiased Confidence Intervals for the Binomial Parameter},
volume = {47},
year = {1960},
}
@Article{Clopper1934,
author = {Clopper, C. J. and Pearson, E. S.},
title = {The Use of Confidence or Fiducial Limits Illustrated In The Case of The Binomial},
journal = {Biometrika},
volume = {26},
number = {4},
pages = {404-413},
year = {1934},
month = {12},
doi = {10.1093/biomet/26.4.404},
}
@Article{Lawrence2001,
title = {Interval Estimation for a Binomial Proportion},
author = {Lawrence D. Brown and T. Tony Cai and Anirban DasGupta},
journal = {Statistical Science},
year = {2001},
volumne = {16},
number = {2},
pages = {101--133},
url = {https://projecteuclid.org/euclid.ss/1009213286},
}
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author = {Geyer, Charles J. and Meeden, Glen D.},
url = {https://www.jstor.org/stable/20061193},
journal = {Statistical Science},
month = {11},
number = {4},
pages = {358--366},
title = {Fuzzy and Randomized Confidence Intervals and P-Values},
volume = {20},
year = {2005}
}
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author = {Wilson, Edwin B.},
title = {Probable inference, the law of succession, and statistical inference},
journal = {Journal of the American Statistical Association},
volume = {22},
number = {158},
pages = {209-212},
year = {1927},
month = {6},
doi = {10.1080/01621459.1927.10502953},
}
@article{Fournier2012,
author = {David A. Fournier and Hans J. Skaug and Johnoel Ancheta and James Ianelli and Arni Magnusson and Mark N. Maunder and Anders Nielsen and John Sibert},
title = {AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models},
journal = {Optimization Methods and Software},
volume = {27},
number = {2},
pages = {233-249},
year = {2012},
doi = {10.1080/10556788.2011.597854},
}
@article{Gelman2015,
author = {Andrew Gelman and Daniel Lee and Jiqiang Guo},
title = {Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization},
journal = {Journal of Educational and Behavioral Statistics},
volume = {40},
number = {5},
pages = {530-543},
year = {2015},
doi = {10.3102/1076998615606113},
URL = {https://doi.org/10.3102/1076998615606113},
abstract = {Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users’ and developers’ perspectives and illustrate with a simple but nontrivial nonlinear regression example.},
}
@article{Hoffman2014,
author = {Matthew D. Hoffman and Andrew Gelman},
title = {The {No-U-Turn} Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
journal = {Journal of Machine Learning Research},
year = {2014},
volume = {15},
number = {1},
pages = {1593-1623},
}
@article{Monnahan2018,
author = {Monnahan, Cole C. and Kristensen, Kasper},
journal = {PLOS ONE},
title = {{No-U-turn} sampling for fast Bayesian inference in {ADMB} and {TMB}: Introducing the {adnuts} and {tmbstan} {R} packages},
year = {2018},
volume = {13},
pages = {1-10},
number = {5},
}
@Inproceedings{Kucukelbir2015,
author = {Kucukelbir, Alp and Ranganath, Rajesh and Gelman, Andrew and Blei, David M.},
title = {Automatic Variational Inference in Stan},
year = {2015},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
booktitle = {Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1},
pages = {568–576},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS'15},
}
@article{Carpenter2017,
author = {Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell},
title = {{Stan}: A Probabilistic Programming Language},
journal = {Journal of Statistical Software},
volume = {76},
number = {1},
year = {2017},
issn = {1548-7660},
pages = {1--32},
doi = {10.18637/jss.v076.i01},
url = {https://www.jstatsoft.org/v076/i01}
}
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title = {Visualization in Bayesian workflow},
author = {Jonah Gabry and Daniel Simpson and Aki Vehtari and Michael Betancourt and Andrew Gelman},
year = {2019},
journal = {Journal of the Royal Statistical Society Series A: Statistics in Society},
volume = {182},
issue = {2},
pages = {389-402},
doi = {10.1111/rssa.12378},
}
@misc{Gelman2020,
title = {Bayesian Workflow},
author = {Andrew Gelman and Aki Vehtari and Daniel Simpson and Charles C. Margossian and Bob Carpenter and Yuling Yao and Lauren Kennedy and Jonah Gabry and Paul-Christian Bürkner and Martin Modrák},
year = {2020},
eprint = {2011.01808},
archivePrefix = {arXiv},
primaryClass = {stat.ME}
}
@misc{Carpenter2015,
title = {The Stan Math Library: Reverse-Mode Automatic Differentiation in C++},
author = {Bob Carpenter and Matthew D. Hoffman and Marcus Brubaker and Daniel Lee and Peter Li and Michael Betancourt},
year = {2015},
eprint = {1509.07164},
archivePrefix = {arXiv},
primaryClass = {cs.MS},
url = {https://arxiv.org/abs/1509.07164},
}
@misc{Dillon2017,
title = {{TensorFlow} Distributions},
author = {Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous},
year = {2017},
eprint = {1711.10604},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1711.10604},
}
@Article{Rcpp2011,
title = {{Rcpp}: Seamless {R} and {C++} Integration},
author = {Dirk Eddelbuettel and Romain Fran\c{c}ois},
journal = {Journal of Statistical Software},
year = {2011},
volume = {40},
number = {8},
pages = {1--18},
doi = {10.18637/jss.v040.i08},
}
@Article{Rcpp2018,
title = {{Extending {R} with {C++}: A Brief Introduction to {Rcpp}}},
author = {Dirk Eddelbuettel and James Joseph Balamuta},
journal = {The American Statistician},
year = {2018},
volume = {72},
number = {1},
pages = {28-36},
doi = {10.1080/00031305.2017.1375990},
}
@Article{Bates2013,
title = {Fast and Elegant Numerical Linear Algebra Using the {RcppEigen} Package},
author = {Douglas Bates and Dirk Eddelbuettel},
journal = {Journal of Statistical Software},
year = {2013},
volume = {52},
number = {5},
pages = {1--24},
doi = {10.18637/jss.v052.i05},
}
@Article{pymc2023,
title = {{PyMC}: a modern, and comprehensive probabilistic programming framework in Python},
author = {Abril-Pla O, Andreani V, Carroll C, Dong L, Fonnesbeck CJ, Kochurov M, Kumar R, Lao J, Luhmann CC, Martin OA, Osthege M, Vieira R, Wiecki T, Zinkov R},
journal = {PeerJ Computer Science},
year = {2023},
volume = {9},
number = {e1516},
doi = {10.7717/peerj-cs.1516},
url = {https://doi.org/10.7717/peerj-cs.1516},
}
@Article{workflowr2019,
title = {Creating and sharing reproducible research code the workflowr way [version 1; peer review: 3 approved]},
author = {John D Blischak and Peter Carbonetto and Matthew Stephens},
journal = {F1000Research},
year = {2019},
volume = {8},
number = {1749},
doi = {10.12688/f1000research.20843.1},
url = {https://doi.org/10.12688/f1000research.20843.1},
}
@article{Rocker2020,
author = {Daniel Nüst and Dirk Eddelbuettel and Dom Bennett and
Robrecht Cannoodt and Dav Clark and Gergely Daróczi and
Mark Edmondson and Colin Fay and Ellis Hughes and Lars
Kjeldgaard and Sean Lopp and Ben Marwick and Heather Nolis
and Jacqueline Nolis and Hong Ooi and Karthik Ram and Noam
Ross and Lori Shepherd and Péter Sólymos and Tyson Lee
Swetnam and Nitesh Turaga and Charlotte Van Petegem and
Jason Williams and Craig Willis and Nan Xiao},
title = {{The Rockerverse: Packages and Applications for Containerisation with R}},
year = {2020},
journal = {{The R Journal}},
doi = {10.32614/RJ-2020-007},
url = {https://doi.org/10.32614/RJ-2020-007},
pages = {437--461},
volume = {12},
number = {1},
}
@Article{targets2021,
title = {The {targets} {R} package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing},
author = {William Michael Landau},
journal = {Journal of Open Source Software},
year = {2021},
volume = {6},
number = {57},
pages = {2959},
url = {https://doi.org/10.21105/joss.02959},
}
@article{Bhadra2019,
author = {Anindya Bhadra and Jyotishka Datta and Nicholas G. Polson and Brandon Willard},
title = {{Lasso Meets Horseshoe: A Survey}},
volume = {34},
journal = {Statistical Science},
number = {3},
publisher = {Institute of Mathematical Statistics},
pages = {405 -- 427},
keywords = {Global-local priors, horseshoe, horseshoe+, hyper-parameter tuning, Lasso, regression, regularization, Sparsity},
year = {2019},
doi = {10.1214/19-STS700},
}
@article{Piironen2017a,
author = {Juho Piironen and Aki Vehtari},
title = {Sparsity information and regularization in the horseshoe and other shrinkage priors},
volume = {11},
journal = {Electronic Journal of Statistics},
number = {2},
publisher = {Institute of Mathematical Statistics and Bernoulli Society},
pages = {5018 -- 5051},
keywords = {Bayesian inference, horseshoe prior, shrinkage priors, Sparse estimation},
year = {2017},
doi = {10.1214/17-EJS1337SI},
}
@Article{Piironen2017b,
title = {Comparison of {{Bayesian}} Predictive Methods for Model Selection},
author = {Juho Piironen and Aki Vehtari},
year = {2017},
journal = {Statistics and Computing},
volume = {27},
number = {3},
pages = {711--735},
doi = {10.1007/s11222-016-9649-y},
}
@Article{Piironen2020,
title = {Projective Inference in High-Dimensional Problems: {{Prediction}} and Feature Selection},
author = {Juho Piironen and Markus Paasiniemi and Aki Vehtari},
year = {2020},
journal = {Electronic Journal of Statistics},
volume = {14},
number = {1},
pages = {2155--2197},
doi = {10.1214/20-EJS1711},
}
@article{TMB2016,
title = {{TMB}: Automatic Differentiation and Laplace Approximation},
volume = {70},
doi = {10.18637/jss.v070.i05},
number = {5},
journal= {Journal of Statistical Software},
author = {Kristensen, Kasper and Nielsen, Anders and Berg, Casper W. and Skaug, Hans and Bell, Bradley M.},
year = {2016},
pages = {1–21},
}
@Article{Brooks2017,
author = {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 Maechler and Benjamin M. Bolker},
title = {{glmmTMB} Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling},
year = {2017},
journal = {The R Journal},
doi = {10.32614/RJ-2017-066},
pages = {378--400},
volume = {9},
number = {2},
}
@Article{Rousset2014,
title = {Testing environmental and genetic effects in the presence of spatial autocorrelation},
author = {François Rousset and Jean-Baptiste Ferdy},
journal = {Ecography},
year = {2014},
volume = {37},
number = {8},
pages = {781-790},
url = {https://dx.doi.org/10.1111/ecog.00566},
}
@Article{Bates2015,
title = {Fitting Linear Mixed-Effects Models Using {lme4}},
author = {Douglas Bates and Martin M{\"a}chler and Ben Bolker and Steve Walker},
journal = {Journal of Statistical Software},