text:
@ARTICLE{Bokulich2018-xf,
  title    = "Optimizing taxonomic classification of marker-gene amplicon
              sequences with {QIIME} 2's q2-feature-classifier plugin",
  author   = "Bokulich, Nicholas A and Kaehler, Benjamin D and Rideout, Jai Ram
              and Dillon, Matthew and Bolyen, Evan and Knight, Rob and Huttley,
              Gavin A and Gregory Caporaso, J",
  abstract = "BACKGROUND: Taxonomic classification of marker-gene sequences is
              an important step in microbiome analysis. RESULTS: We present
              q2-feature-classifier (
              https://github.com/qiime2/q2-feature-classifier ), a QIIME 2
              plugin containing several novel machine-learning and
              alignment-based methods for taxonomy classification. We evaluated
              and optimized several commonly used classification methods
              implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and
              several new methods implemented in QIIME 2 (a scikit-learn naive
              Bayes machine-learning classifier, and alignment-based taxonomy
              consensus methods based on VSEARCH, and BLAST+) for
              classification of bacterial 16S rRNA and fungal ITS marker-gene
              amplicon sequence data. The naive-Bayes, BLAST+-based, and
              VSEARCH-based classifiers implemented in QIIME 2 meet or exceed
              the species-level accuracy of other commonly used methods
              designed for classification of marker gene sequences that were
              evaluated in this work. These evaluations, based on 19 mock
              communities and error-free sequence simulations, including
              classification of simulated ``novel'' marker-gene sequences, are
              available in our extensible benchmarking framework, tax-credit (
              https://github.com/caporaso-lab/tax-credit-data ). CONCLUSIONS:
              Our results illustrate the importance of parameter tuning for
              optimizing classifier performance, and we make recommendations
              regarding parameter choices for these classifiers under a range
              of standard operating conditions. q2-feature-classifier and
              tax-credit are both free, open-source, BSD-licensed packages
              available on GitHub.",
  journal  = "Microbiome",
  volume   =  6,
  number   =  1,
  pages    = "90",
  month    =  may,
  year     =  2018,
  url      = "http://dx.doi.org/10.1186/s40168-018-0470-z",
  language = "en",
  issn     = "2049-2618",
  pmid     = "29773078",
  doi      = "10.1186/s40168-018-0470-z",
  pmc      = "PMC5956843"
}

@ARTICLE{Bolyen2019-sd,
  title    = "Reproducible, interactive, scalable and extensible microbiome
              data science using {QIIME} 2",
  author   = "Bolyen, Evan and Rideout, Jai Ram and Dillon, Matthew R and
              Bokulich, Nicholas A and Abnet, Christian C and Al-Ghalith,
              Gabriel A and Alexander, Harriet and Alm, Eric J and Arumugam,
              Manimozhiyan and Asnicar, Francesco and Bai, Yang and Bisanz,
              Jordan E and Bittinger, Kyle and Brejnrod, Asker and Brislawn,
              Colin J and Brown, C Titus and Callahan, Benjamin J and
              Caraballo-Rodr{\'\i}guez, Andr{\'e}s Mauricio and Chase, John and
              Cope, Emily K and Da Silva, Ricardo and Diener, Christian and
              Dorrestein, Pieter C and Douglas, Gavin M and Durall, Daniel M
              and Duvallet, Claire and Edwardson, Christian F and Ernst,
              Madeleine and Estaki, Mehrbod and Fouquier, Jennifer and
              Gauglitz, Julia M and Gibbons, Sean M and Gibson, Deanna L and
              Gonzalez, Antonio and Gorlick, Kestrel and Guo, Jiarong and
              Hillmann, Benjamin and Holmes, Susan and Holste, Hannes and
              Huttenhower, Curtis and Huttley, Gavin A and Janssen, Stefan and
              Jarmusch, Alan K and Jiang, Lingjing and Kaehler, Benjamin D and
              Kang, Kyo Bin and Keefe, Christopher R and Keim, Paul and Kelley,
              Scott T and Knights, Dan and Koester, Irina and Kosciolek, Tomasz
              and Kreps, Jorden and Langille, Morgan G I and Lee, Joslynn and
              Ley, Ruth and Liu, Yong-Xin and Loftfield, Erikka and Lozupone,
              Catherine and Maher, Massoud and Marotz, Clarisse and Martin,
              Bryan D and McDonald, Daniel and McIver, Lauren J and Melnik,
              Alexey V and Metcalf, Jessica L and Morgan, Sydney C and Morton,
              Jamie T and Naimey, Ahmad Turan and Navas-Molina, Jose A and
              Nothias, Louis Felix and Orchanian, Stephanie B and Pearson,
              Talima and Peoples, Samuel L and Petras, Daniel and Preuss, Mary
              Lai and Pruesse, Elmar and Rasmussen, Lasse Buur and Rivers, Adam
              and Robeson, 2nd, Michael S and Rosenthal, Patrick and Segata,
              Nicola and Shaffer, Michael and Shiffer, Arron and Sinha, Rashmi
              and Song, Se Jin and Spear, John R and Swafford, Austin D and
              Thompson, Luke R and Torres, Pedro J and Trinh, Pauline and
              Tripathi, Anupriya and Turnbaugh, Peter J and Ul-Hasan, Sabah and
              van der Hooft, Justin J J and Vargas, Fernando and
              V{\'a}zquez-Baeza, Yoshiki and Vogtmann, Emily and von Hippel,
              Max and Walters, William and Wan, Yunhu and Wang, Mingxun and
              Warren, Jonathan and Weber, Kyle C and Williamson, Charles H D
              and Willis, Amy D and Xu, Zhenjiang Zech and Zaneveld, Jesse R
              and Zhang, Yilong and Zhu, Qiyun and Knight, Rob and Caporaso, J
              Gregory",
  journal  = "Nature biotechnology",
  volume   =  37,
  number   =  8,
  pages    = "852-857",
  month    =  aug,
  year     =  2019,
  url      = "http://dx.doi.org/10.1038/s41587-019-0209-9",
  language = "en",
  issn     = "1087-0156, 1546-1696",
  pmid     = "31341288",
  doi      = "10.1038/s41587-019-0209-9",
  pmc      = "PMC7015180"
}

@ARTICLE{Buchan2019-mh,
  title    = "The relevance of sink proximity to toilets on the detection of
              Klebsiella pneumoniae carbapenemase inside sink drains",
  author   = "Buchan, Blake W and Graham, Mary Beth and Lindmair-Snell, Jill
              and Arvan, Jennifer and Ledeboer, Nathan A and Nanchal, Rahul and
              Munoz-Price, L Silvia",
  abstract = "We report a higher prevalence of blaKPC in patient room sink
              drains located next to toilets (87.0\%) when compared with sink
              drains located farther away from toilets (21.7\%) using direct
              polymerase chain reaction assay. However, culture methods were
              only able to recover blaKPC-positive isolates from 16\% of
              polymerase chain reaction-positive drains.",
  journal  = "American journal of infection control",
  volume   =  47,
  number   =  1,
  pages    = "98-100",
  month    =  jan,
  year     =  2019,
  url      = "http://dx.doi.org/10.1016/j.ajic.2018.06.021",
  keywords = "Carbapenem-resistant organisms; Disinfection; Kpc; Pipes; Sink
              drains; Sinks",
  language = "en",
  issn     = "0196-6553, 1527-3296",
  pmid     = "30172608",
  doi      = "10.1016/j.ajic.2018.06.021"
}

@ARTICLE{Burkner2017-yr,
  title    = "brms: An {R} Package for Bayesian Multilevel Models Using Stan",
  author   = "B{\"u}rkner, Paul-Christian",
  abstract = "The brms package implements Bayesian multilevel models in R using
              the probabilistic programming language Stan. A wide range of
              distributions and link functions are supported, allowing users to
              fit - among others - linear, robust linear, binomial, Poisson,
              survival, ordinal, zero-inflated, hurdle, and even non-linear
              models all in a multilevel context. Further modeling options
              include autocorrelation of the response variable, user defined
              covariance structures, censored data, as well as meta-analytic
              standard errors. Prior specifications are flexible and explicitly
              encourage users to apply prior distributions that actually
              reflect their beliefs. In addition, model fit can easily be
              assessed and compared with the Watanabe-Akaike information
              criterion and leave-one-out cross-validation.",
  journal  = "Journal of Statistical Software, Articles",
  volume   =  80,
  number   =  1,
  pages    = "1-28",
  year     =  2017,
  url      = "https://www.jstatsoft.org/v080/i01",
  keywords = "Bayesian inference; multilevel model; ordinal data; MCMC; Stan; R",
  issn     = "1548-7660",
  doi      = "10.18637/jss.v080.i01"
}

@ARTICLE{Callahan2017-gx,
  title     = "Exact sequence variants should replace operational taxonomic
               units in marker-gene data analysis",
  author    = "Callahan, Benjamin J and McMurdie, Paul J and Holmes, Susan P",
  abstract  = "Recent advances have made it possible to analyze high-throughput
               marker-gene sequencing data without resorting to the customary
               construction of molecular operational taxonomic units (OTUs):
               clusters of sequencing reads that differ by less than a fixed
               dissimilarity threshold. New methods control errors sufficiently
               such that amplicon sequence variants (ASVs) can be resolved
               exactly, down to the level of single-nucleotide differences over
               the sequenced gene region. The benefits of finer resolution are
               immediately apparent, and arguments for ASV methods have focused
               on their improved resolution. Less obvious, but we believe more
               important, are the broad benefits that derive from the status of
               ASVs as consistent labels with intrinsic biological meaning
               identified independently from a reference database. Here we
               discuss how these features grant ASVs the combined advantages of
               closed-reference OTUs-including computational costs that scale
               linearly with study size, simple merging between independently
               processed data sets, and forward prediction-and of de novo
               OTUs-including accurate measurement of diversity and
               applicability to communities lacking deep coverage in reference
               databases. We argue that the improvements in reusability,
               reproducibility and comprehensiveness are sufficiently great
               that ASVs should replace OTUs as the standard unit of
               marker-gene analysis and reporting.",
  journal   = "The ISME journal",
  publisher = "The Author(s)",
  volume    =  11,
  number    =  12,
  pages     = "2639-2643",
  month     =  dec,
  year      =  2017,
  url       = "http://dx.doi.org/10.1038/ismej.2017.119",
  language  = "en",
  issn      = "1751-7362, 1751-7370",
  pmid      = "28731476",
  doi       = "10.1038/ismej.2017.119",
  pmc       = "PMC5702726"
}

@ARTICLE{Callahan2019-ir,
  title    = "High-throughput amplicon sequencing of the full-length {16S}
              {rRNA} gene with single-nucleotide resolution",
  author   = "Callahan, Benjamin J and Wong, Joan and Heiner, Cheryl and Oh,
              Steve and Theriot, Casey M and Gulati, Ajay S and McGill, Sarah K
              and Dougherty, Michael K",
  abstract = "Targeted PCR amplification and high-throughput sequencing
              (amplicon sequencing) of 16S rRNA gene fragments is widely used
              to profile microbial communities. New long-read sequencing
              technologies can sequence the entire 16S rRNA gene, but higher
              error rates have limited their attractiveness when accuracy is
              important. Here we present a high-throughput amplicon sequencing
              methodology based on PacBio circular consensus sequencing and the
              DADA2 sample inference method that measures the full-length 16S
              rRNA gene with single-nucleotide resolution and a near-zero error
              rate. In two artificial communities of known composition, our
              method recovered the full complement of full-length 16S sequence
              variants from expected community members without residual errors.
              The measured abundances of intra-genomic sequence variants were
              in the integral ratios expected from the genuine allelic variants
              within a genome. The full-length 16S gene sequences recovered by
              our approach allowed Escherichia coli strains to be correctly
              classified to the O157:H7 and K12 sub-species clades. In human
              fecal samples, our method showed strong technical replication and
              was able to recover the full complement of 16S rRNA alleles in
              several E. coli strains. There are likely many applications
              beyond microbial profiling for which high-throughput amplicon
              sequencing of complete genes with single-nucleotide resolution
              will be of use.",
  journal  = "Nucleic acids research",
  volume   =  47,
  number   =  18,
  pages    = "e103",
  month    =  oct,
  year     =  2019,
  url      = "http://dx.doi.org/10.1093/nar/gkz569",
  language = "en",
  issn     = "0305-1048, 1362-4962",
  pmid     = "31269198",
  doi      = "10.1093/nar/gkz569",
  pmc      = "PMC6765137"
}

@ARTICLE{Callahan2016-uq,
  title     = "{DADA2}: High-resolution sample inference from Illumina amplicon
               data",
  author    = "Callahan, Benjamin J and McMurdie, Paul J and Rosen, Michael J
               and Han, Andrew W and Johnson, Amy Jo A and Holmes, Susan P",
  abstract  = "We present the open-source software package DADA2 for modeling
               and correcting Illumina-sequenced amplicon errors
               (https://github.com/benjjneb/dada2). DADA2 infers sample
               sequences exactly and resolves differences of as little as 1
               nucleotide. In several mock communities, DADA2 identified more
               real variants and output fewer spurious sequences than other
               methods. We applied DADA2 to vaginal samples from a cohort of
               pregnant women, revealing a diversity of previously undetected
               Lactobacillus crispatus variants.",
  journal   = "Nature methods",
  publisher = "Nature Publishing Group",
  volume    =  13,
  number    =  7,
  pages     = "581-583",
  month     =  jul,
  year      =  2016,
  url       = "http://dx.doi.org/10.1038/nmeth.3869",
  language  = "en",
  issn      = "1548-7091, 1548-7105",
  pmid      = "27214047",
  doi       = "10.1038/nmeth.3869",
  pmc       = "PMC4927377"
}

@ARTICLE{Carling2018-dl,
  title    = "Wastewater drains: epidemiology and interventions in 23
              carbapenem-resistant organism outbreaks",
  author   = "Carling, Philip C",
  abstract = "For many years, patient-area wastewater drains (ie, sink and
              shower drains) have been considered a potential source of
              bacterial pathogens that can be transmitted to patients.
              Recently, evolving genomic epidemiology tools combined with new
              insights into the ecology of wastewater drain (WWD) biofilm have
              provided new perspectives on the clinical relevance and
              hospital-associated infection (HAI) transmission risks related to
              these fixtures. To further clarify the clinical relevance of
              WWD-associated pathogen transmission, reports of outbreaks
              attributed to WWDs were selected for review that (1) investigated
              the outbreak epidemiology of WWD-associated transmission of
              bacterial pathogens, (2) utilized advanced microbiologic methods
              to establish clonality of outbreak pathogens and/or resistance
              genes, or (3) described interventions implemented to mitigate
              transmission of the outbreak pathogens from WWDs. These reports
              were collated, compared, and analyzed, and the results are
              presented here.",
  journal  = "Infection control and hospital epidemiology: the official journal
              of the Society of Hospital Epidemiologists of America",
  volume   =  39,
  number   =  8,
  pages    = "972-979",
  month    =  aug,
  year     =  2018,
  url      = "http://dx.doi.org/10.1017/ice.2018.138",
  language = "en",
  issn     = "0899-823X, 1559-6834",
  pmid     = "29950189",
  doi      = "10.1017/ice.2018.138"
}

@ARTICLE{Carpenter2017-ec,
  title     = "Stan: A Probabilistic Programming Language",
  author    = "Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D and
               Lee, Daniel and Goodrich, Ben and Betancourt, Michael and
               Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell,
               Allen",
  abstract  = "Stan is a probabilistic programming language for specifying
               statistical models. A Stan program imperatively defines a log
               probability function over parameters conditioned on specified
               data and constants. As of version 2.14.0, Stan provides full
               Bayesian inference for continuous-variable models through Markov
               chain Monte Carlo methods such as the No-U-Turn sampler, an
               adaptive form of Hamiltonian Monte Carlo sampling. Penalized
               maximum likelihood estimates are calculated using optimization
               methods such as the limited memory
               Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a
               platform for computing log densities and their gradients and
               Hessians, which can be used in alternative algorithms such as
               variational Bayes, expectation propagation, and marginal
               inference using approximate integration. To this end, Stan is
               set up so that the densities, gradients, and Hessians, along
               with intermediate quantities of the algorithm such as acceptance
               probabilities, are easily accessible. Stan can be called from
               the command line using the cmdstan package, through R using the
               rstan package, and through Python using the pystan package. All
               three interfaces support sampling and optimization-based
               inference with diagnostics and posterior analysis. rstan and
               pystan also provide access to log probabilities, gradients,
               Hessians, parameter transforms, and specialized plotting.",
  journal   = "Journal of statistical software",
  publisher = "Columbia Univ., New York, NY (United States); Harvard Univ.,
               Cambridge, MA \textbackslashtextellipsis",
  volume    =  76,
  number    =  1,
  pages     = "1-32",
  year      =  2017,
  url       = "https://www.jstatsoft.org/v076/i01",
  keywords  = "probabilistic programming; Bayesian inference; algorithmic
               differentiation; Stan",
  issn      = "1548-7660",
  doi       = "10.18637/jss.v076.i01"
}

@ARTICLE{Chen2019-km,
  title    = "A prospective study of transmission of {Multidrug-Resistant}
              Organisms ({MDROs}) between environmental sites and hospitalized
              patients-the {TransFER} study",
  author   = "Chen, Luke F and Knelson, Lauren P and Gergen, Maria F and
              Better, Olga M and Nicholson, Bradly P and Woods, Christopher W
              and Rutala, William A and Weber, David J and Sexton, Daniel J and
              Anderson, Deverick J and {CDC Prevention Epicenters Program}",
  abstract = "OBJECTIVE: Hospital environmental surfaces are frequently
              contaminated by microorganisms. However, the causal mechanism of
              bacterial contamination of the environment as a source of
              transmission is still debated. This prospective study was
              performed to characterize the nature of multidrug-resistant
              organism (MDRO) transmission between the environment and patients
              using standard microbiological and molecular techniques. SETTING:
              Prospective cohort study at 2 academic medical centers. DESIGN: A
              prospective multicenter study to characterize the nature of
              bacterial transfer events between patients and environmental
              surfaces in rooms that previously housed patients with 1 of 4
              'marker' MDROs: methicillin-resistant Staphylococcus aureus,
              vancomycin-resistant enterococci, Clostridium difficile, and MDR
              Acinetobacter baumannii. Environmental and patient
              microbiological samples were obtained on admission into a freshly
              disinfected inpatient room. Repeat samples from room surfaces and
              patients were taken on days 3 and 7 and each week the patient
              stayed in the same room. The bacterial identity, antibiotic
              susceptibility, and molecular sequences were compared between
              organisms found in the environment samples and patient sources.
              RESULTS: We enrolled 80 patient-room admissions; 9 of these
              patients (11.3\%) were asymptomatically colonized with MDROs at
              study entry. Hospital room surfaces were contaminated with MDROs
              despite terminal disinfection in 44 cases (55\%). Microbiological
              Bacterial Transfer events either to the patient, the environment,
              or both occurred in 12 patient encounters (18.5\%) from the
              microbiologically evaluable cohort. CONCLUSIONS: Microbiological
              Bacterial Transfer events between patients and the environment
              were observed in 18.5\% of patient encounters and occurred early
              in the admission. This study suggests that research on prevention
              methods beyond the standard practice of room disinfection at the
              end of a patient's stay is needed to better prevent acquisition
              of MDROs through the environment.",
  journal  = "Infection control and hospital epidemiology: the official journal
              of the Society of Hospital Epidemiologists of America",
  volume   =  40,
  number   =  1,
  pages    = "47-52",
  month    =  jan,
  year     =  2019,
  url      = "http://dx.doi.org/10.1017/ice.2018.275",
  language = "en",
  issn     = "0899-823X, 1559-6834",
  pmid     = "30426908",
  doi      = "10.1017/ice.2018.275",
  pmc      = "PMC6690181"
}

@ARTICLE{Freedberg2016-bx,
  title    = "Receipt of Antibiotics in Hospitalized Patients and Risk for
              Clostridium difficile Infection in Subsequent Patients Who Occupy
              the Same Bed",
  author   = "Freedberg, Daniel E and Salmasian, Hojjat and Cohen, Bevin and
              Abrams, Julian A and Larson, Elaine L",
  abstract = "Objective: To assess whether receipt of antibiotics by prior
              hospital bed occupants is associated with increased risk for CDI
              in subsequent patients who occupy the same bed. Design, Setting,
              and Participants: This is a retrospective cohort study of adult
              patients hospitalized in any 1 of 4 facilities between 2010 and
              2015. Patients were excluded if they had recent CDI, developed
              CDI within 48 hours of admission, had inadequate follow-up time,
              or if their prior bed occupant was in the bed for less than 24
              hours. Main Outcomes and Measures: The primary exposure was
              receipt of non-CDI antibiotics by the prior bed occupant and the
              primary outcome was incident CDI in the subsequent patient to
              occupy the same bed. Incident CDI was defined as a positive
              result from a stool polymerase chain reaction for the C difficile
              toxin B gene followed by treatment for CDI. Demographics,
              comorbidities, laboratory data, and medication exposures are
              reported. Results: Among 100 615 pairs of patients who
              sequentially occupied a given hospital bed, there were 576 pairs
              (0.57\%) in which subsequent patients developed CDI. Receipt of
              antibiotics in prior patients was significantly associated with
              incident CDI in subsequent patients (log-rank P < .01). This
              relationship remained unchanged after adjusting for factors known
              to influence risk for CDI including receipt of antibiotics by the
              subsequent patient (adjusted hazard ratio [aHR], 1.22; 95\% CI,
              1.02-1.45) and also after excluding 1497 patient pairs among whom
              the prior patients developed CDI (aHR, 1.20; 95\% CI, 1.01-1.43).
              Aside from antibiotics, no other factors related to the prior bed
              occupants were associated with increased risk for CDI in
              subsequent patients. Conclusions and Relevance: Receipt of
              antibiotics by prior bed occupants was associated with increased
              risk for CDI in subsequent patients. Antibiotics can directly
              affect risk for CDI in patients who do not themselves receive
              antibiotics.",
  journal  = "JAMA internal medicine",
  volume   =  176,
  number   =  12,
  pages    = "1801-1808",
  month    =  dec,
  year     =  2016,
  url      = "http://dx.doi.org/10.1001/jamainternmed.2016.6193",
  language = "en",
  issn     = "2168-6106, 2168-6114",
  pmid     = "27723860",
  doi      = "10.1001/jamainternmed.2016.6193",
  pmc      = "PMC5138095"
}

@ARTICLE{Gabry2019-le,
  title         = "Visualization in Bayesian workflow",
  author        = "Gabry, Jonah and Simpson, Daniel and Vehtari, Aki and
                   Betancourt, Michael and Gelman, Andrew",
  abstract      = "Abstract Bayesian data analysis is about more than just
                   computing a posterior distribution, and Bayesian
                   visualization is about more than trace plots of Markov
                   chains. Practical Bayesian data analysis, like all data
                   analysis, is an iterative process of model building,
                   inference, model checking and evaluation, and model
                   expansion. Visualization is helpful in each of these stages
                   of the Bayesian workflow and it is indispensable when
                   drawing inferences from the types of modern, high
                   dimensional models that are used by applied researchers.",
  journal       = "Journal of the Royal Statistical Society. Series A,",
  publisher     = "Wiley",
  volume        =  182,
  number        =  2,
  pages         = "389-402",
  month         =  feb,
  year          =  2019,
  url           = "https://onlinelibrary.wiley.com/doi/abs/10.1111/rssa.12378",
  keywords      = "Bayesian data analysis; Statistical graphics; Statistical
                   workflow",
  copyright     = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
  language      = "en",
  archivePrefix = "arXiv",
  eprint        = "1709.01449",
  primaryClass  = "stat.ME",
  issn          = "0964-1998, 1467-985X",
  arxivid       = "1709.01449",
  doi           = "10.1111/rssa.12378"
}

@ARTICLE{Gelman2014-wn,
  title    = "Beyond Power Calculations: Assessing Type {S} (Sign) and Type {M}
              (Magnitude) Errors",
  author   = "Gelman, Andrew and Carlin, John",
  abstract = "Statistical power analysis provides the conventional approach to
              assess error rates when designing a research study. However,
              power analysis is flawed in that a narrow emphasis on statistical
              significance is placed as the primary focus of study design. In
              noisy, small-sample settings, statistically significant results
              can often be misleading. To help researchers address this problem
              in the context of their own studies, we recommend design
              calculations in which (a) the probability of an estimate being in
              the wrong direction (Type S [sign] error) and (b) the factor by
              which the magnitude of an effect might be overestimated (Type M
              [magnitude] error or exaggeration ratio) are estimated. We
              illustrate with examples from recent published research and
              discuss the largest challenge in a design calculation: coming up
              with reasonable estimates of plausible effect sizes based on
              external information.",
  journal  = "Perspectives on psychological science: a journal of the
              Association for Psychological Science",
  volume   =  9,
  number   =  6,
  pages    = "641-651",
  month    =  nov,
  year     =  2014,
  url      = "http://dx.doi.org/10.1177/1745691614551642",
  keywords = "Type M error; Type S error; design calculation; exaggeration
              ratio; power analysis; replication crisis; statistical
              significance",
  language = "en",
  issn     = "1745-6916, 1745-6924",
  pmid     = "26186114",
  doi      = "10.1177/1745691614551642"
}

@ARTICLE{Gelman2019-bc,
  title     = "R-squared for Bayesian Regression Models",
  author    = "Gelman, Andrew and Goodrich, Ben and Gabry, Jonah and Vehtari,
               Aki",
  abstract  = "AbstractThe usual definition of R2 (variance of the predicted
               values divided by the variance of the data) has a problem for
               Bayesian fits, as the numerator can be larger than the
               denominator. We propose an alternative definition similar to one
               that has appeared in the survival analysis literature: the
               variance of the predicted values divided by the variance of
               predicted values plus the expected variance of the errors.",
  journal   = "The American statistician",
  publisher = "Taylor \& Francis",
  volume    =  73,
  number    =  3,
  pages     = "307-309",
  month     =  jul,
  year      =  2019,
  url       = "https://doi.org/10.1080/00031305.2018.1549100",
  issn      = "0003-1305",
  doi       = "10.1080/00031305.2018.1549100"
}

@BOOK{Gelman2020-ck,
  title     = "Regression and Other Stories",
  author    = "Gelman, Andrew and Hill, Jennifer and Vehtari, Aki",
  abstract  = "Most textbooks on regression focus on theory and the simplest of
               examples. Real statistical problems, however, are complex and
               subtle. This is not a book about the theory of regression. It is
               about using regression to solve real problems of comparison,
               estimation, prediction, and causal inference. Unlike other
               books, it focuses on practical issues such as sample size and
               missing data and a wide range of goals and techniques. It jumps
               right in to methods and computer code you can use immediately.
               Real examples, real stories from the authors' experience
               demonstrate what regression can do and its limitations, with
               practical advice for understanding assumptions and implementing
               methods for experiments and observational studies. They make a
               smooth transition to logistic regression and GLM. The emphasis
               is on computation in R and Stan rather than derivations, with
               code available online. Graphics and presentation aid
               understanding of the models and model fitting.",
  publisher = "Cambridge University Press",
  month     =  jul,
  year      =  2020,
  url       = "https://play.google.com/store/books/details?id=SZFKzQEACAAJ",
  language  = "en",
  isbn      = "9781107676510"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Gelman2000-ih,
  title    = "Type {S} error rates for classical and Bayesian single and
              multiple comparison procedures",
  author   = "Gelman, Andrew and Tuerlinckx, Francis",
  abstract = "In classical statistics, the significance of comparisons (e.g.,
              $\vartheta$1− $\vartheta$2) is calibrated using the Type 1 error
              rate, relying on the assumption that the true difference is zero,
              which makes no sense in many applications. We set up a more
              relevant framework in which a true comparison can be positive or
              negative, and, based on the data, you can state ``$\vartheta$1 >
              $\vartheta$2 with confidence,'' ``$\vartheta$2 > $\vartheta$1
              with confidence,'' or ``no claim with confidence.'' We focus on
              the Type S (for sign) error, which occurs when you claim
              ``$\vartheta$1 > $\vartheta$2 with confidence'' when
              $\vartheta$2> $\vartheta$1 (or vice-versa). We compute the Type S
              error rates for classical and Bayesian confidence statements and
              find that classical Type S error rates can be extremely high (up
              to 50\%). Bayesian confidence statements are conservative, in the
              sense that claims based on 95\% posterior intervals have Type S
              error rates between 0 and 2.5\%. For multiple comparison
              situations, the conclusions are similar.",
  journal  = "Computational statistics",
  volume   =  15,
  number   =  3,
  pages    = "373-390",
  month    =  sep,
  year     =  2000,
  url      = "https://doi.org/10.1007/s001800000040",
  issn     = "0943-4062",
  doi      = "10.1007/s001800000040"
}

@ARTICLE{Gelman2020-yu,
  title         = "Bayesian Workflow",
  author        = "Gelman, Andrew and Vehtari, Aki and Simpson, Daniel and
                   Margossian, Charles C and Carpenter, Bob and Yao, Yuling and
                   Kennedy, Lauren and Gabry, Jonah and B{\"u}rkner,
                   Paul-Christian and Modr{\'a}k, Martin",
  abstract      = "The Bayesian approach to data analysis provides a powerful
                   way to handle uncertainty in all observations, model
                   parameters, and model structure using probability theory.
                   Probabilistic programming languages make it easier to
                   specify and fit Bayesian models, but this still leaves us
                   with many options regarding constructing, evaluating, and
                   using these models, along with many remaining challenges in
                   computation. Using Bayesian inference to solve real-world
                   problems requires not only statistical skills, subject
                   matter knowledge, and programming, but also awareness of the
                   decisions made in the process of data analysis. All of these
                   aspects can be understood as part of a tangled workflow of
                   applied Bayesian statistics. Beyond inference, the workflow
                   also includes iterative model building, model checking,
                   validation and troubleshooting of computational problems,
                   model understanding, and model comparison. We review all
                   these aspects of workflow in the context of several
                   examples, keeping in mind that in practice we will be
                   fitting many models for any given problem, even if only a
                   subset of them will ultimately be relevant for our
                   conclusions.",
  month         =  nov,
  year          =  2020,
  url           = "http://arxiv.org/abs/2011.01808",
  archivePrefix = "arXiv",
  eprint        = "2011.01808",
  primaryClass  = "stat.ME",
  arxivid       = "2011.01808"
}

@ARTICLE{Gloor2016-yy,
  title    = "It's all relative: analyzing microbiome data as compositions",
  author   = "Gloor, Gregory B and Wu, Jia Rong and Pawlowsky-Glahn, Vera and
              Egozcue, Juan Jos{\'e}",
  abstract = "PURPOSE: The ability to properly analyze and interpret large
              microbiome data sets has lagged behind our ability to acquire
              such data sets from environmental or clinical samples. Sequencing
              instruments impose a structure on these data: the natural sample
              space of a 16S rRNA gene sequencing data set is a simplex, which
              is a part of real space that is restricted to nonnegative values
              with a constant sum. Such data are compositional and should be
              analyzed using compositionally appropriate tools and approaches.
              However, most of the tools for 16S rRNA gene sequencing analysis
              assume these data are unrestricted. METHODS: We show that
              existing tools for compositional data (CoDa) analysis can be
              readily adapted to analyze high-throughput sequencing data sets.
              RESULTS: The Human Microbiome Project tongue versus buccal mucosa
              data set shows how the CoDa approach can address the major
              elements of microbiome analysis. Reanalysis of a publicly
              available autism microbiome data set shows that the CoDa approach
              in concert with multiple hypothesis test corrections prevent
              false positive identifications. CONCLUSIONS: The CoDa approach is
              readily scalable to microbiome-sized analyses. We provide example
              code and make recommendations to improve the analysis and
              reporting of microbiome data sets.",
  journal  = "Annals of epidemiology",
  volume   =  26,
  number   =  5,
  pages    = "322-329",
  month    =  may,
  year     =  2016,
  url      = "http://dx.doi.org/10.1016/j.annepidem.2016.03.003",
  keywords = "16S rRNA gene sequencing; Compositional data; Microbiome;
              Multivariate analysis",
  language = "en",
  issn     = "1047-2797, 1873-2585",
  pmid     = "27143475",
  doi      = "10.1016/j.annepidem.2016.03.003"
}

@ARTICLE{Gloor2017-kj,
  title    = "Microbiome Datasets Are Compositional: And This Is Not Optional",
  author   = "Gloor, Gregory B and Macklaim, Jean M and Pawlowsky-Glahn, Vera
              and Egozcue, Juan J",
  abstract = "Datasets collected by high-throughput sequencing (HTS) of 16S
              rRNA gene amplimers, metagenomes or metatranscriptomes are
              commonplace and being used to study human disease states,
              ecological differences between sites, and the built environment.
              There is increasing awareness that microbiome datasets generated
              by HTS are compositional because they have an arbitrary total
              imposed by the instrument. However, many investigators are either
              unaware of this or assume specific properties of the
              compositional data. The purpose of this review is to alert
              investigators to the dangers inherent in ignoring the
              compositional nature of the data, and point out that HTS datasets
              derived from microbiome studies can and should be treated as
              compositions at all stages of analysis. We briefly introduce
              compositional data, illustrate the pathologies that occur when
              compositional data are analyzed inappropriately, and finally give
              guidance and point to resources and examples for the analysis of
              microbiome datasets using compositional data analysis.",
  journal  = "Frontiers in microbiology",
  volume   =  8,
  pages    = "2224",
  month    =  nov,
  year     =  2017,
  url      = "http://dx.doi.org/10.3389/fmicb.2017.02224",
  keywords = "Bayesian estimation; compositional data; correlation; count
              normalization; high-throughput sequencing; microbiota; relative
              abundance",
  language = "en",
  issn     = "1664-302X",
  pmid     = "29187837",
  doi      = "10.3389/fmicb.2017.02224",
  pmc      = "PMC5695134"
}

@ARTICLE{Gloor2016-ka,
  title    = "Compositional analysis: a valid approach to analyze microbiome
              high-throughput sequencing data",
  author   = "Gloor, Gregory B and Reid, Gregor",
  abstract = "A workshop held at the 2015 annual meeting of the Canadian
              Society of Microbiologists highlighted compositional data
              analysis methods and the importance of exploratory data analysis
              for the analysis of microbiome data sets generated by
              high-throughput DNA sequencing. A summary of the content of that
              workshop, a review of new methods of analysis, and information on
              the importance of careful analyses are presented herein. The
              workshop focussed on explaining the rationale behind the use of
              compositional data analysis, and a demonstration of these methods
              for the examination of 2 microbiome data sets. A clear
              understanding of bioinformatics methodologies and the type of
              data being analyzed is essential, given the growing number of
              studies uncovering the critical role of the microbiome in health
              and disease and the need to understand alterations to its
              composition and function following intervention with fecal
              transplant, probiotics, diet, and pharmaceutical agents.",
  journal  = "Canadian journal of microbiology",
  volume   =  62,
  number   =  8,
  pages    = "692-703",
  month    =  aug,
  year     =  2016,
  url      = "http://dx.doi.org/10.1139/cjm-2015-0821",
  keywords = "compositional data; correction en raison de tests multiples;
              correlation; corr{\'e}lation; donn{\'e}es sur la composition;
              microbiome; multiple test correction",
  language = "en",
  issn     = "0008-4166, 1480-3275",
  pmid     = "27314511",
  doi      = "10.1139/cjm-2015-0821"
}

@ARTICLE{Gloor2016-jk,
  title     = "Compositional uncertainty should not be ignored in
               high-throughput sequencing data analysis",
  author    = "Gloor, Gregory Brian and Macklaim, Jean M and Vu, Michael and
               Fernandes, Andrew D",
  abstract  = "High throughput sequencing generates sparse compositional data,
               yet these datasets are rarely analyzed using a compositional
               approach. In addition, the variation inherent in these datasets
               is rarely acknowledged, but ignoring it can result in many false
               positive inferences. We demonstrate that examination of point
               estimates of the data can result in false positive results, even
               with appropriate zero replacement approaches, using an in vitro
               selection dataset with an outside standard of truth. The
               variation inherent in real high-throughput sequencing datasets
               is demonstrated, and we show that this varia- tion can be
               approximated, and hence accounted for, by Monte-Carlo sampling
               from the Dirichlet distribution. This approximation when used by
               itself is itself problematic, but becomes useful when coupled
               with a log-ratio approach commonly used in compositional data
               analysis. Thus, the approach illustrated here that merges
               Bayesian estimation with principles of compositional data
               analysis should be generally useful for high-dimensional count
               compositional data of the type generated by high throughput
               sequencing.",
  journal   = "Austrian Journal of Statistics",
  publisher = "Austrian Statistical Society",
  volume    =  45,
  number    =  4,
  pages     = "73-87",
  month     =  jul,
  year      =  2016,
  url       = "https://ajs.or.at/index.php/ajs/article/view/vol45-4-5",
  issn      = "1026-597X",
  doi       = "10.17713/ajs.v45i4.122"
}

@ARTICLE{Halpin2016-zs,
  title    = "Editorial Commentary: The Dawning of Microbiome Remediation for
              Addressing Antibiotic Resistance",
  author   = "Halpin, Alison Laufer and McDonald, L Clifford",
  journal  = "Clinical infectious diseases: an official publication of the
              Infectious Diseases Society of America",
  volume   =  62,
  number   =  12,
  pages    = "1487-1488",
  month    =  jun,
  year     =  2016,
  url      = "http://dx.doi.org/10.1093/cid/ciw187",
  keywords = "fecal microbiota transplantation; microbiome; resistome",
  language = "en",
  issn     = "1058-4838, 1537-6591",
  pmid     = "27025827",
  doi      = "10.1093/cid/ciw187",
  pmc      = "PMC5728354"
}

@ARTICLE{Huslage2010-rm,
  title    = "A quantitative approach to defining ``high-touch'' surfaces in
              hospitals",
  author   = "Huslage, Kirk and Rutala, William A and Sickbert-Bennett, Emily
              and Weber, David J",
  abstract = "Fifty interactions between healthcare workers and patients were
              observed to obtain a quantifiable definition of ``high-touch''
              (ie, frequently touched) surfaces based on frequency of contact.
              Five surfaces were defined as high-touch surfaces: the bed rails,
              the bed surface, the supply cart, the over-bed table, and the
              intravenous pump.",
  journal  = "Infection control and hospital epidemiology: the official journal
              of the Society of Hospital Epidemiologists of America",
  volume   =  31,
  number   =  8,
  pages    = "850-853",
  month    =  aug,
  year     =  2010,
  url      = "http://dx.doi.org/10.1086/655016",
  language = "en",
  issn     = "0899-823X, 1559-6834",
  pmid     = "20569115",
  doi      = "10.1086/655016"
}

@ARTICLE{Johnson2018-ve,
  title    = "Investigation of a Cluster of Sphingomonas koreensis Infections",
  author   = "Johnson, Ryan C and Deming, Clay and Conlan, Sean and Zellmer,
              Caroline J and Michelin, Angela V and Lee-Lin, Shihqueen and
              Thomas, Pamela J and Park, Morgan and Weingarten, Rebecca A and
              Less, John and Dekker, John P and Frank, Karen M and Musser,
              Kimberlee A and McQuiston, John R and Henderson, David K and Lau,
              Anna F and Palmore, Tara N and Segre, Julia A",
  abstract = "BACKGROUND: Plumbing systems are an infrequent but known
              reservoir for opportunistic microbial pathogens that can infect
              hospitalized patients. In 2016, a cluster of clinical
              sphingomonas infections prompted an investigation. METHODS: We
              performed whole-genome DNA sequencing on clinical isolates of
              multidrug-resistant Sphingomonas koreensis identified from 2006
              through 2016 at the National Institutes of Health (NIH) Clinical
              Center. We cultured S. koreensis from the sinks in patient rooms
              and performed both whole-genome and shotgun metagenomic
              sequencing to identify a reservoir within the infrastructure of
              the hospital. These isolates were compared with clinical and
              environmental S. koreensis isolates obtained from other
              institutions. RESULTS: The investigation showed that two isolates
              of S. koreensis obtained from the six patients identified in the
              2016 cluster were unrelated, but four isolates shared more than
              99.92\% genetic similarity and were resistant to multiple
              antibiotic agents. Retrospective analysis of banked clinical
              isolates of sphingomonas from the NIH Clinical Center revealed
              the intermittent recovery of a clonal strain over the past
              decade. Unique single-nucleotide variants identified in strains
              of S. koreensis elucidated the existence of a reservoir in the
              hospital plumbing. Clinical S. koreensis isolates from other
              facilities were genetically distinct from the NIH isolates.
              Hospital remediation strategies were guided by results of
              microbiologic culturing and fine-scale genomic analyses.
              CONCLUSIONS: This genomic and epidemiologic investigation
              suggests that S. koreensis is an opportunistic human pathogen
              that both persisted in the NIH Clinical Center infrastructure
              across time and space and caused health care-associated
              infections. (Funded by the NIH Intramural Research Programs.).",
  journal  = "The New England journal of medicine",
  volume   =  379,
  number   =  26,
  pages    = "2529-2539",
  month    =  dec,
  year     =  2018,
  url      = "http://dx.doi.org/10.1056/NEJMoa1803238",
  language = "en",
  issn     = "0028-4793, 1533-4406",
  pmid     = "30586509",
  doi      = "10.1056/NEJMoa1803238",
  pmc      = "PMC6322212"
}

@ARTICLE{Kizny_Gordon2017-rw,
  title    = "The Hospital Water Environment as a Reservoir for
              {Carbapenem-Resistant} Organisms Causing {Hospital-Acquired}
              {Infections-A} Systematic Review of the Literature",
  author   = "Kizny Gordon, Alice E and Mathers, Amy J and Cheong, Elaine Y L
              and Gottlieb, Thomas and Kotay, Shireen and Walker, A Sarah and
              Peto, Timothy E A and Crook, Derrick W and Stoesser, Nicole",
  abstract = "Over the last 20 years there have been 32 reports of
              carbapenem-resistant organisms in the hospital water environment,
              with half of these occurring since 2010. The majority of these
              reports have described associated clinical outbreaks in the
              intensive care setting, affecting the critically ill and the
              immunocompromised. Drains, sinks, and faucets were most
              frequently colonized, and Pseudomonas aeruginosa the predominant
              organism. Imipenemase (IMP), Klebsiella pneumoniae carbapenemase
              (KPC), and Verona integron-encoded metallo-$\beta$-lactamase
              (VIM) were the most common carbapenemases found. Molecular typing
              was performed in almost all studies, with pulse field gel
              electrophoresis being most commonly used. Seventy-two percent of
              studies reported controlling outbreaks, of which just more than
              one-third eliminated the organism from the water environment. A
              combination of interventions seems to be most successful,
              including reinforcement of general infection control measures,
              alongside chemical disinfection. The most appropriate
              disinfection method remains unclear, however, and it is likely
              that replacement of colonized water reservoirs may be required
              for long-term clearance.",
  journal  = "Clinical infectious diseases: an official publication of the
              Infectious Diseases Society of America",
  volume   =  64,
  number   =  10,
  pages    = "1435-1444",
  month    =  may,
  year     =  2017,
  url      = "http://dx.doi.org/10.1093/cid/cix132",
  keywords = "carbapenem-resistant; carbapenemase; healthcare-associated
              infections; outbreak; water.",
  language = "en",
  issn     = "1058-4838, 1537-6591",
  pmid     = "28200000",
  doi      = "10.1093/cid/cix132"
}

@ARTICLE{Lax2017-rw,
  title     = "Bacterial colonization and succession in a newly opened hospital",
  author    = "Lax, Simon and Sangwan, Naseer and Smith, Daniel and Larsen,
               Peter and Handley, Kim M and Richardson, Miles and Guyton,
               Kristina and Krezalek, Monika and Shogan, Benjamin D and
               Defazio, Jennifer and Flemming, Irma and Shakhsheer, Baddr and
               Weber, Stephen and Landon, Emily and Garcia-Houchins, Sylvia and
               Siegel, Jeffrey and Alverdy, John and Knight, Rob and Stephens,
               Brent and Gilbert, Jack A",
  abstract  = "The microorganisms that inhabit hospitals may influence patient
               recovery and outcome, although the complexity and diversity of
               these bacterial communities can confound our ability to focus on
               potential pathogens in isolation. To develop a community-level
               understanding of how microorganisms colonize and move through
               the hospital environment, we characterized the bacterial
               dynamics among hospital surfaces, patients, and staff over the
               course of 1 year as a new hospital became operational. The
               bacteria in patient rooms, particularly on bedrails,
               consistently resembled the skin microbiota of the patient
               occupying the room. Bacterial communities on patients and room
               surfaces became increasingly similar over the course of a
               patient's stay. Temporal correlations in community structure
               demonstrated that patients initially acquired room-associated
               taxa that predated their stay but that their own microbial
               signatures began to influence the room community structure over
               time. The $\alpha$- and $\beta$-diversity of patient skin
               samples were only weakly or nonsignificantly associated with
               clinical factors such as chemotherapy, antibiotic usage, and
               surgical recovery, and no factor except for ambulatory status
               affected microbial similarity between the microbiotas of a
               patient and their room. Metagenomic analyses revealed that genes
               conferring antimicrobial resistance were consistently more
               abundant on room surfaces than on the skin of the patients
               inhabiting those rooms. In addition, persistent unique genotypes
               of Staphylococcus and Propionibacterium were identified. Dynamic
               Bayesian network analysis suggested that hospital staff were
               more likely to be a source of bacteria on the skin of patients
               than the reverse but that there were no universal patterns of
               transmission across patient rooms.",
  journal   = "Science translational medicine",
  publisher = "American Association for the Advancement of Science",
  volume    =  9,
  number    =  391,
  pages     = "eaah6500",
  month     =  may,
  year      =  2017,
  url       = "http://dx.doi.org/10.1126/scitranslmed.aah6500",
  language  = "en",
  issn      = "1946-6234, 1946-6242",
  pmid      = "28539477",
  doi       = "10.1126/scitranslmed.aah6500",
  pmc       = "PMC5706123"
}

@ARTICLE{Lee2018-de,
  title    = "A Bayesian Semiparametric Regression Model for Joint Analysis of
              Microbiome Data",
  author   = "Lee, Juhee and Sison-Mangus, Marilou",
  abstract = "The successional dynamics of microbial communities are influenced
              by the synergistic interactions of physical and biological
              factors. In our motivating data, ocean microbiome samples were
              collected from the Santa Cruz Municipal Wharf, Monterey Bay at
              multiple time points and then 16S ribosomal RNA (rRNA) sequenced.
              We develop a Bayesian semiparametric regression model to
              investigate how microbial abundance and succession change with
              covarying physical and biological factors including algal bloom
              and domoic acid concentration level using 16S rRNA sequencing
              data. A generalized linear regression model is built using the
              Laplace prior, a sparse inducing prior, to improve estimation of
              covariate effects on mean abundances of microbial species
              represented by operational taxonomic units (OTUs). A
              nonparametric prior model is used to facilitate borrowing
              strength across OTUs, across samples and across time points. It
              flexibly estimates baseline mean abundances of OTUs and provides
              the basis for improved quantification of covariate effects. The
              proposed method does not require prior normalization of OTU
              counts to adjust differences in sample total counts. Instead, the
              normalization and estimation of covariate effects on OTU
              abundance are simultaneously carried out for joint analysis of
              all OTUs. Using simulation studies and a real data analysis, we
              demonstrate improved inference compared to an existing method.",
  journal  = "Frontiers in microbiology",
  volume   =  9,
  pages    = "522",
  month    =  mar,
  year     =  2018,
  url      = "http://dx.doi.org/10.3389/fmicb.2018.00522",
  keywords = "16S ribosomal RNA sequencing; Laplace prior; count data;
              metagenomics; microbiome; negative binomial model; process
              convolution; regularizing prior",
  language = "en",
  issn     = "1664-302X",
  pmid     = "29632519",
  doi      = "10.3389/fmicb.2018.00522",
  pmc      = "PMC5879107"
}

@ARTICLE{Magnusson2019-eg,
  title         = "Bayesian leave-one-out cross-validation for large data",
  author        = "Magnusson, M{\aa}ns and Andersen, Michael Riis and Jonasson,
                   Johan and Vehtari, Aki",
  abstract      = "Model inference, such as model comparison, model checking,
                   and model selection, is an important part of model
                   development. Leave-one-out cross-validation (LOO) is a
                   general approach for assessing the generalizability of a
                   model, but unfortunately, LOO does not scale well to large
                   datasets. We propose a combination of using approximate
                   inference techniques and
                   probability-proportional-to-size-sampling (PPS) for fast LOO
                   model evaluation for large datasets. We provide both
                   theoretical and empirical results showing good properties
                   for large data.",
  month         =  apr,
  year          =  2019,
  url           = "http://arxiv.org/abs/1904.10679",
  archivePrefix = "arXiv",
  eprint        = "1904.10679",
  primaryClass  = "stat.ML",
  arxivid       = "1904.10679"
}

@ARTICLE{Margossian2020-iu,
  title         = "Hamiltonian Monte Carlo using an adjoint-differentiated
                   Laplace approximation: Bayesian inference for latent
                   Gaussian models and beyond",
  author        = "Margossian, Charles C and Vehtari, Aki and Simpson, Daniel
                   and Agrawal, Raj",
  abstract      = "Gaussian latent variable models are a key class of Bayesian
                   hierarchical models with applications in many fields.
                   Performing Bayesian inference on such models can be
                   challenging as Markov chain Monte Carlo algorithms struggle
                   with the geometry of the resulting posterior distribution
                   and can be prohibitively slow. An alternative is to use a
                   Laplace approximation to marginalize out the latent Gaussian
                   variables and then integrate out the remaining
                   hyperparameters using dynamic Hamiltonian Monte Carlo, a
                   gradient-based Markov chain Monte Carlo sampler. To
                   implement this scheme efficiently, we derive a novel adjoint
                   method that propagates the minimal information needed to
                   construct the gradient of the approximate marginal
                   likelihood. This strategy yields a scalable differentiation
                   method that is orders of magnitude faster than state of the
                   art differentiation techniques when the hyperparameters are
                   high dimensional. We prototype the method in the
                   probabilistic programming framework Stan and test the
                   utility of the embedded Laplace approximation on several
                   models, including one where the dimension of the
                   hyperparameter is $\sim$6,000. Depending on the cases, the
                   benefits can include an alleviation of the geometric
                   pathologies that frustrate Hamiltonian Monte Carlo and a
                   dramatic speed-up.",
  month         =  apr,
  year          =  2020,
  url           = "http://arxiv.org/abs/2004.12550",
  archivePrefix = "arXiv",
  eprint        = "2004.12550",
  primaryClass  = "stat.CO",
  arxivid       = "2004.12550"
}

@ARTICLE{McDonald2012-tf,
  title    = "An improved {Greengenes} taxonomy with explicit ranks for
              ecological and evolutionary analyses of bacteria and archaea",
  author   = "McDonald, Daniel and Price, Morgan N and Goodrich, Julia and
              Nawrocki, Eric P and DeSantis, Todd Z and Probst, Alexander and
              Andersen, Gary L and Knight, Rob and Hugenholtz, Philip",
  abstract = "Reference phylogenies are crucial for providing a taxonomic
              framework for interpretation of marker gene and metagenomic
              surveys, which continue to reveal novel species at a remarkable
              rate. Greengenes is a dedicated full-length 16S rRNA gene
              database that provides users with a curated taxonomy based on de
              novo tree inference. We developed a 'taxonomy to tree' approach
              for transferring group names from an existing taxonomy to a tree
              topology, and used it to apply the Greengenes, National Center
              for Biotechnology Information (NCBI) and cyanoDB (Cyanobacteria
              only) taxonomies to a de novo tree comprising 408,315 sequences.
              We also incorporated explicit rank information provided by the
              NCBI taxonomy to group names (by prefixing rank designations) for
              better user orientation and classification consistency. The
              resulting merged taxonomy improved the classification of 75\% of
              the sequences by one or more ranks relative to the original NCBI
              taxonomy with the most pronounced improvements occurring in
              under-classified environmental sequences. We also assessed
              candidate phyla (divisions) currently defined by NCBI and present
              recommendations for consolidation of 34 redundantly named groups.
              All intermediate results from the pipeline, which includes tree
              inference, jackknifing and transfer of a donor taxonomy to a
              recipient tree (tax2tree) are available for download. The
              improved Greengenes taxonomy should provide important
              infrastructure for a wide range of megasequencing projects
              studying ecosystems on scales ranging from our own bodies (the
              Human Microbiome Project) to the entire planet (the Earth
              Microbiome Project). The implementation of the software can be
              obtained from http://sourceforge.net/projects/tax2tree/.",
  journal  = "The ISME journal",
  volume   =  6,
  number   =  3,
  pages    = "610-618",
  month    =  mar,
  year     =  2012,
  url      = "http://dx.doi.org/10.1038/ismej.2011.139",
  keywords = "Archaea, Bacteria, classification, Databases, Genetic,
              Metagenomics, Phylogeny, RNA, Ribosomal, 16S, Software",
  language = "en",
  issn     = "1751-7362, 1751-7370",
  pmid     = "22134646",
  doi      = "10.1038/ismej.2011.139",
  pmc      = "PMC3280142"
}

@BOOK{McElreath2016-sf,
  title     = "Statistical Rethinking: A Bayesian Course with Examples in {R}
               and Stan",
  author    = "McElreath, Richard",
  abstract  = "Statistical Rethinking: A Bayesian Course with Examples in R and
               Stan builds readers' knowledge of and confidence in statistical
               modeling. Reflecting the need for even minor programming in
               today's model-based statistics, the book pushes readers to
               perform step-by-step calculations that are usually automated.
               This unique computational approach ensures that readers
               understand enough of the details to make reasonable choices and
               interpretations in their own modeling work. The text presents
               generalized linear multilevel models from a Bayesian
               perspective, relying on a simple logical interpretation of
               Bayesian probability and maximum entropy. It covers from the
               basics of regression to multilevel models. The author also
               discusses measurement error, missing data, and Gaussian process
               models for spatial and network autocorrelation. By using
               complete R code examples throughout, this book provides a
               practical foundation for performing statistical inference.
               Designed for both PhD students and seasoned professionals in the
               natural and social sciences, it prepares them for more advanced
               or specialized statistical modeling. Web ResourceThe book is
               accompanied by an R package (rethinking) that is available on
               the author's website and GitHub. The two core functions (map and
               map2stan) of this package allow a variety of statistical models
               to be constructed from standard model formulas.",
  publisher = "CRC Press/Taylor \& Francis Group",
  edition   = "1 edition",
  year      =  2016,
  url       = "https://play.google.com/store/books/details?id=d7fCsgEACAAJ",
  language  = "en",
  isbn      = "9781482253443"
}

@ARTICLE{Mody2019-nr,
  title    = "Multidrug-resistant Organisms in Hospitals: What Is on Patient
              Hands and in Their Rooms?",
  author   = "Mody, Lona and Washer, Laraine L and Kaye, Keith S and Gibson,
              Kristen and Saint, Sanjay and Reyes, Katherine and Cassone, Marco
              and Mantey, Julia and Cao, Jie and Altamimi, Sarah and Perri,
              Mary and Sax, Hugo and Chopra, Vineet and Zervos, Marcus",
  abstract = "BACKGROUND: The impact of healthcare personnel hand contamination
              in multidrug-resistant organism (MDRO) transmission is important
              and well studied; however, the role of patient hand contamination
              needs to be characterized further. METHODS: Patients from 2
              hospitals in southeast Michigan were recruited within 24 hours of
              arrival to their room and followed prospectively using microbial
              surveillance of nares, dominant hand, and 6 high-touch
              environmental surfaces. Sampling was performed on admission, days
              3 and 7, and weekly until discharge. Paired samples of
              methicillin-resistant Staphylococcus aureus (MRSA) isolated from
              the patients' hand and room surfaces were evaluated for
              relatedness using pulsed-field gel electrophoresis and
              staphylococcal cassette chromosome mec, and Panton-Valentine
              leukocidin typing. RESULTS: A total of 399 patients (mean age,
              60.8 years; 49\% male) were enrolled and followed for 710 visits.
              Fourteen percent (n = 56/399) of patients were colonized with an
              MDRO at baseline; 10\% (40/399) had an MDRO on their hands.
              Twenty-nine percent of rooms harbored an MDRO. Six percent
              (14/225 patients with at least 2 visits) newly acquired an MDRO
              on their hands during their stay. New MDRO acquisition in
              patients occurred at a rate of 24.6/1000 patient-days, and in
              rooms at a rate of 58.6/1000 patient-days. Typing demonstrated a
              high correlation between MRSA on patient hands and room surfaces.
              CONCLUSIONS: Our data suggest that patient hand contamination
              with MDROs is common and correlates with contamination on
              high-touch room surfaces. Patient hand hygiene protocols should
              be considered to reduce transmission of pathogens and
              healthcare-associated infections.",
  journal  = "Clinical infectious diseases: an official publication of the
              Infectious Diseases Society of America",
  volume   =  69,
  number   =  11,
  pages    = "1837-1844",
  month    =  nov,
  year     =  2019,
  url      = "http://dx.doi.org/10.1093/cid/ciz092",
  keywords = "acute care hospitals; colonization; contamination;
              multidrug-resistant organisms; new acquisition",
  language = "en",
  issn     = "1058-4838, 1537-6591",
  pmid     = "30980082",
  doi      = "10.1093/cid/ciz092",
  pmc      = "PMC6853699"
}

@ARTICLE{Piironen2018-qe,
  title         = "Projective Inference in High-dimensional Problems:
                   Prediction and Feature Selection",
  author        = "Piironen, Juho and Paasiniemi, Markus and Vehtari, Aki",
  abstract      = "This paper discusses predictive inference and feature
                   selection for generalized linear models with scarce but
                   high-dimensional data. We argue that in many cases one can
                   benefit from a decision theoretically justified two-stage
                   approach: first, construct a possibly non-sparse model that
                   predicts well, and then find a minimal subset of features
                   that characterize the predictions. The model built in the
                   first step is referred to as the
                   \textbackslashemph\{reference model\} and the operation
                   during the latter step as predictive
                   \textbackslashemph\{projection\}. The key characteristic of
                   this approach is that it finds an excellent tradeoff between
                   sparsity and predictive accuracy, and the gain comes from
                   utilizing all available information including prior and that
                   coming from the left out features. We review several methods
                   that follow this principle and provide novel methodological
                   contributions. We present a new projection technique that
                   unifies two existing techniques and is both accurate and
                   fast to compute. We also propose a way of evaluating the
                   feature selection process using fast leave-one-out
                   cross-validation that allows for easy and intuitive model
                   size selection. Furthermore, we prove a theorem that helps
                   to understand the conditions under which the projective
                   approach could be beneficial. The benefits are illustrated
                   via several simulated and real world examples.",
  month         =  oct,
  year          =  2018,
  url           = "http://arxiv.org/abs/1810.02406",
  archivePrefix = "arXiv",
  eprint        = "1810.02406",
  primaryClass  = "stat.ML",
  arxivid       = "1810.02406"
}

@ARTICLE{Piironen2017-wh,
  title         = "Sparsity information and regularization in the horseshoe and
                   other shrinkage priors",
  author        = "Piironen, Juho and Vehtari, Aki",
  abstract      = "Project Euclid - mathematics and statistics online",
  journal       = "Electronic journal of statistics",
  publisher     = "The Institute of Mathematical Statistics and the Bernoulli
                   Society",
  volume        =  11,
  number        =  2,
  pages         = "5018-5051",
  year          =  2017,
  url           = "https://projecteuclid.org/euclid.ejs/1513306866",
  keywords      = "Bayesian inference; sparse estimation; shrinkage priors;
                   horseshoe prior",
  language      = "en",
  archivePrefix = "arXiv",
  eprint        = "1707.01694",
  primaryClass  = "stat.ME",
  issn          = "1935-7524",
  arxivid       = "1707.01694",
  doi           = "10.1214/17-EJS1337SI"
}

@MISC{R_Core_Team2018-xo,
  title       = "R: A Language and Environment for Statistical Computing",
  author      = "{R Core Team}",
  institution = "R Foundation for Statistical Computing",
  year        =  2018,
  url         = "https://www.R-project.org/",
  address     = "Vienna, Austria"
}

@ARTICLE{Regev-Yochay2018-yd,
  title    = "Sink traps as the source of transmission of {OXA-48-producing}
              Serratia marcescens in an intensive care unit",
  author   = "Regev-Yochay, Gili and Smollan, Gill and Tal, Ilana and Pinas
              Zade, Nani and Haviv, Yael and Nudelman, Valery and Gal-Mor, Ohad
              and Jaber, Hanaa and Zimlichman, Eyal and Keller, Nati and Rahav,
              Galia",
  abstract = "BACKGROUND: Carbapenemase-producing Enterobacteriaceae (CPE)
              outbreaks are mostly attributed to patient-to-patient
              transmission via healthcare workers. OBJECTIVE: We describe
              successful containment of a prolonged OXA-48-producing S.
              marcescens outbreak after recognizing the sink traps as the
              source of transmission. METHODS: The Sheba Medical Center
              intensive care unit (ICU), contains 16 single-bed, semi-closed
              rooms. Active CPE surveillance includes twice-weekly rectal
              screening of all patients. A case was defined as a patient
              detected with OXA-48 CPE >72 hours after admission. A root-cause
              analysis was used to investigate the outbreak. All samples were
              inoculated on chrom-agar CRE, and carbapenemase genes were
              detected using commercial molecular Xpert-Carba-R. Environmental
              and patient S. marcescens isolates were characterized using PFGE.
              RESULTS: From January 2016 to May 2017, 32 OXA-48 CPE cases were
              detected, and 81\% of these were S. marcescens. A single clone
              was the cause of all but the first 2 cases. The common factor in
              all cases was the use of relatively large amounts of tap water.
              The outbreak clone was detected in 2 sink outlets and 16 sink
              traps. In addition to routine strict infection control measures,
              measures taken to contain the outbreak included (1) various sink
              decontamination efforts, which eliminated the bacteria from the
              sink drains only temporarily and (2) educational intervention
              that engaged the ICU team and lead to high adherence to
              'sink-contamination prevention guidelines.' No additional cases
              were detected for 12 months. CONCLUSIONS: Despite persistence of
              the outbreak clones in the environmental reservoir for 1 year,
              the outbreak was rapidly and successfully contained. Addressing
              sink traps as hidden reservoirs played a major role in the
              intervention.",
  journal  = "Infection control and hospital epidemiology: the official journal
              of the Society of Hospital Epidemiologists of America",
  volume   =  39,
  number   =  11,
  pages    = "1307-1315",
  month    =  nov,
  year     =  2018,
  url      = "http://dx.doi.org/10.1017/ice.2018.235",
  language = "en",
  issn     = "0899-823X, 1559-6834",
  pmid     = "30284524",
  doi      = "10.1017/ice.2018.235"
}

@ARTICLE{Rothman2018-jg,
  title    = "Planning Study Size Based on Precision Rather Than Power",
  author   = "Rothman, Kenneth J and Greenland, Sander",
  abstract = "Study size has typically been planned based on statistical power
              and therefore has been heavily influenced by the philosophy of
              statistical hypothesis testing. A worthwhile alternative is to
              plan study size based on precision, for example by aiming to
              obtain a desired width of a confidence interval for the targeted
              effect. This article presents formulas for planning the size of
              an epidemiologic study based on the desired precision of the
              basic epidemiologic effect measures.",
  journal  = "Epidemiology",
  volume   =  29,
  number   =  5,
  pages    = "599-603",
  month    =  sep,
  year     =  2018,
  url      = "http://dx.doi.org/10.1097/EDE.0000000000000876",
  language = "en",
  issn     = "1044-3983, 1531-5487",
  pmid     = "29912015",
  doi      = "10.1097/EDE.0000000000000876"
}

@ARTICLE{Shams2016-oq,
  title    = "Assessment of the Overall and {Multidrug-Resistant} Organism
              Bioburden on Environmental Surfaces in Healthcare Facilities",
  author   = "Shams, Alicia M and Rose, Laura J and Edwards, Jonathan R and
              Cali, Salvatore and Harris, Anthony D and Jacob, Jesse T and
              LaFae, Anna and Pineles, Lisa L and Thom, Kerri A and McDonald, L
              Clifford and Arduino, Matthew J and Noble-Wang, Judith A",
  abstract = "OBJECTIVE To determine the typical microbial bioburden (overall
              bacterial and multidrug-resistant organisms [MDROs]) on
              high-touch healthcare environmental surfaces after routine or
              terminal cleaning. DESIGN Prospective 2.5-year microbiological
              survey of large surface areas (>1,000 cm2). SETTING MDRO
              contact-precaution rooms from 9 acute-care hospitals and 2
              long-term care facilities in 4 states. PARTICIPANTS Samples from
              166 rooms (113 routine cleaned and 53 terminal cleaned rooms).
              METHODS Using a standard sponge-wipe sampling protocol, 2
              composite samples were collected from each room; a third sample
              was collected from each Clostridium difficile room. Composite 1
              included the TV remote, telephone, call button, and bed rails.
              Composite 2 included the room door handle, IV pole, and overbed
              table. Composite 3 included toileting surfaces. Total bacteria
              and MDROs (ie, methicillin-resistant Staphylococcus aureus,
              vancomycin-resistant enterococci [VRE], Acinetobacter baumannii,
              Klebsiella pneumoniae, and C. difficile) were quantified,
              confirmed, and tested for drug resistance. RESULTS The mean
              microbial bioburden and range from routine cleaned room
              composites were higher (2,700 colony-forming units [CFU]/100 cm2;
              $\leq$1-130,000 CFU/100 cm2) than from terminal cleaned room
              composites (353 CFU/100 cm2; $\leq$1-4,300 CFU/100 cm2). MDROs
              were recovered from 34\% of routine cleaned room composites
              (range $\leq$1-13,000 CFU/100 cm2) and 17\% of terminal cleaned
              room composites ($\leq$1-524 CFU/100 cm2). MDROs were recovered
              from 40\% of rooms; VRE was the most common (19\%). CONCLUSIONS
              This multicenter bioburden summary provides a first step to
              determining microbial bioburden on healthcare surfaces, which may
              help provide a basis for developing standards to evaluate
              cleaning and disinfection as well as a framework for studies
              using an evidentiary hierarchy for environmental infection
              control. Infect Control Hosp Epidemiol 2016;1426-1432.",
  journal  = "Infection control and hospital epidemiology: the official journal
              of the Society of Hospital Epidemiologists of America",
  volume   =  37,
  number   =  12,
  pages    = "1426-1432",
  month    =  dec,
  year     =  2016,
  url      = "http://dx.doi.org/10.1017/ice.2016.198",
  language = "en",
  issn     = "0899-823X, 1559-6834",
  pmid     = "27619507",
  doi      = "10.1017/ice.2016.198",
  pmc      = "PMC6399740"
}

@ARTICLE{Shaughnessy2011-uf,
  title    = "Evaluation of hospital room assignment and acquisition of
              Clostridium difficile infection",
  author   = "Shaughnessy, Megan K and Micielli, Renee L and DePestel, Daryl D
              and Arndt, Jennifer and Strachan, Cathy L and Welch, Kathy B and
              Chenoweth, Carol E",
  abstract = "BACKGROUND AND OBJECTIVE: Clostridium difficile spores persist in
              hospital environments for an extended period. We evaluated
              whether admission to a room previously occupied by a patient with
              C. difficile infection (CDI) increased the risk of acquiring CDI.
              DESIGN: Retrospective cohort study. SETTING: Medical intensive
              care unit (ICU) at a tertiary care hospital. METHODS: Patients
              admitted from January 1, 2005, through June 30, 2006, were
              evaluated for a diagnosis of CDI 48 hours after ICU admission and
              within 30 days after ICU discharge. Medical, ICU, and pharmacy
              records were reviewed for other CDI risk factors. Admitted
              patients who did develop CDI were compared with admitted patients
              who did not. RESULTS: Among 1,844 patients admitted to the ICU,
              134 CDI cases were identified. After exclusions, 1,770 admitted
              patients remained for analysis. Of the patients who acquired CDI
              after admission to the ICU, 4.6\% had a prior occupant without
              CDI, whereas 11.0\% had a prior occupant with CDI (P = .002). The
              effect of room on CDI acquisition remained a significant risk
              factor (P = .008) when Kaplan-Meier curves were used. The prior
              occupant's CDI status remained significant (p = .01; hazard
              ratio, 2.35) when controlling for the current patient's age,
              Acute Physiology and Chronic Health Evaluation III score,
              exposure to proton pump inhibitors, and antibiotic use.
              CONCLUSIONS: A prior room occupant with CDI is a significant risk
              factor for CDI acquisition, independent of established CDI risk
              factors. These findings have implications for room placement and
              hospital design.",
  journal  = "Infection control and hospital epidemiology: the official journal
              of the Society of Hospital Epidemiologists of America",
  volume   =  32,
  number   =  3,
  pages    = "201-206",
  month    =  mar,
  year     =  2011,
  url      = "http://dx.doi.org/10.1086/658669",
  language = "en",
  issn     = "0899-823X, 1559-6834",
  pmid     = "21460503",
  doi      = "10.1086/658669"
}

@ARTICLE{Vehtari2017-zz,
  title         = "Practical Bayesian model evaluation using leave-one-out
                   cross-validation and {WAIC}",
  author        = "Vehtari, Aki and Gelman, Andrew and Gabry, Jonah",
  abstract      = "Leave-one-out cross-validation (LOO) and the widely
                   applicable information criterion (WAIC) are methods for
                   estimating pointwise out-of-sample prediction accuracy from
                   a fitted Bayesian model using the log-likelihood evaluated
                   at the posterior simulations of the parameter values. LOO
                   and WAIC have various advantages over simpler estimates of
                   predictive error such as AIC and DIC but are less used in
                   practice because they involve additional computational
                   steps. Here we lay out fast and stable computations for LOO
                   and WAIC that can be performed using existing simulation
                   draws. We introduce an efficient computation of LOO using
                   Pareto-smoothed importance sampling (PSIS), a new procedure
                   for regularizing importance weights. Although WAIC is
                   asymptotically equal to LOO, we demonstrate that PSIS-LOO is
                   more robust in the finite case with weak priors or
                   influential observations. As a byproduct of our
                   calculations, we also obtain approximate standard errors for
                   estimated predictive errors and for comparison of predictive
                   errors between two models. We implement the computations in
                   an R package called loo and demonstrate using models fit
                   with the Bayesian inference package Stan.",
  journal       = "Statistics and computing",
  volume        =  27,
  number        =  5,
  pages         = "1413-1432",
  month         =  sep,
  year          =  2017,
  url           = "https://doi.org/10.1007/s11222-016-9696-4",
  archivePrefix = "arXiv",
  eprint        = "1507.04544",
  primaryClass  = "stat.CO",
  issn          = "0960-3174, 1573-1375",
  arxivid       = "1507.04544",
  doi           = "10.1007/s11222-016-9696-4"
}

@ARTICLE{Weber2013-bg,
  title    = "Understanding and preventing transmission of
              healthcare-associated pathogens due to the contaminated hospital
              environment",
  author   = "Weber, David J and Rutala, William A",
  journal  = "Infection control and hospital epidemiology: the official journal
              of the Society of Hospital Epidemiologists of America",
  volume   =  34,
  number   =  5,
  pages    = "449-452",
  month    =  may,
  year     =  2013,
  url      = "http://dx.doi.org/10.1086/670223",
  language = "en",
  issn     = "0899-823X, 1559-6834",
  pmid     = "23571359",
  doi      = "10.1086/670223"
}

@BOOK{Wickham2016-eh,
  title     = "ggplot2: Elegant Graphics for Data Analysis",
  author    = "Wickham, Hadley",
  abstract  = "This new edition to the classic book by ggplot2 creator Hadley
               Wickham highlights compatibility with knitr and RStudio. ggplot2
               is a data visualization package for R that helps users create
               data graphics, including those that are multi-layered, with
               ease. With ggplot2, it's easy to: produce handsome,
               publication-quality plots with automatic legends created from
               the plot specificationsuperimpose multiple layers (points,
               lines, maps, tiles, box plots) from different data sources with
               automatically adjusted common scalesadd customizable smoothers
               that use powerful modeling capabilities of R, such as loess,
               linear models, generalized additive models, and robust
               regressionsave any ggplot2 plot (or part thereof) for later
               modification or reusecreate custom themes that capture in-house
               or journal style requirements and that can easily be applied to
               multiple plotsapproach a graph from a visual perspective,
               thinking about how each component of the data is represented on
               the final plot This book will be useful to everyone who has
               struggled with displaying data in an informative and attractive
               way. Some basic knowledge of R is necessary (e.g., importing
               data into R). ggplot2 is a mini-language specifically tailored
               for producing graphics, and you'll learn everything you need in
               the book. After reading this book you'll be able to produce
               graphics customized precisely for your problems, and you'll find
               it easy to get graphics out of your head and on to the screen or
               page.",
  publisher = "Springer International Publishing",
  edition   = "1st ed. 2009. Corr. 3rd printing 2010 edition",
  month     =  jun,
  year      =  2016,
  url       = "https://play.google.com/store/books/details?id=RTMFswEACAAJ",
  language  = "en",
  isbn      = "9783319242750"
}

branch_name: refs/heads/main


text:
@article{fiat,
    author = {Robert C. Kirby},
    title = {Algorithm 839: {FIAT}, a New Paradigm for Computing Finite Element Basis Functions},
    year = {2004},
    volume = {30},
    number = {4},
    doi = {10.1145/1039813.1039820},
    journal = {ACM Transactions on Mathematical Software},
    pages = {502--516},
}

@article{sympy,
    title = {{SymPy}: symbolic computing in {P}ython},
    author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, AMiT and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
    year = 2017,
    volume = 3,
    pages = {e103},
    journal = {PeerJ Computer Science},
    doi = {10.7717/peerj-cs.103}
}

@misc{basix,
    title={Basix: FEniCS runtime basis evaluation library},
    author={Chris Richardson and Matthew Scroggs and Garth Wells},
    url = {https://github.com/FEniCS/basix},
    year = {2021}
}

@article{fenics,
  title = {The FEniCS Project Version 1.5},
  author = {Martin S. Aln{\ae}s and Jan Blechta and Johan Hake and
  August Johansson and Benjamin Kehlet and Anders Logg and Chris
  Richardson and Johannes Ring and Rognes, Marie E.  and Wells, Garth N.},
  year = {2015},
  journal = {Archive of Numerical Software},
  volume = {3},
  number = {100},
  doi = {10.11588/ans.2015.100.20553},
  pages = {9--23},
}

@book{ciarlet,
  title = {The Finite Element Method for Elliptic Problems},
  author = {Philippe G. Ciarlet},
  year = {2002},
  doi = {10.1137/1.9780898719208}
}

license: MIT

repository: mscroggs/symfem

license_type: permissive

repo_name: mscroggs/symfem

gha_license_id: MIT

gha_language: Python

extension: bib


text:
SUMMARY = "OpenDreambox: Modem task for the OpenDreambox distribution"
SECTION = "opendreambox/base"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://${OPENDREAMBOX_BASE}/LICENSE;md5=0a5ec7423edaca24547634f416478834"
PR = "r2"

inherit task

#
# task-opendreambox-modem
#
SUMMARY_${PN} = "OpenDreambox: Modem support"
DEPENDS = "enigma2"
RDEPENDS_${PN} = "\
  dreambox-modem-ppp-scripts \
  enigma2-plugin-extensions-modem \
  kernel-module-ppp-async \
  kernel-module-ppp-deflate \
  kernel-module-ppp-generic \
  ppp \
"

RRECOMMENDS_${PN} = "\
  enigma2-plugin-extensions-trafficinfo \
"

PACKAGE_ARCH = "${MACHINE_ARCH}"

repository: kiyala/op

repo_name: kiyala/op

branch_name: refs/heads/master

extension: bb


text:
@article{biharieth,
    author={Eberhard, David M., Gary F. Simons and Charles D. Fennig (eds.)},
    title={Ethnologue: Languages of the World. Twenty-second edition.},
    journal={World Language Ethnologue},
    publisher={Dallas, Texas: SIL International.},
    year={2019}
}


text:
package main

// This file contains a demo of using the Datafeed service by creating a
// sample datafeed with a random filename, inserting it, listing the
// existing feeds and retrieving it (to show that it was indeed inserted),
// and then deleting the new feed.

import (
	"fmt"
	"math/rand"

	"golang.org/x/net/context"
	"google.golang.org/api/content/v2.1"
)

func datafeedDemo(ctx context.Context, service *content.APIService, config *merchantInfo) {
	if config.IsMCA {
		fmt.Println("This demo cannot be run on a multi-client account.")
		return
	}
	feedName := fmt.Sprintf("feed%d", rand.Int())

	fmt.Printf("Inserting datafeed with filename %s... ", feedName)
	datafeeds := content.NewDatafeedsService(service)
	feed := createSampleDatafeed(feedName)

	insertedFeed, err := datafeeds.Insert(config.MerchantID, feed).Do()
	if err != nil {
		dumpAPIErrorAndStop(err, "Insertion failed")
	}
	fmt.Printf("done.\n")
	feedID := insertedFeed.Id
	fmt.Printf("New feed ID: %d\n\n", feedID)

	fmt.Printf("Listing datafeeds:\n")
	listCall := datafeeds.List(config.MerchantID)
	// Enable this to change the number of results listed by
	// per page:
	if false {
		listCall.MaxResults(100)
	}
	if err := listCall.Pages(ctx, printFeedsPage); err != nil {
		dumpAPIErrorAndStop(err, "Listing feeds failed")
	}
	fmt.Printf("\n")

	fmt.Printf("Retrieving datafeed %d... ", feedID)
	if _, err := datafeeds.Get(config.MerchantID, uint64(feedID)).Do(); err != nil {
		dumpAPIErrorAndStop(err, "Retrieving feed failed")
	}
	fmt.Printf("done.\n")

	fmt.Printf("Removing datafeed %d... ", feedID)
	if err := datafeeds.Delete(config.MerchantID, uint64(feedID)).Do(); err != nil {
		dumpAPIErrorAndStop(err, "Removing feed failed")
	}
	fmt.Printf("done.\n\n")
}

func printFeedsPage(res *content.DatafeedsListResponse) error {
	for _, feed := range res.Resources {
		fmt.Printf(" - Id %d, Name %s\n", feed.Id, feed.Name)
	}
	return nil
}

func createSampleDatafeed(name string) *content.Datafeed {
	fetchSchedule := content.DatafeedFetchSchedule{
		Weekday:  "monday",
		Hour:     6,
		TimeZone: "America/Los_Angeles",
		FetchUrl: fmt.Sprintf("https://feeds.myshop.com/%s", name),
	}
	feedFormat := content.DatafeedFormat{
		FileEncoding:    "utf-8",
		ColumnDelimiter: "tab",
		QuotingMode:     "value quoting",
	}
	feed := content.Datafeed{
		Name:              name,
		ContentType:       "products",
		AttributeLanguage: "en",
		Targets: []*content.DatafeedTarget{&content.DatafeedTarget{
			Language:             "en",
			IncludedDestinations: []string{"Shopping"},
			Country:              "US",
		}},
		FileName:      name,
		FetchSchedule: &fetchSchedule,
		Format:        &feedFormat,
	}
	return &feed
}

repository: googleads/googleads-shopping-samples

repo_name: googleads/googleads-shopping-samples

gha_language: Java

extension: go


text:
SUMMARY = "Initramfs image to boot from internal flash with initramfs"
DESCRIPTION = "Provides a minimal environment to bootstrap and run LuneOS"

require initramfs-simple-image.inc

repository: shr-distribution/meta-smartphone

repo_name: shr-distribution/meta-smartphone

gha_language: Roff


text:
# Additional clean files

file(REMOVE_RECURSE
  "CMakeFiles/intro_2_autogen.dir/AutogenUsed.txt"
  "CMakeFiles/intro_2_autogen.dir/ParseCache.txt"
  "intro_2_autogen"
)

repository: alantess/QtSimple

repo_name: alantess/QtSimple

extension: cmake