Stan specifications

Information


Unique identifier OMICS_19012
Name Stan
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++, MATLAB, Python, R, Shell (Bash), Stata, Julia
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 2.14.0
Stability Stable
Source code URL https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v076i01/cmdstan-2.14.0.tar.gz
Maintained Yes

Versioning


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Additional information


http://www.stat.columbia.edu/~gelman/research/published/stan_jebs_2.pdf - https://arxiv.org/abs/1509.07164

Publications for Stan

Stan in publications

 (10)
PMCID: 5937180
PMID: 29507048
DOI: 10.1534/genetics.117.300673

[…] included. sample relatedness was modeled by a random term that followed a multivariate normal distribution. model parameters can be estimated by either l-bfgs mle or hmc sampling, as implemented in stan., although the mle implementation in bayes-glmm was efficient and reliable in estimating glms, it was unreliable in estimating glmms. we found that the mle of the random term was skewed toward […]

PMCID: 5902836
PMID: 29661246
DOI: 10.1186/s12981-018-0198-7

[…] to refer to any insertion in the β3–β4 loop of reverse transcriptase between codons 66 and 70., a bayesian approach to statistical analysis was used throughout, with models implemented in the stan probabilistic programming language [] using the rstan [] interface for r. this approach was chosen to guard against the erroneous inferences that can arise from model building based on large […]

PMCID: 5730572
PMID: 29242601
DOI: 10.1038/s41598-017-17779-z

[…] distribution over the unit cell cycle. the parameters of the beta distribution, as well as the boundaries of the cell cycle stages, were inferred from the data using hamiltonian monte carlo via the stan software package. further details of the model are given in the supplementary data., all data generated or analysed during this study are included in this published article and its supplementary […]

PMCID: 5733000
PMID: 29311777
DOI: 10.3389/fnins.2017.00696

[…] ess because we can predict future form current time points. a convenient side-produce of subsampling is reduced computational costs., we implement our model in the probabilistic programming language stan (carpenter et al., ) using r. stan uses hamiltonian monte carlo to sample efficiently from posterior distributions using automatic differentiation. it removes the need for manually deriving […]

PMCID: 5656642
PMID: 29070818
DOI: 10.1038/s41598-017-12652-5

[…] for consistency with the other bayesian models discussed here, but the results are straightforward from either a bayesian or frequentist perspective. we implemented by the bayesian approach with stan, using the rstanarm package for r, with weakly informative cauchy prior distributions with location 0 and scaling factor 2.5 (equivalent to a student-t distribution with 1 degree of freedom, […]


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Stan institution(s)
Columbia University, New York, NY, USA; York University, Toronto, ON, Canada; Indiana University, Bloomington, IN, USA
Stan funding source(s)
Supported by the US government and by a grant from the National Science Foundation (CNS-1205516); grants which indirectly supported the initial research and development included grants from the Department of Energy (DE-SC0002099), the National Science Foundation (ATM-0934516), and the Department of Education Institute of Education Sciences (ED-GRANTS-032309-005 and R305D090006-09A); the high-performance computing facility was made possible through a grant from the National Institutes of Health (1G20RR030893-01).

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