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Stan specifications


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
Maintained Yes


No version available

Additional information -

Publications for Stan

Stan citations


A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late Onset Alzheimer’s Disease

PMCID: 5937180
PMID: 29507048
DOI: 10.1534/genetics.117.300673
call_split See protocol

[…] Our models were built under Stan, which provides a flexible and efficient programming environment for statistical modeling. Inherited from Stan, Bayes-GLMM supported two methods for parameter estimation: limited-memory Broyden–F […]


Association with humans and seasonality interact to reverse predictions for animal space use

PMCID: 5924504
PMID: 29736242
DOI: 10.1186/s40462-018-0123-7

[…] ofauna count would be conflated with the home range size or core range size, so we included only building or macrofauna density. For these analyses, we used Bayesian simple linear regression in R and STAN [], using diffuse priors for intercept and slope (Normal [mean = 0, variance =1×106]), and diffuse priors for variance of residual error (Cauchy[ x0=0, γ=5]). We used 3 chains, with 1×103 step bu […]


Dissecting the null model for biological invasions: A meta analysis of the propagule pressure effect

PLoS Biol
PMCID: 5933808
PMID: 29684017
DOI: 10.1371/journal.pbio.2005987

[…] alculated with the other moderators constrained to their reference levels and with measurement error fixed at the median observed value. All Bayesian models were fitted using the open-source software Stan [, ] and the R package brms []. Sampling was conducted for 20,000 iterations of each of 3 chains with a burn-in of 1,000 samples. Moderator effects used an improper flat prior. The random effect […]


Implications of current therapeutic restrictions for primaquine and tafenoquine in the radical cure of vivax malaria

PLoS Negl Trop Dis
PMCID: 5931686
PMID: 29677199
DOI: 10.1371/journal.pntd.0006440

[…] activity. This visually clustered the genotypes into these two categories. Note that these categories do not correspond to the WHO categories [].The Bayesian beta-binomial model was fitted in R using stan []. See supplementary materials for full model specification and code.To estimate the distributions of G6PD activities in hemizygous males and homozygous females (theoretically identical) we pool […]


Risk factors and outcomes for the Q151M and T69 insertion HIV 1 resistance mutations in historic UK data

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

[…] hroughout 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 numb […]


The Origins and Vulnerabilities of Two Transmissible Cancers in Tasmanian Devils

Cancer Cell
PMCID: 5896245
PMID: 29634948
DOI: 10.1016/j.ccell.2018.03.013

[…] to refit known COSMIC mutational signatures to devil DFT1 and DFT2 somatic spectra. The fitting is done using Markov Chain Monte Carlo sampling (MCMC), using the No-U-Turn sampler implemented in the Stan programming language (). In the model, the mutational signatures are interpreted as the probability parameters of independent multinomial distributions, and the observed mutation counts in the 96 […]


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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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