datalist 
List of data passed in

modelStructure 
list controlling mdoel components
 StrataYear.positiveTows
Effects for strata:year interactions in positive model. Character string that is one of the following: "zero", "fixed", random", "random2", "randomExpanded", or "correlated". The "random", "random2" and "randomExpanded" treatments of normally distributed random effects differ in that "random" assigns a uniform prior on the random effects standard deviation, "random2" assigns an Inverse Gamma prior on the random effects precision, while "randomExpanded" assigns the folded noncentral tdistribution as a prior on the random effect SD (Gelman 2006, Gelman et al. 2007). Specifying the effects as "correlated" also models deviations as random effects, but treats them as multivariate normal, with a correlation between the presenceabsence model estimated. Defaults to "random".
 VesselYear.positiveTows
Effects for vessel:year interactions in positive model. Character string that is one of the following: "zero", "fixed", random", "random2", "randomExpanded", or "correlated". The "random", "random2" and "randomExpanded" treatments of normally distributed random effects differ in that "random" assigns a uniform prior on the random effects standard deviation, "random2" assigns an Inverse Gamma prior on the random effects precision, while "randomExpanded" assigns the folded noncentral tdistribution as a prior on the random effect SD (Gelman 2006, Gelman et al. 2007). Specifying the effects as "correlated" also models deviations as random effects, but treats them as multivariate normal, with a correlation between the presenceabsence model estimated. Defaults to "random".
 StrataYear.zeroTows
Effects for strata:year interactions in binomial model. Character string that is one of the following: "zero", "fixed", random", "random2", "randomExpanded", or "correlated". The "random", "random2" and "randomExpanded" treatments of normally distributed random effects differ in that "random" assigns a uniform prior on the random effects standard deviation, "random2" assigns an Inverse Gamma prior on the random effects precision, while "randomExpanded" assigns the folded noncentral tdistribution as a prior on the random effect SD (Gelman 2006, Gelman et al. 2007). Specifying the effects as "correlated" also models deviations as random effects, but treats them as multivariate normal, with a correlation between the presenceabsence model estimated. Defaults to "random".
 VesselYear.zeroTows
Effects for vessel:year interactions in binomial model. Character string that is one of the following: "zero", "fixed", random", "random2", "randomExpanded", or "correlated". The "random", "random2" and "randomExpanded" treatments of normally distributed random effects differ in that "random" assigns a uniform prior on the random effects standard deviation, "random2" assigns an Inverse Gamma prior on the random effects precision, while "randomExpanded" assigns the folded noncentral tdistribution as a prior on the random effect SD (Gelman 2006, Gelman et al. 2007). Specifying the effects as "correlated" also models deviations as random effects, but treats them as multivariate normal, with a correlation between the presenceabsence model estimated. Defaults to "random".
 Vessel.positiveTows
Effects for vessel interactions in positive model. Character string that is one of the following: "zero", "fixed", random", "random2", "randomExpanded", or "correlated". The "random", "random2" and "randomExpanded" treatments of normally distributed random effects differ in that "random" assigns a uniform prior on the random effects standard deviation, "random2" assigns an Inverse Gamma prior on the random effects precision, while "randomExpanded" assigns the folded noncentral tdistribution as a prior on the random effect SD (Gelman 2006, Gelman et al. 2007). Specifying the effects as "correlated" also models deviations as random effects, but treats them as multivariate normal, with a correlation between the presenceabsence model estimated. Defaults to "zero".
 Vessel.zeroTows
Effects for vessel interactions in binomial model. Character string that is one of the following: "zero", "fixed", random", "random2", "randomExpanded", or "correlated". The "random", "random2" and "randomExpanded" treatments of normally distributed random effects differ in that "random" assigns a uniform prior on the random effects standard deviation, "random2" assigns an Inverse Gamma prior on the random effects precision, while "randomExpanded" assigns the folded noncentral tdistribution as a prior on the random effect SD (Gelman 2006, Gelman et al. 2007). Specifying the effects as "correlated" also models deviations as random effects, but treats them as multivariate normal, with a correlation between the presenceabsence model estimated. Defaults to "zero".
 Catchability.positiveTows
Specify offset for the positive model. Can be fixed ("one"), or estimated ("linear", "quadratic"). If "E" represents effort, the offset is included in the following form, ln(u) = ... + B1ln(E) + B2(ln(E)^2). Defaults to "one".
 Catchability.zeroTows
Specify offset for the presenceabsence model. Can be fixed ("zero","one"), or estimated ("linear", "quadratic"). If "E" represents effort, the offset is included in the following form, logit(p) = ... + B1E + B2(E^2). Defaults to "zero".
 year.deviations
Argument ("correlated","uncorrelated") specifying whether year deviations should be estimated as correlated random effects (correlation between presenceabsence and positive model deviates estimated). By default, this is "uncorrelated", meaning year deviations are included as fixed effects.
 strata.deviations
Argument ("correlated","uncorrelated") specifying whether strata deviations should be estimated as correlated random effects (correlation between presenceabsence and positive model deviates estimated). By default, this is "uncorrelated", meaning strata deviations are included as fixed effects.

covariates 
Two element vector specifying whether covariates are included.
 positive
Boolean, defaults to FALSE
 binomial
Boolean, defaults to FALSE

likelihood 
Character string specifying the form of the positive model. Can be one of the following: "gamma" (or "gammaFixedCV"), "lognormal" (or "lognormalFixedCV"), "invGaussian" (or "invGaussianFixedCV"), "lognormalECE", "gammaECE", "poisson", "zt_poisson", or "negbin". Defaults to "gamma". The forms of the model are as follows:
 gamma
Models the response as continuous from a Gamma distribution, with a loglink. The form of the Gamma distribution used is Y ~ Gamma(shape = a, rate = b), where E(Y) = a / b, and CV(Y) = 1 / sqrt(a). For consistency with the lognormal, the CV^2 is assigned an Inverse Gamma (0.001,0.001) prior. If the distribution is specified as "gammaFixedCV", the CV = 1
 lognormal
Models the response as continuous from a lognormal distribution, with a loglink. The CV^2 is assigned an Inverse Gamma (0.001,0.001) prior, and the variance of the lognormal distribution is calculated as var = (log(CV^2)+1). If the distribution is specified as "lognormalFixedCV", the CV = 1
 invGaussian
Models the response as continuous from an inverse Gaussian (Wald) distribution, with a loglink. The parameterization is in terms of the mean (u) and variance (u^3 / lambda). This model is considerably slower than the lognormal or gamma because the "zeros" trick needs to be implemented. For consistency with the other distributions, the CV^2 is assigned an Inverse Gamma (0.001,0.001) prior. If the distribution is specified as "invGaussianFixedCV", the CV = 1.
 gammaECE
Extends the gamma distribution to model the positive distribution as a 2component mixture (normal and "extreme catch events", Thorson 2011). The probability of membership in each class ("normal", "extreme") is estimated. Extreme catch events are allowed to have a separate estimated variance parameter (or CV). The mean of extreme catch events is also estimated; for identifiability, this is estimated as an offset from normal catch events in linkspace, e.g. log(u_extreme) = log(u_normal) + logratio, where the "logratio" parameter is assigned a loguniform(0,5) prior distribution.
 lognormalECE
Extends the lognormal distribution to model the positive distribution as a 2component mixture (normal and "extreme catch events", Thorson 2011). The probability of membership in each class ("normal", "extreme") is estimated. Extreme catch events are allowed to have a separate estimated variance parameter (or CV). The mean of extreme catch events is also estimated; for identifiability, this is estimated as an offset from normal catch events in linkspace, e.g. log(u_extreme) = log(u_normal) + logratio, where the "logratio" parameter is assigned a loguniform(0,5) prior distribution.
 poisson
Models the response as discrete counts from the Poisson distribution, parameterized using the loglink function, Y ~ Poisson(u), with log(u) = B0 + B1*X. Because this is a deltaGLM model, applying the Poisson distribution isn't really appropriate when counts are small (zeros are a possibility). The zt_poisson distribution is more appropriate for these settings.
 ztpoisson
Models the response as discrete counts from the zerotruncated Poisson distribution, parameterized using the loglink Y ~ Poisson(u), log(u) = B0 + B1X. This is more appropriate for small count data than the poisson. Because this implements the zeros trick to calculate the likelihood, estimation is slower than the traditional Poisson
 negbin
Models the response as discrete counts from the Negative binomial distribution, parameterized using the loglink function, Y ~ NegBin(u, r), with log(u) = B0 + B1X.

model.name 
Character string specifying the name of the JAGS txt file that is written to the working directory. Defaults "to deltaGLM.txt"

fit.model 
Boolean, specifying whether the model should be fit or not. Defaults to TRUE; if FALSE, just the JAGS script file is written.

write.model 
Boolean, whether to write model to a file. Defaults to TRUE

mcmc.control 
List of parameter to control MCMC estimation
 chains
Number of MCMC chains to estimate
 thin
Thinning rate, defaults to 1
 burn
Burn in period, defaults to 5000
 iterToSave
iterations to save after burnin, defaults to 2000

Parallel 
Whether to conduct estimation using parallel computing (faster), using the function jags.parallel().

Species 
Species name, defaults to "NULL"

logitBounds 
2 element vector specifying bounds of logit link space. Defaults c(20,20)

logBounds 
2 element vector specifying bounds of log link space. Defaults c(20,20)

prior.scale 
Scale parameter for the variance parameter of the "randomExpanded" random effects. This is a 6 element vector, defaults to (25, 25, 25, 25, 25, 25), where the elements are (strataYear.positive, strataYear.presenceAbsence, vesselYear.positive, vesselYear.presenceAbsence, vessel.positive, vessel.presenceAbsence)

dgammaNum 
Prior on precision parameters, e.g. tau ~ dgamma(dgammaNum, dgammaNum). Defaults to 0.001
