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[][]$\OT1/cmtt/m/n/10 https : / / drive . google . com / folderview ? id = 0Bzo
muXDutCcRT2VHMUlqWDhqNlk&usp =
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\OT1/cmr/m/n/10 age be-tween sta-ble CRAN re-leases. There are sev-eral ways to
 in-stall \OT1/cmtt/m/n/10 hierarchicalDS
 []

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[][]\OT1/cmtt/m/n/10 install.packages[]([]"hierarchicalDS.tar.gz"[], repos = NU
LL, type = []"source"[])[] 
 []

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[]\OT1/cmtt/m/n/10 S = 100  []#Number of grid cells; square grid assumed for si
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[]    \OT1/cmtt/m/n/10 Observers[, i] = []sample[]([]c[](1, 2, 3), size = 2, re
place = FALSE)[] 
 []


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[]\OT1/cmtt/m/n/10 Sim = []simulate_data[](S = S, Observers = Observers, misID 
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[]\OT1/cmtt/m/n/10 Hab.cov = []data.frame[]([]rep[]([]log[]([]c[](1:[]sqrt[](S)
/[]sqrt[](S))), each = []sqrt[](S)))[] 
 []


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[][]\OT1/cmtt/m/n/10 # (covariate on abundance intensity same as used to simula
te data)[][] 
 []


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[]\OT1/cmtt/m/n/10 Cov.prior.pdf[, 1] = []c[]([]"pois1"[], []"pois1"[])  #model
 group size as a zero truncated poisson[] 
 []


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[]\OT1/cmtt/m/n/10 Control = []list[](iter = 21000, burnin = 1000, thin = 10, M
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[]    \OT1/cmtt/m/n/10 2, S), MH.misID = []matrix[](0.1, 3, 1), RJ.N = []matrix
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[]\OT1/cmtt/m/n/10 hab.pois = []matrix[](0, n.species, 2)  []#covariates are in
tercept, index[][] 
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[][]\OT1/cmtt/m/n/10 # provide some initial values to ensure MCMC doesn
t start
 out at weird[][] 
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[]\OT1/cmtt/m/n/10 Inits = []list[](hab.pois = hab.pois, hab.bern = hab.bern, t
au.nu = []c[](500, 500),[] 
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[]\OT1/cmtt/m/n/10 Prior.pars = []list[](a.eta = 1, b.eta = 0.01, a.nu = 1, b.n
u = 0.01, beta.tau = 0.01,[] 
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[][]\OT1/cmtt/m/n/10 # (1,.01) prior makes it closer to a uniform distribution 
near the origin[][] 
 []


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[]\OT1/cmtt/m/n/10 Out = []hierarchical_DS[](Dat = Dat, Adj = Adj, Area.hab = A
rea.hab, Mapping = Mapping,[] 
 []


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[]    \OT1/cmtt/m/n/10 Area.trans = Area.trans, Observers = Observers, Bin.leng
th = Bin.length,[] 
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[]    \OT1/cmtt/m/n/10 Hab.cov = Hab.cov, Obs.cov = Obs.cov, n.obs.cov = n.obs.
cov, Hab.pois.formula = Hab.pois.formula,[] 
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[]    \OT1/cmtt/m/n/10 Hab.bern.formula = Hab.bern.formula, detect = detect, De
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[]    \OT1/cmtt/m/n/10 Cov.prior.pdf = Cov.prior.pdf, Cov.prior.parms = Cov.pri
or.parms, Cov.prior.fixed = Cov.prior.fixed,[] 
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[]    \OT1/cmtt/m/n/10 Cov.prior.n = Cov.prior.n, pol.eff = NULL, ZIP = ZIP, po
int.ind = point.ind,[] 
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[]    \OT1/cmtt/m/n/10 spat.ind = spat.ind, fix.tau.nu = fix.tau.nu, srr = srr,
 srr.tol = srr.tol,[] 
 []


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[]    \OT1/cmtt/m/n/10 misID = misID, last.ind = last.ind, cor.const = cor.cons
t, Inits = Inits,[] 
 []


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[]    \OT1/cmtt/m/n/10 grps = grps, M = M, Control = Control, adapt = adapt, Pr
ior.pars = Prior.pars,[] 
 []


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[]    \OT1/cmtt/m/n/10 misID.mat = misID.mat, misID.models = misID.models, misI
D.symm = misID.symm,[] 
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[][]\OT1/cmtt/m/n/10 ### 3) plot and summarize results; note that chain would n
eed to be run a[][] 
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