fixing stuff for CRAN

DESCRIPTION

@@ -2,12 +2,12 @@ Package: spAbundance
 Type: Package
 Title: Univariate and Multivariate Spatial Modeling of Species Abundance 
 Version: 0.1.0
-Author: Jeffrey Doser [aut, cre], Andrew O. Finley [aut]
-Maintainer: Jeffrey Doser <[email protected]>
+Authors@R: c(person("Jeffrey", "Doser", role=c("aut", "cre"), email="[email protected]"), person("Andrew", "Finley", role = c("aut")))
 Description: Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2020) <arXiv:2001.09111>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency. 
 License: GPL (>= 3)
 Encoding: UTF-8
 LazyData: true
+URL: https://www.jeffdoser.com/files/spabundance-web
 BugReports: https://github.com/doserjef/spAbundance/issues
 Depends: R (>= 3.5.0)
 Imports: 

man/dataNMixSim.rda.Rd

@@ -38,70 +38,68 @@ data(dataNMixSim)
 \keyword{datasets}
 
 \examples{
-\dontrun{
-  set.seed(6)
-  J.x <- 15
-  J.y <- 15
-  J <- J.x * J.y
-  n.rep <- sample(3, size = J, replace = TRUE)
-  # n.rep <- rep(5, J) 
-  n.sp <- 6
-  # Community-level covariate effects
-  # Occurrence
-  beta.mean <- c(-1, 0.5)
-  p.abund <- length(beta.mean)
-  tau.sq.beta <- c(0.4, 1.2)
-  # Detection
-  alpha.mean <- c(0, 0.5, 0.8)
-  tau.sq.alpha <- c(0.2, 1, 1.5)
-  p.det <- length(alpha.mean)
-  # Random effects
-  mu.RE <- list()
-  mu.RE <- list(levels = c(10),
-  	       sigma.sq.mu = c(0.5),
-                 beta.indx = list(1))
-  p.RE <- list()
-  # Draw species-level effects from community means.
-  beta <- matrix(NA, nrow = n.sp, ncol = p.abund)
-  alpha <- matrix(NA, nrow = n.sp, ncol = p.det)
-  for (i in 1:p.abund) {
-    beta[, i] <- rnorm(n.sp, beta.mean[i], sqrt(tau.sq.beta[i]))
-  }
-  for (i in 1:p.det) {
-    alpha[, i] <- rnorm(n.sp, alpha.mean[i], sqrt(tau.sq.alpha[i]))
-  }
-  alpha.true <- alpha
-  sp <- TRUE 
-  n.factors <- 3
-  factor.model <- TRUE
-  phi <- runif(n.factors, 3/1, 3 / .2)
-  kappa <- runif(n.sp, 0.1, 1)
-  family <- 'Poisson'
-
-  dat <- simMsNMix(J.x = J.x, J.y = J.y, n.rep = n.rep, n.sp = n.sp, beta = beta, alpha = alpha,
-  	        mu.RE = mu.RE, p.RE = p.RE, sp = sp, kappa = kappa, family = family, 
-                  factor.model = factor.model, phi = phi, 
-                  cov.model = 'exponential', n.factors = n.factors)
-  table(dat$N)
-  apply(dat$N, 1, sum)
-
-  y <- dat$y
-  X <- dat$X
-  X.p <- dat$X.p
-  X.re <- dat$X.re
-  X.p.re <- dat$X.p.re
-  coords <- dat$coords
-  dimnames(coords)[[2]] <- c('X', 'Y')
-
-  # Package all data into a list
-  abund.covs <- cbind(X, X.re)
-  colnames(abund.covs) <- c('int', 'abund.cov.1', 'abund.factor.1')
-  abund.covs <- abund.covs[, -1]
-  det.covs <- list(det.cov.1 = X.p[, , 2], 
-  		 det.cov.2 = X.p[, , 3]) 
-  dataNMixSim <- list(y = y, 
-  		    abund.covs = abund.covs, 
-  		    det.covs = det.covs, 
-                      coords = coords)
+set.seed(6)
+J.x <- 15
+J.y <- 15
+J <- J.x * J.y
+n.rep <- sample(3, size = J, replace = TRUE)
+# n.rep <- rep(5, J) 
+n.sp <- 6
+# Community-level covariate effects
+# Occurrence
+beta.mean <- c(-1, 0.5)
+p.abund <- length(beta.mean)
+tau.sq.beta <- c(0.4, 1.2)
+# Detection
+alpha.mean <- c(0, 0.5, 0.8)
+tau.sq.alpha <- c(0.2, 1, 1.5)
+p.det <- length(alpha.mean)
+# Random effects
+mu.RE <- list()
+mu.RE <- list(levels = c(10),
+	       sigma.sq.mu = c(0.5),
+               beta.indx = list(1))
+p.RE <- list()
+# Draw species-level effects from community means.
+beta <- matrix(NA, nrow = n.sp, ncol = p.abund)
+alpha <- matrix(NA, nrow = n.sp, ncol = p.det)
+for (i in 1:p.abund) {
+  beta[, i] <- rnorm(n.sp, beta.mean[i], sqrt(tau.sq.beta[i]))
 }
+for (i in 1:p.det) {
+  alpha[, i] <- rnorm(n.sp, alpha.mean[i], sqrt(tau.sq.alpha[i]))
+}
+alpha.true <- alpha
+sp <- TRUE 
+n.factors <- 3
+factor.model <- TRUE
+phi <- runif(n.factors, 3/1, 3 / .2)
+kappa <- runif(n.sp, 0.1, 1)
+family <- 'Poisson'
+
+dat <- simMsNMix(J.x = J.x, J.y = J.y, n.rep = n.rep, n.sp = n.sp, beta = beta, alpha = alpha,
+	        mu.RE = mu.RE, p.RE = p.RE, sp = sp, kappa = kappa, family = family, 
+                factor.model = factor.model, phi = phi, 
+                cov.model = 'exponential', n.factors = n.factors)
+table(dat$N)
+apply(dat$N, 1, sum)
+
+y <- dat$y
+X <- dat$X
+X.p <- dat$X.p
+X.re <- dat$X.re
+X.p.re <- dat$X.p.re
+coords <- dat$coords
+dimnames(coords)[[2]] <- c('X', 'Y')
+
+# Package all data into a list
+abund.covs <- cbind(X, X.re)
+colnames(abund.covs) <- c('int', 'abund.cov.1', 'abund.factor.1')
+abund.covs <- abund.covs[, -1]
+det.covs <- list(det.cov.1 = X.p[, , 2], 
+		 det.cov.2 = X.p[, , 3]) 
+dataNMixSim <- list(y = y, 
+		    abund.covs = abund.covs, 
+		    det.covs = det.covs, 
+                    coords = coords)
 }