fix svcAbund

DESCRIPTION

@@ -2,12 +2,13 @@ Package: spAbundance
 Type: Package
 Title: Univariate and Multivariate Spatial Modeling of Species Abundance 
 Version: 0.2.0
-Authors@R: c(person("Jeffrey", "Doser", role=c("aut", "cre"), email="doserjef@msu.edu"), person("Andrew", "Finley", role = c("aut")))
+Authors@R: c(person("Jeffrey", "Doser", role=c("aut", "cre"), email="jwdoser@ncsu.edu"), 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 (2022) <doi:10.18637/jss.v103.i05>. 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
+URL: https://www.doserlab.com/files/spabundance-web
+     https://groups.google.com/g/spocc-spabund-users
 BugReports: https://github.com/doserjef/spAbundance/issues
 Depends: R (>= 3.5.0)
 Imports: 

README.Rmd

@@ -11,13 +11,13 @@ knitr::opts_chunk$set(
   out.width = "100%")
 ```
 
-# spAbundance <a href='https://www.jeffdoser.com/files/spabundance-web/'><img src="man/figures/logo.png" align="right" height="139" width="120"/></a>
+# spAbundance <a href='https://www.doserlab.com/files/spabundance-web/'><img src="man/figures/logo.png" align="right" height="139" width="120"/></a>
 
 [![](http://cranlogs.r-pkg.org/badges/grand-total/spAbundance?color=blue)](https://CRAN.R-project.org/package=spAbundance)
 [![CRAN](https://www.r-pkg.org/badges/version/spAbundance)](https://CRAN.R-project.org/package=spAbundance)
 [![Codecov test coverage](https://codecov.io/gh/doserjef/spAbundance/branch/main/graph/badge.svg)](https://codecov.io/gh/doserjef/spAbundance?branch=main)
 
-`spAbundance` fits univariate (i.e., single-species) and multivariate (i.e., multi-species) spatial N-mixture models, hierarchical distance sampling models, and generalized linear mixed models using Markov chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs) to facilitate model fitting to large spatial datasets. `spAbundance` uses analogous syntax to its "sister package" [spOccupancy](https://www.jeffdoser.com/files/spoccupancy-web/) (Doser et al. 2022). Below we provide a very brief introduction to some of the package's functionality, and illustrate just one of the model fitting functions. For more information, see the resources referenced at the bottom of this page and the "Articles" tab at the top of the page. 
+`spAbundance` fits univariate (i.e., single-species) and multivariate (i.e., multi-species) spatial N-mixture models, hierarchical distance sampling models, and generalized linear mixed models using Markov chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs) to facilitate model fitting to large spatial datasets. `spAbundance` uses analogous syntax to its "sister package" [spOccupancy](https://www.doserlab.com/files/spoccupancy-web/) (Doser et al. 2022). Below we provide a very brief introduction to some of the package's functionality, and illustrate just one of the model fitting functions. For more information, see the resources referenced at the bottom of this page and the "Articles" tab at the top of the page. Please also consider joining the [`spAbundance` and `spOccupancy` users google group](https://groups.google.com/g/spocc-spabund-users).  
 
 ## Installation
 
@@ -65,7 +65,7 @@ Note that because we implement the MCMC in C++, you will need a C++ compiler on
 |`simAbund()`       | Simulate single-species count data                                        |
 |`simMsAbund()`     | Simulate multi-species count data                                         |
 
-All model fitting functions allow for Poisson and negative binomial distributions for the abundance portion of the model. All GLM(M)s also allow for Gaussian and zero-inflated Gaussian models. Note the two functions for fitting spatailly-varying coefficient models are only available for Gaussian and zero-inflated Gaussian models. 
+All model fitting functions allow for Poisson and negative binomial distributions for the abundance portion of the model. All GLM(M)s also allow for Gaussian and zero-inflated Gaussian models. Note the multi-species spatailly-varying coefficient models are only available for Gaussian and zero-inflated Gaussian models. 
 
 ## Example usage
 
@@ -147,15 +147,15 @@ out.pred <- predict(out, X.0, coords.0, verbose = FALSE)
 
 ## Learn more
 
-The `vignette("distanceSampling")`, `vignette("nMixtureModels")`, and `vignette("glmm")` provide detailed descriptions and tutorials of all hierarchical distance sampling models, N-mixture models, and generalized linear mixed models in `spAbundance`, respectively. Given the similarity in syntax to fitting occupancy models in the `spOccupancy` package, much of the documentation on the [`spOccupancy` website](https://www.jeffdoser.com/files/spoccupancy-web/) will also be helpful for fitting models in `spAbundance`.
+The `vignette("distanceSampling")`, `vignette("nMixtureModels")`, and `vignette("glmm")` provide detailed descriptions and tutorials of all hierarchical distance sampling models, N-mixture models, and generalized linear mixed models in `spAbundance`, respectively. Given the similarity in syntax to fitting occupancy models in the `spOccupancy` package, much of the documentation on the [`spOccupancy` website](https://www.doserlab.com/files/spoccupancy-web/) will also be helpful for fitting models in `spAbundance`. Please also consider joining the [`spAbundance` and `spOccupancy` users google group](https://groups.google.com/g/spocc-spabund-users) to learn from others who use the two packages.  
 
 ## Citing `spAbundance`
 
 Please cite `spAbundance` as: 
 
-Doser, J. W., Finley, A. O., Kéry, M., and Zipkin, E. F. (2023). spAbundance: An R package for single-species and multi-species spatially-explicit abundance models. arXiv Preprint. 
+Doser, J. W., Finley A. O., Kéry, M., & Zipkin E. F. (2024). spAbundance: An R package for single-species and multi-species spatially explicit abundance models, Methods in Ecology and Evolution. 15, 1024-1033. https://doi.org/10.1111/2041-210X.14332")
 
 ## References
 
-Doser, J. W., Finley, A. O., Kéry, M., and Zipkin, E. F. (2022). spOccupancy: An R package for single-species, multi-species, and integrated spatial occupancy models. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.13897.
+Doser, J. W., Finley, A. O., Kéry, M., and Zipkin, E. F. (2022). spOccupancy: An R package for single-species, multi-species, and integrated spatial occupancy models. Methods in Ecology and Evolution, 13(8), 1670-1678. https://doi.org/10.1111/2041-210X.13897.
 

README.md

@@ -1,24 +1,26 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# spAbundance <a href='https://www.jeffdoser.com/files/spabundance-web/'><img src="man/figures/logo.png" align="right" height="139" width="120"/></a>
+# spAbundance <a href='https://www.doserlab.com/files/spabundance-web/'><img src="man/figures/logo.png" align="right" height="139" width="120"/></a>
 
 [![](http://cranlogs.r-pkg.org/badges/grand-total/spAbundance?color=blue)](https://CRAN.R-project.org/package=spAbundance)
 [![CRAN](https://www.r-pkg.org/badges/version/spAbundance)](https://CRAN.R-project.org/package=spAbundance)
 [![Codecov test
-coverage](https://codecov.io/gh/doserjef/spAbundance/branch/main/graph/badge.svg)](https://app.codecov.io/gh/doserjef/spAbundance?branch=main)
+coverage](https://codecov.io/gh/doserjef/spAbundance/branch/main/graph/badge.svg)](https://codecov.io/gh/doserjef/spAbundance?branch=main)
 
 `spAbundance` fits univariate (i.e., single-species) and multivariate
 (i.e., multi-species) spatial N-mixture models, hierarchical distance
 sampling models, and generalized linear mixed models using Markov chain
 Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor
 Gaussian Processes (NNGPs) to facilitate model fitting to large spatial
 datasets. `spAbundance` uses analogous syntax to its “sister package”
-[spOccupancy](https://www.jeffdoser.com/files/spoccupancy-web/) (Doser
-et al. 2022). Below we provide a very brief introduction to some of the
+[spOccupancy](https://www.doserlab.com/files/spoccupancy-web/) (Doser et
+al. 2022). Below we provide a very brief introduction to some of the
 package’s functionality, and illustrate just one of the model fitting
 functions. For more information, see the resources referenced at the
 bottom of this page and the “Articles” tab at the top of the page.
+Please also consider joining the [`spAbundance` and `spOccupancy` users
+google group](https://groups.google.com/g/spocc-spabund-users).
 
 ## Installation
 
@@ -74,9 +76,9 @@ C++ on a Mac, you can install `XCode` from the Mac app store.
 
 All model fitting functions allow for Poisson and negative binomial
 distributions for the abundance portion of the model. All GLM(M)s also
-allow for Gaussian and zero-inflated Gaussian models. Note the two
-functions for fitting spatailly-varying coefficient models are only
-available for Gaussian and zero-inflated Gaussian models.
+allow for Gaussian and zero-inflated Gaussian models. Note the
+multi-species spatailly-varying coefficient models are only available
+for Gaussian and zero-inflated Gaussian models.
 
 ## Example usage
 
@@ -158,7 +160,7 @@ summary(out)
 #> Thinning Rate: 5
 #> Number of Chains: 3
 #> Total Posterior Samples: 6000
-#> Run Time (min): 0.7641
+#> Run Time (min): 0.9219
 #> 
 #> Abundance (log scale): 
 #>                  Mean     SD    2.5%     50%   97.5%   Rhat ESS
@@ -248,20 +250,24 @@ hierarchical distance sampling models, N-mixture models, and generalized
 linear mixed models in `spAbundance`, respectively. Given the similarity
 in syntax to fitting occupancy models in the `spOccupancy` package, much
 of the documentation on the [`spOccupancy`
-website](https://www.jeffdoser.com/files/spoccupancy-web/) will also be
-helpful for fitting models in `spAbundance`.
+website](https://www.doserlab.com/files/spoccupancy-web/) will also be
+helpful for fitting models in `spAbundance`. Please also consider
+joining the [`spAbundance` and `spOccupancy` users google
+group](https://groups.google.com/g/spocc-spabund-users) to learn from
+others who use the two packages.
 
 ## Citing `spAbundance`
 
 Please cite `spAbundance` as:
 
-Doser, J. W., Finley, A. O., Kéry, M., and Zipkin, E. F. (2023).
-spAbundance: An R package for single-species and multi-species
-spatially-explicit abundance models. arXiv Preprint.
+Doser, J. W., Finley A. O., Kéry, M., & Zipkin E. F. (2024).
+spAbundance: An R package for single-species and multi-species spatially
+explicit abundance models, Methods in Ecology and Evolution. 15,
+1024-1033. <https://doi.org/10.1111/2041-210X.14332>“)
 
 ## References
 
 Doser, J. W., Finley, A. O., Kéry, M., and Zipkin, E. F. (2022).
 spOccupancy: An R package for single-species, multi-species, and
-integrated spatial occupancy models. Methods in Ecology and Evolution.
-<https://doi.org/10.1111/2041-210X.13897>.
+integrated spatial occupancy models. Methods in Ecology and Evolution,
+13(8), 1670-1678. <https://doi.org/10.1111/2041-210X.13897>.

inst/CITATION

@@ -1,20 +1,36 @@
-if (!exists("meta") || is.null(meta)) meta <- packageDescription("spOccupancy")
+if (!exists("meta") || is.null(meta)) meta <- packageDescription("spAbundance")
 
-year <- sub(".*(2[[:digit:]]{3})-.*", "\\1", meta$Date, perl = TRUE)
-vers <- paste("R package version", meta$Version)
+citHeader("To cite package", sQuote(meta$Package), "in publications, please cite the Doser, Finley, Kéry, Zipkin (2024) paper. If using functions lfMsAbund, sfMsAbund, lfMsDS, or sfMsDS, please also cite the Doser, Finley, Banerjee (2023) paper that developed much of the code used for these functions.") 
 
 bibentry(bibtype = "Article",
   title        = "{spAbundance: An R package for univariate and multivariate spatially-explicit abundance models}",
   author       = c(as.person("Jeffrey W. Doser"), as.person("Andrew O. Finley"), as.person("Marc Kéry"), as.person("Elise F. Zipkin")),
-  journal      = "arXiv Preprint",
+  journal      = "Methods in Ecology and Evolution",
+  year         = "2024",
+  volume       = "15",
+  number       = "",
+  pages        = "1024-1033",
+  url          = "https://doi.org/10.1111/2041-210X.14332",
+
+  textVersion  =
+  paste("Doser, J. W., Finley A. O., Kéry, M., & Zipkin E. F. (2024).",
+        "spAbundance: An R package for single-species and multi-species spatially explicit abundance models",
+        "Methods in Ecology and Evolution, 15, 1024-1033. https://doi.org/10.1111/2041-210X.14332")
+)
+
+bibentry(bibtype = "Article",
+  title        = "Joint species distribution models with imperfect detection for high-dimensional spatial data",
+  author       = c(as.person("Jeffrey W. Doser"), as.person("Andrew O. Finley"), as.person("Sudipto Banerjee")),
+  journal      = "Ecology",
   year         = "2023",
   volume       = "",
   number       = "",
-  pages        = "",
-  url          = "",
+  pages        = "e4137",
+  url          = "https://doi.org/10.1002/ecy.4137",
 
   textVersion  =
-  paste("Doser, J. W., Finley A. O., Kéry, M., & Zipkin E. F. (2022).",
-        "spAbundance: An R package for univariate and multivariate spatially-explicit abundance models",
-        "arXiv Preprint.")
+  paste("Doser, J. W., Finley A. O., & Banerjee, S. (2023).",
+        "Joint species distribution models with imperfect detection for high-dimensional spatial data",
+        "Ecology e4137. https://doi.org/10.1002/ecy.4137")
 )
+

src/spAbundGaussianNNGP.cpp

@@ -432,9 +432,9 @@ extern "C" {
             for (j = 0; j < J; j++) {
               if (XRE[betaStarIndx[l] * J + j] == betaLevelIndx[l]) {
                 tmp_02 = 0.0;
-		for (ll = 0; ll < pRE; ll++) {
+                for (ll = 0; ll < pRE; ll++) {
                   tmp_02 += betaStar[betaStarLongIndx[ll * J + j]] * XRandom[ll * J + j];
-		}
+                }
                 tmp_one[0] += XRandom[betaStarIndx[l] * J + j] * (y[j] - F77_NAME(ddot)(&p, &X[j], &J, beta, &inc) -
           		    tmp_02 + (betaStar[l] * XRandom[betaStarIndx[l] * J + j]) -
 			    w[j]) / tauSq;

src/spAbundNNGP.cpp

@@ -403,7 +403,7 @@ extern "C" {
         // Proposal
         for (k = 0; k < pAbund; k++) {
           logPostBetaCand = 0.0;
-	  logPostBetaCurr = 0.0;
+          logPostBetaCurr = 0.0;
           betaCand[k] = rnorm(beta[k], exp(tuning[betaAMCMCIndx + k]));
           logPostBetaCand += dnorm(betaCand[k], muBeta[k], sqrt(SigmaBeta[k * pAbund + k]), 1);
 	  logPostBetaCurr += dnorm(beta[k], muBeta[k], sqrt(SigmaBeta[k * pAbund + k]), 1);

src/svcAbundGaussianNNGP.cpp

@@ -440,7 +440,6 @@ extern "C" {
         /********************************************************************
          *Update random effects
          *******************************************************************/
-	// TODO: The random slopes component of this does not work.
         if (pRE > 0) {
           // Update each individual random effect one by one.
           for (l = 0; l < nRE; l++) {
@@ -455,13 +454,12 @@ extern "C" {
             for (j = 0; j < J; j++) {
               if (XRE[betaStarIndx[l] * J + j] == betaLevelIndx[l]) {
                 tmp_02 = 0.0;
-		for (ll = 0; ll < pRE; ll++) {
+                for (ll = 0; ll < pRE; ll++) {
                   tmp_02 += betaStar[betaStarLongIndx[ll * J + j]] * XRandom[ll * J + j];
-		}
+                }
                 tmp_one[0] += XRandom[betaStarIndx[l] * J + j] * (y[j] - F77_NAME(ddot)(&p, &X[j], &J, beta, &inc) -
-          		    tmp_02 + (betaStar[l] * XRandom[betaStarIndx[l] * J + j]) -
-			    wSites[j]) / tauSq;
-		// TODO: check this out. This matches postHocLM.
+                              tmp_02 + (betaStar[l] * XRandom[betaStarIndx[l] * J + j]) -
+                              wSites[j]) / tauSq;
                 tmp_0 += XRandom[betaStarIndx[l] * J + j] * XRandom[betaStarIndx[l] * J + j] / tauSq;
               }
             }

src/svcAbundNNGP.cpp

@@ -549,7 +549,7 @@ extern "C" {
 	                e += B[ll * nIndx + nnIndxLU[jj]+i]*wCand[(nnIndx[nnIndxLU[jj]+i]) * pTilde + ll];
                 }
                 b = wCand[jj * pTilde + ll] - e;
-                a += b*b/F[ll * J + j];
+                a += b*b/F[ll * J + jj];
               }
 	          }
 	          // MVN for j

src/svcAbundNNGPPredict.cpp

@@ -1,4 +1,3 @@
-// TODO: there is a memory problem here at 255 and 260 for spAbund type models (at least).
 #define USE_FC_LEN_T
 #include <string>
 #include "util.h"