diff --git a/vignettes/Manuscript/Manuscript.Rmd b/vignettes/Manuscript/Manuscript.Rmd
index 44a1eaa..95e3f68 100644
--- a/vignettes/Manuscript/Manuscript.Rmd
+++ b/vignettes/Manuscript/Manuscript.Rmd
@@ -28,7 +28,8 @@ abstract: >
   It can also be on *multiple* lines.
 header-includes: >
   \usepackage{lipsum}
-bibliography: sample.bib
+bibliography: bibliography.bib
+link-citations: true
 output: 
   bookdown::pdf_book:
     base_format: rticles::peerj_article # for using bookdown features like \@ref()
@@ -36,18 +37,18 @@ output:
 
 # Introduction {.unnumbered}
 
-Spatial epidemiology is the description and analysis of geographically indexed health data with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors [5]. Broadly speaking, the field of spatial epidemiology can be divided into three principal subfields: disease mapping, spatial regression/geographic correlation studies, and analysis of disease clusters. The SpatialEpi package implements methods for these subfields.\
+Spatial epidemiology is the description and analysis of geographically indexed health data with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors @Elliott04. Broadly speaking, the field of spatial epidemiology can be divided into three principal subfields: disease mapping, spatial regression/geographic correlation studies, and analysis of disease clusters. The SpatialEpi package implements methods for these subfields.\
 
 ```{r}
 library(SpatialEpi)
-Rcode <- system.file("doc", "SpatialEpi.Rnw", package = "SpatialEpi")
-options(device.ask.default = FALSE)
-Stangle(Rcode)
+# Rcode <- system.file("doc", "SpatialEpi.Rnw", package = "SpatialEpi")
+# options(device.ask.default = FALSE)
+# Stangle(Rcode)
 ```
 
 ## 2 Producing Maps
 
-The production of disease atlases is one of the chief tasks in spatial epidemiology. In order to facilitate producing such maps, the SpatialEpi package uses the sp package to process objects of class SpatialPoly-gons [11]. Further information on the sp package can be found in Applied Spatial Data Analysis with R [3].
+The production of disease atlases is one of the chief tasks in spatial epidemiology. In order to facilitate producing such maps, the SpatialEpi package uses the sp package to process objects of class SpatialPoly-gons [@Pebesma05]. Further information on the sp package can be found in Applied Spatial Data Analysis with R [@Bivand08].
 
 ##### 2.1 Converting Different Map Formats into SpatialPolygons
 
@@ -58,20 +59,20 @@ Several different formats of maps can be converted into objects of class Spatial
 A polygon file consists of a 2-column matrix of coordinates, where each complete subpolygon representing some subarea of the study region (counties, census tracts, zip/postal codes) is separated by NA values. All subpolygons are assumed to be closed by joining the last point to the first point. Using the polygon2spatial.polygon() function, we can convert the polygon file into an object of class SpatialPolygons. In the case when certain subareas consist of more than one contiguous land mass, we specify the nrepeats vector where each element represents the number of subpolygons corresponding to that subarea. The advantages of plotting maps as a SpatialPolygon rather than a simple polygon are: a) the aspect ratio of the x and y axes in plots is preserved to reflect geography b) specific subareas can be highlighted easily c) subareas that consist of more than one contiguous land mass can be treated as one unit As an demonstration of these three advantages, in Figure 1 we plot a map of Scotland with all 56 counties of Scotland in 1975 both as a polygon (using the R polygon() function) and as a SpatialPolygons object. Several of the counties of Scotland consist of more than one contiguous land mass, e.g. the county Argyll-Bute consists of 8 separate land masses.
 
 ```{r}
-data(scotland)
-polygon <- scotland$polygon$polygon
-nrepeats <- scotland$polygon$nrepeats
-names <- scotland$data$county.names
-spatial.polygon <- polygon2spatial.polygon(polygon, coordinate.system = "+proj=utm", + names, nrepeats)
+# data(scotland)
+# polygon <- scotland$polygon$polygon
+# nrepeats <- scotland$polygon$nrepeats
+# names <- scotland$data$county.names
+# spatial.polygon <- polygon2spatial.polygon(polygon, coordinate.system = "+proj=utm", + names, nrepeats)
 ```
 
 ```{r}
-par(mfrow = c(1, 2))
-plot(polygon, type = "n", xlab = "Eastings (km)", ylab = "Northings (km)", main = "Polygon File")
-polygon(polygon)
-plot(spatial.polygon, axes = TRUE)
-title(xlab = "Eastings (km)", ylab = "Northings (km)", main = "Spatial Polygon")
-plot(spatial.polygon[23], add = TRUE, col = "red")
+# par(mfrow = c(1, 2))
+# plot(polygon, type = "n", xlab = "Eastings (km)", ylab = "Northings (km)", main = "Polygon File")
+# polygon(polygon)
+# plot(spatial.polygon, axes = TRUE)
+# title(xlab = "Eastings (km)", ylab = "Northings (km)", main = "Spatial Polygon")
+# plot(spatial.polygon[23], add = TRUE, col = "red")
 ```
 
 ##### 2.1.2 Converting maps objects to SpatialPolygons
@@ -79,14 +80,14 @@ plot(spatial.polygon[23], add = TRUE, col = "red")
 The maps R package includes several commonly used maps, which can be converted into SpatialPolygons objects using the map2SpatialPolygons command. For instance, a county-level map of just the states Pennsylvania and Vermont with red borders, along with the boundaries of neighboring states with slightly thicker black borders can be produced with the resulting plot shown in Figure 2).
 
 ```{r}
-library(maps)
-county.map <- map("county", c("pennsylvania", "vermont"), fill = TRUE, plot = FALSE)
-county.names <- as.character(county.map$names)
-county <- map2SpatialPolygons(county.map, IDs = county.names, proj4string = CRS("+proj=longlat"))
-state.map <- map("state", c(), fill = TRUE, plot = FALSE)
-state.names <- as.character(state.map$names)
-state <- map2SpatialPolygons(state.map, IDs = state.names, proj4string = CRS("+proj=longlat")) > plot(county, axes = TRUE, border = "red")
-plot(state, add = TRUE, lwd = 2)
+# library(maps)
+# county.map <- map("county", c("pennsylvania", "vermont"), fill = TRUE, plot = FALSE)
+# county.names <- as.character(county.map$names)
+# county <- map2SpatialPolygons(county.map, IDs = county.names, proj4string = CRS("+proj=longlat"))
+# state.map <- map("state", c(), fill = TRUE, plot = FALSE)
+# state.names <- as.character(state.map$names)
+# state <- map2SpatialPolygons(state.map, IDs = state.names, proj4string = CRS("+proj=longlat")) > plot(county, axes = TRUE, border = "red")
+# plot(state, add = TRUE, lwd = 2)
 ```
 
 ## 2.2 Converting Between Coordinate Systems
@@ -94,10 +95,10 @@ plot(state, add = TRUE, lwd = 2)
 In the Pennsylvania and Vermont example in Section 2.1.2, all coordinates are in longitude/latitude. How- ever, this coordinate system is not appropriate for many distance-based methods as degrees of longitude are not equidistant, and must be converted to a grid based system. The function latlong2grid() can convert either a) an n×2 matrix of coordinates b) a SpatialPolygons object based on longitude/latitude (expressed in decimal values) into kilometer-based grid coordinates. Figure 3 shows the resulting transformed map.
 
 ```{r}
-county.grid <- latlong2grid(county)
-state.grid <- latlong2grid(state)
-plot(county.grid, axes = TRUE, border = "red")
-plot(state.grid, add = TRUE, lwd = 2)
+# county.grid <- latlong2grid(county)
+# state.grid <- latlong2grid(state)
+# plot(county.grid, axes = TRUE, border = "red")
+# plot(state.grid, add = TRUE, lwd = 2)
 ```
 
 Or a simple 2-column matrix of coordinates can be converted as well. As an example, consider the latitude and longitudes of Montreal QC (latitude: 45deg 28' 0" N (deg min sec), longitude: 73deg 45' 0" W) and Vancouver BC (latitude: 45deg 39' 38" N (deg min sec), longitude: 122deg 36' 15" W) in decimal format. These also can be converted to a grid-based coordinate system.
@@ -136,7 +137,8 @@ ggplot() +
 
 ##### 3.2 Scotland Lip Cancer among Males in 1975-1980
 
-AFF agriculture farming and fishing The expected numbers of disease were calculated by the method of Mantel and Stark
+AFF agriculture farming and fishing The expected numbers of disease were calculated by the method of Mantel and Stark [@Mantel68].
+[@Kemp85]
 
 Figure \@ref(fig:scotland)
 
@@ -163,7 +165,7 @@ ggplot() +
 
 ## 4.1 Expected Numbers of Disease and Standardized Mortality Ratios
 
-In order to control for known risk factors (in this case strata) using internal indirect standardization, we can compute the (indirect) expected number of diseases [6] for each area using the expected() command. It is important that the population and cases vectors are balanced: all counts are sorted by area first, and then within each area the counts for all strata are listed (even if 0 count) in the same order. i.e. if considering 16 strata, the first 16 elements correspond to the first area, the next 16 correspond to the second area, etc. and the strata are always listed in the same order.\
+In order to control for known risk factors (in this case strata) using internal indirect standardization, we can compute the (indirect) expected number of diseases [@Wakefield00] for each area using the expected() command. It is important that the population and cases vectors are balanced: all counts are sorted by area first, and then within each area the counts for all strata are listed (even if 0 count) in the same order. i.e. if considering 16 strata, the first 16 elements correspond to the first area, the next 16 correspond to the second area, etc. and the strata are always listed in the same order.\
 \
 
 ```{r}
@@ -178,7 +180,7 @@ expected.cases <- expected(pennLC$data$population, pennLC$data$cases, n.strata)
 
 ### 4.2.1 Empricial Bayes
 
-Given that SMR estimates for areas with small values of expected numbers of disease can be highly variable, Clayton and Kaldor proposed an empirical Bayes approach to estimate disease rates [4]. The estimates represent a weighted compromise between an area's SMR and the overall mean relative risk. These estimates are much more stable than the raw SMR's. In this example, we use a linear model $\alpha + \beta_1 x + \beta_2 x_2$ in the eBayes() function to estimate relative risk with the resulting plot in Figure
+Given that SMR estimates for areas with small values of expected numbers of disease can be highly variable, Clayton and Kaldor proposed an empirical Bayes approach to estimate disease rates [@Clayton87]. The estimates represent a weighted compromise between an area's SMR and the overall mean relative risk. These estimates are much more stable than the raw SMR's. In this example, we use a linear model $\alpha + \beta_1 x + \beta_2 x_2$ in the eBayes() function to estimate relative risk with the resulting plot in Figure
 
 ```{r}
 data(scotland)
@@ -192,11 +194,11 @@ mapvariable(results$RR, scotland.map)
 
 # 4.3 Cluster Detection
 
-Cluster detection is the routine surveillance of a large expanse of small administrative zones for evidence of individual "hot-spots" of disease without any preconceptions about their locations. [2]. For aggregated count data, a typical procedure is to consider a set of zones, each zone being some amalgamation of areas in the study regions. Each zone is then viewed as a potential cluster.
+Cluster detection is the routine surveillance of a large expanse of small administrative zones for evidence of individual "hot-spots" of disease without any preconceptions about their locations. [@Besag91]. For aggregated count data, a typical procedure is to consider a set of zones, each zone being some amalgamation of areas in the study regions. Each zone is then viewed as a potential cluster.
 
 ## 4.3.1 Kulldorff
 
-The kulldorff() function implements the Kulldorff method for finding the most likely cluster as described in Kulldorff and Nagarwalla (1995) [9] and Kulldorff (1997) [8]. The kulldorff()
+The kulldorff() function implements the Kulldorff method for finding the most likely cluster as described in Kulldorff and Nagarwalla (1995) [@Kulldorff95] and Kulldorff (1997) [@Kulldorff97]. The kulldorff()
 
 ```{r}
 data <- pennLC$data
@@ -230,7 +232,7 @@ title("Most Likely Cluster Controlling for Strata")
 
 ## 4.3.2 Besag-Newell
 
-The besag.newell() function implements the Besag-Newell method as described in Besag and Newell (1995) [2]. Using the same dataset as in Section 4.3.1
+The besag.newell() function implements the Besag-Newell method as described in Besag and Newell (1995) [@Besag91]. Using the same dataset as in Section 4.3.1
 
 ```{r}
 k <- 1250
@@ -314,5 +316,7 @@ param2 <- GammaPriorCh(log(5), 0.975, 2)
 ```
 
 ## 4.4.3 Choosing the Prior on the Spatial Residuals
+Unfinished section
 
 # References
+
diff --git a/vignettes/Manuscript/bibliography.bib b/vignettes/Manuscript/bibliography.bib
new file mode 100644
index 0000000..814097a
--- /dev/null
+++ b/vignettes/Manuscript/bibliography.bib
@@ -0,0 +1,941 @@
+@ARTICLE{Besag77,
+	author = {J. Besag and P. J. Diggle},
+	title = {Simple {M}onte {C}arlo tests for spatial pattern},
+	journal = {Applied Statistics},
+	year = {1977},
+	volume = {26},
+	number = {3},
+	pages = {327-333},
+}
+
+@ARTICLE{Openshaw87,
+	author = {S. Openshaw and M. Charlton and C. Wymer and A.W. Craft},
+	title = {A mark 1 analysis machine for the automated analysis of point data sets},
+	journal = {International Journal of Geographical Information Systems},
+	year = {1987},
+	volume = {1},
+	number = {4},
+	pages = {335-358},
+}
+
+@ARTICLE{Besag91,
+	author = {J. Besag and J. Newell},
+	title = {The detection of clusters in rare diseases},
+	journal = {Journal of the Royal Statistical Society: Series A},
+	year = {1991},
+	volume = {154},
+	number = {1},
+	pages = {143-155},
+}
+
+@ARTICLE{Kulldorff97,
+	author = {M. Kulldorff},
+	title = {A spatial scan statistic},
+	journal = {Communication in Statistics: Theory and Methods},
+	year = {1997},
+	volume = {26},
+	number = {6},
+	pages = {1481-1496},
+}
+
+@ARTICLE{Kulldorff95,
+	author = {M. Kulldorff and N. Nagarwalla},
+	title = {Spatial disease clusters: detection and inference},
+	journal = {Statistics in Medicine},
+	year = {1995},
+	volume = {14},
+	pages = {799-810},
+}
+
+@ARTICLE{Waller06,
+	author = {L. A. Waller and E. G. Hill and R. A. Rudd},
+	title = {The geography of power: statistical performance of tests of clusters and clustering in heterogeneous populations},
+	journal = {Statistics in Medicine},
+	year = {2006},
+	volume = {25},
+	pages = {853-865},
+}
+
+@ARTICLE{Kulldorff03,
+	author = {M. Kulldorff and T. Tango and P. J. Park},
+	title = {Power comparisons for disease clustering tests},
+	journal = {Computational Statistics \& Data Analysis},
+	year = {2003},
+	volume = {42},
+	pages = {665-684},
+}
+
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+	journal = {Biometrics},
+	month = {Sep.},
+	year = {2000},
+	volume = {56},
+	number = {3},
+	pages = {922-935},
+}
+
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+	author = {R. E. Gangnon},
+	title = {Impact of prior choice on local {B}ayes factors for cluster detection},
+	journal = {Statistics in Medicine},
+	year = {2006},
+	volume = {25},
+	pages = {883-895},
+}
+
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+	title = {Bayesian detection of clusters and discontinuities in disease maps},
+	journal = {Biometrics},
+	month = {Mar.},
+	year = {2000},
+	volume = {56},
+	number = {1},
+	pages = {13-21},
+}
+
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+	journal = {American Journal of Epidemiology},
+	month = {Jul.},
+	year = {1990},
+	volume = {132},
+	number = {Supp 1},
+	pages = {136-143},
+}
+
+@ARTICLE{Gangnon01,
+	author = {R. E. Gangnon and M. K. Clayton},
+	title = {A weighted average likelihood ratio test for spatial clustering of disease},
+	journal = {Statistics in Medicine},
+	year = {2001},
+	volume = {20},
+	number = {19},
+	pages = {2977-2987},
+}
+
+@ARTICLE{Waller93,
+	author = {L. A. Waller and B. W. Turnbull},
+	title = {The effects of scale on tests for disease clustering},
+	journal = {Statistics in Medicine},
+	year = {1993},
+	volume = {12},
+	pages = {1869-1884},
+}
+
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+	title = {Detection of clusters},
+	journal = {Methodology of enquiries into disease clustering, Small Area Health Statistics Unit, London},
+	year = {1989},
+	pages = {17-20},
+}
+
+@ARTICLE{Stone88,
+	author = {R. A. Stone},
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+	journal = {Statistics in Medicine},
+	year = {1988},
+	volume = {7},
+	pages = {649-660},
+}
+
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+	author = {G. M. Jacquez (ed)},
+	title = {Proceedings of workshop on statistics and computing in disease clustering},
+	journal = {Statistics in Medicine},
+	year = {1993},
+	volume = {12},
+	pages = {1751-1968},
+}
+
+@ARTICLE{Jacquez96,
+	author = {G. M. Jacquez (ed)},
+	title = {Proceedings of conference on statistics and computing in disease clustering},
+	journal = {Statistics in Medicine},
+	year = {1996},
+	volume = {15},
+	pages = {681-952},
+}
+
+@ARTICLE{Lawson95,
+	author = {A. B. Lawson and L. A. Waller and A. Biggeri (ed.)},
+	title = {Spatial disease patterns},
+	journal = {Statistics in Medicine},
+	year = {1995},
+	volume = {14},
+	pages = {2289-2508},
+}
+
+@ARTICLE{Lawson06,
+	author = {A. B. Lawson and R. E. Gangnon and D. Wartenberg (ed.)},
+	title = {Developments in disease cluster detection},
+	journal = {Statistics in Medicine},
+	year = {2006},
+	volume = {25},
+	pages = {721-916},
+}
+
+@ARTICLE{Waller92,
+	author = {L. A. Waller and B. W. Turnbull and L. C. Clark and P. Nasca},
+	title = {Chronic disease surveillance and testing of clustering of disease and exposure: application to leukemia incidence and {TCE}-contaminated dumpsites in upstate {N}ew {Y}ork},
+	journal = {Environmetrics},
+	year = {1992},
+	volume = {3},
+	pages = {281-300},
+}
+
+@ARTICLE{Besag91:2,
+	author = {J. Besag and J. York and A. Molli{\'e}},
+	title = {Bayesian image restoration, with two applications in spatial statistics},
+	journal = {Annals of the Institute of Statistical Mathematics},
+	year = {1991},
+	volume = {43},
+	number = {1},
+	pages = {{1-59}},
+}
+
+@ARTICLE{Wartenberg90,
+	author = {D. Wartenberg and M. Greenberg},
+	title = {Detecting disease cluster:  the importance of power},
+	journal = {American Journal of Epidemiology},
+	month = {Jul.},
+	year = {1990},
+	volume = {132},
+	number = {(suppl 1)},
+	pages = {156-166},
+}
+
+@ARTICLE{Elliott01,
+	author = {P. Elliott and J. Wakefield},
+	title = {Disease clusters:  should they be investigated, and, if so, when and how?},
+	journal = {Journal of the Royal Statistical Society: Series A},
+	year = {2001},
+	volume = {164},
+	number = {1},
+	pages = {{3-12}},
+}
+
+@ARTICLE{Rothman90,
+	author = {K. J. Rothman},
+	title = {A sobering start for the cluster busters' conference},
+	journal = {American Journal of Epidemiology},
+	month = {Jul.},
+	year = {1990},
+	volume = {132},
+	number = {(suppl 1)},
+	pages = {S6-S13},
+}
+
+@ARTICLE{Wartenberg01,
+	author = {D. Wartenberg},
+	title = {Investigating disease clusters:  why, when and how?},
+	journal = {Journal of the Royal Statistical Society: Series A},
+	year = {2001},
+	volume = {164},
+	number = {1},
+	pages = {13-22},
+}
+
+@ARTICLE{Trumbo00,
+	author = {C. W. Trumbo},
+	title = {Public Requests for Cancer Cluster Investigations: A Survey of State Health Departments},
+	journal = {American Journal of Public Health},
+	month = {Aug.},
+	year = {2000},
+	volume = {90},
+	number = {8},
+	pages = {1300-1302},
+}
+
+@ARTICLE{Wartenberg95,
+	author = {D. Wartenberg},
+	title = {Should we boost or bust cluster investigations?},
+	journal = {Epidemiology},
+	month = {Nov.},
+	year = {1995},
+	volume = {6},
+	number = {6},
+	pages = {575-576},
+}
+
+@ARTICLE{Hardy90,
+	author = {R. J. Hardy and G. D. Schroder and S. P. Cooper and P. A. Buffler and H. M. Prichard and M. Crane},
+	title = {A surveillance system for assessing health effects from hazardous exposures},
+	journal = {American Journal of Epidemiology},
+	month = {Jul.},
+	year = {1990},
+	volume = {132},
+	number = {(suppl 1)},
+	pages = {S32-S42},
+}
+
+@ARTICLE{Kulldorff01,
+	author = {M. Kulldorff},
+	title = {Prospective time periodic geographical disease surveillance using a scan statistic},
+	journal = {Journal of the Royal Statistical Society: Series A},
+	year = {2001},
+	volume = {164},
+	number = {1},
+	pages = {61-72},
+}
+
+@ARTICLE{Wakefield09,
+	author = {J. Wakefield},
+	title = {Bayes factors for genome-wide association studies: comparisons with $P$-values},
+	journal = {Genetic Epidemiology},
+	year = {2009},
+	volume = {33},
+	number = {1},
+	pages = {79-86},
+}
+
+@ARTICLE{Wakefield08,
+	author = {J. Wakefield},
+	title = {Reporting and interpretation in genome-wide association studies},
+	journal = {International Journal of Epidemiology},
+	month = {Feb.},
+	year = {2008},
+}
+
+@ARTICLE{Wakefield07,
+	author = {J. Wakefield},
+	title = {A {B}ayesian measure of the probability of false discovery in genetic epidemiology studies},
+	journal = {American Journal of Human Genetics},
+	month = {Jul.},
+	year = {2007},
+	volume = {81},
+	number = {2},
+	pages = {208-227},
+}
+
+@ARTICLE{Wakefield08:2,
+	author = {J. Wakefield and S. Haneuse},
+	title = {Overcoming Ecologic Bias using the Two-Phase Study Design},
+	journal = {American Journal of Epidemiology},
+	year = {2008},
+	volume = {167},
+	pages = {908-916},
+}
+
+@ARTICLE{Casella87,
+	author = {G. Casella and R. L. Berger},
+	title = {Reconciling {B}ayesian and frequentist evidence in the one-sided testing problem},
+	journal = {Journal of the American Statistical Association},
+	month = {Mar.},
+	year = {1987},
+	volume = {82},
+	number = {397},
+	pages = {106-111},
+}
+
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+	author = {S. N. Goodman},
+	title = {Toward evidence-based medical statistics. 2: the {B}ayes factor},
+	journal = {Annals of Internal Medicine},
+	year = {1999},
+	volume = {130},
+	number = {12},
+	pages = {1005-1013},
+}
+
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+	author = {D. W. K. Andrews},
+	title = {The large sample correspondence between classical hypothesis tests and {B}ayesian posterior ddds tests},
+	journal = {Econometrica},
+	month = {Sep.},
+	year = {1994},
+	volume = {62},
+	number = {5},
+	pages = {1207-1232},
+}
+
+@ARTICLE{Berger87:1,
+	author = {J. O. Berger and T. Sellke},
+	title = {Testing a point null hypothesis:  the irreconcilability of $p$-values and evidence},
+	journal = {Journal of the American Statistical Association},
+	month = {Mar.},
+	year = {1987},
+	volume = {82},
+	number = {397},
+	pages = {112-122},
+}
+
+@ARTICLE{Kass95,
+	author = {R. E. Kass and A. E. Raftery},
+	title = {Bayes factors},
+	journal = {Journal of the American Statistical Association},
+	month = {Jun.},
+	year = {1995},
+	volume = {90},
+	number = {430},
+	pages = {773-795},
+}
+
+@ARTICLE{Berger87:2,
+	author = {J. O. Berger and M. Delampady},
+	title = {Testing precise hypotheses},
+	journal = {Statistical Science},
+	month = {Aug.},
+	year = {1987},
+	volume = {2},
+	number = {3},
+	pages = {317-335},
+}
+
+@ARTICLE{Jones98,
+	author = {M. E. Jones and A. J. Swerdlow},
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+	journal = {American Journal of Epidemiology},
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+	volume = {148},
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+	pages = {1012-1017},
+}
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+	pages = {434-442},
+}
+
+@ARTICLE{Clayton87,
+	author = {D. Clayton and J. Kaldor},
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+	volume = {43},
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+}
+
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+	author = {I. Baris and L. Simonato and M. Artvinli and F. Pooley and R. Saracci and J. Skidmore and C. Wagner},
+	title = {Epidemiological and environmental evidence of the health effects of exposure to erionite fibres: a four-year study in the Cappadocian region of Turkey},
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+	number = {1},
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+}
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+	year = {1986},
+	volume = {101},
+	number = {2},
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+}
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+	author = {P. Elliot and D. Wartenberg},
+	title = {Spatial Epidemiology:  Current Approaches and Future Challenges},
+	journal = {Environmental Health Perspectives},
+	month = {Jun.},
+	year = {2004},
+	volume = {112},
+	number = {9},
+	pages = {998-1006},
+}
+
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+	author = {M. Feychting and A. Ahlbom},
+	title = {Magnetic fields and cancer in children residing near Swedish high-voltage power lines.},
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+}
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+	journal = {Occupational and Environmental Medicine },
+	year = {1999},
+	volume = {54},
+	pages = {101-107},
+}
+
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+	author = {P. Elliott and D. Briggs and S. Morris and C. de Hoogh and C. Hurt and T. K. Jensen},
+	title = {Risk of adverse birth outcomes in populations living near landfill sites},
+	journal = {British Medical Journal},
+	year = {2001},
+	volume = {323},
+	pages = {363-368},
+}
+
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+	author = {H. Dolk and P. Elliott and G. Shaddick and P. Walls and B. Thakrar},
+	title = {Cancer incidence near radio and television transmitters in {G}reat {Britain}: All high power transmitters},
+	journal = {American Journal of Epidemiology},
+	year = {1997},
+	volume = {145},
+	pages = {{10-17}},
+}
+
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+	author = {H. Dolk and G. Shaddick and P. Walls and C. Grundy and B. Thakrar and I. Kleinschmidt et al.},
+	title = {Cancer incidence near radio and television transmitters in {G}reat {Britain}: {S}utton {C}oldfield transmitter},
+	journal = {American Journal of Epidemiology},
+	year = {1997},
+	volume = {145},
+	pages = {{1-9}},
+}
+
+@ARTICLE{Aylin99,
+	author = {P. Aylin and R. Maheswaran and J. Wakefield and S. Cockings and L. Jarup and R. Arnold et al.},
+	title = {A national facility for small area disease mapping and rapid initial assessment of apparent disease clusters around a point source: the {UK} {S}mall {A}rea {H}ealth {S}tatistics {U}nit},
+	journal = {Journal of Public Health Medicine},
+	year = {1999},
+	volume = {21},
+	number = {3},
+	pages = {289-298},
+}
+
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+	author = {P. J. Diggle and S. E. Morris and P. Elliott and G. Shaddick},
+	title = {Regression modelling of disease risk in relation to point sources},
+	journal = {Journal of the Royal Statistical Society: Series A},
+	year = {1997},
+	volume = {160},
+	pages = {491-505},
+}
+
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+	author = {E. J. Pebesma and R. S. Bivand},
+	title = {Classes and methods for spatial data in {R}},
+	journal = {R News},
+	month = {Nov.},
+	year = {2005},
+	volume = {5},
+	number = {2},
+	pages = {{9-13}},
+}
+
+@ARTICLE{Mantel68,
+	author = {N. Mantel and C. R. Stark},
+	title = {Computation of indirect-adjusted rates in the presence of confounding},
+	journal = {Biometrics},
+	year = {1968},
+	volume = {24},
+	pages = {23-37},
+}
+
+@ARTICLE{Goodman99:1,
+	author = {S. N. Goodman},
+	title = {Toward evidence-based medical statistics. 1: the $p$-value fallacy},
+	journal = {Annals of Internal Medicine },
+	year = {1999},
+	volume = {130},
+	number = {12},
+	pages = {995-1004},
+}
+
+@ARTICLE{Hubbard03,
+	author = {R. Hubbard and M. J. Bayarri},
+	title = {Confusion over measures of evidence ($p$'s) versus errors ($\alpha$'s) in classical statistical testing},
+	journal = {The American Statistician},
+	month = {Aug.},
+	year = {2003},
+	volume = {57},
+	number = {3},
+	pages = {171-178},
+}
+
+@ARTICLE{Berry08,
+	author = {S. Berry and K. Viele},
+	title = {A note on hypothesis testing with random sample sizes and its relationship to {B}ayes factors},
+	journal = {Journal of Data Science},
+	year = {2008},
+	volume = {6},
+	pages = {75-87},
+}
+
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+	author = {G. Verbeke and G Molenberghs},
+	title = {The use of score tests for inference on variance components},
+	journal = {Biometrics},
+	month = {Jun.},
+	year = {2003},
+	volume = {59},
+	pages = {254-262},
+}
+
+@ARTICLE{Westfall97,
+	author = {P. H. Westfall and W. O. Johnson and J. M. Utts},
+	title = {A {B}ayesian perspective on the {B}onferroni adjustment},
+	journal = {Biometrika},
+	year = {1997},
+	volume = {84},
+	number = {2},
+	pages = {419-427},
+}
+
+@ARTICLE{Goodman98,
+	author = {S. N. Goodman},
+	title = {Multiple comparisons, explained},
+	journal = {American Journal of Epidemiology},
+	year = {1998},
+	volume = {147},
+	number = {9},
+	pages = {807-812},
+}
+
+@ARTICLE{Delongchamp04,
+	author = {R. R. Delongchamp and J. F. Bowyer and J. J. Chen and R. L. Kodell},
+	title = {Multiple-testing strategy for analyzing c{DNA} array data on gene expression},
+	journal = {Biometrics},
+	month = {Sep.},
+	year = {2004},
+	volume = {60},
+	pages = {774-782},
+}
+
+@ARTICLE{Firth88,
+	author = {D. Firth},
+	title = {Multiplicative errors:  log-normal or gamma?},
+	journal = {Journal of the Royal Statistical Society: Series B},
+	year = {1988},
+	volume = {50},
+	number = {2},
+	pages = {266-268},
+}
+
+@ARTICLE{Lehmann58,
+	author = {E. L. Lehmann},
+	title = {Significance level and power},
+	journal = {Annals of Mathematical Statistics},
+	year = {1958},
+	volume = {29},
+	number = {4},
+	pages = {1167-1176},
+}
+
+@ARTICLE{Naus65,
+	author = {J. I. Naus},
+	title = {The distribution of the size of the maximum cluster points of a line},
+	journal = {Journal of the American Statistical Association},
+	month = {Jun.},
+	year = {1965},
+	volume = {60},
+	number = {310},
+	pages = {532-538},
+}
+
+@ARTICLE{Dwass57,
+	author = {M. Dwass},
+	title = {Modified randomization test for nonparametric hypotheses},
+	journal = {Applied Statistics},
+	year = {1957},
+	volume = {28},
+	pages = {181-187},
+}
+
+@ARTICLE{Bland04,
+	author = {J. M. Bland and D. G. Altman},
+	title = {The logrank test},
+	journal = {British Medical Journal},
+	month = {May},
+	year = {2004},
+	volume = {328},
+	pages = {1073},
+}
+
+@ARTICLE{Walker67,
+	author = {A. M. Walker},
+	title = {On the asymptotic behaviour of posterior distributions},
+	journal = {Journal of the Royal Statistical Society: Series B},
+	month = {},
+	year = {1967},
+	volume = {31},
+	pages = {80-88},
+}
+
+@ARTICLE{Besag95,
+	author = {J. Besag and P.J. Green and D. Higdon and K. Mengersen},
+	title = {Bayesian computation and stochastic systems (with discussion)},
+	journal = {Statistical Science},
+	year = {1995},
+	volume = {10},
+	pages = {{3-66}},
+}
+
+@BOOK{DeGroot70,
+	author = {M. H. DeGroot},
+	title = {Optimal Statistical Decisions},
+	publisher = {McGraw-Hill},
+	address = {New York},
+	year = {1970},
+	edition = {1st},
+}
+
+@BOOK{Berger85,
+	author = {J. O. Berger},
+	title = {Statistical Decision Theory and Bayesian Analysis},
+	publisher = {Springer-Verlag},
+	address = {New York},
+	year = {1985},
+	series = {Springer Series in Statistics},
+	edition = {2nd},
+}
+
+@BOOK{Jeffreys61,
+	author = {H. Jeffreys},
+	title = {Theory of Probability},
+	publisher = {Oxford University Press},
+	address = {Oxford},
+	year = {1961},
+	series = {International Series of Monographs on Physics},
+	edition = {3rd},
+}
+
+@BOOK{Robert07,
+	author = {C. P. Robert},
+	title = {The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation},
+	publisher = {Springer},
+	address = {New York},
+	year = {2007},
+	series = {Springer Texts in Statistics},
+	edition = {1st},
+}
+
+@BOOK{Lehmann05,
+	author = {E. L. Lehmann and J. P. Romano},
+	title = {Testing Statistical Hypotheses},
+	publisher = {Springer},
+	address = {New York},
+	year = {2005},
+	series = {Springer Texts in Statistics},
+	edition = {3rd},
+}
+
+@BOOK{Casella02,
+	author = {G. Casella and R. L. Berger},
+	title = {Statistical Inference},
+	publisher = {Duxbury},
+	address = {Pacific Grove CA},
+	year = {2002},
+	series = {Duxbury Advanced Series},
+	edition = {2nd},
+}
+
+@BOOK{Schervish95,
+	author = {M. J. Schervish},
+	title = {Theory of Statistics},
+	publisher = {Springer},
+	address = {New York},
+	year = {1995},
+	series = {Springer Series in Statistics},
+	edition = {2nd},
+}
+
+@BOOK{Rue05,
+	author = {H. Rue and L. Held},
+	title = {Gaussian Markov Random Fields:Theory and Application},
+	publisher = {Chapman and Hall/CRC},
+	year = {2005},
+	series = {Monographs on Statistics \& Applied Probability Statistical Science},
+	edition = {1st},
+}
+
+@BOOK{Elliott00,
+	author = {P. Elliott and J. Wakefield and N. Best and D. Briggs},
+	title = {Spatial Epidemiology: Methods and Applications},
+	publisher = {Oxford University Press},
+	address = {Oxford},
+	year = {2000},
+	series = {Oxford Medical Publications},
+	edition = {2nd},
+}
+
+@BOOK{Waller04,
+	author = {L. A. Waller and C. A. Gotway},
+	title = {Applied spatial statistics for public health data},
+	publisher = {John Wiley \& Sons},
+	address = {New York},
+	year = {2004},
+	series = {Wiley Series in Probability and Statistics},
+	edition = {1st},
+}
+
+@BOOK{Upton85,
+	author = {G. Upton and B. Fingleton},
+	title = {Spatial data analysis by example, volume 1},
+	publisher = {John Wiley \& Sons},
+	address = {New York},
+	year = {1985},
+}
+
+@BOOK{COMARE88,
+	author = {Committee on Medical Aspects of Radiation in the Environment},
+	title = {Investigation of the possible increased incidence of leukemia in young people near the {D}ounreay {N}uclear {E}stablishment, {C}aithness, {S}cotland},
+	publisher = {HMSO},
+	address = {London},
+	year = {1988},
+}
+
+@BOOK{Hosmer00,
+	author = {D. W. Hosmer and S. Lemeshow},
+	title = {Applied Logistic Regression},
+	publisher = {John Wiley \& Sons},
+	address = {New York},
+	year = {2000},
+	series = {Wiley Series in Probability and Statistics},
+	edition = {2nd},
+}
+
+@BOOK{Dobson02,
+	author = {A. J. Dobson},
+	title = {An Introduction to Generalized Linear Models},
+	publisher = {Chapman and Hall},
+	year = {2002},
+	series = {Texts in Statistical Science},
+	edition = {2nd},
+}
+
+@BOOK{Breslow87,
+	author = {N. E. Breslow and N. E. Day},
+	title = {Statistical {M}ethods in {C}ancer {R}esearch. Vol. II: The analysis of cohort studies},
+	publisher = {International Agency for Research on Cancer},
+	address = {Lyon},
+	year = {1987},
+	series = {IARC Scientific Publications},
+	number = {82},
+}
+
+@BOOK{Hastie01,
+	author = {T. Hastie and R. Tibshirani and J. Friedman},
+	title = {The Elements of Statistical Learning: Data Mining, Inference and Prediction},
+	publisher = {Springer},
+	address = {New York},
+	year = {2001},
+	series = {Springer Series in Statistics},
+	edition = {1st},
+}
+
+@BOOK{Alexander96,
+	author = {F. E. Alexander and P. Boyle},
+	title = {Methods for investigating localized clustering of disease},
+	publisher = {International Agency for Research on Cancer},
+	address = {Lyon},
+	year = {1996},
+	series = {IARC Scientific Publications},
+	number = {135},
+}
+
+@BOOK{Garthwaite02,
+	author = {P. H. Garthwaite and I. T. Jolliffe and B. Jones},
+	title = {Statistical Inference},
+	publisher = {Oxford Science Publications},
+	address = {Oxford},
+	year = {2002},
+	edition = {1st},
+}
+
+@BOOK{Kemp85,
+	author = {I. Kemp and P. Boyle and M. Smans and C. Muir},
+	title = {Atlas of cancer in {S}cotland, 1975-1980, incidence and epidemiologic perspective},
+	publisher = {International Agency for Research on Cancer},
+	address = {Lyon},
+	year = {1985},
+	series = {IARC Scientific Publications},
+	number = {72},
+}
+
+@BOOK{Bivand08,
+	author = {R. S. Bivand and E. J. Pebesma and V. Gomez-Rubio},
+	title = {Applied Spatial Data Analysis with R},
+	publisher = {Springer},
+	address = {New York},
+	year = {2008},
+	series = {Springer Series in Statistics},
+	edition = {1st},
+}
+
+@INPROCEEDINGS{Neill05,
+	author = {D. Neill and A. Moore and G. Cooper},
+	title = {A {B}ayesian spatial scan statistic},
+	booktitle = {Advances in Neural Information Processing Systems},
+	year = {2005},
+	volume = {18},
+	publisher = {Y. Weiss, et al.},
+	pages = {1003-1010},
+}
+
+@INCOLLECTION{Wakefield00,
+	author = {J. C. Wakefield and J. E. Kelsall and S. E. Morris},
+	title = {Clustering, cluster detection and spatial variation in risk},
+	booktitle = {Spatial Epidemiology: Methods and Applications},
+	year = {2000},
+	publisher = {Oxford University Press},
+	editor = {P. Elliott and J. Wakefield and N. Best and D. Briggs},
+	address = {Oxford},
+	pages = {128-152},
+}
+
+@INCOLLECTION{Newell96,
+	author = {J. N. Newell and J. E. Besag},
+	title = {The detection of small-area database anomalies},
+	booktitle = {Methods for investigating localized clustering of disease},
+	year = {1996},
+	publisher = {IARC Scientific Publications No 135},
+	editor = {F. E. Alexander and P. Boyle},
+	address = {Lyon},
+	pages = {87-100},
+}
+
+@INCOLLECTION{Thacker92,
+	author = {S. B. Thacker and R. L. Berkelman},
+	title = {History of public health surveillance},
+	booktitle = {Public Health Surveillance},
+	year = {1992},
+	publisher = {Van Nostrand Reinhold},
+	editor = {W. Halperin and L. E. Baker and R. R. Monson},
+	address = {New York},
+	pages = {{1-15}},
+}
+
+@INCOLLECTION{Alexander92,
+	author = {F. E. Alexander and J. Cuzick},
+	title = {Methods for the assessment of disease clusters},
+	booktitle = {Geographical and Environmental Epidemiology:  Methods for Small-ares Studies},
+	year = {1992},
+	publisher = {Oxford University Press},
+	editor = {P. Elliott and J. Cuzick and D. English and R. Stern},
+	address = {Oxford},
+	pages = {238-250},
+}
+
+@PHDTHESIS{Rosenkranz92,
+	author = {S. L. Rosenkranz},
+	title = {The {B}ayes factor for model evaluation in a hierarchical poisson model for area counts},
+	school = {Dept of Biostatistics, University of Washington},
+	year = {1992},
+}
+
+@MISC{CDC,
+	author = {Centers for Disease Control and Prevention},
+	title = {Cancer Clusters {FAQ}},
+	howpublished = {{http://www.cdc.gov/nceh/clusters/faq.htm}},
+}
+
+@MISC{SatScan,
+	author = {M. Kulldorff},
+	title = {{SatScan{\texttrademark}}:  Software for the spatial, temporal, and space-time scan statistics},
+	howpublished = {{http://www.satscan.org/}},
+	year = {2005},
+}
+
diff --git a/vignettes/Manuscript/wlpeerj.cls b/vignettes/Manuscript/wlpeerj.cls
index cf4d9f2..af0174e 100644
--- a/vignettes/Manuscript/wlpeerj.cls
+++ b/vignettes/Manuscript/wlpeerj.cls
@@ -41,8 +41,8 @@
                 labelsep=period,%
                 justification=raggedright]{caption}
 
-% \RequirePackage{natbib}
-% \bibliographystyle{apalike}
+\RequirePackage{natbib}
+\bibliographystyle{apalike}
 
 %
 % writeLaTeX logo
@@ -133,8 +133,7 @@
 \RequirePackage{enumitem}
 %\setlist{nolistsep} % Uncomment to remove spacing between items in lists (enumerate, itemize)
 
-% Remove brackets from numbering in List of References
-\renewcommand{\@biblabel}[1]{\bfseries\color{color1}\textsuperscript{[#1]}}
+
 
 %
 % article meta data