add install_github instructions

DESCRIPTION

@@ -7,7 +7,8 @@ BugReports: https://github.com/ConnorDonegan/geostan/issues
 Authors@R: c(
     person("Connor", "Donegan", , "connor.donegan@gmail.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-9698-5443")),
-    person("Mitzi", "Morris", role = "ctb")
+    person("Mitzi", "Morris", role = "ctb"),
+    person("Amy", "Tims", role = "ctb")
   )
 Description: For Bayesian inference with spatial data, provides exploratory spatial analysis tools, multiple spatial model specifications, spatial model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health surveillance data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
 License: GPL (>= 3) 

NEWS.md

@@ -9,6 +9,8 @@ The model fitting functions (`stan_glm`, `stan_car`, etc.) now allow for missing
 
 The `prep_icar_data` function, which is used inside `stan_icar`, did not have the expected behavior in all cases - this has been fixed thanks to this [pull request](https://github.com/ConnorDonegan/geostan/pull/18).
 
+This update can be installed from github and is not on CRAN, at least for now. The package may move off of CRAN in the future. New install instructions are found on the package home page https://connordonegan.github.io/geostan/
+
 # geostan 0.5.4
 
 Minor updates to the vignettees and documentation, also re-compiled geostan models using the latest StanHeaders (fixing an error on CRAN).

README.Rmd

@@ -32,12 +32,26 @@ Features include:
 
 ## Installation
 
-Install **geostan** from CRAN using:
+Using your R console, you can install **geostan** from CRAN:
 
 ``` r
 install.packages("geostan")
 ```
 
+You can install the latest version (0.6.0) from the package github repository:
+
+```r
+if (!require('devtools')) install.packages('devtools')
+devtools::install_github("connordonegan/geostan")
+```
+
+If you are using Windows, you may need to install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) first. To install Rtools:
+
+1. Visit the Rtools site: https://cran.r-project.org/bin/windows/Rtools/
+2. Select the version that corresponds to the version of R that you have installed (e.g., R 4.3).
+3. After selecting the correct version, look for the "Install Rtools" section (just below the introductory text) and click on the "installer" to download it. For example, for Rtools43 (for R version 4.3), click on "<span style="color:blue">Rtools43 installer</span>."
+4. Go to the `.exe` file you just downloaded and double-click to begin installation of Rtools.
+
 ## Support
 
 All functions and methods are documented (with examples) on the website [reference](https://connordonegan.github.io/geostan/reference/index.html) page. See the package [vignettes](https://connordonegan.github.io/geostan/articles/index.html) for more on exploratory spatial analysis, spatial measurement error models, spatial regression with raster layers, and building custom spatial model in Stan.

README.md

@@ -38,12 +38,36 @@ Features include:
 
 ## Installation
 
-Install **geostan** from CRAN using:
+Using your R console, you can install **geostan** from CRAN:
 
 ``` r
 install.packages("geostan")
 ```
 
+You can install the latest version (0.6.0) from the package github
+repository:
+
+``` r
+if (!require('devtools')) install.packages('devtools')
+devtools::install_github("connordonegan/geostan")
+```
+
+If you are using Windows, you may need to install
+[Rtools](https://cran.r-project.org/bin/windows/Rtools/) first. To
+install Rtools:
+
+1.  Visit the Rtools site:
+    <https://cran.r-project.org/bin/windows/Rtools/>
+2.  Select the version that corresponds to the version of R that you
+    have installed (e.g., R 4.3).
+3.  After selecting the correct version, look for the “Install Rtools”
+    section (just below the introductory text) and click on the
+    “installer” to download it. For example, for Rtools43 (for R
+    version 4.3), click on “<span style="color:blue">Rtools43
+    installer</span>.”
+4.  Go to the `.exe` file you just downloaded and double-click to begin
+    installation of Rtools.
+
 ## Support
 
 All functions and methods are documented (with examples) on the website
@@ -66,6 +90,7 @@ Load the package and the `georgia` county mortality data set:
 
 ``` r
 library(geostan)
+#> This is geostan version 0.6.0
 data(georgia)
 ```
 
@@ -82,8 +107,8 @@ A <- shape2mat(georgia, style = "B")
 mortality_rate <- georgia$rate.female * 10e3
 sp_diag(mortality_rate, georgia, w = A)
 #> 3 NA values found in x will be dropped from data x and matrix w
-#> Warning: Removed 3 rows containing
-#> non-finite values (`stat_bin()`).
+#> Warning: Removed 3 rows containing non-finite outside the scale range
+#> (`stat_bin()`).
 ```
 
 <img src="man/figures/README-unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
@@ -111,6 +136,8 @@ fit <- stan_car(deaths.female ~ offset(log(pop.at.risk.female)),
         family = poisson(),
         cores = 4, # for multi-core processing
         refresh = 0) # to silence some printing
+#> 3 NA values identified in the outcome variable
+#> Found in rows: 55, 126, 157
 #> 
 #> *Setting prior parameters for intercept
 #> Distribution: normal
@@ -135,8 +162,8 @@ diagnostics for spatial models:
 sp_diag(fit, georgia, w = A)
 #> Using sp_diag(y, shape, rates = TRUE, ...). To examine data as (unstandardized) counts, use rates = FALSE.
 #> 3 NA values found in x will be dropped from data x and matrix w
-#> Warning: Removed 3 rows containing missing
-#> values (`geom_pointrange()`).
+#> Warning: Removed 3 rows containing missing values or values outside the scale range
+#> (`geom_pointrange()`).
 ```
 
 <img src="man/figures/README-unnamed-chunk-5-1.png" style="display: block; margin: auto;" />
@@ -154,20 +181,20 @@ print(fit)
 #> Spatial method (outcome):  CAR 
 #> Likelihood function:  poisson 
 #> Link function:  log 
-#> Residual Moran Coefficient:  0.0015345 
-#> WAIC:  1290.42 
-#> Observations:  159 
+#> Residual Moran Coefficient:  0.0028665 
+#> WAIC:  1228.09 
+#> Observations:  156 
 #> Data models (ME): none
 #> Inference for Stan model: foundation.
 #> 4 chains, each with iter=2000; warmup=1000; thin=1; 
 #> post-warmup draws per chain=1000, total post-warmup draws=4000.
 #> 
-#>             mean se_mean    sd   2.5%    25%    50%    75%  97.5% n_eff  Rhat
-#> intercept -4.679   0.011 0.178 -4.888 -4.718 -4.674 -4.633 -4.480   267 1.014
-#> car_rho    0.925   0.001 0.059  0.776  0.896  0.937  0.968  0.998  2766 1.001
-#> car_scale  0.457   0.001 0.035  0.394  0.432  0.455  0.480  0.532  3518 1.000
+#>             mean se_mean    sd   2.5%    20%    50%    80%  97.5% n_eff Rhat
+#> intercept -4.673   0.002 0.089 -4.842 -4.730 -4.674 -4.621 -4.495  1764    1
+#> car_rho    0.923   0.001 0.058  0.777  0.882  0.937  0.972  0.995  2899    1
+#> car_scale  0.458   0.001 0.035  0.392  0.428  0.456  0.486  0.531  3989    1
 #> 
-#> Samples were drawn using NUTS(diag_e) at Thu Apr  4 16:19:11 2024.
+#> Samples were drawn using NUTS(diag_e) at Thu Apr 11 09:12:28 2024.
 #> For each parameter, n_eff is a crude measure of effective sample size,
 #> and Rhat is the potential scale reduction factor on split chains (at 
 #> convergence, Rhat=1).
@@ -183,19 +210,19 @@ mortality_est <- fitted(fit) * 10e3
 county_name <- georgia$NAME
 head( cbind(county_name, mortality_est) )
 #>           county_name      mean        sd      2.5%       20%       50%
-#> fitted[1]       Crisp 101.64041  9.519141  83.73978  93.36796 101.31662
-#> fitted[2]     Candler 137.30390 15.999801 108.73333 123.23183 136.35141
-#> fitted[3]      Barrow  94.19814  6.141840  82.55457  88.89596  94.11255
-#> fitted[4]      DeKalb  59.73895  1.559960  56.71691  58.39545  59.69778
-#> fitted[5]    Columbia  53.31653  3.216973  47.31274  50.57770  53.26367
-#> fitted[6]        Cobb  54.11649  1.518304  51.17859  52.82489  54.11122
+#> fitted[1]       Crisp 101.69298  9.508124  84.21700  93.70998 101.31671
+#> fitted[2]     Candler 137.51901 16.501093 107.21564 123.28521 136.65931
+#> fitted[3]      Barrow  94.31354  6.325440  82.41186  88.81864  94.08767
+#> fitted[4]      DeKalb  59.76484  1.635268  56.60461  58.36384  59.74816
+#> fitted[5]    Columbia  53.30993  3.258305  47.14342  50.62473  53.25321
+#> fitted[6]        Cobb  54.11545  1.491715  51.19031  52.86885  54.10357
 #>                 80%     97.5%
-#> fitted[1] 109.55245 120.85305
-#> fitted[2] 150.48176 170.97901
-#> fitted[3]  99.37661 106.39628
-#> fitted[4]  61.05971  62.76113
-#> fitted[5]  56.08382  59.67599
-#> fitted[6]  55.39974  57.13810
+#> fitted[1] 109.46104 121.41962
+#> fitted[2] 151.09549 172.61239
+#> fitted[3]  99.79433 106.73475
+#> fitted[4]  61.14575  62.90701
+#> fitted[5]  56.00826  59.86768
+#> fitted[6]  55.36081  57.12056
 ```
 
 The mortality estimates are stored in the column named “mean”, and the