preparing for cran submission

.Rbuildignore

@@ -4,3 +4,5 @@
 sandbox
 ^data-raw$
 ^\.github$
+^figs$
+^cran-comments\.md$

.gitignore

@@ -2,4 +2,4 @@
 .Rhistory
 .RData
 .Ruserdata
-*.Rproj
+*.Rproj

README.Rmd

@@ -23,38 +23,7 @@ knitr::opts_chunk$set(
 
 `saeczi` is an R package that implements a small area estimator that uses a two-stage modeling approach for zero-inflated response variables. In particular, we are working with variables that follow a semi-continuous distribution with a mixture of zeroes and positive continuously distributed values. An example can be seen below.
 
-```{r zi-plot, dpi = 600, fig.width = 10, fig.height=6, echo=F, message=F, warning=F}
-set.seed(6)
-library(tidyverse)
-
-z <- tibble(
-  z = rep(0, 2500)
-)
-
-nz <- tibble(
-  nz = rgamma(1000, 3, 4)
-)
-
-ggplot() +
-  geom_density(
-    data = filter(nz, nz > 0.25),
-    aes(x = nz, y = ..count..),
-    fill = "#023047",
-    color = "white",
-    alpha = 0.9
-    ) +
-  geom_histogram(
-    data = z,
-    aes(x = z),
-    fill = "#023047",
-    color = "white",
-    alpha = 0.9
-    ) +
-  labs(
-    x = "Response Variable"
-  ) +
-  theme_bw()
-```
+![](figs/README-zi-plot-1.png)
 
 `saeczi` first fits a linear mixed model to the non-zero portion of the response and then a generalized linear mixed model with binomial response to classify the probability of zero for a given data point. In estimation these models are each applied to new data points and combined to compute a final prediction. 
 
@@ -108,20 +77,4 @@ result$res |> head()
 
 This output format allows for easy results plotting
 
-```{r res_plot, dpi = 600, echo = FALSE, warning = FALSE, message=FALSE}
-library(tidyverse)
-result$res |>
-  mutate(domain = fct_reorder(domain, est)) |> 
-  ggplot() +
-  geom_point(aes(x = domain, y = est), alpha = 0.7, size = 0.8) +
-  geom_errorbar(aes(x = domain,
-                    ymin = est - 1.96*sqrt(mse),
-                    ymax = est + 1.96*sqrt(mse)),
-                color = "midnightblue") +
-  theme_bw() +
-  theme(
-    axis.text.x = element_text(angle = 45, vjust = 0.7)
-  ) +
-  labs(x = "County", y = "Biomass")
-```
-
+![](figs/README-res_plot-1.png)

README.md

@@ -17,7 +17,7 @@ In particular, we are working with variables that follow a
 semi-continuous distribution with a mixture of zeroes and positive
 continuously distributed values. An example can be seen below.
 
-![](README-zi-plot-1.png)<!-- -->
+![](figs/README-zi-plot-1.png)
 
 `saeczi` first fits a linear mixed model to the non-zero portion of the
 response and then a generalized linear mixed model with binomial
@@ -70,8 +70,13 @@ result <- saeczi(samp_dat = samp,
                  mse_est = TRUE,
                  B = 500,
                  parallel = FALSE)
-#> creating bootstraps...
-#> estimating mse...
+#> ================================================================================
+#> • Fitting Models...
+#> • Beginning Bootstrap...
+#>                                                                   - Fitting to Bootstrap samples ✓
+#>                                       - Estimating MSE ✓
+#> • Compiling Results...
+#> ================================================================================
 ```
 
 The function returns the original call, a data frame containing the
@@ -91,14 +96,14 @@ few rows of the results:
 ``` r
 result$res |> head()
 #>   domain       mse      est
-#> 1  41001 374.82344 14.85495
-#> 2  41003  10.97949 97.74967
-#> 3  41005 708.82223 86.02207
-#> 4  41007 251.39503 76.24752
-#> 5  41009 382.08787 70.28624
-#> 6  41011  77.06537 87.65072
+#> 1  41001 173.58940 14.85495
+#> 2  41003  42.43279 97.74967
+#> 3  41005 581.01815 86.02207
+#> 4  41007  35.98329 76.24752
+#> 5  41009 222.58951 70.28624
+#> 6  41011 213.38928 87.65072
 ```
 
 This output format allows for easy results plotting
 
-![](README-res_plot-1.png)<!-- -->
+![](figs/README-res_plot-1.png)

cran-comments.md

@@ -0,0 +1,5 @@
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* This is a new release.