fix check issues

NAMESPACE

@@ -62,17 +62,20 @@ importFrom(rlang,syms)
 importFrom(stats,.lm.fit)
 importFrom(stats,as.formula)
 importFrom(stats,cov)
+importFrom(stats,dnorm)
 importFrom(stats,family)
 importFrom(stats,fitted)
 importFrom(stats,formula)
 importFrom(stats,get_all_vars)
+importFrom(stats,lm.wfit)
 importFrom(stats,median)
 importFrom(stats,model.frame)
 importFrom(stats,model.matrix)
 importFrom(stats,na.fail)
 importFrom(stats,na.omit)
 importFrom(stats,nobs)
 importFrom(stats,residuals)
+importFrom(stats,sd)
 importFrom(stats,setNames)
 importFrom(stats,simulate)
 importFrom(stats,terms)

R/birdie-package.R

@@ -12,7 +12,7 @@
 #' @importFrom cli cli_inform cli_warn cli_abort
 #' @importFrom Rcpp sourceCpp
 #' @importFrom RcppParallel CxxFlags
-#' @importFrom stats na.omit na.fail .lm.fit fitted simulate median cov
+#' @importFrom stats na.omit na.fail .lm.fit lm.wfit fitted simulate median sd cov dnorm
 #' @importFrom stats terms get_all_vars model.frame model.matrix setNames
 #' @importFrom stats formula update.formula as.formula nobs family vcov residuals
 #' @importFrom utils tail unstack relist head packageVersion

R/birdie.R

@@ -11,7 +11,7 @@
 #' `birdie()` uses an expectation-maximization (EM) routine to find the maximum
 #' *a posteriori* (MAP) estimate for the specified model. Asymptotic
 #' variance-covariance matrices for the MAP estimate are available for the
-#' Categorical-Dirichlet model via bootstrapping (`se_boot`).
+#' Categorical-Dirichlet and Normal linear models via bootstrapping (`se_boot`).
 #'
 #' The Categorical-Dirichlet model is specified as follows: \deqn{
 #'     Y_i \mid R_i, X_i, \Theta \sim \text{Categorical}(\theta_{R_iX_i}) \\
@@ -36,8 +36,9 @@
 #'
 #' The Normal linear model is specified as follows: \deqn{
 #'     Y_i \mid R_i, \vec X_i, \Theta \sim \mathcal{N}(\vec X_i^\top\vec\theta, \sigma^2) \\
-#'     \pi(\sigma^2) \propto \sigma^{-2} \\
-#'     \beta \sim \mathcal{N}(0, \Sigma), \\
+#'     \sigma^2 \sim \text{Inv-Gamma}(n_\sigma/2, l_\sigma^2 n_\sigma/2) \\
+#'     \beta_{\text{intercept}} \sim \mathcal{N}(0, s^2_\text{int}) \\
+#'     \beta_k \sim \mathcal{N}(0, s^2_\beta), \\
 #' } where \eqn{\vec\theta} is a vector of linear model coefficients.
 #' Estimates for \eqn{\theta} and \eqn{\sigma} are stored in the
 #' `$beta` and `$sigma` elements of the fitted model object.
@@ -95,10 +96,10 @@
 #'   The default will work with the output of [bisg()].
 #' @param se_boot The number of bootstrap replicates to use to compute
 #'   approximate standard errors for the main model estimates. Only available
-#'   when `model="dir"`. When there are fewer than 1,000 individuals or 100 or
-#'   fewer replicates, a Bayesian bootstrap is used instead (i.e., weights are
-#'   drawn from a \eqn{\text{Dirichlet}(1, 1, ..., 1)} distribution, which produces more
-#'   reliable estimates.
+#'   when `familiy=cat_dir()` or `gaussian()`. When there are fewer than 1,000
+#'   individuals or 100 or fewer replicates, a Bayesian bootstrap is used
+#'   instead (i.e., weights are drawn from a \eqn{\text{Dirichlet}(1, 1, ...,
+#'   1)} distribution, which produces more reliable estimates.
 #' @param ctrl A list containing control parameters for the EM algorithm and
 #'   optimization routines. A list in the proper format can be made using
 #'   [birdie.ctrl()].
@@ -126,7 +127,7 @@
 #' birdie(r_probs, turnout ~ zip, data=pseudo_vf)
 #'
 #' fit = birdie(r_probs, turnout ~ (1 | zip), data=pseudo_vf,
-#'        ctrl=birdie.ctrl(abstol=1e-3))
+#'              family=cat_mixed(), ctrl=birdie.ctrl(abstol=1e-3))
 #'
 #' summary(fit)
 #' coef(fit)

cran-comments.md

@@ -18,9 +18,9 @@
 fine in a browser, but not at the command line with `curl -I -L`, 
 where it returns a 403 error.
 
-* C++14 is a requirement in order to compile Stan models as well.
+* C++14 is a requirement in order to compile Stan models.
 
-* GNU make is a SystemRequirement in order to compile Stan models.
+* GNU make is a SystemRequirement in order to compile Stan models as well.
 
 * Examples are \dontrun{} in census_race_geo_table() since they require an API
 key and may take some time to run.

vignettes/birdie.Rmd

@@ -110,14 +110,15 @@ For example, among White voters in a particular ZIP code, this assumption would
 
 We're now ready to estimate turnout by race.
 For this we'll use the `birdie()` function, and provide it with a formula describing our BIRDiE model, including our variable of interest `turnout` and our geography variable `zip`.
+We provide `family=cat_mixed()` to indicate that we want to fit a Categorical mixed-effects regression model for turnout.
 Here, we wrap `zip` in the `proc_zip()` function, which, among other things, recodes missing ZIP codes as "Other" so that the model doesn't encounter any missing data.
 The first argument to `birdie()` is `r_probs`, the racial probabilities.
 `birdie` knows how to handle its columns of because they came from this package.
 If you use a different package, the columns may be named differently.
 The `prefix` parameter to `birdie()` lets you specify the naming convention for your probabilities.
 
 ```{r messages=FALSE}
-fit = birdie(r_probs, turnout ~ (1 | proc_zip(zip)), data=pseudo_vf)
+fit = birdie(r_probs, turnout ~ (1 | proc_zip(zip)), data=pseudo_vf, family=cat_mixed())
 print(fit)
 ```