cran fixes

IQSS · Apr 11, 2024 · dfa3b86 · dfa3b86
1 parent 6a60450
commit dfa3b86
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Showing 7 changed files with 16 additions and 16 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: Amelia
 Version: 1.8.2
-Date: 2024-03-04
+Date: 2024-04-10
 Title: A Program for Missing Data
 Authors@R: c(
     person("James", "Honaker", email = "[email protected]", role = c("aut")),

diff --git a/R/ameliagui.r b/R/ameliagui.r
@@ -1,4 +1,4 @@
-#' Interactive GUI for Amelia
+q#' Interactive GUI for Amelia
 #'
 #' @name ameliagui
 #'
@@ -457,7 +457,7 @@ load.session <- function() {
                            "Re-save imputed data sets to the working directory?", icon =
                            "question", default = "yes", type = "yesno")
     if (tcltk::tclvalue(resave) == "yes") {
-      amelia.save(getAmelia("ameliaObject"),
+      amelia_save(getAmelia("ameliaObject"),
                   tcltk::tclvalue(getAmelia("outname")), as.numeric(tcltk::tclvalue(getAmelia("outnum"))))
     }
 
@@ -633,7 +633,7 @@ run.amelia <- function() {
                      "normal")
     tcltk::tkentryconfigure(getAmelia("main.menu.output"), 2, state = "normal")
     tcltk::tkconfigure(getAmelia("showLogButton"), state = "normal")
-    amelia.save(getAmelia("ameliaObject"),
+    amelia_save(getAmelia("ameliaObject"),
                 tcltk::tclvalue(getAmelia("outname")), as.numeric(tcltk::tclvalue(getAmelia("outnum"))))
     tcltk::tkgrid(getAmelia("allgood.label"), row = 2, column = 7,
            sticky ="e", padx = 10)
@@ -642,7 +642,7 @@ run.amelia <- function() {
 
 }
 
-amelia.save <- function(out,outname,m)  {
+amelia_save <- function(out,outname,m)  {
   save.type <- as.numeric(tcltk::tclvalue(getAmelia("outtype")))
   if (save.type == 1) {
     write.amelia(out, file.stem = outname, format = "csv",
@@ -2702,7 +2702,7 @@ environment(activateGUI) <- ameliaEnv
 environment(save.session) <- ameliaEnv
 environment(load.session) <- ameliaEnv
 environment(run.amelia) <- ameliaEnv
-environment(amelia.save) <- ameliaEnv
+environment(amelia_save) <- ameliaEnv
 environment(set.out) <- ameliaEnv
 environment(setTS) <- ameliaEnv
 environment(unsetTS) <- ameliaEnv

diff --git a/R/diag.r b/R/diag.r
@@ -292,7 +292,7 @@ overimpute <- function(output, var, draws = 20, subset, legend = TRUE, xlab,
 
   ## The argument list for an amelia output is now
   ## at "output$arguments"
-  prepped <- amelia.prep(x = data, arglist = output$arguments, incheck = FALSE)
+  prepped <- amelia_prep(x = data, arglist = output$arguments, incheck = FALSE)
 
   stacked.var <- match(var, prepped$subset.index[prepped$p.order])
   subset.var <- match(var, prepped$subset.index)
@@ -532,7 +532,7 @@ disperse <- function(output, m = 5, dims = 1, p2s = 0, frontend = FALSE, ...,
   }
 
                                         # prep the data and arguments
-  prepped<-amelia.prep(x=data, arglist=output$arguments)
+  prepped<-amelia_prep(x=data, arglist=output$arguments)
 
   if (p2s) cat("-- Imputation", "1", "--")
   if (frontend) {
@@ -897,7 +897,7 @@ tscsPlot <- function(output, var, cs, draws = 100, conf = .90,
     par(mfcol = c(nr, nc))
   }
 
-  prepped <- amelia.prep(x = data, arglist = output$arguments)
+  prepped <- amelia_prep(x = data, arglist = output$arguments)
   if (!is.null(prepped$blanks)) {
     data <- data[-prepped$blanks,]
     unit.rows <- which(csvar %in% cs)
@@ -919,7 +919,7 @@ tscsPlot <- function(output, var, cs, draws = 100, conf = .90,
   if (sum(miss) > 0) {
     for (i in 1:draws) {
       currtheta <- output$theta[,,ceiling(i/drawsperimp)]
-      imps[,i] <- amelia.impute(x = cross.sec, thetareal = currtheta,
+      imps[,i] <- amelia_impute(x = cross.sec, thetareal = currtheta,
                                 bounds = prepped$bounds,
                                 priors = prepped$priors,
                                 max.resample = output$arguments$max.resample)[,stacked.var]

diff --git a/R/emb.r b/R/emb.r
@@ -270,7 +270,7 @@ emarch<-function(x,p2s=TRUE,thetaold=NULL,startvals=0,tolerance=0.0001,priors=NU
 }
 
 ## Draw imputations for missing values from a given theta matrix
-amelia.impute<-function(x,thetareal,priors=NULL,bounds=NULL,max.resample=NULL){
+amelia_impute<-function(x,thetareal,priors=NULL,bounds=NULL,max.resample=NULL){
 
   indx<-indxs(x)                      # This needs x.NA
   if (!identical(priors,NULL)){
@@ -771,7 +771,7 @@ amelia.default <- function(x, m = 5, p2s = 1, frontend = FALSE, idvars = NULL,
   am.call <- match.call(expand.dots = TRUE)
   archv <- am.call
 
-  prepped<-amelia.prep(x = x, m = m, idvars = idvars, empri = empri, ts = ts,
+  prepped<-amelia_prep(x = x, m = m, idvars = idvars, empri = empri, ts = ts,
                        cs = cs, tolerance = tolerance, polytime = polytime,
                        splinetime = splinetime, lags = lags, leads = leads,
                        logs = logs, sqrts = sqrts, lgstc = lgstc, p2s = p2s,
@@ -852,7 +852,7 @@ amelia.default <- function(x, m = 5, p2s = 1, frontend = FALSE, idvars = NULL,
       return(impdata)
     }
 
-    ximp <- amelia.impute(prepped$x, thetanew$thetanew, priors = prepped$priors,
+    ximp <- amelia_impute(prepped$x, thetanew$thetanew, priors = prepped$priors,
                           bounds = prepped$bounds, max.resample)
     ximp <- amunstack(ximp, n.order = prepped$n.order,
                       p.order = prepped$p.order)

diff --git a/R/prep.r b/R/prep.r
@@ -676,7 +676,7 @@ combine.output <- function(...) {
 }
 
 
-amelia.prep <- function(x,m=5,p2s=1,frontend=FALSE,idvars=NULL,logs=NULL,
+amelia_prep <- function(x,m=5,p2s=1,frontend=FALSE,idvars=NULL,logs=NULL,
                         ts=NULL,cs=NULL,empri=NULL,
                         tolerance=0.0001,polytime=NULL,splinetime=NULL,startvals=0,lags=NULL,
                         leads=NULL,intercs=FALSE,sqrts=NULL,

diff --git a/vignettes/intro-mi.Rmd b/vignettes/intro-mi.Rmd
@@ -29,7 +29,7 @@ The Amelia program goes several significant steps beyond the capabilities of the
 
 Multiple imputation involves imputing $m$ values for each missing cell in your data matrix and creating $m$ "completed" data sets. Across these completed data sets, the observed values are the same, but the missing values are filled in with a distribution of imputations that reflect the uncertainty about the missing data. After imputation with Amelia's EMB algorithm, you can apply whatever statistical method you would have used if there had been no missing values to each of the $m$ data sets, and use a simple procedure, described below, to combine the results[^combine]. Under normal circumstances, you only need to impute once and can then analyze the $m$ imputed data sets as many times and for as many purposes as you wish. The advantage of Amelia is that it combines the comparative speed and ease-of-use of our algorithm with the power of multiple imputation, to let you focus on your substantive research questions rather than spending time developing complex application-specific models for nonresponse in each new data set. Unless the rate of missingness is very high, $m = 5$ (the program default) is probably adequate.
 
-[^combine]: You can combine the results automatically by doing your data analyses within [Zelig for R](https://zeligproject.org), or within [Clarify for Stata](https://gking.harvard.edu/clarify).
+[^combine]: You can combine the results automatically by doing your data analyses within [Zelig for R](https://docs.zeligproject.org/index.html), or within [Clarify for Stata](https://gking.harvard.edu/clarify).
 
 ### Assumptions
 

diff --git a/vignettes/using-amelia.Rmd b/vignettes/using-amelia.Rmd
@@ -508,7 +508,7 @@ out <- mi.combine(imp.models, conf.int = TRUE)
 out
 ```
 
-The combination of the results depends on the [broom](https://broom.tidymodels.org) package and results can be combined if a `tidy()` method exists for the estimation function passed to `with()`. Other packages such as [Zelig](https://zeligproject.org) can also combine imputed data sets across a number of statistical models. Furthermore, users can easily export their imputations using the `write.amelia()` function as described in \@ref(sec_saving) and use statistical packages other than R for the analysis model.
+The combination of the results depends on the [broom](https://broom.tidymodels.org) package and results can be combined if a `tidy()` method exists for the estimation function passed to `with()`. Other packages such as [Zelig](https://docs.zeligproject.org/index.html) can also combine imputed data sets across a number of statistical models. Furthermore, users can easily export their imputations using the `write.amelia()` function as described in \@ref(sec_saving) and use statistical packages other than R for the analysis model.
 
 In addition to the resources available in R, users can draw on
 Stata to implement their analysis models. As of version 11,