try appveyor fix; re-style code; update docs; update pkgdown site

Makefile

@@ -2,7 +2,7 @@ md:
 	Rscript -e "rmarkdown::render('README.Rmd', output_file = 'README.md')"
 
 site:
-	Rscript -e "rmarkdown::render('README-norefs.Rmd', output_file = 'README.md')"
+	Rscript -e "rmarkdown::render('README.Rmd', output_file = 'README.md')"
 	Rscript -e "pkgdown::build_site()"
 
 check:

R/censoring_estimate.R

@@ -170,7 +170,8 @@ estimateCensoring <- function(dataList,
           g_dC <-
             suppressWarnings(
               1 - predict(
-                ctimeMod, newdata = x[, c(
+                ctimeMod,
+                newdata = x[, c(
                   "t", "trt",
                   names(adjustVars)
                 )],

R/checkInputs.R

@@ -306,7 +306,8 @@ checkInputs <- function(ftime,
       glm.ftime <- paste0(
         "-1 + ", paste0(
           "I(t == ", unique(ftime[ftype > 0]),
-          ")", collapse = "+"
+          ")",
+          collapse = "+"
         ), "+",
         paste0(
           "I(trt*t == ", unique(ftime[ftype > 0]), ")",
@@ -323,7 +324,8 @@ checkInputs <- function(ftime,
     glm.ctime <- paste0(
       "-1 + ", paste0(
         "I(t == ", unique(ftime[ftype == 0]),
-        ")", collapse = "+"
+        ")",
+        collapse = "+"
       ), "+",
       paste0(
         "I(trt*t == ", unique(ftime[ftype == 0]), ")",
@@ -342,7 +344,8 @@ checkInputs <- function(ftime,
       glm.ctime <- paste0(
         "-1 + ", paste0(
           "I(t == ", unique(ftime[ftype == 0]),
-          ")", collapse = "+"
+          ")",
+          collapse = "+"
         ), "+",
         paste0(
           "I(trt*t == ", unique(ftime[ftype == 0]), ")",
@@ -354,7 +357,8 @@ checkInputs <- function(ftime,
       glm.ftime <- paste0(
         "-1 + ", paste0(
           "I(t==", unique(ftime[ftype > 0]),
-          ")", collapse = "+"
+          ")",
+          collapse = "+"
         ), "+",
         paste0(
           "I(trt*t == ", unique(ftime[ftype > 0]), ")",
@@ -382,15 +386,17 @@ checkInputs <- function(ftime,
           ), "+",
           paste0(
             "I(trt*t == ", unique(ftime[ftype == 0]),
-            ")", collapse = "+"
+            ")",
+            collapse = "+"
           )
         )
       }
       if (is.null(glm.ftime)) {
         glm.ftime <- paste0(
           "-1 + ", paste0(
             "I(t == ", unique(ftime[ftype > 0]),
-            ")", collapse = "+"
+            ")",
+            collapse = "+"
           ), "+",
           paste0(
             "I(trt*t == ", unique(ftime[ftype > 0]), ")",

R/glm_fast.R

@@ -35,37 +35,36 @@ fast_glm <- function(reg_form, data, family, ...) {
   )[[1]][1])
 
   # fit speedglm or glm as appropriate
-  out <- tryCatch(
-    {
-      # Obviously, a sparse design matrix is not used when fitting an intercept
-      # model. In such cases, 'sparse=TRUE' is an inappropriate choice, though
-      # this is only expected when estimating the treatment mechanism.
-      speedglm::speedglm(
-        formula = reg_form,
-        data = data,
-        family = family,
-        method = "Cholesky",
-        sparse = ifelse(calling_fun == "estimateTreatment",
-          FALSE, TRUE
-        ),
-        trace = FALSE,
-        ...
-      )
-    },
-    error = function(cond) {
-      message(paste0(
-        "'speedglm' ran into an error in ", calling_fun,
-        ".", "'glm' will be used instead."
-      ))
-      # Choose a return value in case of error
-      mod <- stats::glm(
-        formula = reg_form,
-        data = data,
-        family = family,
-        ...
-      )
-      return(mod)
-    }
+  out <- tryCatch({
+    # Obviously, a sparse design matrix is not used when fitting an intercept
+    # model. In such cases, 'sparse=TRUE' is an inappropriate choice, though
+    # this is only expected when estimating the treatment mechanism.
+    speedglm::speedglm(
+      formula = reg_form,
+      data = data,
+      family = family,
+      method = "Cholesky",
+      sparse = ifelse(calling_fun == "estimateTreatment",
+        FALSE, TRUE
+      ),
+      trace = FALSE,
+      ...
+    )
+  },
+  error = function(cond) {
+    message(paste0(
+      "'speedglm' ran into an error in ", calling_fun,
+      ".", "'glm' will be used instead."
+    ))
+    # Choose a return value in case of error
+    mod <- stats::glm(
+      formula = reg_form,
+      data = data,
+      family = family,
+      ...
+    )
+    return(mod)
+  }
   )
   return(out)
 }

R/hazards_estimate.R

@@ -117,7 +117,8 @@ estimateHazards <- function(dataList,
         dataList <- lapply(dataList, function(x, j) {
           suppressWarnings(
             x[[paste0("Q", j, "PseudoHaz")]] <- predict(
-              Qj_mod, newdata = x,
+              Qj_mod,
+              newdata = x,
               type = "response"
             )
           )
@@ -215,7 +216,8 @@ estimateHazards <- function(dataList,
       dataList <- lapply(dataList, function(x, j) {
         suppressWarnings(
           x[[paste0("Q", j, "PseudoHaz")]] <- predict(
-            Qj_mod, onlySL = TRUE,
+            Qj_mod,
+            onlySL = TRUE,
             newdata = x[, c("t", "trt", names(adjustVars))]
           )[[1]]
         )

R/iterated_mean_fluctuate.R

@@ -84,7 +84,8 @@ fluctuateIteratedMean <- function(wideDataList, t, uniqtrt, whichJ, allJ, t0,
     flucForm <- paste(
       outcomeName, "~ -1 + offset(stats::qlogis(Q", whichJ,
       ".", t, ")) +",
-      paste0("H", uniqtrt, ".", t, collapse = "+"), sep = ""
+      paste0("H", uniqtrt, ".", t, collapse = "+"),
+      sep = ""
     )
 
     if (!Gcomp) {
@@ -101,7 +102,8 @@ fluctuateIteratedMean <- function(wideDataList, t, uniqtrt, whichJ, allJ, t0,
       wideDataList <- lapply(wideDataList, function(x, t) {
         suppressWarnings(
           x[[paste0("Q", whichJ, "star.", t)]] <- predict(
-            flucMod, newdata = x,
+            flucMod,
+            newdata = x,
             type = "response"
           )
         )

R/makeWideDataList.R

@@ -69,7 +69,8 @@ makeWideDataList <- function(dat,
             ))],
           direction = "wide", timevar = "t", idvar = "id"
         )
-        , row.names = NULL
+        ,
+        row.names = NULL
       )
       out[, paste0("C.", 1:t0)] <- 0
       names(out)[1:(ncol(adjustVars))] <- names(adjustVars)

R/printing.R

@@ -50,7 +50,8 @@ print.tp.survtmle <- function(x, ...) {
 
   # structure point estimates
   est_only <- t(matrix(
-    unlist(lapply(x, FUN = `[[`, "est")), ncol = len_groups,
+    unlist(lapply(x, FUN = `[[`, "est")),
+    ncol = len_groups,
     byrow = TRUE
   ))
   est_only <- as.data.frame(est_only)

R/timepoints.R

@@ -113,7 +113,8 @@ timepoints <- function(object, times, returnModels = FALSE) {
       outList[[ct]] <- list(
         est = rep(0, length(object$est)),
         var = matrix(
-          NA, nrow = length(object$est),
+          NA,
+          nrow = length(object$est),
           ncol = length(object$est)
         )
       )

R/tmle_hazard.R

@@ -277,7 +277,7 @@ hazard_tmle <- function(ftime,
   infCurves <- dat[, grep("D.j", names(dat))]
   meanIC <- colMeans(infCurves)
 
-  attr(dataList, "fluc") <- rep(Inf, ntrt * nJ ^ 2)
+  attr(dataList, "fluc") <- rep(Inf, ntrt * nJ^2)
   ct <- 0
   while (any(abs(meanIC) > tol) & ct <= maxIter) {
     ct <- ct + 1
@@ -319,15 +319,16 @@ hazard_tmle <- function(ftime,
     for (z in uniqtrt) {
       eval(parse(text = paste(
         "est <- rbind(est, dat$margF", j, ".z", z,
-        ".t0[1])", sep = ""
+        ".t0[1])",
+        sep = ""
       )))
       rowNames <- c(rowNames, paste(c(z, j), collapse = " "))
     }
   }
   row.names(est) <- rowNames
 
   # calculate standard error
-  var <- t(as.matrix(infCurves)) %*% as.matrix(infCurves) / n ^ 2
+  var <- t(as.matrix(infCurves)) %*% as.matrix(infCurves) / n^2
   row.names(var) <- colnames(var) <- rowNames
 
   out <- list(

R/tmle_mean.R

@@ -294,13 +294,15 @@ mean_tmle <- function(ftime,
     for (z in seq_along(uniqtrt)) {
       thisEst <- eval(parse(text = paste(
         "mean(wideDataList[[", z + 1, "]]$Q",
-        j, "star.1)", sep = ""
+        j, "star.1)",
+        sep = ""
       )))
       est <- rbind(est, thisEst)
       rowNames <- c(rowNames, paste(c(uniqtrt[z], j), collapse = " "))
       eval(parse(text = paste(
         "wideDataList[[1]]$Q", j, "star.0.Z", uniqtrt[z],
-        " <- rep(thisEst,n)", sep = ""
+        " <- rep(thisEst,n)",
+        sep = ""
       )))
       eval(parse(text = paste(
         "wideDataList[[1]]$Q", j, "star.1.Z", uniqtrt[z],
@@ -323,14 +325,16 @@ mean_tmle <- function(ftime,
           "star.", t, " <- wideDataList[[1]]$H",
           uniqtrt[z], ".", t,
           "*(wideDataList[[1]][,outcomeName] - wideDataList[[1]]$Q",
-          j, "star.", t, ")", sep = ""
+          j, "star.", t, ")",
+          sep = ""
         )))
       }
       eval(parse(text = paste(
         "wideDataList[[1]]$D.Z", uniqtrt[z], ".", j,
         "star.0 <- wideDataList[[1]]$Q", j, "star.1.Z",
         uniqtrt[z], " - wideDataList[[1]]$Q", j,
-        "star.0.Z", uniqtrt[z], sep = ""
+        "star.0.Z", uniqtrt[z],
+        sep = ""
       )))
       ind <- eval(parse(text = paste(
         "grep('D.Z", uniqtrt[z], ".", j,
@@ -351,7 +355,7 @@ mean_tmle <- function(ftime,
     drop = FALSE
   ]
   meanIC <- apply(infCurves, MARGIN = 2, FUN = mean)
-  var <- t(as.matrix(infCurves)) %*% as.matrix(infCurves) / (n ^ 2)
+  var <- t(as.matrix(infCurves)) %*% as.matrix(infCurves) / (n^2)
   row.names(var) <- colnames(var) <- rowNames
 
   out <- list(