version 2.8.5

DESCRIPTION

@@ -1,16 +1,13 @@
 Package: qgcomp
 Title: Quantile G-Computation
-Version: 2.8.0
-Date: 2021-07-16
+Version: 2.8.5
+Date: 2021-12-12
 Authors@R: 
-    person(given = "Alexander",
-           family = "Keil",
-           role = c("aut", "cre"),
-           email = "akeil@unc.edu")
+    person("Alexander", "Keil", , "akeil@unc.edu", role = c("aut", "cre"))
 Author: Alexander Keil [aut, cre]
 Maintainer: Alexander Keil <akeil@unc.edu>
-Description: G-computation for a set of time-fixed exposures
-    with quantile-based basis functions, possibly under linearity and
+Description: G-computation for a set of time-fixed exposures with
+    quantile-based basis functions, possibly under linearity and
     homogeneity assumptions. This approach estimates a regression line
     corresponding to the expected change in the outcome (on the link
     basis) given a simultaneous increase in the quantile-based category
@@ -20,16 +17,17 @@ Description: G-computation for a set of time-fixed exposures
     Alexandra J. White (2019) A quantile-based g-computation approach to
     addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
 License: GPL (>= 2)
+URL: https://github.com/alexpkeil1/qgcomp/
+BugReports: https://github.com/alexpkeil1/qgcomp/issues
 Depends: R (>= 3.5.0)
 Imports: arm, future, future.apply, generics, ggplot2 (>= 3.3.0),
         grDevices, grid, gridExtra, pscl, stats, survival, tibble
 Suggests: broom, devtools, knitr, markdown, MASS, mice
 VignetteBuilder: knitr
 Encoding: UTF-8
 Language: en-US
-LazyData: true
-RoxygenNote: 7.1.1
+RoxygenNote: 7.1.2
 NeedsCompilation: no
-Packaged: 2021-07-16 05:11:12 UTC; akeil
+Packaged: 2021-12-12 16:40:15 UTC; akeil
 Repository: CRAN
-Date/Publication: 2021-07-16 05:30:02 UTC
+Date/Publication: 2021-12-12 17:20:02 UTC

MD5

@@ -1,25 +1,25 @@
-ea0907dd8d8bf0253dbeee55a4ef41ac *DESCRIPTION
+b6afd953f51f1073a6caf1c5b4228b0a *DESCRIPTION
 45b94bbaaed7650e00a75f4dd64263ab *NAMESPACE
-72211dc027952d22b7234f76983988d6 *NEWS.md
-b0578a2fafc1900ec78e1a12197c7fb3 *R/base.R
+45a9e6e780256018a5dbca48d618d380 *NEWS.md
+a97b917ce3e1d74e4f7e04451f01f9bb *R/base.R
 7bb09da9bf7f3d5610f6f4d8854ffc15 *R/base_bounds.R
 ad02dc8706a119714c07e79b625cc3c5 *R/base_experimental.R
 0864472a76fb326a0384aebcf5debb6e *R/base_extensions.R
-ce715f03b6d7b17f17644b84e9b3c747 *R/base_generics.R
-9afb7640c5aa4f7f62b797906a77b69e *R/base_hurdle.R
+a2ec4b87648a50dbcf983a7418c01d6d *R/base_generics.R
+1e7d87f01647c3a449e00a4b49a03d83 *R/base_hurdle.R
 48dcec918baba27637817e8e9c157e7e *R/base_plots.R
 9c350212698df84906f888384afa314c *R/base_samplesplits.R
 3d883410bf8ee1ce5493c3470a37792a *R/base_simhelpers.R
-5f9e610e1a041a46ab3acb05138a8699 *R/base_surv.R
-8c822958f5f73b7c02bfea863709251f *R/base_utility.R
-97497e491cdd6cfd510859cd1a1de3a6 *R/base_zi.R
-c1010c23e7a3ff99d466b057d7a03ea7 *R/data.R
-08c5cf7c518fd7e9f289d8517ab2535b *README.md
-cc39b8f0f4f849f87f442e4003561109 *build/vignette.rds
+f1ba7eb98effce3834743d0d94c10ce1 *R/base_surv.R
+c140b5c60f253c3651016aa3f5af5ca0 *R/base_utility.R
+2438f922c307df5aa315664442190186 *R/base_zi.R
+daf60f4a2a0ea425235870c7988de990 *R/data.R
+c6010d73c541566f61b6c5eb7b291cc9 *README.md
+88d4e09aa6fa6930befc110a511d079c *build/vignette.rds
 c52baf72a0ded2860f1fc7bcc0a44587 *data/metals.RData
-d4606c509dc71895eb141f340de0ec41 *inst/doc/qgcomp-vignette.R
-be2718a92a2cd546d7d7197fa12c406a *inst/doc/qgcomp-vignette.Rmd
-bc2aadc9beed1205b96ed4ed153ec66b *inst/doc/qgcomp-vignette.html
+d9d367fdeeccbc78a57d67bb0bacfdeb *inst/doc/qgcomp-vignette.R
+40b9e2cdd5cbc36fd96d19d1b51a6004 *inst/doc/qgcomp-vignette.Rmd
+90da432a06376c82f56483b3d5bdef73 *inst/doc/qgcomp-vignette.html
 dcba3c6855f754b7494b841d285e3197 *inst/fig/fighex.png
 d4f062a438e514faa03a0ae2c9f38a12 *inst/fig/fighex_social.png
 a728173c2b39a9a95c900810feca0c26 *inst/fig/res1.png
@@ -32,7 +32,7 @@ aae600adc3ab853fb0e1ad835bc7bbd6 *inst/fig/res5.png
 882b058937456f008fa1ed843cabea7c *man/glance.qgcompfit.Rd
 8e8229ff4022716ab2f581007b4b8ab1 *man/hurdlemsm.fit.Rd
 e8ce60028517f93774d710b3f1304b7e *man/hurdlemsm.fit.control.Rd
-fe4d3e1f0eb95fff9501963f9c7f9b9a *man/metals.Rd
+fb3a53ea7529ed58840a9dd391e16031 *man/metals.Rd
 246ed51a262b533facebbba4f18b0894 *man/mice.impute.leftcenslognorm.Rd
 d00e920bfd6bb988c6e9ab09b1d53d4b *man/modelbound.boot.Rd
 b07ea7c4d35d3a68e4d6ce8241ad6510 *man/msm.fit.Rd
@@ -43,15 +43,15 @@ e03bf2686ec795d743c27e2eb16979e4 *man/msm.predict.Rd
 be9635de94dc97e73cd42e4fe0bee060 *man/predict.qgcompfit.Rd
 16e6bf82624318b907d18d714d6fa418 *man/print.qgcompfit.Rd
 fc8955636f8d783a2a535db417402d13 *man/qgcomp.Rd
-5fe305bf50bd9e80ad5bde30280641b6 *man/qgcomp.boot.Rd
-8c056a34c46b1964cef0d1b857fd80cc *man/qgcomp.cox.boot.Rd
+8584daf5f1542fdb28a88ccfc366ea81 *man/qgcomp.boot.Rd
+73e7e074a52fabd27f4b16d6ed18d3bc *man/qgcomp.cox.boot.Rd
 1e252c99b0270454f0da900ab76d0b90 *man/qgcomp.cox.noboot.Rd
-5c87bfc251d53f7d2adcd7cd78c15fcf *man/qgcomp.hurdle.boot.Rd
+5e68bc7f8be2aa7c643f2ca4f12441d1 *man/qgcomp.hurdle.boot.Rd
 1418f7d295a1d046cd8e932b6b82f006 *man/qgcomp.hurdle.noboot.Rd
 83d33c2554c16afe6960ed07bc16b255 *man/qgcomp.noboot.Rd
 82c91b83d893c9fb3c920f3ac4be0502 *man/qgcomp.partials.Rd
 754ef31f92fca5d8ff88e47d29e73cf8 *man/qgcomp.survcurve.boot.Rd
-1eed0974da01dd7506b42d7d32f990d4 *man/qgcomp.zi.boot.Rd
+7299dba0c2dac6a56b6faa9d1d08f78d *man/qgcomp.zi.boot.Rd
 272ff788437b05b20ee98ba277f0e37f *man/qgcomp.zi.noboot.Rd
 1f80778d431ea68be45ffbfd5b57263c *man/quantize.Rd
 877cd72913f4eefc3f902c93d8db94d7 *man/se_comb.Rd
@@ -61,16 +61,16 @@ ce81e1ffbeb2e8f4d0ebda7b11e2156f *man/tidy.qgcompfit.Rd
 adafbb49bfd55970000807640f274224 *man/vc_comb.Rd
 21be15986a04cf91ac6c94d79806917b *man/zimsm.fit.Rd
 fb7e649e05de6cc54a94061cf824942a *man/zimsm.fit.control.Rd
-14f5ad5eb01480c666bd951c24e101aa *tests/test_asis.R
+a5c483300a969c7486c668dd4ffc0255 *tests/test_asis.R
 c7c524d4e7a45d818706709a57a6529d *tests/test_basics.R
 4fe496ca8422555ef455b43f27240f77 *tests/test_bayesqgcomp.R
 3adbce145f25a6436905f10c97898295 *tests/test_boot_ints.R
-74219b40d4fb44b4eb414e9d0f45b018 *tests/test_bootchooser.R
+01f20ca185dbcc7a95a366fca634ffcc *tests/test_bootchooser.R
 2adc75ff8664eea0314992fd19ebff78 *tests/test_factor.R
 3ecea9330c4be292acaed8d39ae81c87 *tests/test_id.R
 5e757e8b3ebaa84f6f0dc3b3f78ebb38 *tests/test_mice.R
 20abdeb552c2efed1352526d2baf8c88 *tests/test_numeric.R
 1132ffeaf05f6fe7b409e9049f826bf2 *tests/test_poisson.R
 34309c92ce57d4598adf623550be8cb5 *tests/test_splits.R
 055d4f8d4b83a240f614fb268ffb5dbe *tests/test_weights.R
-be2718a92a2cd546d7d7197fa12c406a *vignettes/qgcomp-vignette.Rmd
+40b9e2cdd5cbc36fd96d19d1b51a6004 *vignettes/qgcomp-vignette.Rmd

NEWS.md

@@ -1,3 +1,11 @@
+# qgcomp v2.8.5
+## Major changes
+- None
+
+## Minor changes
+- Handling of parallel processing through futures: bringing in line with package recommendations and planned deprecations.
+- Documentation changes
+
 # qgcomp v2.8.0
 ## Major changes
 - Exposed simulation functions to the user

R/base.R

@@ -377,7 +377,6 @@ qgcomp.noboot <- function(f,
         res$zstat <- tstat
         res$pval <- pvalz
       }
-    #attr(res, "class") <- "qgcompfit"
     res
 }
 
@@ -458,7 +457,7 @@ qgcomp.boot <- function(
   #'  error at a given value of MCsize). This likely won't matter much in linear models, but may
   #'  be important with binary or count outcomes.
   #' @param parallel use (safe) parallel processing from the future and future.apply packages
-  #' @param parplan (logical, default=FALSE) automatically set future::plan to plan(multiprocess) (and set to plan(invisible) after bootstrapping)
+  #' @param parplan (logical, default=FALSE) automatically set future::plan to plan(multisession) (and set to existing plan, if any, after bootstrapping)
   #' @param ... arguments to glm (e.g. family)
   #' @seealso \code{\link[qgcomp]{qgcomp.noboot}}, and \code{\link[qgcomp]{qgcomp}}
   #' @return a qgcompfit object, which contains information about the effect
@@ -551,7 +550,7 @@ qgcomp.boot <- function(
   #'
   #' qgcomp.boot(y ~ z + x1iqr + I(x2iqr>0.1) + I(x2>0.4) + I(x2>0.9),
   #'   family="binomial", expnms = c('x1iqr', 'x2iqr'), data=dat, q=NULL, rr=TRUE, B=200,
-  #'   degree=2, parallel=TRUE)
+  #'   degree=2, parallel=TRUE, parplan=TRUE)
   #'
   #'
   #' # weighted model
@@ -691,14 +690,17 @@ qgcomp.boot <- function(
     set.seed(seed)
     if(parallel){
       #Sys.setenv(R_FUTURE_SUPPORTSMULTICORE_UNSTABLE="quiet")
-      if(parplan) future::plan(strategy = future::multisession)
+      if (parplan) {
+        oplan <- future::plan(strategy = future::multisession)
+        on.exit(future::plan(oplan), add = TRUE)      
+      }
       bootsamps <- future.apply::future_lapply(X=seq_len(B), FUN=psi.only,f=f, qdata=qdata, intvals=intvals,
                           expnms=expnms, rr=rr, degree=degree, nids=nids, id=id,
                           weights=qdata$weights,MCsize=MCsize, hasintercept = hasintercept,
                           future.seed=TRUE,
                           ...)
 
-      if(parplan) future::plan(strategy = future::transparent)
+
     }else{
       bootsamps <- lapply(X=seq_len(B), FUN=psi.only,f=f, qdata=qdata, intvals=intvals,
                           expnms=expnms, rr=rr, degree=degree, nids=nids, id=id,
@@ -755,7 +757,6 @@ qgcomp.boot <- function(
         res$zstat <- tstat
         res$pval <- pvalz
       }
-    attr(res, "class") <- "qgcompfit"
     res
 }
 

R/base_generics.R

@@ -185,7 +185,7 @@ summary.qgcompfit <- function(object, ...){
   colnames(pdat)[which(colnames(pdat)=="test")] = eval(paste(testtype, "value"))
   colnames(pdat)[which(colnames(pdat)=="pval")] = eval(paste(plab))
   rownames(pdat) <- rnm
-  list(coefficents=pdat)
+  list(coefficients=pdat)
 }
 
 family.qgcompfit <- function(object, ...){

R/base_hurdle.R

@@ -491,7 +491,7 @@ qgcomp.hurdle.boot <- function(
   #'  linear fits with qgcomp.hurdle.noboot to gain some intuition for the level of expected simulation 
   #'  error at a given value of MCsize)
   #' @param msmcontrol named list from \code{\link[qgcomp]{hurdlemsm.fit.control}}
-  #' @param parplan (logical, default=FALSE) automatically set future::plan to plan(multiprocess) (and set to plan(invisible) after bootstrapping)
+  #' @param parplan (logical, default=FALSE) automatically set future::plan to plan(multisession) (and set to existing plan, if any, after bootstrapping)
   #' @param ... arguments to glm (e.g. family)
   #' @seealso \code{\link[qgcomp]{qgcomp.hurdle.noboot}},\code{\link[qgcomp]{qgcomp.boot}}, 
   #' \code{\link[qgcomp]{qgcomp.cox.boot}},  and \code{\link[pscl]{hurdle}}
@@ -511,15 +511,15 @@ qgcomp.hurdle.boot <- function(
   #' \dontrun{
   #' # warning: the examples below can take a long time to run
   #' res = qgcomp.hurdle.boot(f=y ~ x1 + x2 | x1 + x2, expnms = c('x1', 'x2'), 
-  #'     data=dat, q=4, dist="poisson", B=1000, MCsize=10000, parallel=TRUE)
+  #'     data=dat, q=4, dist="poisson", B=1000, MCsize=10000, parallel=TRUE, parplan=TRUE)
   #' qgcomp.hurdle.noboot(f=y ~ x1 + x2 | x1 + x2, expnms = c('x1', 'x2'), 
   #'     data=dat, q=4, dist="poisson")
   #' res
   #' 
   #' # accuracy for small MCsize is suspect (compare coefficients between boot/noboot versions), 
   #' # so re-check with MCsize set to larger value (this takes a long time to run)
   #' res2 = qgcomp.hurdle.boot(f=y ~ x1 + x2 | x1 + x2, expnms = c('x1', 'x2'), 
-  #'     data=dat, q=4, dist="poisson", B=1000, MCsize=50000, parallel=TRUE)
+  #'     data=dat, q=4, dist="poisson", B=1000, MCsize=50000, parallel=TRUE, parplan=TRUE)
   #'  res2
   #' plot(density(res2$bootsamps[4,]))
   #' 
@@ -672,7 +672,10 @@ qgcomp.hurdle.boot <- function(
   set.seed(seed)
   if(parallel){
     #Sys.setenv(R_FUTURE_SUPPORTSMULTICORE_UNSTABLE="quiet")
-    if(parplan) future::plan(strategy = future::multisession)
+    if (parplan) {
+        oplan <- future::plan(strategy = future::multisession)
+        on.exit(future::plan(oplan), add = TRUE)
+      }
     #testenv <- list2env(list(qdata=qdata, weights=weights))
     bootsamps <- future.apply::future_lapply(X=seq_len(B), FUN=psi.only,f=newform, qdata=qdata, intvals=intvals, 
                                              expnms=expnms, degree=degree, nids=nids, id=id, 
@@ -681,7 +684,7 @@ qgcomp.hurdle.boot <- function(
                                              future.seed=TRUE,
                                              ...)
 
-    if(parplan) future::plan(strategy = future::transparent)
+
   }else{
     bootsamps <- lapply(X=seq_len(B), FUN=psi.only,f=newform, qdata=qdata, intvals=intvals, 
                         expnms=expnms, degree=degree, nids=nids, id=id, 

R/base_surv.R

@@ -489,7 +489,7 @@ qgcomp.cox.boot <- function(f, data, expnms=NULL, q=4, breaks=NULL,
   #'  to 2 decimal places).
   #' @param seed integer or NULL: random number seed for replicable bootstrap results
   #' @param parallel logical (default FALSE): use future package to speed up bootstrapping
-  #' @param parplan (logical, default=FALSE) automatically set future::plan to plan(multiprocess) (and set to plan(invisible) after bootstrapping)
+  #' @param parplan (logical, default=FALSE) automatically set future::plan to plan(multisession) (and set to existing plan, if any, after bootstrapping)
   #' @param ... arguments to glm (e.g. family)
   #' @seealso \code{\link[qgcomp]{qgcomp.cox.noboot}}, and \code{\link[qgcomp]{qgcomp}}
   #' @return a qgcompfit object, which contains information about the effect
@@ -517,7 +517,7 @@ qgcomp.cox.boot <- function(f, data, expnms=NULL, q=4, breaks=NULL,
   #' 
   #' # using future package, marginalizing over confounder z
   #' (obj3 <- qgcomp.cox.boot(survival::Surv(time, d)~x1 + x2 + z, expnms = expnms, data = dat, 
-  #'                          B=1000, MCsize=20000, parallel=TRUE))
+  #'                          B=1000, MCsize=20000, parallel=TRUE, parplan=TRUE))
   #' # non-constant hazard ratio, non-linear terms
   #' (obj4 <- qgcomp.cox.boot(survival::Surv(time, d)~factor(x1) + splines::bs(x2) + z, 
   #'                          expnms = expnms, data = dat, 
@@ -623,13 +623,16 @@ qgcomp.cox.boot <- function(f, data, expnms=NULL, q=4, breaks=NULL,
   set.seed(seed)
   if(parallel){
     #Sys.setenv(R_FUTURE_SUPPORTSMULTICORE_UNSTABLE="quiet")
-    if(parplan) future::plan(strategy = future::multisession)
+    if (parplan) {
+        oplan <- future::plan(strategy = future::multisession)
+        on.exit(future::plan(oplan), add = TRUE)
+      }
     bootsamps <- future.apply::future_lapply(X=seq_len(B), FUN=psi.only,
                                     f=newform, qdata=qdata, intvals=intvals, 
                                     expnms=expnms, degree=degree, nids=nids, id=id,
                                     future.seed=TRUE,
                                     weights=qdata$weights, MCsize=MCsize, ...)
-    if(parplan) future::plan(strategy = future::transparent)
+
   }else {
     bootsamps <- lapply(X=seq_len(B), FUN=psi.only,
                         f=newform, qdata=qdata, intvals=intvals, 

R/base_utility.R

@@ -291,7 +291,7 @@ checknames <- function(terms){
   if(is.na(match("pos.size", nms))) res$pos.size = NULL
   if(is.na(match("neg.size", nms))) res$neg.size = NULL
   if(is.na(match("df", nms))) res$df = NULL
-  attr(res, "class") <- c("list", "qgcompfit")
+  attr(res, "class") <- c("qgcompfit", "list")
   res
 }
 

R/base_zi.R

@@ -481,7 +481,7 @@ qgcomp.zi.boot <- function(f,
   #'  linear fits with qgcomp.zi.noboot to gain some intuition for the level of expected simulation 
   #'  error at a given value of MCsize)
   #' @param msmcontrol named list from \code{\link[qgcomp]{zimsm.fit.control}}
-  #' @param parplan (logical, default=FALSE) automatically set future::plan to plan(multiprocess) (and set to plan(invisible) after bootstrapping)
+  #' @param parplan (logical, default=FALSE) automatically set future::plan to plan(multisession) (and set to existing plan, if any, after bootstrapping)
   #' @param ... arguments to glm (e.g. family)
   #' @seealso \code{\link[qgcomp]{qgcomp.zi.noboot}},\code{\link[qgcomp]{qgcomp.boot}}, 
   #' \code{\link[qgcomp]{qgcomp.cox.boot}},  and \code{\link[pscl]{zeroinfl}}
@@ -501,15 +501,15 @@ qgcomp.zi.boot <- function(f,
   #' \dontrun{
   #' # warning: the examples below can take a long time to run
   #' res = qgcomp.zi.boot(f=y ~ x1 + x2 | x1 + x2, expnms = c('x1', 'x2'), 
-  #'     data=dat, q=4, dist="poisson", B=1000, MCsize=10000, parallel=TRUE)
+  #'     data=dat, q=4, dist="poisson", B=1000, MCsize=10000, parallel=TRUE, parplan=TRUE)
   #' qgcomp.zi.noboot(f=y ~ x1 + x2 | x1 + x2, expnms = c('x1', 'x2'), 
   #'     data=dat, q=4, dist="poisson")
   #' res
   #' 
   #' # accuracy for small MCsize is suspect (compare coefficients between boot/noboot versions), 
   #' # so re-check with MCsize set to larger value (this takes a long time to run)
   #' res2 = qgcomp.zi.boot(f=y ~ x1 + x2 | x1 + x2, expnms = c('x1', 'x2'), 
-  #'     data=dat, q=4, dist="poisson", B=1000, MCsize=50000, parallel=TRUE)
+  #'     data=dat, q=4, dist="poisson", B=1000, MCsize=50000, parallel=TRUE, parplan=TRUE)
   #'  res2
   #' plot(density(res2$bootsamps[4,]))
   #' 
@@ -665,7 +665,10 @@ qgcomp.zi.boot <- function(f,
   set.seed(seed)
   if(parallel){
     #Sys.setenv(R_FUTURE_SUPPORTSMULTICORE_UNSTABLE="quiet")
-    if(parplan) future::plan(strategy = future::multisession)
+    if (parplan) {
+        oplan <- future::plan(strategy = future::multisession)
+        on.exit(future::plan(oplan), add = TRUE)
+      }
     #testenv <- list2env(list(qdata=qdata, weights=weights))
     bootsamps <- future.apply::future_lapply(X=seq_len(B), FUN=psi.only,f=newform, qdata=qdata, intvals=intvals, 
                                              expnms=expnms, degree=degree, nids=nids, id=id, 
@@ -674,7 +677,7 @@ qgcomp.zi.boot <- function(f,
                                              future.seed=TRUE,
                                              ...)
 
-    if(parplan) future::plan(strategy = future::transparent)
+
   }else{
     bootsamps <- lapply(X=seq_len(B), FUN=psi.only,f=newform, qdata=qdata, intvals=intvals, 
                         expnms=expnms, degree=degree, nids=nids, id=id, 

R/data.R

@@ -6,7 +6,7 @@
 #'
 #' A dataset containing well water measurements and health outcomes for 253 individuals. 
 #' All continuous variables are standardized to have mean 0, standard deviation 1.
-#'
+#' @usage data(metals)
 #' @format A data frame with 253 rows and 24 variables:
 #' \describe{
 #'   \item{y}{continuous birth outcome}

README.md

@@ -1,4 +1,4 @@
-`qgcomp` v2.8.0
+`qgcomp` v2.8.5