version 0.1.3

DESCRIPTION

@@ -1,17 +1,20 @@
 Package: metaforest
 Type: Package
+Date: 2020-03-01
 Title: Exploring Heterogeneity in Meta-Analysis using Random Forests
-Version: 0.1.2
+Version: 0.1.3
 Author: Caspar J. van Lissa
 Maintainer: Caspar J. van Lissa <c.j.vanlissa@gmail.com>
 Description: Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates, even with as little as 20 studies (Van Lissa, in preparation). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.
-Depends: R (>= 3.4.0), ggplot2, metafor, ranger, mmpf
+Depends: R (>= 3.5.0), ggplot2, metafor, ranger, data.table, methods
 Imports: gtable, grid
+Suggests: testthat, caret, knitr, rmarkdown, covr
 License: GPL-3
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 6.0.1
+RoxygenNote: 6.1.1
+VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2018-05-29 13:33:58 UTC; Lissa102
+Packaged: 2020-01-07 13:31:24 UTC; Lissa102
 Repository: CRAN
-Date/Publication: 2018-05-31 06:38:14 UTC
+Date/Publication: 2020-01-08 04:50:02 UTC

MD5

@@ -1,32 +1,58 @@
-6cf86b703bd62821377b54af3b556051 *DESCRIPTION
-7b9fb2aca1d6fa05d72a826ec35d7574 *NAMESPACE
-a5a999a06589e5a7d8e81e082b7effe6 *NEWS.md
-a6b6a30829b06ea29a08d9d893d1887d *R/ClusterMF.R
-458465013bcbfd57182feefc6cf5b655 *R/MetaForest.R
-a40d81adf65b5d3d2e9777073970d8ee *R/ModelInfo_mf.R
-008381af037cfb6539376fe3fe5620df *R/ModelInfo_rma.R
-c19d5b5ce20171de6fe5500e21844194 *R/PartialDependence.R
+b3c6e75036c0b1bbae721f93168fa60a *DESCRIPTION
+8c1c14d0b47981263cc6444b3a72f897 *NAMESPACE
+02711f100a7abd4374dae7347070f286 *NEWS.md
+409e8e06ae2e11fcbb13a81dfc4c7f81 *R/MF.R
+c219f24fb9d32bd2465c88bdec87502d *R/MF_cluster.R
+3c0613d0e4c3b3343413611ae670ae7c *R/MetaForest.R
+b6355e26020055df3889907d199934f7 *R/ModelInfo_mf.R
+66533fca6c675f33afea961fc69588b1 *R/ModelInfo_rma.R
+6bf1b32406a4a6340cc180e371fbe9be *R/PartialDependence.R
 5bee56163ee1ed1ca34c3084ae29976e *R/SimulateSMD.R
-5119f1a320f5df1f122d1e83db22f1c3 *R/VarImpPlot.R
-c24c3a207c5f9ca40b367461972f5a17 *R/WeightedScatter.R
+e397b8e51fd13c1e7a1a3f76914a8fdd *R/VarImpPlot.R
+7dc6b16572b5d954dc8198d6bfef4117 *R/WeightedScatter.R
+0d2f06c2b3bb9ac7ec556aa3dce737a3 *R/coef_test.R
+946c50248d6ed432e57a54a093a89b83 *R/curry.R
+d09bdd7b302e8953b5ffa9e2ec1f18bf *R/deprecated_functions.R
 b705eb0b3619b86be53f698cfe84fb2b *R/extract_proximity.MetaForest.R
-efe228a1a76a6f67cb805ccee6e390c6 *R/plot.MetaForest.R
+74232bd46fd9a3ff291b6724a0835fe5 *R/fukkink_lont.R
+b3f6b2a586762766c2784e68fedccd0c *R/helpers.R
+89f2449a2d4e0a001d3462e1953f777f *R/marginal_predictions.R
+32285741dea0d32ff58c4cdf279712aa *R/metaforest-deprecated.r
+763d5e514e47360adebcf24797b268db *R/plot.MetaForest.R
 a79e0de646f739fd1b7682cd920dcce1 *R/predict.MetaForest.R
-70eb09253ffcfab8ef1638d4401a4ab0 *R/print.MetaForest.R
-a202d7e137f1edb58544384e892c65c8 *R/print.summary.MetaForest.R
-bb297a23b03e87eae7183ce9bae3a7b7 *R/summary.MetaForest.R
-d05dfb908762615ad0a2d9f24c38ac3e *README.md
-a8893d971eab3fd2ddba3de21da6e3ab *man/ClusterMF.Rd
-f52506f04d79e9379a0fbf94feb2881f *man/MetaForest.Rd
+abbf351cb879ddaa0080fed56efe0090 *R/preselect.R
+18ab42f4bc720a04cbb5584614976c15 *R/print.MetaForest.R
+8debd94bf6d50872a0958520095f96ea *R/print.summary.MetaForest.R
+4bd7a30acbc88c2383175353c8aec34d *R/summary.MetaForest.R
+1b9852c57eee5eafa518c70af03b74c8 *README.md
+eafaec85b832d041f9b1adb37a6c8728 *build/vignette.rds
+289b6b0183d882e1df481e4c452eb69c *data/curry.csv
+dc1e97e1e613ff72c4bfc96c288f0ed4 *data/fukkink_lont.csv
+ad136ffcd7a8a3133de768ae87b37982 *inst/doc/Introduction_to_metaforest.R
+a7cd2280f981053d7b72322ce0bd764f *inst/doc/Introduction_to_metaforest.Rmd
+ceb0fdc44312cc11af9ed41af32daf90 *inst/doc/Introduction_to_metaforest.html
+3e5a340127b2b559f2efbb46cb08ff6e *man/ClusterMF-deprecated.Rd
+b2ce464db687a1a75969aa31e5f502c8 *man/MetaForest.Rd
 13f1dea9b8bad12306667bc2ca1ece4e *man/ModelInfo_mf.Rd
 1f5aaf3113ff9333addda2f507408b1f *man/ModelInfo_rma.Rd
-eca7bb1eedf3684a7cad1bd9e0043b1b *man/PartialDependence.Rd
-6cef1a7d2d8a494746d0171c89703579 *man/SimulateSMD.Rd
-f077035565cc15ba394d3281a0cc9602 *man/VarImpPlot.Rd
-d667fb3107c077918f57180064e7837c *man/WeightedScatter.Rd
+2288fde62aa54898f110a377d3812c0b *man/PartialDependence.Rd
+9416057960800caae33541931c570aab *man/SimulateSMD.Rd
+e878b5cb026252dad7a3709553050197 *man/VarImpPlot.Rd
+4671d451dfe4afde1d6fa3039620b99e *man/WeightedScatter.Rd
+00a6f08db4fe25a9b0588fe54ac305eb *man/coef_test.Rd
+a77839d8b2064517231b0de254cfdaf0 *man/curry.Rd
 544cd29d80b072cb33938c11f74722c0 *man/extract_proximity.Rd
+1e6476f7146902b8620ed9c9565f4e64 *man/fukkink_lont.Rd
+30619d14393326e116d3c099ba9ecc23 *man/metaforest-deprecated.Rd
 999dfbbaa2f02ca0baf94d3f67c60ead *man/plot.MetaForest.Rd
-10e36f7940d7d3109a1a2a615e5305aa *man/predict.MetaForest.Rd
-9814c28572d0991f6e1b132944cb7abf *man/print.MetaForest.Rd
+d0729a89e979374167fa3c668a779821 *man/predict.MetaForest.Rd
+b5f02e9b9cebc6c7c207573a5ff52a6d *man/preselect.Rd
+f0dc022b81b24284fe4b10a425b95033 *man/preselect_vars.Rd
 453ada4b241e83b52f432a7b71fde3de *man/print.summary.MetaForest.Rd
-15eeb6b2069b4e977b194eced864d52e *man/summary.MetaForest.Rd
+7fed682f72cc90ced35057f79f6c32e1 *man/report.Rd
+503bfde986e5c277a21e525c47848697 *tests/testthat.R
+0d7c818303933b894c4f69625dd47e83 *tests/testthat/test-clusterMF.R
+14275530d269f2ad35162d0e365ff431 *tests/testthat/test-rma_nonconverge.R
+18f6524126de70d301970ab6794e3ef3 *tests/testthat/test-s4.R
+5df7d4e4da960accaed4f79d892e4565 *tests/testthat/test-summary.R
+a7cd2280f981053d7b72322ce0bd764f *vignettes/Introduction_to_metaforest.Rmd

NAMESPACE

@@ -1,11 +1,17 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(PartialDependence,MetaForest)
-S3method(PartialDependence,rma)
+S3method(PartialDependence,ranger)
+S3method(coef_test,rma)
 S3method(extract_proximity,MetaForest)
 S3method(plot,MetaForest)
+S3method(plot,mf_preselect)
+S3method(plot,ranger)
 S3method(predict,MetaForest)
+S3method(preselect,MetaForest)
 S3method(print,MetaForest)
+S3method(print,coef_test_results)
+S3method(print,mf_preselect)
 S3method(print,summary.MetaForest)
 S3method(summary,MetaForest)
 export(ClusterMF)
@@ -16,11 +22,18 @@ export(PartialDependence)
 export(SimulateSMD)
 export(VarImpPlot)
 export(WeightedScatter)
+export(coef_test)
 export(extract_proximity)
+export(preselect)
+export(preselect_vars)
+import(data.table)
 import(ggplot2)
+import(grid)
+import(gtable)
 import(metafor)
 import(ranger)
 import(stats)
+importFrom(data.table,setcolorder)
 importFrom(grid,grid.draw)
 importFrom(grid,grid.newpage)
 importFrom(grid,grobWidth)
@@ -29,6 +42,7 @@ importFrom(grid,textGrob)
 importFrom(gtable,gtable_add_cols)
 importFrom(gtable,gtable_add_grob)
 importFrom(gtable,gtable_matrix)
-importFrom(mmpf,marginalPrediction)
+importFrom(metafor,rma)
+importFrom(methods,hasArg)
 importFrom(stats,predict)
 importFrom(utils,installed.packages)

NEWS.md

@@ -1,4 +1,17 @@
-##### Version 0.1.2
+# Version 0.1.3
+
+* ClusterMF is soft deprecated; it has the same functionality as 
+  MetaForest. You can simply replace any call to ClusterMF with a call to
+  MetaForest with the same arguments.
+* A clustered MetaForest analysis no longer automatically doubles the number of
+  trees estimated. Instead, it divides num.trees trees by two, rounding up to 
+  the nearest even number.
+* Generic S3 methods are now properly declared as such, instead of being 
+  exported with their own documentation.
+* Reduce dependencies by calculating partial dependence manually
+
+# Version 0.1.2
+
 * Rewrote WeightedScatter to jointly plot numeric and factor variables
 * Rewrote PartialDependence to be an S3 generic, with methods for metaforest and
   rma models
@@ -7,7 +20,8 @@
   caret
 * Added ModelInfo_rma(), which returns a ModelInfo list for using rma with caret
 
-##### Version 0.1.1
+# Version 0.1.1
+
 * Substantial update to PartialDependence
 * PartialDependence now plots percentile interval for predictions
 * PartialDependence now plots weighted raw data