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FBartos authored and cran-robot committed Apr 6, 2022
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13 changes: 7 additions & 6 deletions DESCRIPTION
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@@ -1,6 +1,6 @@
Package: RoBMA
Title: Robust Bayesian Meta-Analyses
Version: 2.1.2
Version: 2.2.1
Maintainer: František Bartoš <f.bartos96@gmail.com>
Authors@R: c(
person("František", "Bartoš", role = c("aut", "cre"),
Expand Down Expand Up @@ -37,14 +37,15 @@ RoxygenNote: 7.1.1
SystemRequirements: JAGS >= 4.3.0 (https://mcmc-jags.sourceforge.io/)
NeedsCompilation: yes
Depends: R (>= 4.0.0)
Imports: BayesTools (>= 0.1.3), runjags, bridgesampling, rjags, coda,
psych, stats, graphics, extraDistr, scales, callr, Rdpack,
ggplot2
Imports: BayesTools (>= 0.2.0), runjags, bridgesampling, rjags, coda,
psych, stats, graphics, extraDistr, mvtnorm, scales, callr,
Rdpack, ggplot2
Suggests: parallel, rstan, metaBMA, testthat, vdiffr, knitr, rmarkdown,
covr
LinkingTo: mvtnorm
RdMacros: Rdpack
VignetteBuilder: knitr
Packaged: 2022-01-05 21:37:49 UTC; fbart
Packaged: 2022-04-06 11:22:33 UTC; fbart
Author: František Bartoš [aut, cre] (<https://orcid.org/0000-0002-0018-5573>),
Maximilian Maier [aut] (<https://orcid.org/0000-0002-9873-6096>),
Eric-Jan Wagenmakers [ths] (<https://orcid.org/0000-0003-1596-1034>),
Expand All @@ -54,4 +55,4 @@ Author: František Bartoš [aut, cre] (<https://orcid.org/0000-0002-0018-5573>),
Martyn Plummer [cph] (Original copyright holder of some modified code
where indicated.)
Repository: CRAN
Date/Publication: 2022-01-12 08:32:42 UTC
Date/Publication: 2022-04-06 13:32:34 UTC
115 changes: 69 additions & 46 deletions MD5
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@@ -1,25 +1,25 @@
30bd7fd1420fc5e3cea3018093aaa6ae *DESCRIPTION
bebb5fac9bedcc30de543eccb9ec099d *DESCRIPTION
2723104b2f68b322211c88171a2dfb4d *NAMESPACE
0ae080c4c34b104e0f67d443374a16a0 *NEWS.md
784b080c7c9832bf39799db7cafe3bc1 *NEWS.md
1c2efbb44d4f84af0da4bca0d5495d23 *R/RoBMA-package.R
43a305505d6f00fdc56010907a696f80 *R/check-input-and-settings.R
645fe5f115e3c9262173ac755b611c2d *R/check-priors-and-models.R
d4a80d56de0a1daed5244b10305042c9 *R/check-input-and-settings.R
26fd7adf8da97e6ca0e467dc87a7d034 *R/check-priors-and-models.R
2611c65fd6537461b882cefdce2c7e70 *R/data.R
46c0f5790311be300e59f978d06c745e *R/diagnostics.R
a72ea2f538d05806699a536d733e53e7 *R/distributions.R
1e1208bc6e239a8f6760eda294df3869 *R/fit-and-marglik.R
77f0090f41a0c5958b9217f8cc29002f *R/inference-and-model-averaging.R
795ba93e06e09a2b51335c94069c3137 *R/main.R
6316075cbad4df02f901fd14b1d16c26 *R/plots.R
c9b44ea228a2fc69e915fd0663ac267a *R/distributions.R
6ddcd40584f7311abd306108263d41ff *R/fit-and-marglik.R
b8dd3d2586515950b15e2036d6ffbb58 *R/inference-and-model-averaging.R
349d283ce1f949973b6b9ab18f04a68b *R/main.R
33ab5cfdc64b422953a20dacfb6151cb *R/plots.R
452bcef9c5cce6a2c34851ab75fca450 *R/priors.R
20456045641a790c4230b73b4fa3a0ed *R/summary.R
cd22dde138d15b99465b04eb97e49d17 *R/tools.R
afe49393ad7223c73a3f7b4940d20895 *R/summary.R
df1be979571c2c6be9a6411339104cf5 *R/tools.R
cc0fc23f9331953e8fa547a01b36226a *R/transformations-tools.R
0455cd7fb4bbd536cdcbc151d26c4cd4 *R/transformations.R
a4cb727df4c91b72678a25b808b16e2b *R/utilities.R
60dc1e710fdc90848b25dde2130644f7 *R/zzz.R
b1db4cf7f89c168131908261d4e3d9c8 *README.md
7b649b24a1daec7d44665f8791bf8216 *build/partial.rdb
90c91372c74b977f8b075690d193504f *R/transformations.R
0bd1ffef08f9afab2e1b7f4e3cc5d74e *R/utilities.R
cd363eeefe876971e527b1a4fe266cce *R/zzz.R
219bcffa2dcbe7dec5867f8861415f95 *README.md
56ecf8632f916e4c176e1e66a27b6aa2 *build/partial.rdb
aab22ea31319c88be663c6f78faefff3 *build/vignette.rds
760dc96e9df5cdd5e94af60cede37069 *cleanup
d57e4d76f7d4fb66d04dcd60b679ac0f *configure
Expand All @@ -29,27 +29,27 @@ bf90b60ca5e8c2ffa72fa100846c10d4 *data/Anderson2010.RData
cbcc7963e5c57e89d42dc60e368a36cb *data/Bem2011.RData
96e56d0b8da282a0878752dc95629fb0 *data/Poulsen2006.RData
c7cc6f9a65faceb858ad7dba5876eecd *inst/CITATION
2dd5ac933d12333c2b8a87ad1d482b1a *inst/REFERENCES.bib
1104c45ede2bb772092ea9588dca9c59 *inst/REFERENCES.bib
bb4253fc50b5772dcbafae0756498e0b *inst/apa.csl
ac2e03d3e7e6e59360ffe5c372956810 *inst/doc/CustomEnsembles.R
b004b57447e2dcb83e2339ec2b3e10b8 *inst/doc/CustomEnsembles.Rmd
1b13d9582a16f06ff737da5a26149753 *inst/doc/CustomEnsembles.html
62e8e58cc0b4e1683e5dd8e1769a7104 *inst/doc/MedicineBMA.R
81f1f65d1d6e88ebc484c096b8ad1b74 *inst/doc/MedicineBMA.Rmd
cbda6afc88bfeb1ac5f0d89ba03c15ec *inst/doc/MedicineBMA.html
9a1dbc7f078c153388f460c8dc983392 *inst/doc/ReproducingBMA.R
b1c1852c7de459c4421ae87949cd9802 *inst/doc/ReproducingBMA.Rmd
8d58bca285cbc21944f046bb0207747a *inst/doc/ReproducingBMA.html
fd3c67a42dbdd24982cc36f9dc612130 *inst/doc/CustomEnsembles.R
ff0426b2da58fda280e1fcd90352f623 *inst/doc/CustomEnsembles.Rmd
7fbdb2909546d13636424938bdbf2ebf *inst/doc/CustomEnsembles.html
cb3994c095bc53bda0bfd6a09ce8a29b *inst/doc/MedicineBMA.R
7a0b6454ac3dc0ed4144d934328265da *inst/doc/MedicineBMA.Rmd
6822db525bfafbf909a0a635142235de *inst/doc/MedicineBMA.html
df79337a88d6ba01256bd5d048c26935 *inst/doc/ReproducingBMA.R
f1653fabc1fbf3ba4b081334942fa41b *inst/doc/ReproducingBMA.Rmd
7b286c38b06fed7b95bd1674bb195b9f *inst/doc/ReproducingBMA.html
60cf5e03077f6969f5d0c85c17260e4d *man/Anderson2010.Rd
5324a3e1e554e2e230ebce87149a7b6c *man/Bem2011.Rd
223236109c7a979824a985d3cc3cac37 *man/Poulsen2006.Rd
cecae2c7ef8b708b820b6f802888af33 *man/Poulsen2006.Rd
1566ef4dc00fbfdff5d60fd1abc76ed9 *man/RoBMA-package.Rd
088d1e0cb83b052447fbe1c2ca50a217 *man/RoBMA.Rd
67f0f1e5f55394ddc16173d67ac7835b *man/RoBMA.Rd
aad724c2ea0020421a2dc6cd17b904da *man/RoBMA_control.Rd
32dba31d6713776b551b1bb3699d205c *man/RoBMA_options.Rd
54e11cbfac1177f9c595a8296235bee2 *man/check_RoBMA.Rd
96a5ad80c9be1722c52a96bfc4326d7e *man/check_setup.Rd
a22902d0f12aaf6b0487b8253d2a0f5c *man/combine_data.Rd
d0115d2acc36e6c7083563469708a186 *man/check_setup.Rd
d8d506927f32710bd19f58023b287e90 *man/combine_data.Rd
1cd37833fa52968342de1f6d73e6cd2e *man/diagnostics.Rd
82f3509c87644717fd2d0b44f2481326 *man/effect_sizes.Rd
d183f4a728a7404cb6a6920a4f40cbc1 *man/figures/README-fig_PETPEESE-1.png
Expand All @@ -70,18 +70,31 @@ ed38a06f79291ab08611815f2a99baaa *man/print.RoBMA.Rd
563c13b89d4cc6eaaf296ecd0e11826b *man/prior.Rd
cb9a3e6b91761a8f770104ded283e182 *man/prior_PEESE.Rd
761c23f04005e611e438e7adf47ab9b0 *man/prior_PET.Rd
43c197aba67c9e94ffe825151c124fb1 *man/prior_informed.Rd
819c718c417d3fbba4addfb165e6b7c1 *man/prior_informed.Rd
c1c1f994c4e52b88d35a01ba98780fd6 *man/prior_none.Rd
98e3842e71b622f838a4417736be5c5b *man/prior_weightfunction.Rd
68b5330a4acab62097773922c1711d6c *man/sample_sizes.Rd
86a651259d1563f8623bcec699c50a0b *man/standard_errors.Rd
785fa6f9a38af87ab52e635cb64b5805 *man/summary.RoBMA.Rd
c03013f1f30dff82fecb19366048a53f *man/update.RoBMA.Rd
b110a37ae7a55dc250c3e40ba856374a *man/update.RoBMA.Rd
5b439cec675847fc8c4968af6316f4da *man/weighted_multivariate_normal.Rd
d0709638b1704656ba678caddfc8fd68 *man/weighted_normal.Rd
69fe3562edeef5b116af2a1fc145d2b8 *src/Makevars.in
612b3fe82ecd0c90e7d37525e33f1b44 *src/Makevars.ucrt
60fe6dfe3087d85baa810358806c43e0 *src/Makevars.win
fdb4160bcc24b8f24262463f4f6e40a3 *src/RoBMA.cc
a82e6591d0eff4f1f81da8efcece21d2 *src/Makevars.in
43483df7de06a4dc27cf69958c804603 *src/Makevars.ucrt
b8ac24fa41901ab920cb747459327a96 *src/Makevars.win
3c36becc2075f9c2ee843a7b1686b92f *src/RoBMA.cc
5852a93a65376b3980869a5c04d37b6c *src/distributions/DMN.cc
9f65f3782608efe8263c42f2084f6acd *src/distributions/DMN.h
ab0e2a1ee3ec92b274de8240f399b3c8 *src/distributions/DMNv.cc
8e30b1693eedbac0120abd5782677b18 *src/distributions/DMNv.h
52e8bedfaeaeb8c4d9fdfd0d18fe8a64 *src/distributions/DWMN1.cc
762d85714f269fb8354fd5378d58469e *src/distributions/DWMN1.h
44553b0db7de43a9f18485076f1e6010 *src/distributions/DWMN1v.cc
f8635c3a4fec1dd1f977915b93cbe516 *src/distributions/DWMN1v.h
cd671ef892ace1fadd9d09b5e84cb731 *src/distributions/DWMN2.cc
e674121b4ff7d21eb72ebc14c7a00e8e *src/distributions/DWMN2.h
f212c233a87be54e9f9005eacf26e44e *src/distributions/DWMN2v.cc
36822d89d26ace478f32cd21522040d9 *src/distributions/DWMN2v.h
3057280c857f91e278b62eca9148aba5 *src/distributions/DWN1.cc
e2236c2afcab47f86b04fd834827f8c1 *src/distributions/DWN1.h
62c1d641fe2f286964782c40da903b1b *src/distributions/DWN2.cc
Expand All @@ -90,21 +103,31 @@ e14412c791c50c48d2eb0c99ab27d770 *src/distributions/DWN2.h
c44dfe0b0d861a937e913c0a73dbed3e *src/distributions/DWT1.h
59dcc141232421266e08da9aabc21ec6 *src/distributions/DWT2.cc
695ff2065c4046851b48d09bb8c67ff3 *src/distributions/DWT2.h
d9cabc87174f8c2b7baeba21f535a136 *src/functions/mnorm.cc
4bac11e67c3bcb848ec4e2cd6ab62bd4 *src/functions/mnorm.h
071f6916620aa90e35b5037b57aff44f *src/functions/wmnorm.cc
e7c28a3eab2be697b65719887db90259 *src/functions/wmnorm.h
bf113377ea31751bd409ae196bb39451 *src/init.c
15c16b8f9ef0d427578e77328f62fd4d *src/jagsversions.h
c4a81c2453703e507809476797693ed8 *src/source/mnorm.cc
0e6bd149c4ed9c8a9358800d08b8e32d *src/source/mnorm.h
b68f64fb699403b21302e2844a57e579 *src/source/tools.cc
59b80fde8225f6388886b96e9fefd3ff *src/source/tools.h
b2933d35db911f73fd977f08b704eb42 *src/source/transformations.cc
903017eb905c9a3133662511ed50ee3d *src/source/transformations.h
1b061ccd4450cb726081859632af2345 *src/source/wmnorm.cc
a8f9faa604c6f8ca7d6372ac02d9aea8 *src/source/wmnorm.h
94bfcf9342a57cbae1600b8bcef8b152 *src/testRoBMA.cc
309a4636a84d3085cf17255f1a3e6cfe *src/transformations/d.cc
9d85bc95b5b7c6b35c10f61cf1fcee6b *src/transformations/d.cc
13cf6c4eed77fb401c106275a085c198 *src/transformations/d.h
f7d37918118160039f9e9d0e19bf48ba *src/transformations/logOR.cc
bb947f8092dea981c0963ceeaa5658ab *src/transformations/logOR.cc
5df1ac1d986cc34808ff8b147dfcb589 *src/transformations/logOR.h
00c6f1b813c791fe72a52f29adf59a82 *src/transformations/r.cc
f061ce25636080bc3ac7f45b647dd7ea *src/transformations/r.cc
6a54e1b212fc2ce31ee8b4841cb51fc0 *src/transformations/r.h
31bf6c1d38f60b45e4e1727d8347db7b *src/transformations/transformations_common.cc
0876121aef39dcdaca948eebe29c0cab *src/transformations/transformations_common.h
9a0a76c93ecc50bfb6071fa572096a03 *src/transformations/z.cc
743b59c8467d78924632f06fb21ca3bd *src/transformations/z.cc
420cd69dfbbad14035813bda1b0d0ff7 *src/transformations/z.h
a44b750a42f3ec1655e214439ed62f98 *tests/testthat.R
bef8892dff0ab25ee7359dbff70fe60e *tests/testthat/test-0-CRAN.R
b004b57447e2dcb83e2339ec2b3e10b8 *vignettes/CustomEnsembles.Rmd
81f1f65d1d6e88ebc484c096b8ad1b74 *vignettes/MedicineBMA.Rmd
b1c1852c7de459c4421ae87949cd9802 *vignettes/ReproducingBMA.Rmd
27f4a9679ca4610981ea17183741d924 *tests/testthat/test-0-CRAN.R
ff0426b2da58fda280e1fcd90352f623 *vignettes/CustomEnsembles.Rmd
7a0b6454ac3dc0ed4144d934328265da *vignettes/MedicineBMA.Rmd
f1653fabc1fbf3ba4b081334942fa41b *vignettes/ReproducingBMA.Rmd
9 changes: 9 additions & 0 deletions NEWS.md
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@@ -1,3 +1,12 @@
## version 2.2.1
### Changes
- message about the effect size scale of parameter estimates is always shown
- compatibility with BayesTools 0.2.0+

## version 2.2
### Features
- three-level meta-analysis by specifying `study_ids` argument in `RoBMA`. However, note that this is (1) an experimental feature and (2) the computational expense of fitting selection models with clustering is extreme. As of now, it is almost impossible to have more than 2-3 estimates clustered within a single study).

## version 2.1.2
### Fixes
- adding Windows ucrt patch (thanks to Tomas Kalibera)
Expand Down
6 changes: 4 additions & 2 deletions R/check-input-and-settings.R
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Expand Up @@ -29,11 +29,13 @@ check_setup <- function(
priors_effect_null = prior(distribution = "point", parameters = list(location = 0)),
priors_heterogeneity_null = prior(distribution = "point", parameters = list(location = 0)),
priors_bias_null = prior_none(),
priors_rho = prior("beta", parameters = list(alpha = 1, beta = 1)),
priors_rho_null = NULL,
models = FALSE, silent = FALSE){

object <- list()
object$priors <- .check_and_list_priors(tolower(model_type), priors_effect_null, priors_effect, priors_heterogeneity_null, priors_heterogeneity, priors_bias_null, priors_bias, object$add_info[["prior_scale"]])
object$models <- .make_models(object[["priors"]])
object$priors <- .check_and_list_priors(tolower(model_type), priors_effect_null, priors_effect, priors_heterogeneity_null, priors_heterogeneity, priors_bias_null, priors_bias, priors_rho, priors_rho_null, object$add_info[["prior_scale"]])
object$models <- .make_models(object[["priors"]], multivariate = FALSE)


### model types overview
Expand Down
51 changes: 40 additions & 11 deletions R/check-priors-and-models.R
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@@ -1,5 +1,5 @@
### functions for creating model objects
.check_and_list_priors <- function(model_type, priors_effect_null, priors_effect, priors_heterogeneity_null, priors_heterogeneity, priors_bias_null, priors_bias, prior_scale){
.check_and_list_priors <- function(model_type, priors_effect_null, priors_effect, priors_heterogeneity_null, priors_heterogeneity, priors_bias_null, priors_bias, priors_rho_null, priors_rho, prior_scale){

if(!is.null(model_type) & length(model_type == 1)){
# precanned models
Expand All @@ -13,32 +13,38 @@
prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.025, 0.05)), prior_weights = 1/12),
prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.5)), prior_weights = 1/12),
prior_weightfunction(distribution = "one.sided", parameters = list(alpha = c(1, 1, 1, 1), steps = c(0.025, 0.05, 0.5)), prior_weights = 1/12),
prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4),
prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4)
prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/4),
prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/4)
)
priors_rho <- NULL
priors_effect_null <- prior(distribution = "point", parameters = list(location = 0))
priors_heterogeneity_null <- prior(distribution = "point", parameters = list(location = 0))
priors_bias_null <- prior_none()
priors_rho_null <- NULL
}else if(model_type == "pp"){
priors_effect <- prior(distribution = "normal", parameters = list(mean = 0, sd = 1))
priors_heterogeneity <- prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15))
priors_bias <- list(
prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/2),
prior_PET(distribution = "Cauchy", parameters = list(0,1), truncation = list(0, Inf), prior_weights = 1/2),
prior_PEESE(distribution = "Cauchy", parameters = list(0,5), truncation = list(0, Inf), prior_weights = 1/2)
)
priors_rho <- NULL
priors_effect_null <- prior(distribution = "point", parameters = list(location = 0))
priors_heterogeneity_null <- prior(distribution = "point", parameters = list(location = 0))
priors_bias_null <- prior_none()
priors_rho_null <- NULL
}else if(model_type == "2w"){
priors_effect <- prior(distribution = "normal", parameters = list(mean = 0, sd = 1))
priors_heterogeneity <- prior(distribution = "invgamma", parameters = list(shape = 1, scale = .15))
priors_bias <- list(
prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1), steps = c(0.05)), prior_weights = 1/2),
prior_weightfunction(distribution = "two.sided", parameters = list(alpha = c(1, 1, 1), steps = c(0.05, 0.10)), prior_weights = 1/2)
)
priors_rho <- NULL
priors_effect_null <- prior(distribution = "point", parameters = list(location = 0))
priors_heterogeneity_null <- prior(distribution = "point", parameters = list(location = 0))
priors_bias_null <- prior_none()
priors_rho_null <- NULL
}else{
stop("Unknown 'model_type'.")
}
Expand All @@ -48,13 +54,14 @@
priors$effect <- .check_and_list_component_priors(priors_effect_null, priors_effect, "effect")
priors$heterogeneity <- .check_and_list_component_priors(priors_heterogeneity_null, priors_heterogeneity, "heterogeneity")
priors$bias <- .check_and_list_component_priors(priors_bias_null, priors_bias, "bias")
priors$rho <- .check_and_list_component_priors(priors_rho_null, priors_rho, "rho")

return(priors)
}
.check_and_list_component_priors <- function(priors_null, priors_alt, component){

# check that at least one prior is specified (either null or alternative)
if(is.null(priors_null) & is.null(priors_alt))
if(component != "rho" && (is.null(priors_null) & is.null(priors_alt)))
stop(paste0("At least one prior needs to be specified for the ", component," parameter (either null or alternative)."))

# create an empty list if user didn't specified priors
Expand Down Expand Up @@ -132,28 +139,45 @@
if(!(is.prior.PET(priors[[p]]) | is.prior.PEESE(priors[[p]]) | is.prior.weightfunction(priors[[p]]) | is.prior.none(priors[[p]])))
stop(paste0("'", print(priors[[p]], silent = TRUE),"' prior distribution is not supported for the bias component."))
}
}else if(component == "rho"){

for(p in seq_along(priors)){

# check for allowed priors
if(!(priors[[p]][["distribution"]] == "beta"))
stop(paste0("'", print(priors[[p]], silent = TRUE),"' prior distribution is not supported for the rho component."))
}
}

return(priors)
}
.make_models <- function(priors){
.make_models <- function(priors, multivariate){

# create models according to the set priors
models <- NULL
for(effect in priors[["effect"]]){
for(heterogeneity in priors[["heterogeneity"]]){
for(bias in priors[["bias"]]){
models <- c(
models,
list(.make_model(effect, heterogeneity, bias, effect[["prior_weights"]] * heterogeneity[["prior_weights"]] * bias[["prior_weights"]]))
)
if(!is.null(priors[["rho"]]) && multivariate){
for(rho in priors[["rho"]]){
models <- c(
models,
list(.make_model(effect, heterogeneity, bias, rho, effect[["prior_weights"]] * heterogeneity[["prior_weights"]] * bias[["prior_weights"]] * rho[["prior_weights"]]))
)
}
}else{
models <- c(
models,
list(.make_model(effect, heterogeneity, bias, NULL, effect[["prior_weights"]] * heterogeneity[["prior_weights"]] * bias[["prior_weights"]]))
)
}
}
}
}

return(models)
}
.make_model <- function(prior_effect, prior_heterogeneity, prior_bias, prior_weights){
.make_model <- function(prior_effect, prior_heterogeneity, prior_bias, prior_rho, prior_weights){

priors <- list()

Expand All @@ -166,13 +190,18 @@
}else if(is.prior.weightfunction(prior_bias)){
priors$omega <- prior_bias
}
# add 3 level structure only if there is heterogeneity
if(!(prior_heterogeneity[["distribution"]] == "point" && prior_heterogeneity$parameters[["location"]] == 0) && !is.null(prior_rho)){
priors$rho <- prior_rho
}

model <- list(
priors = priors,
prior_weights = prior_weights,
prior_weights_set = prior_weights
)
class(model) <- "RoBMA.model"
attr(model, "multivariate") <- !is.null(priors$rho)

return(model)
}

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