version 0.1.0

DESCRIPTION

@@ -0,0 +1,39 @@
+Package: publipha
+Title: Bayesian Meta-Analysis with Publications Bias and P-Hacking
+Version: 0.1.0
+Authors@R: c(
+    person("Jonas", "Moss", , "jonas.gjertsen@gmail.com", role = c("aut", "cre"), 
+      comment = c(ORCID = "0000-0002-6876-6964")
+    ),
+    person("Trustees of Columbia University", role = "cph"))
+Description: Tools for Bayesian estimation of meta-analysis models that account 
+    for publications bias or p-hacking. For publication bias, this package 
+    implements a variant of the p-value based selection model of Hedges (1992)
+    <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also 
+    implements the mixture of truncated normals model for p-hacking described 
+    in Moss and De Bin (2019) <arXiv:1911.12445>.
+License: GPL-3
+Depends: methods, R (>= 3.5.0), Rcpp (>= 0.12.19)
+Imports: rstan (>= 2.18.1), rstantools (>= 1.5.1), loo, truncnorm
+LinkingTo: BH (>= 1.72.0-2), Rcpp (>= 0.12.19), RcppEigen (>=
+        0.3.3.4.0), rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
+Encoding: UTF-8
+LazyData: true
+NeedsCompilation: yes
+SystemRequirements: GNU make
+RoxygenNote: 7.0.2
+Suggests: testthat (>= 2.1.0), covr, knitr, rmarkdown, metafor, qtl,
+        spelling
+Collate: 'data-anderson2010.R' 'data-baskerville2012.R'
+        'data-cuddy2018.R' 'data-dang2018.R' 'data-motyl2017.R'
+        'densities-helpers.R' 'densities-mpsnorm.R'
+        'densities-phnorm.R' 'densities-psnorm.R' 'densities-snorm.R'
+        'ma.R' 'generics.R' 'publipha-package.R' 'stanmodels.R'
+        'tools.R' 'utility.R' 'zzz.R'
+Language: en-US
+Packaged: 2020-01-07 13:39:23 UTC; jonas
+Author: Jonas Moss [aut, cre] (<https://orcid.org/0000-0002-6876-6964>),
+  Trustees of Columbia University [cph]
+Maintainer: Jonas Moss <jonas.gjertsen@gmail.com>
+Repository: CRAN
+Date/Publication: 2020-01-08 17:20:02 UTC

MD5

@@ -0,0 +1,69 @@
+c606c742e3bbfef8938b4d38397931d4 *DESCRIPTION
+8f2795df89df69a2e11a857aa1b61b00 *NAMESPACE
+65c50aaf39cdeb26273e88fa08e4052a *R/data-anderson2010.R
+d8951aee5aadcd1c2032159aa648093f *R/data-baskerville2012.R
+0d93988093faab5cbef35be16f76e5f1 *R/data-cuddy2018.R
+3a57557a83c083fde241f9d4046eca69 *R/data-dang2018.R
+27bdca04a12d92c6991099e929cc1a1f *R/data-motyl2017.R
+d370e088aa75db54e7cc19e0426ce179 *R/densities-helpers.R
+878228ed844892d8c99e5c18c8475c01 *R/densities-mpsnorm.R
+9cd9c42759f519a9512bb28ecc3b4d66 *R/densities-phnorm.R
+147deb30e3768768b2fed6f893f95c13 *R/densities-psnorm.R
+09b96567ca0af8919a7ac1f1ddab7aa3 *R/densities-snorm.R
+1f6206c46f5cc83a45b8ab5925fece6a *R/generics.R
+221e41dd289b1290497446601fd45470 *R/ma.R
+30cecd37711be11e96810849a217f009 *R/publipha-package.R
+d144914c93a54f8fa80a7417bfa68226 *R/stanmodels.R
+06d1e90aff26586e18fd7d55f7e79515 *R/tools.R
+004b66099a1bb1f4647488b06623b1dd *R/utility.R
+51bcd7b693c038f673551122b58e541c *R/zzz.R
+e27356694a61d1ae217807a387f345c5 *README.md
+59a375086c7ae1ef5c6f86152172973d *data/dat.anderson2010.rda
+62f18b4612982cc7b77c8f95c6f68fef *data/dat.baskerville2012.rda
+d7180e56782d5467768676f0484dd5ac *data/dat.cuddy2018.rda
+05ad6dc4ba3d6d44cb8e192503db7431 *data/dat.dang2018.rda
+b2481e064295118fb14341794394f13d *data/dat.motyl2017.rda
+4d7ec23b5d3a4e6be7f4f884f18098eb *inst/WORDLIST
+855c064dbf8deaff96c8dc8a87169109 *inst/include/meta_header.hpp
+dac06014d2103797cf1224c100b5a241 *man/ExtractParameters.Rd
+4d2ec08822c679893e469860cbe0e0a5 *man/add_elements.Rd
+4c1bef2a62c31ca6380c291ba14f68a4 *man/alist2.Rd
+ee823fcae3b5e055c7b8ad9992248099 *man/arguments.Rd
+15d7d72609fa685fead3fb42cbb62fb6 *man/dat.anderson2010.Rd
+9ce7f61a6e8a834c99a156767a2b0137 *man/dat.baskerville2012.Rd
+69f521b1a321f6d35ede4c30c1295fc9 *man/dat.cuddy2018.Rd
+41debbd624365b9210a9151582e5b825 *man/dat.dang2018.Rd
+4f9c96fad0049d57f23568722ab2cc8c *man/dat.motyl2017.Rd
+d7594887c11f9cff3455256b02004c07 *man/do_call.Rd
+5395e40a3407d034974523668765f09f *man/figures/README-unnamed-chunk-4-1.png
+e6d53af036ce9626e0048a7427c80e9e *man/figures/logo.png
+36b32e7f4dcfacb506adebaab417bde2 *man/loo-mafit-method.Rd
+5c0485dabf6bcdab6df0d381505ddc99 *man/ma.Rd
+e427ab72830996588ea4c9e7a9c6ff67 *man/mafit-class.Rd
+ff8afc0ef1c7a9df53eb89b660f799d5 *man/mpsnorm.Rd
+50c3e2485e8a1c489ee599aa1f14d296 *man/normalizing_constant.Rd
+0346e3ac2f8a910c95b2df0ba54a86c0 *man/phnorm.Rd
+11259a1823341c75dbec376057f9ea12 *man/psnorm.Rd
+b0925a5a8e945d07ec3f100b513421cf *man/publipha-package.Rd
+ed78d3ed114918426e045922f014f9ae *man/snorm.Rd
+eee0022bd55de759febe4642bff4aa33 *src/Makevars
+252c1b3991f8907ce7750aadf6ae131f *src/Makevars.win
+14f404dcf467c6b7c2571640f759ab2c *src/init.cpp
+a239d444a0460e4abed452df5926ef8c *src/stan_files/chunks/densities.stan
+ef217dbc12ab476c518879db0491d35e *src/stan_files/chunks/license.stan
+fd10473e68b647f27491b652f5b17cef *src/stan_files/chunks/phma_likelihoods.stan
+848d6b4b0334dfe2f6c15f08613a7d8f *src/stan_files/chunks/psma_likelihoods.stan
+97d8113ccca3b4f522c5e7a230b4a903 *src/stan_files/cma.stan
+cfd0ddaa90149c4e469fe1b3b399b67f *src/stan_files/phma.stan
+585357b05819120d8b20b20527aceda8 *src/stan_files/psma.stan
+f8903adefb4c1844616f994332abd6d8 *tests/testthat.R
+62e6623f220d0d4b18bf80a847f8eced *tests/testthat/setup.R
+39a3a114c602153ccf96b6bdb021054d *tests/testthat/test_densities-helpers.R
+2a0bdc39dc18a15180966818d8e43a55 *tests/testthat/test_densities-mpsnorm.R
+38b10cfcf4644d6617fdaeaaa2ce686a *tests/testthat/test_densities-phnorm.R
+93a8c5582e2126b20e7457b655db1a32 *tests/testthat/test_densities-psnorm.R
+0cf1642706955fe8fba93cfb2eda0651 *tests/testthat/test_densities-snorm.R
+ed7d178f2c8de723a14074d330596ec1 *tests/testthat/test_ma.R
+08ab993a451e673e55557d4ba7c5f497 *tests/testthat/test_tools.R
+66c06f829934efe5e5633417d810df55 *tests/testthat/test_utility.R
+240510fe0f2844a94e141a76a4eabb1a *tools/make_cc.R

NAMESPACE

@@ -0,0 +1,31 @@
+# Generated by roxygen2: do not edit by hand
+
+export(allma)
+export(cma)
+export(dmpsnorm)
+export(dphnorm)
+export(dpsnorm)
+export(dsnorm)
+export(esnorm)
+export(extract_eta)
+export(extract_isq)
+export(extract_tau)
+export(extract_theta)
+export(extract_theta0)
+export(ma)
+export(phma)
+export(pmpsnorm)
+export(pphnorm)
+export(ppsnorm)
+export(psma)
+export(rmpsnorm)
+export(rphnorm)
+export(rpsnorm)
+export(rsnorm)
+exportClasses(mafit)
+exportMethods(loo)
+import(Rcpp)
+import(methods)
+import(rstantools)
+importFrom(rstan,sampling)
+useDynLib(publipha, .registration = TRUE)

R/data-anderson2010.R

@@ -0,0 +1,28 @@
+#' Studies on Effect of Violent Video Games on Negative Outcomes
+#'
+#' Results from 477 studies on the effect of violent video games on negative
+#'    outcomes.
+#'
+#' @usage dat.anderson2010
+#'
+#' @format The data frame contains the following columns:
+#'     \tabular{lll}{
+#'         \strong{author}       \tab \code{character} \tab first author \cr
+#'         \strong{year}         \tab \code{numeric}   \tab publication year \cr
+#'         \strong{outcome}      \tab \code{character} \tab one of seven outcomes \cr
+#'         \strong{best   }      \tab \code{boolean}   \tab if \code{TRUE}, the was a best practice study \cr
+#'         \strong{experimental} \tab \code{boolean}   \tab if \code{TRUE}, the study was experimental \cr
+#'         \strong{adult}        \tab \code{boolean}   \tab if \code{TRUE}, the study subjects were adults \cr
+#'         \strong{country}      \tab \code{character} \tab country of study \cr
+#'         \strong{ni}           \tab \code{numeric}   \tab sample size \cr
+#'         \strong{yi}           \tab \code{numeric}   \tab observed mean difference in outcome (violent vs. non-violent) \cr
+#'         \strong{vi}           \tab \code{numeric}   \tab corresponding sampling variance
+#'    }
+#' @keywords datasets
+#'
+#' @references
+#'     Baskerville, N. B., Liddy, C., & Hogg, W. (2012). Systematic review and meta-analysis of practice facilitation within primary care settings. The Annals of Family Medicine, 10(1), 63-74.
+#'     Hilgard, J., Engelhardt, C. R., & Rouder, J. N. (2017). Overstated evidence for short-term effects of violent games on affect and behavior: A reanalysis of Anderson et al.(2010).
+#'
+#' @source \url{https://github.com/Joe-Hilgard/Anderson-meta}
+"dat.anderson2010"

R/data-baskerville2012.R

@@ -0,0 +1,22 @@
+#' Studies on Practice Facilitation
+#'
+#' Results from 23 studies on the effect of practice facilitation in a
+#'     primary care setting.
+#'
+#' @usage dat.baskerville2012
+#'
+#' @format The tibble contains the following columns:
+#'     \tabular{lll}{
+#'         \strong{author}    \tab \code{character} \tab first author of study \cr
+#'         \strong{year}      \tab \code{numeric}   \tab publication year \cr
+#'         \strong{design}    \tab \code{character} \tab study design (RCT, C-RCT, or CCT) \cr
+#'         \strong{blinded}   \tab \code{boolean}   \tab if \code{TRUE}, the study was blinded \cr
+#'         \strong{concealed} \tab \code{boolean}   \tab if \code{TRUE}, the study was concealed \cr
+#'         \strong{yi}        \tab \code{numeric}   \tab observed mean difference in outcome (facilitated vs non-facilitated) \cr
+#'         \strong{vi}        \tab \code{numeric}   \tab corresponding sampling variance
+#'    }
+#' @keywords datasets
+#'
+#' @source Baskerville, N. B., Liddy, C., & Hogg, W. (2012). Systematic review and meta-analysis of practice facilitation within primary care settings. The Annals of Family Medicine, 10(1), 63-74.
+#'
+"dat.baskerville2012"

R/data-cuddy2018.R

@@ -0,0 +1,27 @@
+#' Studies on the Effect of Power Posing
+#'
+#' Results from 27 studies related to power posing.
+#'
+#' The data points are taken from the p-curve analysis of Cuddy et al. (2018),
+#'    restricted to 2 cell designs with mean difference as the outcome
+#'    variable.
+#'
+#' @usage dat.cuddy2018
+#'
+#' @format The data frame contains the following columns:
+#'     \tabular{lll}{
+#'         \strong{author}       \tab \code{character} \tab first author \cr
+#'         \strong{year}         \tab \code{numeric}   \tab publication year \cr
+#'         \strong{power}        \tab \code{boolean}   \tab if \code{TRUE}, the outcome was feeling of power \cr
+#'         \strong{ease}         \tab \code{boolean}   \tab if \code{TRUE}, the outcome was an EASE variable \cr
+#'         \strong{yi}           \tab \code{numeric}   \tab standardized mean difference \cr
+#'         \strong{vi}           \tab \code{numeric}   \tab corresponding sampling variance
+#'    }
+#'
+#' @keywords datasets
+#'
+#' @references
+#'     Cuddy, A. J., Schultz, S. J., & Fosse, N. E. (2018). P-curving a more comprehensive body of research on postural feedback reveals clear evidential value for power-posing effects: Reply to Simmons and Simonsohn (2017). Psychological science, 29(4), 656-666.
+#'
+#' @source \url{https://osf.io/jx3av/}
+"dat.cuddy2018"

R/data-dang2018.R

@@ -0,0 +1,33 @@
+#' Meta-analysis on Ego Depletion
+#'
+#' Results from 150 studies of ego depletion, the claim that self-control is a
+#'     limited resource which is tapped whenever self-control is exerted.
+#'
+#' @usage data(dat.dang2018)
+#'
+#' @format The tibble contains the following columns:
+#'     \tabular{lll}{
+#'         \strong{author}       \tab \code{character} \tab the last name of the first author and the first letter of the last name of the second author;  \cr
+#'         \strong{year}         \tab \code{numeric}   \tab publication year \cr
+#'         \strong{in_carter}    \tab \code{character} \tab was the study in the meta-analysis of Carter et al. (2015) \cr
+#'         \strong{study}        \tab \code{character} \tab the number given to the study in the original paper (0 = only one study was reported in the original paper; the addition of a letter indicates subsamples);  \cr
+#'         \strong{dv}           \tab \code{boolean}   \tab the dependent variable \cr
+#'         \strong{iv}           \tab \code{boolean}   \tab the independent variable \cr
+#'         \strong{n1i}          \tab \code{numeric}   \tab the number of participants in the depletion condition \cr
+#'         \strong{n2i}          \tab \code{numeric}   \tab the number of participants in the control condition \cr
+#'         \strong{yi}           \tab \code{numeric}   \tab the adjusted standardized mean difference \cr
+#'         \strong{vi}           \tab \code{numeric}   \tab the variance
+#'    }
+#'
+#' @keywords datasets
+#'
+#' @references Dang, J. (2018). An updated meta-analysis of the ego
+#' depletion effect. Psychological Research, 82(4), 645-651.
+#'
+#' Carter, E. C., Kofler, L. M., Forster, D. E., & McCullough, M. E. (2015).
+#' A series of meta-analytic tests of the depletion effect: self-control does
+#' not seem to rely on a limited resource. Journal of
+#' Experimental Psychology: General, 144(4), 796.
+#'
+#' @source \url{https://link.springer.com/article/10.1007%2Fs00426-017-0862-x#SupplementaryMaterial}
+"dat.dang2018"

R/data-motyl2017.R

@@ -0,0 +1,29 @@
+#' Effect Sizes from 875 Studies in Psychology.
+#'
+#' Effect sizes from 875 studies in psychology. Adopted from Motyl et al.
+#' (2017).
+#'
+#' @usage data(dat.motyl2017)
+#'
+#' @format The tibble contains the following columns:
+#'     \tabular{lll}{
+#'         \strong{author}       \tab \code{character} \tab first author of study \cr
+#'         \strong{year}         \tab \code{numeric}   \tab publication year \cr
+#'         \strong{study}        \tab \code{numeric}   \tab the number given to the study in the original paper (0 = only one study was reported in the original paper; \cr
+#'         \strong{journal}      \tab \code{character} \tab journal where the study was published \cr
+#'         \strong{concealed}    \tab \code{character} \tab design of the study; "Between", "Within", or "Mixed"   \cr
+#'         \strong{experimental} \tab \code{numeric}   \tab `TRUE` for an experimental study \cr
+#'         \strong{ni}           \tab \code{numeric}   \tab sample size \cr
+#'         \strong{yi}           \tab \code{numeric}   \tab observed mean difference in outcome \cr
+#'         \strong{vi}           \tab \code{numeric}   \tab corresponding sampling variance
+#'    }
+#'
+#' @keywords datasets
+#'
+#' @references Motyl, M., Demos, A. P., Carsel, T. S., Hanson, B. E.,
+#' Melton, Z. J., Mueller, A. B., ... & Yantis, C. (2017). The state of social
+#' and personality science: Rotten to the core, not so bad, getting better,
+#' or getting worse?. Journal of personality and social psychology, 113(1), 34.
+#'
+#' @source \url{https://osf.io/he8mu/}
+"dat.motyl2017"

R/densities-helpers.R

@@ -0,0 +1,69 @@
+#' Normalizing Constants for the Publication Selection Meta-Analysis Model
+#'
+#' Normalizing constants for the publication selection meta-Analysis model.
+#'     These are used in several other functions. The underlying effect size
+#'     distribution is normal and the selection is one-sided.
+#'
+#' The function \code{I} calculates the normalizing constant for the density of
+#'     the observed effect sizes. The function \code{J} calculates the
+#'     normalizing constant for the density of the effect size distribution.
+#'
+#' @keywords internal
+#' @name normalizing_constant
+#' @param sigma Numeric; The standard deviation of the study, due to
+#'     sampling error.
+#' @param theta Numeric; The mean of the underlying effect size.
+#' @param theta0 Numeric; The mean of the underlying effect size
+#'     distribution.
+#' @param tau Numeric; The standard deviation of the underlying effect
+#'     size distribution.
+#' @param alpha Numeric vector; Specifies the thresholds for publication
+#'     bias.
+#' @param eta Numeric vector; Containing the probabilities of being a study
+#'     with the given p-value from being published. This is normalized so that
+#'     the maximal element is 1.
+#' @return The normalizing constant.
+
+I <- function(sigma, theta, alpha, eta) {
+  k <- length(alpha)
+  cutoffs <- stats::qnorm(1 - alpha)
+
+  sapply(sigma, function(sigma) {
+    cdfs <- stats::pnorm(cutoffs, theta / sigma, 1)
+    sum(sapply(1:(k - 1), function(i) eta[i] * (cdfs[i] - cdfs[i + 1])))
+  })
+}
+
+#' @rdname normalizing_constant
+J <- function(sigma, theta0, tau, alpha, eta) {
+  k <- length(alpha)
+  cutoffs <- stats::qnorm(1 - alpha)
+
+  sapply(sigma, function(sigma) {
+    cdfs <- stats::pnorm(cutoffs, theta0 / sigma, sqrt(tau^2 + sigma^2) / sigma)
+    sum(sapply(1:(k - 1), function(i) eta[i] * (cdfs[i] - cdfs[i + 1])))
+  })
+}
+
+density_input_checker <- function(x, theta0 = NULL, theta = NULL, sigma = NULL,
+                                  tau = NULL) {
+  if (any(!is.numeric(x))) {
+    stop("'x' must be numeric")
+  }
+
+  if (any(!is.numeric(c(theta0, theta, tau, sigma)))) {
+    stop("parameters must be numeric")
+  }
+
+  if (any(is.na(c(theta0, sigma, theta, tau)))) {
+    stop("parameters cannot be na")
+  }
+
+  if (any(tau <= 0)) {
+    stop("'tau' must be positive")
+  }
+
+  if (any(sigma <= 0)) {
+    stop("'sigma' must be positive")
+  }
+}