Automated build.

.setup/latex/pdf/.gitignore

@@ -0,0 +1,5 @@
+*
+*/
+!*.pdf
+!.gitignore
+!bib.bib

CITATION.cff

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+# --------------------------------------------
+# CITATION file created with {cffr} R package
+# See also: https://docs.ropensci.org/cffr/
+# --------------------------------------------
+
+cff-version: 1.2.0
+message: 'To cite package "cTMed" in publications use:'
+type: software
+license: GPL-3.0-or-later
+title: 'cTMed: Continuous Time Mediation'
+version: 1.0.5.9000
+identifiers:
+- type: doi
+  value: 10.32614/CRAN.package.cTMed
+abstract: Calculates standard errors and confidence intervals for effects in continuous-time
+  mediation models. This package extends the work of Deboeck and Preacher (2015) <https://doi.org/10.1080/10705511.2014.973960>
+  and Ryan and Hamaker (2021) <https://doi.org/10.1007/s11336-021-09767-0> by providing
+  methods to generate standard errors and confidence intervals for the total, direct,
+  and indirect effects in these models.
+authors:
+- family-names: Pesigan
+  given-names: Ivan Jacob Agaloos
+  email: [email protected]
+  orcid: https://orcid.org/0000-0003-4818-8420
+preferred-citation:
+  type: manual
+  title: 'cTMed: Continuous Time Mediation'
+  authors:
+  - family-names: Pesigan
+    given-names: Ivan Jacob Agaloos
+    email: [email protected]
+    orcid: https://orcid.org/0000-0003-4818-8420
+  year: '2024'
+  notes: R package version 1.0.5.9000
+repository: https://CRAN.R-project.org/package=cTMed
+repository-code: https://github.com/jeksterslab/cTMed
+url: https://jeksterslab.github.io/cTMed/
+contact:
+- family-names: Pesigan
+  given-names: Ivan Jacob Agaloos
+  email: [email protected]
+  orcid: https://orcid.org/0000-0003-4818-8420
+keywords:
+- centrality
+- continuous-time
+- delta-method
+- mediation
+- monte-carlo-method
+- network
+- r
+- r-package
+

DESCRIPTION

@@ -30,5 +30,6 @@ Suggests:
     knitr,
     rmarkdown,
     testthat,
-    expm
+    expm,
+    bootStateSpace
 RoxygenNote: 7.3.2

R/RcppExports.R

@@ -2,122 +2,122 @@
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 .DirectStd <- function(phi, sigma, delta_t, from, to, med) {
-    .Call('_cTMed_DirectStd', PACKAGE = 'cTMed', phi, sigma, delta_t, from, to, med)
+    .Call(`_cTMed_DirectStd`, phi, sigma, delta_t, from, to, med)
 }
 
 .Direct <- function(phi, delta_t, from, to, med) {
-    .Call('_cTMed_Direct', PACKAGE = 'cTMed', phi, delta_t, from, to, med)
+    .Call(`_cTMed_Direct`, phi, delta_t, from, to, med)
 }
 
 .ExpCov <- function(phi, sigma, delta_t) {
-    .Call('_cTMed_ExpCov', PACKAGE = 'cTMed', phi, sigma, delta_t)
+    .Call(`_cTMed_ExpCov`, phi, sigma, delta_t)
 }
 
 .ExpMean <- function(phi, iota, delta_t) {
-    .Call('_cTMed_ExpMean', PACKAGE = 'cTMed', phi, iota, delta_t)
+    .Call(`_cTMed_ExpMean`, phi, iota, delta_t)
 }
 
 .IndirectCentrals <- function(phi, delta_t) {
-    .Call('_cTMed_IndirectCentrals', PACKAGE = 'cTMed', phi, delta_t)
+    .Call(`_cTMed_IndirectCentrals`, phi, delta_t)
 }
 
 .IndirectCentralVec <- function(phi_vec, delta_t) {
-    .Call('_cTMed_IndirectCentralVec', PACKAGE = 'cTMed', phi_vec, delta_t)
+    .Call(`_cTMed_IndirectCentralVec`, phi_vec, delta_t)
 }
 
 .IndirectCentral <- function(phi, delta_t) {
-    .Call('_cTMed_IndirectCentral', PACKAGE = 'cTMed', phi, delta_t)
+    .Call(`_cTMed_IndirectCentral`, phi, delta_t)
 }
 
 .IndirectStd <- function(phi, sigma, delta_t, from, to, med) {
-    .Call('_cTMed_IndirectStd', PACKAGE = 'cTMed', phi, sigma, delta_t, from, to, med)
+    .Call(`_cTMed_IndirectStd`, phi, sigma, delta_t, from, to, med)
 }
 
 .Indirect <- function(phi, delta_t, from, to, med) {
-    .Call('_cTMed_Indirect', PACKAGE = 'cTMed', phi, delta_t, from, to, med)
+    .Call(`_cTMed_Indirect`, phi, delta_t, from, to, med)
 }
 
 .MCPhiI <- function(phi, vcov_phi_vec_l, test_phi = TRUE) {
-    .Call('_cTMed_MCPhiI', PACKAGE = 'cTMed', phi, vcov_phi_vec_l, test_phi)
+    .Call(`_cTMed_MCPhiI`, phi, vcov_phi_vec_l, test_phi)
 }
 
 .MCPhiSigmaI <- function(theta, vcov_theta, test_phi = TRUE) {
-    .Call('_cTMed_MCPhiSigmaI', PACKAGE = 'cTMed', theta, vcov_theta, test_phi)
+    .Call(`_cTMed_MCPhiSigmaI`, theta, vcov_theta, test_phi)
 }
 
 .MCPhiSigma <- function(theta, vcov_theta, R, test_phi = TRUE) {
-    .Call('_cTMed_MCPhiSigma', PACKAGE = 'cTMed', theta, vcov_theta, R, test_phi)
+    .Call(`_cTMed_MCPhiSigma`, theta, vcov_theta, R, test_phi)
 }
 
 .MCPhi <- function(phi, vcov_phi_vec_l, R, test_phi = TRUE) {
-    .Call('_cTMed_MCPhi', PACKAGE = 'cTMed', phi, vcov_phi_vec_l, R, test_phi)
+    .Call(`_cTMed_MCPhi`, phi, vcov_phi_vec_l, R, test_phi)
 }
 
 .Meds <- function(phi, delta_t, from, to, med) {
-    .Call('_cTMed_Meds', PACKAGE = 'cTMed', phi, delta_t, from, to, med)
+    .Call(`_cTMed_Meds`, phi, delta_t, from, to, med)
 }
 
 .MedStds <- function(phi, sigma, delta_t, from, to, med) {
-    .Call('_cTMed_MedStds', PACKAGE = 'cTMed', phi, sigma, delta_t, from, to, med)
+    .Call(`_cTMed_MedStds`, phi, sigma, delta_t, from, to, med)
 }
 
 .MedStdVec <- function(v, delta_t, from, to, med) {
-    .Call('_cTMed_MedStdVec', PACKAGE = 'cTMed', v, delta_t, from, to, med)
+    .Call(`_cTMed_MedStdVec`, v, delta_t, from, to, med)
 }
 
 .MedStd <- function(phi, sigma, delta_t, from, to, med) {
-    .Call('_cTMed_MedStd', PACKAGE = 'cTMed', phi, sigma, delta_t, from, to, med)
+    .Call(`_cTMed_MedStd`, phi, sigma, delta_t, from, to, med)
 }
 
 .MedVec <- function(phi_vec, delta_t, from, to, med) {
-    .Call('_cTMed_MedVec', PACKAGE = 'cTMed', phi_vec, delta_t, from, to, med)
+    .Call(`_cTMed_MedVec`, phi_vec, delta_t, from, to, med)
 }
 
 .Med <- function(phi, delta_t, from, to, med) {
-    .Call('_cTMed_Med', PACKAGE = 'cTMed', phi, delta_t, from, to, med)
+    .Call(`_cTMed_Med`, phi, delta_t, from, to, med)
 }
 
 .TestPhi <- function(phi) {
-    .Call('_cTMed_TestPhi', PACKAGE = 'cTMed', phi)
+    .Call(`_cTMed_TestPhi`, phi)
 }
 
 .TestStable <- function(x) {
-    .Call('_cTMed_TestStable', PACKAGE = 'cTMed', x)
+    .Call(`_cTMed_TestStable`, x)
 }
 
 .TotalCentrals <- function(phi, delta_t) {
-    .Call('_cTMed_TotalCentrals', PACKAGE = 'cTMed', phi, delta_t)
+    .Call(`_cTMed_TotalCentrals`, phi, delta_t)
 }
 
 .TotalCentralVec <- function(phi_vec, delta_t) {
-    .Call('_cTMed_TotalCentralVec', PACKAGE = 'cTMed', phi_vec, delta_t)
+    .Call(`_cTMed_TotalCentralVec`, phi_vec, delta_t)
 }
 
 .TotalCentral <- function(phi, delta_t) {
-    .Call('_cTMed_TotalCentral', PACKAGE = 'cTMed', phi, delta_t)
+    .Call(`_cTMed_TotalCentral`, phi, delta_t)
 }
 
 .TotalDeltaT <- function(phi, delta_t) {
-    .Call('_cTMed_TotalDeltaT', PACKAGE = 'cTMed', phi, delta_t)
+    .Call(`_cTMed_TotalDeltaT`, phi, delta_t)
 }
 
 .TotalStdDeltaT <- function(phi, sigma, delta_t) {
-    .Call('_cTMed_TotalStdDeltaT', PACKAGE = 'cTMed', phi, sigma, delta_t)
+    .Call(`_cTMed_TotalStdDeltaT`, phi, sigma, delta_t)
 }
 
 .TotalStdVec <- function(v, delta_t) {
-    .Call('_cTMed_TotalStdVec', PACKAGE = 'cTMed', v, delta_t)
+    .Call(`_cTMed_TotalStdVec`, v, delta_t)
 }
 
 .TotalStd <- function(phi, sigma, delta_t) {
-    .Call('_cTMed_TotalStd', PACKAGE = 'cTMed', phi, sigma, delta_t)
+    .Call(`_cTMed_TotalStd`, phi, sigma, delta_t)
 }
 
 .TotalVec <- function(phi_vec, delta_t) {
-    .Call('_cTMed_TotalVec', PACKAGE = 'cTMed', phi_vec, delta_t)
+    .Call(`_cTMed_TotalVec`, phi_vec, delta_t)
 }
 
 .Total <- function(phi, delta_t) {
-    .Call('_cTMed_Total', PACKAGE = 'cTMed', phi, delta_t)
+    .Call(`_cTMed_Total`, phi, delta_t)
 }
 

README.md

@@ -0,0 +1,98 @@
+cTMed
+================
+Ivan Jacob Agaloos Pesigan
+2025-01-27
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+<!-- badges: start -->
+
+[![CRAN
+Status](https://www.r-pkg.org/badges/version/cTMed)](https://cran.r-project.org/package=cTMed)
+[![R-Universe
+Status](https://jeksterslab.r-universe.dev/badges/cTMed)](https://jeksterslab.r-universe.dev/cTMed)
+[![Make
+Project](https://github.com/jeksterslab/cTMed/actions/workflows/make.yml/badge.svg)](https://github.com/jeksterslab/cTMed/actions/workflows/make.yml)
+[![R-CMD-check](https://github.com/jeksterslab/cTMed/actions/workflows/check-full.yml/badge.svg)](https://github.com/jeksterslab/cTMed/actions/workflows/check-full.yml)
+[![R Package Test
+Coverage](https://github.com/jeksterslab/cTMed/actions/workflows/test-coverage.yml/badge.svg)](https://github.com/jeksterslab/cTMed/actions/workflows/test-coverage.yml)
+[![Lint R
+Package](https://github.com/jeksterslab/cTMed/actions/workflows/lint.yml/badge.svg)](https://github.com/jeksterslab/cTMed/actions/workflows/lint.yml)
+[![Package Website (GitHub
+Pages)](https://github.com/jeksterslab/cTMed/actions/workflows/pkgdown-gh-pages.yml/badge.svg)](https://github.com/jeksterslab/cTMed/actions/workflows/pkgdown-gh-pages.yml)
+[![Compile
+LaTeX](https://github.com/jeksterslab/cTMed/actions/workflows/latex.yml/badge.svg)](https://github.com/jeksterslab/cTMed/actions/workflows/latex.yml)
+[![Shell
+Check](https://github.com/jeksterslab/cTMed/actions/workflows/shellcheck.yml/badge.svg)](https://github.com/jeksterslab/cTMed/actions/workflows/shellcheck.yml)
+[![pages-build-deployment](https://github.com/jeksterslab/cTMed/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/jeksterslab/cTMed/actions/workflows/pages/pages-build-deployment)
+[![codecov](https://codecov.io/gh/jeksterslab/cTMed/branch/main/graph/badge.svg?token=KVLUET3DJ6)](https://codecov.io/gh/jeksterslab/cTMed)
+<!-- badges: end -->
+
+## Description
+
+Calculates standard errors and confidence intervals for effects in
+continuous time mediation models.
+
+## Installation
+
+You can install the development version of `cTMed` from
+[GitHub](https://github.com/jeksterslab/cTMed) with:
+
+``` r
+if (!require("remotes")) install.packages("remotes")
+remotes::install_github("jeksterslab/cTMed")
+```
+
+## Documentation
+
+See [GitHub Pages](https://jeksterslab.github.io/cTMed/index.html) for
+package documentation.
+
+## References
+
+<div id="refs" class="references csl-bib-body hanging-indent"
+entry-spacing="0" line-spacing="2">
+
+<div id="ref-Bollen-1987" class="csl-entry">
+
+Bollen, K. A. (1987). Total, direct, and indirect effects in structural
+equation models. *Sociological Methodology*, *17*, 37.
+<https://doi.org/10.2307/271028>
+
+</div>
+
+<div id="ref-Deboeck-Preacher-2015" class="csl-entry">
+
+Deboeck, P. R., & Preacher, K. J. (2015). No need to be discrete: A
+method for continuous time mediation analysis. *Structural Equation
+Modeling: A Multidisciplinary Journal*, *23*(1), 61–75.
+<https://doi.org/10.1080/10705511.2014.973960>
+
+</div>
+
+<div id="ref-RCoreTeam-2024" class="csl-entry">
+
+R Core Team. (2024). *R: A language and environment for statistical
+computing*. R Foundation for Statistical Computing.
+<https://www.R-project.org/>
+
+</div>
+
+<div id="ref-Ryan-Hamaker-2021" class="csl-entry">
+
+Ryan, O., & Hamaker, E. L. (2021). Time to intervene: A continuous-time
+approach to network analysis and centrality. *Psychometrika*, *87*(1),
+214–252. <https://doi.org/10.1007/s11336-021-09767-0>
+
+</div>
+
+<div id="ref-Wang-Zhang-2020" class="csl-entry">
+
+Wang, L., & Zhang, Q. (2020). Investigating the impact of the time
+interval selection on autoregressive mediation modeling: Result
+interpretations, effect reporting, and temporal designs. *Psychological
+Methods*, *25*(3), 271–291. <https://doi.org/10.1037/met0000235>
+
+</div>
+
+</div>