Automated build.

.setup/latex/betaSandwich-001-description.Rtex

@@ -108,7 +108,7 @@ See
 \href{https://jeksterslab.github.io/betaSandwich/index.html}{GitHub Pages}
 for package documentation.
 
-\nocite{RCoreTeam-2023}
+\nocite{RCoreTeam-2024}
 
 \printbibliography
 

.setup/latex/betaSandwich-999-session.Rtex

@@ -85,7 +85,7 @@
 % unname(installed.packages()[, 1])
 %% end.rcode
 
-\nocite{RCoreTeam-2023}
+\nocite{RCoreTeam-2024}
 
 \nocite{Pesigan-Cheung-2023}
 

.setup/latex/betaSandwich-zzz-tests-benchmark.Rtex

@@ -117,7 +117,7 @@
 % )
 %% end.rcode
 
-\nocite{RCoreTeam-2023}
+\nocite{RCoreTeam-2024}
 
 \nocite{Pesigan-Sun-Cheung-2023}
 

.setup/latex/betaSandwich-zzz-tests-external.Rtex

@@ -115,7 +115,7 @@
 % )
 %% end.rcode
 
-\nocite{RCoreTeam-2023}
+\nocite{RCoreTeam-2024}
 
 \nocite{Pesigan-Sun-Cheung-2023}
 

.setup/latex/betaSandwich-zzz-tests-internal.Rtex

@@ -115,7 +115,7 @@
 % )
 %% end.rcode
 
-\nocite{RCoreTeam-2023}
+\nocite{RCoreTeam-2024}
 
 \nocite{Pesigan-Sun-Cheung-2023}
 

.setup/latex/betaSandwich-zzz-tests-staging.Rtex

@@ -153,7 +153,7 @@ Staging...
 % confint(hc3)
 %% end.rcode
 
-\nocite{RCoreTeam-2023}
+\nocite{RCoreTeam-2024}
 
 \nocite{Pesigan-Sun-Cheung-2023}
 

.setup/latex/bib/bib.bib

@@ -3870,7 +3870,9 @@ @Article{Cheung-Pesigan-2023a
   journaltitle = {Multivariate Behavioral Research},
   title = {{FINDOUT}: Using either {SPSS} commands or graphical user interface to identify influential cases in structural equation modeling in {AMOS}},
   doi = {10.1080/00273171.2022.2148089},
-  pages = {1--5},
+  number = {5},
+  pages = {964--968},
+  volume = {58},
   abstract = {The results in a structural equation modeling (SEM) analysis can be influenced by just a few observations, called influential cases. Tools have been developed for users of R to identify them. However, similar tools are not available for AMOS, which is also a popular SEM software package. We introduce the FINDOUT toolset, a group of SPSS extension commands, and an AMOS plugin, to identify influential cases and examine how these cases influence the results. The SPSS commands can be used either as syntax commands or as custom dialogs from pull-down menus, and the AMOS plugin can be run from AMOS pull-down menu. We believe these tools can help researchers to examine the robustness of their findings to influential cases.},
   publisher = {Informa {UK} Limited},
   keywords = {influential cases, outliers, structural equation modeling, AMOS, sensitivity analysis, SPSS},
@@ -3882,7 +3884,9 @@ @Article{Cheung-Pesigan-2023b
   journaltitle = {Structural Equation Modeling: A Multidisciplinary Journal},
   title = {{semlbci}: An {R} package for forming likelihood-based confidence intervals for parameter estimates, correlations, indirect effects, and other derived parameters},
   doi = {10.1080/10705511.2023.2183860},
-  pages = {1--15},
+  number = {6},
+  pages = {985--999},
+  volume = {30},
   abstract = {There are three common types of confidence interval (CI) in structural equation modeling (SEM): Wald-type CI, bootstrapping CI, and likelihood-based CI (LBCI). LBCI has the following advantages: (1) it has better coverage probabilities and Type I error rate compared to Wald-type CI when the sample size is finite; (2) it correctly tests the null hypothesis of a parameter based on likelihood ratio chi-square difference test; (3) it is less computationally intensive than bootstrapping CI; and (4) it is invariant to transformations. However, LBCI is not available in many popular SEM software packages. We developed an R package, semlbci, for forming LBCI for parameters in models fitted by lavaan, a popular open-source SEM package, such that researchers have more options in forming CIs for parameters in SEM. The package supports both unstandardized and standardized estimates, derived parameters such as indirect effect, multisample models, and the robust LBCI proposed by Falk.},
   publisher = {Informa {UK} Limited},
   keywords = {confidence interval, likelihood-based confidence interval, robust method, structural equation modeling},
@@ -3924,6 +3928,9 @@ @Article{Didier-King-Polley-etal-2023
   title = {Signal processing and machine learning with transdermal alcohol concentration to predict natural environment alcohol consumption.},
   doi = {10.1037/pha0000683},
   issn = {1064-1297},
+  number = {2},
+  pages = {245--254},
+  volume = {32},
   abstract = {Wrist-worn alcohol biosensors continuously and discreetly record transdermal alcohol concentration (TAC) and may allow alcohol researchers to monitor alcohol consumption in participants’ natural environments. However, the field lacks established methods for signal processing and detecting alcohol events using these devices. We developed software that streamlines analysis of raw data (TAC, temperature, and motion) from a wrist-worn alcohol biosensor (BACtrack Skyn) through a signal processing and machine learning pipeline: biologically implausible skin surface temperature readings (< 28C) were screened for potential device removal and TAC artifacts were corrected, features that describe TAC (e.g., rise duration) were calculated and used to train models (random forest and logistic regression) that predict self-reported alcohol consumption, and model performances were measured and summarized in autogenerated reports. The software was tested using 60 Skyn data sets recorded during 30 alcohol drinking episodes and 30 nonalcohol drinking episodes. Participants (N = 36; 13 with alcohol use disorder) wore the Skyn during one alcohol drinking episode and one nonalcohol drinking episode in their natural environment. In terms of distinguishing alcohol from nonalcohol drinking, correcting artifacts in the data resulted in 10\% improvement in model accuracy relative to using raw data. Random forest and logistic regression models were both accurate, correctly predicting 97\% (58/60; AUC-ROCs = 0.98, 0.96) of episodes. Area under TAC curve, rise duration of TAC curve, and peak TAC were the most important features for predictive accuracy. With promising model performance, this protocol will enhance the efficiency and reliability of TAC sensors for future alcohol monitoring research.},
   publisher = {American Psychological Association (APA)},
 }
@@ -4140,6 +4147,8 @@ @Article{Park-Chow-Epskamp-etal-2023
   date = {2023},
   journaltitle = {Multivariate Behavioral Research},
   title = {Subgrouping with chain graphical {VAR} models},
+  doi = {10.1080/00273171.2023.2289058},
+  pages = {1--23},
   abstract = {Recent years have seen the emergence of an ``idio-thetic'' class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel ``idio-thetic'' model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach–the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.},
   publisher = {Informa UK Limited},
 }
@@ -4188,6 +4197,9 @@ @Article{Pesigan-Cheung-2023
   journaltitle = {Behavior Research Methods},
   title = {{Monte Carlo} confidence intervals for the indirect effect with missing data},
   doi = {10.3758/s13428-023-02114-4},
+  number = {3},
+  pages = {1678--1696},
+  volume = {56},
   abstract = {Missing data is a common occurrence in mediation analysis. As a result, the methods used to construct confidence intervals around the indirect effect should consider missing data. Previous research has demonstrated that, for the indirect effect in data with complete cases, the Monte Carlo method performs as well as nonparametric bootstrap confidence intervals (see MacKinnon et al., Multivariate Behavioral Research, 39(1), 99–128, 2004; Preacher \& Selig, Communication Methods and Measures, 6(2), 77–98, 2012; Tofighi \& MacKinnon, Structural Equation Modeling: A Multidisciplinary Journal, 23(2), 194–205, 2015). In this manuscript, we propose a simple, fast, and accurate two-step approach for generating confidence intervals for the indirect effect, in the presence of missing data, based on the Monte Carlo method. In the first step, an appropriate method, for example, full-information maximum likelihood or multiple imputation, is used to estimate the parameters and their corresponding sampling variance-covariance matrix in a mediation model. In the second step, the sampling distribution of the indirect effect is simulated using estimates from the first step. A confidence interval is constructed from the resulting sampling distribution. A simulation study with various conditions is presented. Implications of the results for applied research are discussed.},
   publisher = {Springer Science and Business Media {LLC}},
   keywords = {Monte Carlo method, nonparametric bootstrap, indirect effect, mediation, missing completely at random, missing at random, full-information maximum likelihood, multiple imputation},
@@ -4200,7 +4212,9 @@ @Article{Pesigan-Sun-Cheung-2023
   journaltitle = {Multivariate Behavioral Research},
   title = {{betaDelta} and {betaSandwich}: Confidence intervals for standardized regression coefficients in {R}},
   doi = {10.1080/00273171.2023.2201277},
-  pages = {1--4},
+  number = {6},
+  pages = {1183--1186},
+  volume = {58},
   abstract = {The multivariate delta method was used by Yuan and Chan to estimate standard errors and confidence intervals for standardized regression coefficients. Jones and Waller extended the earlier work to situations where data are nonnormal by utilizing Browne’s asymptotic distribution-free (ADF) theory. Furthermore, Dudgeon developed standard errors and confidence intervals, employing heteroskedasticity-consistent (HC) estimators, that are robust to nonnormality with better performance in smaller sample sizes compared to Jones and Waller’s ADF technique. Despite these advancements, empirical research has been slow to adopt these methodologies. This can be a result of the dearth of user-friendly software programs to put these techniques to use. We present the betaDelta and the betaSandwich packages in the R statistical software environment in this manuscript. Both the normal-theory approach and the ADF approach put forth by Yuan and Chan and Jones and Waller are implemented by the betaDelta package. The HC approach proposed by Dudgeon is implemented by the betaSandwich package. The use of the packages is demonstrated with an empirical example. We think the packages will enable applied researchers to accurately assess the sampling variability of standardized regression coefficients.},
   publisher = {Informa {UK} Limited},
   keywords = {standardized regression coefficients, confidence intervals, delta method standard errors, heteroskedasticity-consistent standard errors, R package},
@@ -4256,6 +4270,9 @@ @Article{Russell-Smyth-Turrisi-Rodriguez-2023
   title = {Baseline protective behavioral strategy use predicts more moderate transdermal alcohol concentration dynamics and fewer negative consequences of drinking in young adults’ natural settings.},
   doi = {10.1037/adb0000941},
   issn = {0893-164X},
+  number = {3},
+  pages = {347--359},
+  volume = {38},
   abstract = {Objective: Test whether frequent protective behavioral strategies (PBS) users report (a) fewer alcohol-related consequences and (b) less risky alcohol intoxication dynamics (measured via transdermal alcohol concentration [TAC] sensor ``features'') in daily life. Method: Two hundred twenty-two frequently heavy-drinking young adults ($M_{\mathrm{age}} = 22.3$ years) wore TAC sensors for 6 consecutive days. TAC features peak (maximum TAC), rise rate (speed of TAC increase), and area under the curve (AUC) were derived for each day. Negative alcohol-related consequences were measured in the morning after each self-reported drinking day. Past-year PBS use was measured at baseline. Results: Young adults reporting more frequent baseline PBS use showed (a) fewer alcohol-related consequences and (b) lower intoxication dynamics on average (less AUC, lower peaks, and slower rise rates). Limiting/stopping and manner of drinking PBS showed the same pattern of findings as the total score. Serious harm reduction PBS predicted fewer negative alcohol-related consequences, but not TAC features. Multilevel path models showed that TAC features peak and rise rate partially explained associations between PBS (total, limiting/stopping, and manner of drinking) and consequences. Independent contributions of PBS subscales were small and nonsignificant, suggesting that total PBS use was a more important predictor of risk/protection than the specific types of PBS used. Conclusions: Young adults using more total PBS may experience fewer alcohol-related consequences during real-world drinking episodes in part through less risky intoxication dynamics (TAC features). Future research measuring PBS at the daily level is needed to formally test TAC features as day-level mechanisms of protection from acute alcohol-related consequences.},
   publisher = {American Psychological Association (APA)},
 }
@@ -4514,6 +4531,16 @@ @Manual{RCoreTeam-2023
   annotation = {r, r-manual},
 }
 
+@Manual{RCoreTeam-2024,
+  title = {{R}: A language and environment for statistical computing},
+  author = {{R Core Team}},
+  organization = {R Foundation for Statistical Computing},
+  date = {2024},
+  location = {Vienna, Austria},
+  url = {https://www.R-project.org/},
+  annotation = {r, r-manual},
+}
+
 @Manual{SAMHSA-2020,
   author = {{SAMHSA}},
   title = {Key substance use and mental health indicators in the {United States}: Results from the {2019 National Survey on Drug Use and Health} ({HHS Publication No. PEP20-07-01-001, NSDUH Series H-55})},

.setup/latex/pdf/.gitignore

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