Automated build.

.setup/cpp/cTMed-direct-std.cpp

@@ -26,6 +26,7 @@ double DirectStd(const arma::mat& phi, const arma::mat& sigma,
   arma::mat sd_row = arma::diagmat(arma::sqrt(total_cov.diag()));
   arma::mat sd_col_inv = arma::diagmat(1.0 / arma::sqrt(total_cov.diag()));
   arma::mat direct = arma::expmat(delta_t * d * phi * d);
-  arma::mat direct_std = d * (sd_row * direct * sd_col_inv) * d;
+  // arma::mat direct_std = d * (sd_row * direct * sd_col_inv) * d;
+  arma::mat direct_std = sd_row * direct * sd_col_inv;
   return direct_std(to - 1, from - 1);
 }

.setup/cpp/cTMed-indirect-std.cpp

@@ -28,7 +28,8 @@ double IndirectStd(const arma::mat& phi, const arma::mat& sigma,
   arma::mat total_std = sd_row * total * sd_col_inv;
   double total_dbl = total_std(to - 1, from - 1);
   arma::mat direct = arma::expmat(delta_t * d * phi * d);
-  arma::mat direct_std = d * (sd_row * direct * sd_col_inv) * d;
+  // arma::mat direct_std = d * (sd_row * direct * sd_col_inv) * d;
+  arma::mat direct_std = sd_row * direct * sd_col_inv;
   double direct_dbl = direct_std(to - 1, from - 1);
   return total_dbl - direct_dbl;
 }

.setup/cpp/cTMed-med-std-s.cpp

@@ -40,7 +40,8 @@ arma::mat MedStds(const arma::mat& phi, const arma::mat& sigma,
     total_std = sd_row * total * sd_col_inv;
     total_dbl = total_std(to - 1, from - 1);
     direct = arma::expmat(delta_t[t] * d * phi * d);
-    direct_std = d * (sd_row * direct * sd_col_inv) * d;
+    // direct_std = d * (sd_row * direct * sd_col_inv) * d;
+    direct_std = sd_row * direct * sd_col_inv;
     direct_dbl = direct_std(to - 1, from - 1);
     indirect_dbl = total_dbl - direct_dbl;
     output(t, 0) = total_dbl;

.setup/cpp/cTMed-med-std-vec.cpp

@@ -42,7 +42,8 @@ Rcpp::NumericVector MedStdVec(const arma::vec& v, const double& delta_t,
   arma::mat total_std = sd_row * total * sd_col_inv;
   double total_dbl = total_std(to - 1, from - 1);
   arma::mat direct = arma::expmat(delta_t * d * phi * d);
-  arma::mat direct_std = d * (sd_row * direct * sd_col_inv) * d;
+  // arma::mat direct_std = d * (sd_row * direct * sd_col_inv) * d;
+  arma::mat direct_std = sd_row * direct * sd_col_inv;
   double direct_dbl = direct_std(to - 1, from - 1);
   double indirect_dbl = total_dbl - direct_dbl;
   Rcpp::NumericVector output(3);

.setup/cpp/cTMed-med-std.cpp

@@ -28,7 +28,8 @@ Rcpp::NumericVector MedStd(const arma::mat& phi, const arma::mat& sigma,
   arma::mat total_std = sd_row * total * sd_col_inv;
   double total_dbl = total_std(to - 1, from - 1);
   arma::mat direct = arma::expmat(delta_t * d * phi * d);
-  arma::mat direct_std = d * (sd_row * direct * sd_col_inv) * d;
+  // arma::mat direct_std = d * (sd_row * direct * sd_col_inv) * d;
+  arma::mat direct_std = sd_row * direct * sd_col_inv;
   double direct_dbl = direct_std(to - 1, from - 1);
   double indirect_dbl = total_dbl - direct_dbl;
   Rcpp::NumericVector output(4);

.setup/latex/bib/bib.bib

@@ -5467,45 +5467,3 @@ @Manual{Waller-2022
   publisher = {The R Foundation},
   annotation = {r, r-package},
 }
-
-@Article{Collins-Wugalter-1992,
-  author = {Linda M. Collins and Stuart E. Wugalter},
-  date = {1992-01},
-  journaltitle = {Multivariate Behavioral Research},
-  title = {Latent class models for stage-sequential dynamic latent variables},
-  doi = {10.1207/s15327906mbr2701_8},
-  issn = {1532-7906},
-  number = {1},
-  pages = {131--157},
-  volume = {27},
-  abstract = {Stage-sequential dynamic latent variables are of interest in many longitudinal studies. Measurement theory for these latent variables, called Latent Transition Analysis (LTA), can be found in recent generalizations of latent class theory. LTA expands the latent Markov model to allow applications to more complex latent variables and the use of multiple indicators. Because complex latent class models result in sparse contingency tables, that may lead to poor parameter estimation, a simulation study was conducted in order to determine whether model parameters are recovered adequately by LTA, and whether additional indicators result in better measurement or in impossibly sparse tables. The results indicated that parameter recovery was satisfactory overall, although as expected the standard errors were large in some conditions with few subjects. The simulation also indicated that at least within the conditions examined here, the benefits of adding indicators outweigh the costs. Additional indicators improved standard errors, even in conditions producing extremely sparse tables. An example of LTA analysis of empirical data on math skill development is presented.},
-  publisher = {Informa UK Limited},
-}
-
-@Article{Greenfield-Ye-Bond-etal-2014,
-  author = {Thomas K Greenfield and Yu Ye and Jason Bond and William C Kerr and Madhabika B Nayak and Lee Ann Kaskutas and Raymond F Anton and Raye Z Litten and Henry R Kranzler},
-  date = {2014-03},
-  journaltitle = {Journal of Studies on Alcohol and Drugs},
-  title = {Risks of alcohol use disorders related to drinking patterns in the {U.S.} general population},
-  doi = {10.15288/jsad.2014.75.319},
-  issn = {1938-4114},
-  number = {2},
-  pages = {319--327},
-  volume = {75},
-  abstract = {Objective: The purpose of this study was to examine the relations between drinking (mean quantity and heavy drinking patterns) and alcohol use disorders (AUDs) in the U.S. general population. Method: Data from three telephone National Alcohol Surveys (in 2000, 2005, and 2010) were pooled, with separate analyses for men and women restricted to current drinkers ($ns = 5,922$ men, 6,270 women). Predictors were 12-month volume (mean drinks per day), rates of heavy drinking (5+/4+ drinks in a day for men/women), and very heavy drinking (8+, 12+, and 24+ drinks in a day). Outcomes were negative alcohol-related consequences constituting abuse (1+ of 4 DSM-IV–based domains assessed by 13 items) and alcohol dependence (symptoms in 3+ of 7 DSM-IV–based domains), together taken to indicate an AUD. Segmentation analyses were used to model risks of problem outcomes from drinking patterns separately by gender. Results: In the general population, men and women who consumed $\leq 1$ drink/day on average with no heavy drinking days did not incur substantial risks of an AUD ($< 10\%$). Men who drank from 1 to 2 drinks/day on average but never 5+ incurred a $16\%$ risk of reporting an AUD ($3.5\%$ alcohol dependence). At higher volumes, men and women who indicated higher rates of drinking larger amounts per day and/or involving 8+ and 12+ drinks/day (and even 24+ drinks/day for men) showed much higher risks of experiencing AUDs. Conclusions: The findings provide quantitative guidance for primary care practitioners who wish to make population-based recommendations to patients who might benefit by reducing both overall intake and amounts per occasion in an effort to lower their risks of developing AUDs.},
-  publisher = {Alcohol Research Documentation, Inc.},
-}
-
-@Article{Northcote-Livingston-2011,
-  author = {Jeremy Northcote and Michael Livingston},
-  date = {2011-09},
-  journaltitle = {Alcohol and Alcoholism},
-  title = {Accuracy of self-reported drinking: Observational verification of `last occasion' drink estimates of young adults},
-  doi = {10.1093/alcalc/agr138},
-  issn = {0735-0414},
-  number = {6},
-  pages = {709--713},
-  volume = {46},
-  abstract = {Aims: As a formative step towards determining the accuracy of self-reported drinking levels commonly used for estimating population alcohol use, the validity of a `last occasion' self-reporting approach is tested with corresponding field observations of participants’ drinking quantity. This study is the first known attempt to validate the accuracy of self-reported alcohol consumption using data from a natural setting. Methods: A total of 81 young adults (aged 18–25 years) were purposively selected in Perth, Western Australia. Participants were asked to report the number of alcoholic drinks consumed at nightlife venues 1–2 days after being observed by peer-based researchers on 239 occasions. Complete observation data and self-report estimates were available for 129 sessions, which were fitted with multi-level models assessing the relationship between observed and reported consumption. Results: Participants accurately estimated their consumption when engaging in light to moderate drinking (eight or fewer drinks in a single session), with no significant difference between the mean reported consumption and the mean observed consumption. In contrast, participants underestimated their own consumption by increasing amounts when engaging in heavy drinking of more than eight drinks. Conclusion: It is suggested that recent recall methods in self-report surveys are potentially reasonably accurate measures of actual drinking levels for light to moderate drinkers, but that underestimating of alcohol consumption increases with heavy consumption. Some of the possible reasons for underestimation of heavy drinking are discussed, with both cognitive and socio-cultural factors considered.},
-  publisher = {Oxford University Press (OUP)},
-}

.setup/latex/pdf/.gitignore

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