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diff --git a/.setup/build/cTMed.pdf b/.setup/build/cTMed.pdf
diff --git a/.setup/build/cTMed_1.0.4.9000.tar.gz b/.setup/build/cTMed_1.0.4.9000.tar.gz
diff --git a/.setup/latex/bib/quarto.bib b/.setup/latex/bib/quarto.bib
diff --git a/.setup/latex/cTMed-zzz-tests-internal.Rtex b/.setup/latex/cTMed-zzz-tests-internal.Rtex
@@ -75,6 +75,11 @@
 
 \maketitle
 
+%% begin.rcode
+% #| include = FALSE
+% Sys.setenv(NOT_CRAN = "true")
+%% end.rcode
+
 \section*{Tests}
 
 %% begin.rcode

diff --git a/.setup/latex/pdf/.gitignore b/.setup/latex/pdf/.gitignore
@@ -0,0 +1,5 @@
+*
+*/
+!*.pdf
+!.gitignore
+!bib.bib
diff --git a/.setup/latex/pdf/bib.bib b/.setup/latex/pdf/bib.bib
diff --git a/.setup/latex/pdf/cTMed-001-description.pdf b/.setup/latex/pdf/cTMed-001-description.pdf
diff --git a/.setup/latex/pdf/cTMed-999-session.pdf b/.setup/latex/pdf/cTMed-999-session.pdf
diff --git a/.setup/latex/pdf/cTMed-zzz-references.pdf b/.setup/latex/pdf/cTMed-zzz-references.pdf
diff --git a/.setup/latex/pdf/cTMed-zzz-tests-benchmark.pdf b/.setup/latex/pdf/cTMed-zzz-tests-benchmark.pdf
diff --git a/.setup/latex/pdf/cTMed-zzz-tests-external.pdf b/.setup/latex/pdf/cTMed-zzz-tests-external.pdf
diff --git a/.setup/latex/pdf/cTMed-zzz-tests-internal.pdf b/.setup/latex/pdf/cTMed-zzz-tests-internal.pdf
diff --git a/.setup/latex/pdf/cTMed-zzz-tests-staging.pdf b/.setup/latex/pdf/cTMed-zzz-tests-staging.pdf
diff --git a/.setup/vignettes/fit-ct-var-dynr.Rmd.orig b/.setup/vignettes/fit-ct-var-dynr.Rmd.orig
@@ -31,7 +31,7 @@ knitr::opts_chunk$set(
 ```{r}
 #| echo = FALSE
 set.seed(42)
-n <- 50
+n <- 5
 time <- 100
 delta_t <- 0.10
 k <- p <- 3

diff --git a/.setup/vignettes/fit-ct-var-mx.Rmd.orig b/.setup/vignettes/fit-ct-var-mx.Rmd.orig
@@ -32,7 +32,7 @@ knitr::opts_chunk$set(
 ```{r}
 #| echo = FALSE
 set.seed(42)
-n <- 50
+n <- 5
 time <- 100
 delta_t <- 0.10
 k <- p <- 3

diff --git a/.setup/vignettes/med-boot.Rmd.orig b/.setup/vignettes/med-boot.Rmd.orig
@@ -34,7 +34,7 @@ The `cTMed` package provides a bootstrap approach, in addition to the delta and
 ```{r}
 #| include = FALSE
 set.seed(42)
-n <- 50
+n <- 5
 time <- 100
 delta_t <- 0.10
 k <- p <- 3
@@ -357,13 +357,13 @@ theta_l <- t(chol(theta))
 ```
 
 ```{r}
-R <- 1000L
+R <- 5L # use at least 1000 in actual research
 path <- getwd()
 prefix <- "ou"
 ```
 
 The estimated parameters are then passed as arguments to the `PBSSMOUFixed` function from the `simStateSpace` package, which generates a parametric bootstrap sampling distribution of the parameter estimates. The argument `R` specifies the number of bootstrap replications.
-The generated data and model estimates are be stored in `path` using the specified `prefix` for the file names.
+The generated data and model estimates are stored in `path` using the specified `prefix` for the file names.
 The `ncores = parallel::detectCores()` argument instructs the function to use all available CPU cores in the system.
 
 > **_NOTE:_**  Fitting the CT-VAR model multiple times is computationally intensive. 

diff --git a/.setup/vignettes/med-std.Rmd.orig b/.setup/vignettes/med-std.Rmd.orig
@@ -139,7 +139,7 @@ mc <- MCMedStd(
   from = "x",
   to = "y",
   med = "m",
-  R = 20000L,
+  R = 1000L,
   ncores = parallel::detectCores() # use multiple cores
 )
 plot(mc)

diff --git a/.setup/vignettes/med.Rmd.orig b/.setup/vignettes/med.Rmd.orig
@@ -97,7 +97,7 @@ mc <- MCMed(
   from = "x",
   to = "y",
   med = "m",
-  R = 20000L,
+  R = 1000L,
   ncores = parallel::detectCores() # use multiple cores
 )
 plot(mc)

diff --git a/R/cTMed-plot-central-ci-dot.R b/R/cTMed-plot-central-ci-dot.R
@@ -117,10 +117,10 @@
   if (object$args$method == "boot") {
     ylab <- "Estimate"
     if (type == "pc") {
-      method <- "Percentile Bootstrap Method"
+      method <- "PC Bootstrap Method"
     }
     if (type == "bc") {
-      method <- "Bias-Corrected Bootstrap Method"
+      method <- "BC Bootstrap Method"
     }
   }
   if (object$args$method %in% c("mc", "posterior")) {

diff --git a/R/cTMed-plot-med-ci-dot.R b/R/cTMed-plot-med-ci-dot.R
@@ -120,10 +120,10 @@
   if (object$args$method == "boot") {
     ylab <- "Estimate"
     if (type == "pc") {
-      method <- "Percentile Bootstrap Method"
+      method <- "PC Bootstrap Method"
     }
     if (type == "bc") {
-      method <- "Bias-Corrected Bootstrap Method"
+      method <- "BC Bootstrap Method"
     }
   }
   if (object$args$method %in% c("mc", "posterior")) {

diff --git a/README.md b/README.md
@@ -1,7 +1,7 @@
 cTMed
 ================
 Ivan Jacob Agaloos Pesigan
-2025-01-13
+2025-01-14
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 

diff --git a/vignettes/fig-vignettes-med-boot-boot-1.png b/vignettes/fig-vignettes-med-boot-boot-1.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-2.png b/vignettes/fig-vignettes-med-boot-boot-2.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-3.png b/vignettes/fig-vignettes-med-boot-boot-3.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-4.png b/vignettes/fig-vignettes-med-boot-boot-4.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-5.png b/vignettes/fig-vignettes-med-boot-boot-5.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-6.png b/vignettes/fig-vignettes-med-boot-boot-6.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-std-1.png b/vignettes/fig-vignettes-med-boot-boot-std-1.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-std-2.png b/vignettes/fig-vignettes-med-boot-boot-std-2.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-std-3.png b/vignettes/fig-vignettes-med-boot-boot-std-3.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-std-4.png b/vignettes/fig-vignettes-med-boot-boot-std-4.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-std-5.png b/vignettes/fig-vignettes-med-boot-boot-std-5.png
diff --git a/vignettes/fig-vignettes-med-boot-boot-std-6.png b/vignettes/fig-vignettes-med-boot-boot-std-6.png
diff --git a/vignettes/fig-vignettes-med-mc-1.png b/vignettes/fig-vignettes-med-mc-1.png
diff --git a/vignettes/fig-vignettes-med-mc-2.png b/vignettes/fig-vignettes-med-mc-2.png
diff --git a/vignettes/fig-vignettes-med-mc-3.png b/vignettes/fig-vignettes-med-mc-3.png
diff --git a/vignettes/fig-vignettes-med-std-mc-1.png b/vignettes/fig-vignettes-med-std-mc-1.png
diff --git a/vignettes/fig-vignettes-med-std-mc-2.png b/vignettes/fig-vignettes-med-std-mc-2.png
diff --git a/vignettes/fig-vignettes-med-std-mc-3.png b/vignettes/fig-vignettes-med-std-mc-3.png
diff --git a/vignettes/fit-ct-var-dynr.Rmd b/vignettes/fit-ct-var-dynr.Rmd
diff --git a/vignettes/fit-ct-var-mx.Rmd b/vignettes/fit-ct-var-mx.Rmd
diff --git a/vignettes/med-boot.Rmd b/vignettes/med-boot.Rmd
@@ -1,7 +1,7 @@
 ---
 title: "Total, Direct, and Indirect Effects in Continuous-Time Mediation Model (Bootstrap)"
 author: Ivan Jacob Agaloos Pesigan
-date: "2025-01-13"
+date: "2025-01-14"
 bibliography: "vignettes.bib"
 csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
 nocite: |
@@ -45,40 +45,40 @@ In this example, we will use the fitted model from [Fit the Continuous-Time Vect
 ``` r
 summary(fit)
 #> Coefficients:
-#>             Estimate Std. Error t value  ci.lower  ci.upper Pr(>|t|)    
-#> phi_1_1    -0.293807   0.082574  -3.558 -0.455648 -0.131966   0.0002 ***
-#> phi_2_1     0.824396   0.056914  14.485  0.712846  0.935946   <2e-16 ***
-#> phi_3_1    -0.454316   0.057647  -7.881 -0.567303 -0.341329   <2e-16 ***
-#> phi_1_2    -0.071916   0.068787  -1.045 -0.206735  0.062904   0.1479    
-#> phi_2_2    -0.568999   0.048532 -11.724 -0.664119 -0.473878   <2e-16 ***
-#> phi_3_2     0.707647   0.048636  14.550  0.612322  0.802972   <2e-16 ***
-#> phi_1_3     0.025791   0.062589   0.412 -0.096882  0.148463   0.3402    
-#> phi_2_3     0.082668   0.043529   1.899 -0.002647  0.167983   0.0288 *  
-#> phi_3_3    -0.687502   0.044092 -15.593 -0.773920 -0.601084   <2e-16 ***
-#> sigma_1_1   0.235806   0.022759  10.361  0.191198  0.280413   <2e-16 ***
-#> sigma_2_1   0.018668   0.010045   1.858 -0.001020  0.038356   0.0316 *  
-#> sigma_3_1  -0.064687   0.010768  -6.007 -0.085793 -0.043582   <2e-16 ***
-#> sigma_2_2   0.077088   0.009447   8.160  0.058572  0.095603   <2e-16 ***
-#> sigma_3_2   0.008278   0.006655   1.244 -0.004765  0.021321   0.1068    
-#> sigma_3_3   0.080654   0.010196   7.910  0.060670  0.100639   <2e-16 ***
-#> theta_1_1   0.201033   0.005107  39.363  0.191023  0.211043   <2e-16 ***
-#> theta_2_2   0.191640   0.004373  43.823  0.183069  0.200211   <2e-16 ***
-#> theta_3_3   0.197449   0.004521  43.677  0.188588  0.206309   <2e-16 ***
-#> mu0_1_1    -0.068547   0.142210  -0.482 -0.347273  0.210180   0.3149    
-#> mu0_2_1     0.084573   0.176685   0.479 -0.261722  0.430869   0.3161    
-#> mu0_3_1     0.115721   0.141485   0.818 -0.161585  0.393026   0.2067    
-#> sigma0_1_1  0.942679   0.205369   4.590  0.540164  1.345195   <2e-16 ***
-#> sigma0_2_1  0.161788   0.179880   0.899 -0.190769  0.514346   0.1842    
-#> sigma0_3_1  0.103257   0.143856   0.718 -0.178696  0.385209   0.2365    
-#> sigma0_2_2  1.489721   0.311204   4.787  0.879772  2.099670   <2e-16 ***
-#> sigma0_3_2 -0.100464   0.178078  -0.564 -0.449491  0.248563   0.2863    
-#> sigma0_3_3  0.946144   0.204219   4.633  0.545882  1.346406   <2e-16 ***
+#>              Estimate Std. Error t value   ci.lower   ci.upper Pr(>|t|)    
+#> phi_1_1    -0.4698862  0.3734948  -1.258 -1.2019225  0.2621501   0.1045    
+#> phi_2_1     0.4809285  0.1781928   2.699  0.1316771  0.8301800   0.0036 ** 
+#> phi_3_1    -0.2711150  0.2083709  -1.301 -0.6795145  0.1372845   0.0969 .  
+#> phi_1_2    -0.1084163  0.2321649  -0.467 -0.5634512  0.3466185   0.3204    
+#> phi_2_2    -0.2331007  0.1105811  -2.108 -0.4498357 -0.0163658   0.0178 *  
+#> phi_3_2     0.5796100  0.1363571   4.251  0.3123549  0.8468651   <2e-16 ***
+#> phi_1_3     0.1213907  0.1565825   0.775 -0.1855054  0.4282868   0.2193    
+#> phi_2_3    -0.0744270  0.0735117  -1.012 -0.2185074  0.0696533   0.1559    
+#> phi_3_3    -0.5738682  0.0916326  -6.263 -0.7534648 -0.3942715   <2e-16 ***
+#> sigma_1_1   0.3007396  0.1122916   2.678  0.0806521  0.5208271   0.0038 ** 
+#> sigma_2_1   0.0498648  0.0401853   1.241 -0.0288969  0.1286265   0.1076    
+#> sigma_3_1  -0.0741897  0.0428654  -1.731 -0.1582043  0.0098249   0.0421 *  
+#> sigma_2_2   0.0457407  0.0234338   1.952 -0.0001887  0.0916700   0.0258 *  
+#> sigma_3_2   0.0122009  0.0206733   0.590 -0.0283181  0.0527198   0.2777    
+#> sigma_3_3   0.0881988  0.0337203   2.616  0.0221082  0.1542894   0.0046 ** 
+#> theta_1_1   0.1991190  0.0178903  11.130  0.1640546  0.2341833   <2e-16 ***
+#> theta_2_2   0.2112520  0.0148880  14.189  0.1820720  0.2404319   <2e-16 ***
+#> theta_3_3   0.1913294  0.0140468  13.621  0.1637981  0.2188606   <2e-16 ***
+#> mu0_1_1    -0.2189028  0.1678919  -1.304 -0.5479650  0.1101593   0.0965 .  
+#> mu0_2_1     0.5735116  0.2813058   2.039  0.0221625  1.1248608   0.0210 *  
+#> mu0_3_1     0.6406938  0.7675896   0.835 -0.8637542  2.1451418   0.2022    
+#> sigma0_1_1  0.0663477  0.0789889   0.840 -0.0884677  0.2211631   0.2007    
+#> sigma0_2_1  0.0480017  0.1080453   0.444 -0.1637632  0.2597665   0.3285    
+#> sigma0_3_1 -0.3808122  0.3502062  -1.087 -1.0672038  0.3055793   0.1387    
+#> sigma0_2_2  0.3573753  0.2484766   1.438 -0.1296299  0.8443804   0.0755 .  
+#> sigma0_3_2  0.1946208  0.5038433   0.386 -0.7928940  1.1821355   0.3497    
+#> sigma0_3_3  2.8986422  1.8778449   1.544 -0.7818662  6.5791505   0.0617 .  
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 #> 
-#> -2 log-likelihood value at convergence = 21626.57
-#> AIC = 21680.57
-#> BIC = 21856.53
+#> -2 log-likelihood value at convergence = 2176.87
+#> AIC = 2230.87
+#> BIC = 2344.67
 ```
 
 We need to extract the estimated parameters from the fitted object, which will be used to generate bootstrap samples.
@@ -87,7 +87,7 @@ We need to extract the estimated parameters from the fitted object, which will b
 ``` r
 est <- coef(fit)
 n
-#> [1] 50
+#> [1] 5
 time
 #> [1] 100
 delta_t
@@ -109,8 +109,8 @@ mu0 <- est[
   )
 ]
 mu0
-#>     mu0_1_1     mu0_2_1     mu0_3_1 
-#> -0.06854665  0.08457349  0.11572075
+#>    mu0_1_1    mu0_2_1    mu0_3_1 
+#> -0.2189028  0.5735116  0.6406938
 sigma0 <- matrix(
   data = est[
     c(
@@ -129,10 +129,10 @@ sigma0 <- matrix(
   ncol = 3
 )
 sigma0
-#>           [,1]       [,2]       [,3]
-#> [1,] 0.9426793  0.1617881  0.1032567
-#> [2,] 0.1617881  1.4897213 -0.1004635
-#> [3,] 0.1032567 -0.1004635  0.9461439
+#>             [,1]       [,2]       [,3]
+#> [1,]  0.06634768 0.04800168 -0.3808122
+#> [2,]  0.04800168 0.35737526  0.1946208
+#> [3,] -0.38081224 0.19462078  2.8986422
 sigma0_l <- t(chol(sigma0))
 phi <- matrix(
   data = est[
@@ -152,10 +152,10 @@ phi <- matrix(
   ncol = 3
 )
 phi
-#>            [,1]        [,2]        [,3]
-#> [1,] -0.2938068 -0.07191554  0.02579061
-#> [2,]  0.8243958 -0.56899879  0.08266777
-#> [3,] -0.4543161  0.70764697 -0.68750180
+#>            [,1]       [,2]        [,3]
+#> [1,] -0.4698862 -0.1084163  0.12139067
+#> [2,]  0.4809285 -0.2331007 -0.07442704
+#> [3,] -0.2711150  0.5796100 -0.57386817
 sigma <- matrix(
   data = est[
     c(
@@ -168,10 +168,10 @@ sigma <- matrix(
   ncol = 3
 )
 sigma
-#>             [,1]        [,2]         [,3]
-#> [1,]  0.23580555 0.018668186 -0.064687273
-#> [2,]  0.01866819 0.077087752  0.008277734
-#> [3,] -0.06468727 0.008277734  0.080654489
+#>             [,1]       [,2]        [,3]
+#> [1,]  0.30073959 0.04986477 -0.07418968
+#> [2,]  0.04986477 0.04574067  0.01220087
+#> [3,] -0.07418968 0.01220087  0.08819880
 sigma_l <- t(chol(sigma))
 theta <- diag(3)
 diag(theta) <- est[
@@ -182,22 +182,22 @@ diag(theta) <- est[
   )
 ]
 theta
-#>           [,1]      [,2]      [,3]
-#> [1,] 0.2010327 0.0000000 0.0000000
-#> [2,] 0.0000000 0.1916402 0.0000000
-#> [3,] 0.0000000 0.0000000 0.1974485
+#>          [,1]     [,2]      [,3]
+#> [1,] 0.199119 0.000000 0.0000000
+#> [2,] 0.000000 0.211252 0.0000000
+#> [3,] 0.000000 0.000000 0.1913294
 theta_l <- t(chol(theta))
 ```
 
 
 ``` r
-R <- 1000L
+R <- 5L # use at least 1000 in actual research
 path <- getwd()
 prefix <- "ou"
 ```
 
 The estimated parameters are then passed as arguments to the `PBSSMOUFixed` function from the `simStateSpace` package, which generates a parametric bootstrap sampling distribution of the parameter estimates. The argument `R` specifies the number of bootstrap replications.
-The generated data and model estimates are be stored in `path` using the specified `prefix` for the file names.
+The generated data and model estimates are stored in `path` using the specified `prefix` for the file names.
 The `ncores = parallel::detectCores()` argument instructs the function to use all available CPU cores in the system.
 
 > **_NOTE:_**  Fitting the CT-VAR model multiple times is computationally intensive. 

diff --git a/vignettes/med-std.Rmd b/vignettes/med-std.Rmd
@@ -1,7 +1,7 @@
 ---
 title: "Standardized Total, Direct, and Indirect Effects in Continuous-Time Mediation Model"
 author: Ivan Jacob Agaloos Pesigan
-date: "2025-01-13"
+date: "2025-01-14"
 bibliography: "vignettes.bib"
 csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
 nocite: |
@@ -133,7 +133,7 @@ mc <- MCMedStd(
   from = "x",
   to = "y",
   med = "m",
-  R = 20000L,
+  R = 1000L,
   ncores = parallel::detectCores() # use multiple cores
 )
 plot(mc)

diff --git a/vignettes/med.Rmd b/vignettes/med.Rmd
@@ -1,7 +1,7 @@
 ---
 title: "Total, Direct, and Indirect Effects in Continuous-Time Mediation Model"
 author: Ivan Jacob Agaloos Pesigan
-date: "2025-01-13"
+date: "2025-01-14"
 bibliography: "vignettes.bib"
 csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
 nocite: |
@@ -91,7 +91,7 @@ mc <- MCMed(
   from = "x",
   to = "y",
   med = "m",
-  R = 20000L,
+  R = 1000L,
   ncores = parallel::detectCores() # use multiple cores
 )
 plot(mc)