Automated build.

.setup/cpp/cTMed-indirect-central-s.cpp

@@ -7,7 +7,7 @@
 // [[Rcpp::depends(RcppArmadillo)]]
 // [[Rcpp::export(.IndirectCentrals)]]
 arma::mat IndirectCentrals(const arma::mat& phi, const arma::vec& delta_t) {
-  arma::mat output(phi.n_rows, delta_t.n_elem, arma::fill::none);
+  arma::mat output(phi.n_rows, delta_t.n_elem, arma::fill::zeros);
   arma::mat total(phi.n_rows, phi.n_cols, arma::fill::none);
   arma::mat direct(phi.n_rows, phi.n_cols, arma::fill::none);
   arma::mat d = arma::eye(phi.n_rows, phi.n_cols);

.setup/latex/bib/bib.bib

@@ -4098,7 +4098,7 @@ @Article{Yu-Pesigan-Zhang-etal-2019
 }
 
 @Article{Yu-Wu-Pesigan-2015,
-  author = {Shu Yu and Anise Man Sze Wu and Ivan Jacob Agaloos Pesigan},
+  author = {Shu M. Yu and Anise Man Sze Wu and Ivan Jacob Agaloos Pesigan},
   date = {2015-12},
   journaltitle = {International Journal of Mental Health and Addiction},
   title = {Cognitive and psychosocial health risk factors of social networking addiction},
@@ -4168,6 +4168,20 @@ @Article{Zamboanga-Pesigan-Tomaso-etal-2015
   publisher = {Elsevier BV},
 }
 
+@Article{Zhang-2018,
+  author = {Guangjian Zhang},
+  date = {2018-01},
+  journaltitle = {Multivariate Behavioral Research},
+  title = {Testing process factor analysis models using the parametric bootstrap},
+  doi = {10.1080/00273171.2017.1415123},
+  issn = {1532-7906},
+  number = {2},
+  pages = {219--230},
+  volume = {53},
+  abstract = {Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.},
+  publisher = {Informa UK Limited},
+}
+
 @Article{Zhang-Ku-Wu-et-al-2019,
   author = {Meng Xuan Zhang and Lisbeth Ku and Anise M. S. Wu and Shu M. Yu and Ivan Jacob Agaloos Pesigan},
   date = {2019-09},
@@ -4186,7 +4200,7 @@ @Article{Zhang-Pesigan-Kahler-etal-2019
   author = {Meng Xuan Zhang and Ivan Jacob Agaloos Pesigan and Christopher W. Kahler and Michael C.W. Yip and Shu Yu and Anise M.S. Wu},
   date = {2019-03},
   journaltitle = {Addictive Behaviors},
-  title = {Psychometric properties of a Chinese version of the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ)},
+  title = {Psychometric properties of a {Chinese} version of the {Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ)}},
   doi = {10.1016/j.addbeh.2018.11.045},
   issn = {0306-4603},
   pages = {389--394},

.setup/latex/pdf/.gitignore

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

R/cTMed-posterior-phi.R

@@ -17,25 +17,34 @@
 #' @keywords cTMed posterior
 #' @export
 PosteriorPhi <- function(object) { # nocov start
-  stopifnot(
-    inherits(
-      x = object,
-      what = "ctStanFit"
+  if (requireNamespace("ctsem", quietly = TRUE)) {
+    stopifnot(
+      inherits(
+        x = object,
+        what = "ctStanFit"
+      )
     )
-  )
-  varnames <- object$ctstanmodelbase$latentNames
-  posterior <- ctsem::ctExtract(object)$pop_DRIFT
-  out <- lapply(
-    X = seq_len(dim(posterior)[1]),
-    FUN = function(i) {
-      phi <- posterior[i, , ]
-      colnames(phi) <- rownames(phi) <- varnames
-      return(phi)
-    }
-  )
-  class(out) <- c(
-    "ctmedposteriorphi",
-    class(out)
-  )
-  return(out)
+    varnames <- object$ctstanmodelbase$latentNames
+    posterior <- ctsem::ctExtract(object)$pop_DRIFT
+    out <- lapply(
+      X = seq_len(dim(posterior)[1]),
+      FUN = function(i) {
+        phi <- posterior[i, , ]
+        colnames(phi) <- rownames(phi) <- varnames
+        return(phi)
+      }
+    )
+    class(out) <- c(
+      "ctmedposteriorphi",
+      class(out)
+    )
+    return(out)
+  } else {
+    stop(
+      paste(
+        "The 'ctsem' package is required for this functionality.",
+        "Please install it manually."
+      )
+    )
+  }
 } # nocov end

README.md

@@ -1,7 +1,7 @@
 cTMed
 ================
 Ivan Jacob Agaloos Pesigan
-2024-11-26
+2024-12-13
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 

src/source.cpp

@@ -112,7 +112,7 @@ Rcpp::NumericVector ExpMean(const arma::mat& phi, const arma::vec& iota,
 // [[Rcpp::depends(RcppArmadillo)]]
 // [[Rcpp::export(.IndirectCentrals)]]
 arma::mat IndirectCentrals(const arma::mat& phi, const arma::vec& delta_t) {
-  arma::mat output(phi.n_rows, delta_t.n_elem, arma::fill::none);
+  arma::mat output(phi.n_rows, delta_t.n_elem, arma::fill::zeros);
   arma::mat total(phi.n_rows, phi.n_cols, arma::fill::none);
   arma::mat direct(phi.n_rows, phi.n_cols, arma::fill::none);
   arma::mat d = arma::eye(phi.n_rows, phi.n_cols);

tests/testthat/test-indirect-central.R

@@ -36,6 +36,26 @@ lapply(
         )
       }
     )
+    # testthat::test_that(
+    #   paste(text, "IndirectCentrals"),
+    #   {
+    #     testthat::expect_true(
+    #       all(
+    #         (
+    #           answer - c(
+    #             as.vector(
+    #               cTMed:::.IndirectCentrals(
+    #                 phi = phi,
+    #                 delta_t = delta_t
+    #               )
+    #             ),
+    #             delta_t
+    #           )
+    #         ) <= tol
+    #       )
+    #     )
+    #   }
+    # )
     indirect_central <- IndirectCentral(
       phi = phi,
       delta_t = 1:5

vignettes/fit-ct-var-dynr.Rmd

@@ -1,7 +1,7 @@
 ---
 title: "Fit the Continuous-Time Vector Autoregressive Model Using the dynr Package"
 author: Ivan Jacob Agaloos Pesigan
-date: "2024-11-26"
+date: "2024-12-13"
 bibliography: "vignettes.bib"
 csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
 nocite: |
@@ -297,8 +297,8 @@ fit <- dynr.cook(
 #> 
 #> Doing end processing
 #> Successful trial
-#> Total Time: 6.121341 
-#> Backend Time: 6.108856
+#> Total Time: 4.905515 
+#> Backend Time: 4.893933
 summary(fit)
 #> Coefficients:
 #>             Estimate Std. Error t value   ci.lower   ci.upper Pr(>|t|)    

vignettes/fit-ct-var-mx.Rmd

@@ -1,7 +1,7 @@
 ---
 title: "Fit the Continuous-Time Vector Autoregressive Model Using the OpenMx Package"
 author: Ivan Jacob Agaloos Pesigan
-date: "2024-11-26"
+date: "2024-12-13"
 bibliography: "vignettes.bib"
 csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
 nocite: |
@@ -531,8 +531,8 @@ summary(fit)
 #> RMSEA:  0  [95% CI (NA, NA)]
 #> Prob(RMSEA <= 0.05): NA
 #> To get additional fit indices, see help(mxRefModels)
-#> timestamp: 2024-11-26 01:18:11 
-#> Wall clock time: 200.0285 secs 
+#> timestamp: 2024-12-13 02:53:07 
+#> Wall clock time: 175.3345 secs 
 #> optimizer:  SLSQP 
 #> OpenMx version number: 2.21.13 
 #> Need help?  See help(mxSummary)

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: "2024-11-26"
+date: "2024-12-13"
 bibliography: "vignettes.bib"
 csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
 nocite: |

vignettes/med.Rmd

@@ -1,7 +1,7 @@
 ---
 title: "Total, Direct, and Indirect Effects in Continuous-Time Mediation Model"
 author: Ivan Jacob Agaloos Pesigan
-date: "2024-11-26"
+date: "2024-12-13"
 bibliography: "vignettes.bib"
 csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
 nocite: |

vignettes/session.Rmd

@@ -83,7 +83,16 @@ sessionInfo()
 ``` r
 installed.packages()[, c("Package", "Version")]
 #>                   Package             Version      
+#> cpp11             "cpp11"             "0.5.1"      
 #> cTMed             "cTMed"             "1.0.2"      
+#> httr2             "httr2"             "1.0.7"      
+#> later             "later"             "1.4.1"      
+#> lubridate         "lubridate"         "1.9.4"      
+#> promises          "promises"          "1.3.2"      
+#> rProject          "rProject"          "0.0.14"     
+#> testthat          "testthat"          "3.2.2.9000" 
+#> textshaping       "textshaping"       "0.4.1"      
+#> usethis           "usethis"           "3.1.0"      
 #> abind             "abind"             "1.4-8"      
 #> arm               "arm"               "1.14-4"     
 #> arrow             "arrow"             "17.0.0.1"   
@@ -475,51 +484,51 @@ installed.packages()[, c("Package", "Version")]
 ```
 #> Architecture:                         x86_64
 #> CPU op-mode(s):                       32-bit, 64-bit
-#> Address sizes:                        46 bits physical, 48 bits virtual
+#> Address sizes:                        39 bits physical, 48 bits virtual
 #> Byte Order:                           Little Endian
-#> CPU(s):                               24
-#> On-line CPU(s) list:                  0-23
+#> CPU(s):                               8
+#> On-line CPU(s) list:                  0-7
 #> Vendor ID:                            GenuineIntel
-#> Model name:                           Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz
+#> Model name:                           Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz
 #> CPU family:                           6
-#> Model:                                79
-#> Thread(s) per core:                   1
-#> Core(s) per socket:                   12
-#> Socket(s):                            2
-#> Stepping:                             1
-#> CPU max MHz:                          2900.0000
-#> CPU min MHz:                          1200.0000
-#> BogoMIPS:                             4389.66
-#> Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
-#> L1d cache:                            768 KiB (24 instances)
-#> L1i cache:                            768 KiB (24 instances)
-#> L2 cache:                             6 MiB (24 instances)
-#> L3 cache:                             60 MiB (2 instances)
-#> NUMA node(s):                         2
-#> NUMA node0 CPU(s):                    0,2,4,6,8,10,12,14,16,18,20,22
-#> NUMA node1 CPU(s):                    1,3,5,7,9,11,13,15,17,19,21,23
-#> Vulnerability Gather data sampling:   Not affected
-#> Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
-#> Vulnerability L1tf:                   Mitigation; PTE Inversion
-#> Vulnerability Mds:                    Mitigation; Clear CPU buffers; SMT disabled
-#> Vulnerability Meltdown:               Mitigation; PTI
-#> Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT disabled
+#> Model:                                142
+#> Thread(s) per core:                   2
+#> Core(s) per socket:                   4
+#> Socket(s):                            1
+#> Stepping:                             12
+#> CPU max MHz:                          4100.0000
+#> CPU min MHz:                          400.0000
+#> BogoMIPS:                             3799.90
+#> Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp vnmi md_clear flush_l1d arch_capabilities
+#> Virtualization:                       VT-x
+#> L1d cache:                            128 KiB (4 instances)
+#> L1i cache:                            128 KiB (4 instances)
+#> L2 cache:                             1 MiB (4 instances)
+#> L3 cache:                             6 MiB (1 instance)
+#> NUMA node(s):                         1
+#> NUMA node0 CPU(s):                    0-7
+#> Vulnerability Gather data sampling:   Mitigation; Microcode
+#> Vulnerability Itlb multihit:          KVM: Mitigation: VMX disabled
+#> Vulnerability L1tf:                   Not affected
+#> Vulnerability Mds:                    Not affected
+#> Vulnerability Meltdown:               Not affected
+#> Vulnerability Mmio stale data:        Mitigation; Clear CPU buffers; SMT vulnerable
 #> Vulnerability Reg file data sampling: Not affected
-#> Vulnerability Retbleed:               Not affected
+#> Vulnerability Retbleed:               Mitigation; Enhanced IBRS
 #> Vulnerability Spec rstack overflow:   Not affected
 #> Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
 #> Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
-#> Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; IBRS_FW; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
-#> Vulnerability Srbds:                  Not affected
-#> Vulnerability Tsx async abort:        Mitigation; Clear CPU buffers; SMT disabled
+#> Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
+#> Vulnerability Srbds:                  Mitigation; Microcode
+#> Vulnerability Tsx async abort:        Mitigation; TSX disabled
 ```
 
 # Memory
 
 
 ```
 #>                total        used        free      shared  buff/cache   available
-#> Mem:           125Gi       4.1Gi        40Gi       5.1Gi        80Gi       115Gi
-#> Swap:             0B          0B          0B
-#> Total:         125Gi       4.1Gi        40Gi
+#> Mem:            23Gi       4.8Gi       3.7Gi       599Mi        14Gi        17Gi
+#> Swap:           11Gi          0B        11Gi
+#> Total:          34Gi       4.8Gi        15Gi
 ```

vignettes/vignettes.bib

@@ -4098,7 +4098,7 @@ @Article{Yu-Pesigan-Zhang-etal-2019
 }
 
 @Article{Yu-Wu-Pesigan-2015,
-  author = {Shu Yu and Anise Man Sze Wu and Ivan Jacob Agaloos Pesigan},
+  author = {Shu M. Yu and Anise Man Sze Wu and Ivan Jacob Agaloos Pesigan},
   date = {2015-12},
   journaltitle = {International Journal of Mental Health and Addiction},
   title = {Cognitive and psychosocial health risk factors of social networking addiction},
@@ -4168,6 +4168,20 @@ @Article{Zamboanga-Pesigan-Tomaso-etal-2015
   publisher = {Elsevier BV},
 }
 
+@Article{Zhang-2018,
+  author = {Guangjian Zhang},
+  date = {2018-01},
+  journaltitle = {Multivariate Behavioral Research},
+  title = {Testing process factor analysis models using the parametric bootstrap},
+  doi = {10.1080/00273171.2017.1415123},
+  issn = {1532-7906},
+  number = {2},
+  pages = {219--230},
+  volume = {53},
+  abstract = {Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.},
+  publisher = {Informa UK Limited},
+}
+
 @Article{Zhang-Ku-Wu-et-al-2019,
   author = {Meng Xuan Zhang and Lisbeth Ku and Anise M. S. Wu and Shu M. Yu and Ivan Jacob Agaloos Pesigan},
   date = {2019-09},
@@ -4186,7 +4200,7 @@ @Article{Zhang-Pesigan-Kahler-etal-2019
   author = {Meng Xuan Zhang and Ivan Jacob Agaloos Pesigan and Christopher W. Kahler and Michael C.W. Yip and Shu Yu and Anise M.S. Wu},
   date = {2019-03},
   journaltitle = {Addictive Behaviors},
-  title = {Psychometric properties of a Chinese version of the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ)},
+  title = {Psychometric properties of a {Chinese} version of the {Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ)}},
   doi = {10.1016/j.addbeh.2018.11.045},
   issn = {0306-4603},
   pages = {389--394},