version 2.1.1

cran · Mar 20, 2023 · cc83242 · cc83242
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: hmcdm
 Type: Package
 Title: Hidden Markov Cognitive Diagnosis Models for Learning
-Version: 2.1.0
+Version: 2.1.1
 Authors@R: c(
      person("Susu", "Zhang", email = "[email protected]", role = c("aut")),
      person("Shiyu", "Wang", email = "[email protected]", role = c("aut")),
@@ -12,29 +12,24 @@ Description: Fitting hidden Markov models of learning under the cognitive diagno
   The estimation of the hidden Markov diagnostic classification model,
   the first order hidden Markov model, the reduced-reparameterized unified learning model,
   and the joint learning model for responses and response times.
-  Chen, Y., Culpepper, S. A., Wang, S., & Douglas, J. (2018) <doi:10.1177/0146621617721250>.
-  Wang, S., Yang, Y., Culpepper, S. A., & Douglas, J. A. (2018) <doi:10.3102/1076998617719727>.
-  Wang, S., Zhang, S., Douglas, J., & Culpepper, S. (2018) <doi:10.1080/15366367.2018.1435105>.
-  Zhang, S., Douglas, J. A., Wang, S. & Culpepper, S. A. (2019) <doi:10.1007/978-3-030-05584-4_24>.
 License: GPL (>= 2)
 URL: https://github.com/tmsalab/hmcdm
 BugReports: https://github.com/tmsalab/hmcdm/issues
 Depends: R (>= 3.5.0)
 Imports: Rcpp (>= 1.0.0), stats (>= 3.0.0), bayesplot (>= 1.9.0),
         rstantools (>= 1.0.0)
 LinkingTo: Rcpp, RcppArmadillo, progress
-SystemRequirements: C++11
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.2.1
 Suggests: knitr, rmarkdown
 VignetteBuilder: knitr
 NeedsCompilation: yes
-Packaged: 2023-01-25 20:48:54 UTC; sunbeomkwon
+Packaged: 2023-03-20 00:43:14 UTC; sunbeomkwon
 Author: Susu Zhang [aut],
   Shiyu Wang [aut],
   Yinghan Chen [aut],
   Sunbeom Kwon [aut, cre]
 Maintainer: Sunbeom Kwon <[email protected]>
 Repository: CRAN
-Date/Publication: 2023-01-25 21:20:05 UTC
+Date/Publication: 2023-03-20 01:40:02 UTC
diff --git a/MD5 b/MD5
@@ -1,10 +1,10 @@
-9415e1c854c59ae359843f28092af3a3 *DESCRIPTION
+a5e2afb624fc2fdfc8a389587d1b9e4f *DESCRIPTION
 2e8eef1059907d49cab1fed0096745d8 *NAMESPACE
 bc72548d9527527fe92ab5ab9c845506 *NEWS.md
 81ae8d0de4895aecadda44a85ac3e501 *R/RcppExports.R
 a1bb66afd6a4b557715aa9a95ac55a86 *R/Spatial_rotation_data.R
-503452fa7d4573608d55c4a1579beb4b *R/hmcdm-package.R
-77443f5e71f98ede26def98db6dfa1a3 *R/pp_check.R
+999a1da29ad78412eaadf64d3ec2fb4a *R/hmcdm-package.R
+b5cd8366528997ebcb0da13a3679abd1 *R/pp_check.R
 603543066e0f5e674be2caca1f06b8d8 *R/summary.R
 44beb5eabad01203b11375651fa18699 *README.md
 ea124a494ad7922985532ad3b308e4d5 *build/partial.rdb
@@ -17,22 +17,22 @@ d30ab1872863d4cdacfe59949ad86888 *data/Test_order.rda
 2e08eb7e514783e5cdd69ffadbe56982 *data/Y_real_array.rda
 83c48a8d23f9433791f39e753ae6067c *inst/doc/DINA_FOHM.R
 b4fa2524e2e10afc334bba6dbadb7334 *inst/doc/DINA_FOHM.Rmd
-eee0f5a6c3e3198b7ca2dce004b1c263 *inst/doc/DINA_FOHM.html
+312d4d87863d3202955cd1860d2e4916 *inst/doc/DINA_FOHM.html
 ddbad45806cfb267c25bb5f4958570a5 *inst/doc/DINA_HO_RT_joint.R
 bb13553266bd1ec0ac2e2f52277394a1 *inst/doc/DINA_HO_RT_joint.Rmd
-69f6462e9125ffea1ae82dd41ca27305 *inst/doc/DINA_HO_RT_joint.html
+c8c4b3930abbaa3b767e2a1e05533be5 *inst/doc/DINA_HO_RT_joint.html
 2748fc5fc32afc48d3e067dd7f953eb0 *inst/doc/DINA_HO_RT_sep.R
 4495fff09296899af968a9b24523da9b *inst/doc/DINA_HO_RT_sep.Rmd
-0042701f4e8f6e6d9a344a42d341c28f *inst/doc/DINA_HO_RT_sep.html
+fe6e3b29449171bba830667e9bc9083f *inst/doc/DINA_HO_RT_sep.html
 3ac0cc2fe4ef8623ce51b02f3f71d791 *inst/doc/HMDCM.R
 f4012674ba14e7d9d1344f50241470b5 *inst/doc/HMDCM.Rmd
-ffbff7669d1445b5bc14daf570855479 *inst/doc/HMDCM.html
+24b15eac04fe4579eff2f5b0413cd3b3 *inst/doc/HMDCM.html
 396a35a89edd64a43590bd610c37b434 *inst/doc/NIDA_indept.R
 5e0591a73fdc8f02b5784e4eb3394293 *inst/doc/NIDA_indept.Rmd
-fa37c514bdd162c28af1002586d57552 *inst/doc/NIDA_indept.html
+e5cfac19243d6674493c6afc3fdcefdb *inst/doc/NIDA_indept.html
 5a37701db62ee7e321223769548a8fba *inst/doc/rRUM_indept.R
 dffc6020cb49638408da3133d5e57dd1 *inst/doc/rRUM_indept.Rmd
-60fd811a2267715d1d83357b0853ea41 *inst/doc/rRUM_indept.html
+8ec05dbe725cd4b814c595076bd718e8 *inst/doc/rRUM_indept.html
 6b3e26f12535811721e2938165afc266 *man/Design_array.Rd
 5bc5a732efbb28280ea71eb295bdce11 *man/ETAmat.Rd
 613a4c0a9425ab6402a364829f6bee22 *man/L_real_array.Rd
@@ -43,18 +43,18 @@ caf3659e55d411a09527bc07c0b5eb51 *man/TPmat.Rd
 bfcfb75bd675ab3f925d48f532957056 *man/Test_order.Rd
 f5ceac12367947f9bd81ed1e9e8dff3f *man/Test_versions.Rd
 e5a773ba8a7d0697e777aacc7a630f9a *man/Y_real_array.Rd
-b93dd652f7436c7ed18fd634a7988844 *man/hmcdm-package.Rd
+f358b3b8a2353c2fdaa89232a4e5ea0b *man/hmcdm-package.Rd
 81494ef78a0cee13e9bf91f2bc7779ff *man/hmcdm.Rd
 3e8c17e5b3ccb201a515bd78626868de *man/inv_bijectionvector.Rd
-013a17de2e694f3e5a6712b120876171 *man/pp_check.hmcdm.Rd
+71e1e1ed0e6e5267be007b5b94de4831 *man/pp_check.hmcdm.Rd
 2289cb7a5d4f96b614c84b2321f50de0 *man/rOmega.Rd
 0ba6de346e896c592d7ffb85091d08e4 *man/random_Q.Rd
 5ea1bbc4a5df5cd5fe99b1a3612e7d03 *man/sim_RT.Rd
 476f0863fe924a71245325b2a9500c85 *man/sim_alphas.Rd
 39289a816e1470ad9e96fe129693d1db *man/sim_hmcdm.Rd
 551a5d34ed7c8d98747e907ea6d8b2f1 *man/summary.hmcdm.Rd
-56c4d0d96659f0be8a7e7036dabd5277 *src/Makevars
-56c4d0d96659f0be8a7e7036dabd5277 *src/Makevars.win
+2d67cd6d207f2da3c748e0f045f89e4f *src/Makevars
+2d67cd6d207f2da3c748e0f045f89e4f *src/Makevars.win
 9b43ec737b9ad9c2d884047f41b0d35c *src/RcppExports.cpp
 913f97a478b17f6fb6d560c870df2bcc *src/basic_functions.cpp
 db7d4b9462b30b4a409d56b98a366e63 *src/basic_functions.h

diff --git a/R/hmcdm-package.R b/R/hmcdm-package.R
@@ -6,9 +6,13 @@
 #' @import bayesplot
 #' @import rstantools
 #' @references 
-#" Wang, S., Yang, Y., Culpepper, S. A., & Douglas, J. A. (2018) <doi:10.3102/1076998617719727> "Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates."
+#" Wang, S., Yang, Y., Culpepper, S. A., & Douglas, J. A. (2018) \doi{10.3102/1076998617719727} "Tracking Skill Acquisition With Cognitive Diagnosis Models: A Higher-Order, Hidden Markov Model With Covariates."
 #' 
-#" Chen, Y., Culpepper, S. A., Wang, S., & Douglas, J. (2018) <doi:10.1177/0146621617721250> "A hidden Markov model for learning trajectories in cognitive diagnosis with application to spatial rotation skills."
+#" Chen, Y., Culpepper, S. A., Wang, S., & Douglas, J. (2018) \doi{10.1177/0146621617721250} "A hidden Markov model for learning trajectories in cognitive diagnosis with application to spatial rotation skills."
 #' 
-#' Wang, S., Zhang, S., Douglas, J., & Culpepper, S. (2018) <doi:10.1080/15366367.2018.1435105> "Using Response Times to Assess Learning Progress: A Joint Model for Responses and Response Times."
+#' Wang, S., Zhang, S., Douglas, J., & Culpepper, S. (2018) \doi{10.1080/15366367.2018.1435105} "Using Response Times to Assess Learning Progress: A Joint Model for Responses and Response Times."
+#' 
+#' Zhang, S., Douglas, J. A., Wang, S. & Culpepper, S. A. (2019) \doi{10.1007/978-3-030-05584-4_24} "Reduced Reparameterized Unified Model Applied to Learning Spatial Rotation Skills."
 "_PACKAGE"
+
+
diff --git a/R/pp_check.R b/R/pp_check.R
@@ -9,6 +9,7 @@
 #' Type `"item_mean"` is related to the first order moment and examines mean scores of all the items included in the test. 
 #' Type `"item_OR"` is related to the second order moment and examines odds ratios of all item pairs.
 #' Types `"latency_mean"` and `"total_latency"` are available only for `hmcdm` objects that include item response time information (i.e., `hmcdm` object fitted with "`DINA_HO_RT`" model).
+#' @param ... Additional arguments 
 #' @return Plots for checking the posterior predictive distributions. The default `Plotfun` `"dens_overlay"` plots density of each dataset are overlaid with the distribution of the observed values.
 #' @seealso 
 #' [bayesplot::ppc_dens_overlay()]
@@ -17,7 +18,7 @@
 #' [bayesplot::ppc_scatter_avg()]
 #' [bayesplot::ppc_error_scatter_avg()]
 #' @references 
-#" Zhang, S., Douglas, J. A., Wang, S. & Culpepper, S. A. (2019) <doi:10.1007/978-3-030-05584-4_24>
+#" Zhang, S., Douglas, J. A., Wang, S. & Culpepper, S. A. (2019) <\doi{10.1007/978-3-030-05584-4_24}>
 #' @examples
 #' \donttest{
 #' output_FOHM = hmcdm(Y_real_array,Q_matrix,"DINA_FOHM",Design_array,1000,500)
@@ -26,7 +27,7 @@
 #' pp_check(output_FOHM, plotfun="hist", type="item_mean")
 #' }
 #' @export
-pp_check.hmcdm <- function(object,plotfun="dens_overlay",type="total_score"){
+pp_check.hmcdm <- function(object, plotfun = "dens_overlay", type = "total_score", ...){
   N <- dim(object$input_data$Response)[1]
   L <- dim(object$input_data$Response)[3]
   J <- dim(object$input_data$Response)[2]
@@ -145,3 +146,7 @@ pp_check.hmcdm <- function(object,plotfun="dens_overlay",type="total_score"){
 
 
 
+
+
+
+