update doi

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: spinBayes
 Type: Package
 Title: Semi-Parametric Gene-Environment Interaction via Bayesian Variable Selection
-Version: 0.1.0
+Version: 0.2.0
 Author: Jie Ren, Fei Zhou, Xiaoxi Li, Cen Wu, Yu Jiang
 Maintainer: Jie Ren <[email protected]>
 Description: Many complex diseases are known to be affected by the interactions between 
@@ -16,7 +16,7 @@ Description: Many complex diseases are known to be affected by the interactions
    and group level to shrink coefficients corresponding to irrelevant main and interaction 
    effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and 
    alternative methods are  efficiently implemented in C++. 
-Depends: R (>= 3.5.0)
+Depends: R (>= 4.0.0)
 License: GPL-2
 Encoding: UTF-8
 LazyData: true

NEWS.md

@@ -0,0 +1,13 @@
+# spinBayes 0.2.0 [2024-2-21]
+
+* Added a generic function plot() for plotting identified varying effects.
+* Updated the documentation.
+
+
+
+
+
+
+
+
+

R/BVCFit.R

@@ -57,7 +57,7 @@
 #'
 #' @references
 #' Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., Wu, C. (2020) Semiparametric Bayesian variable selection for gene-environment interactions.
-#' {\emph{Statistics in Medicine}, 39(5): 617– 638} \url{https://doi.org/10.1002/sim.8434}
+#' {\emph{Statistics in Medicine}, 39(5): 617– 638} \doi{10.1002/sim.8434}
 #'
 #' @examples
 #' data(gExp)

R/BVSelection.R

@@ -11,7 +11,7 @@
 #'
 #' @references
 #' Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., Wu, C. (2020) Semiparametric Bayesian variable selection for gene-environment interactions.
-#' {\emph{Statistics in Medicine}, 39(5): 617– 638} \url{https://doi.org/10.1002/sim.8434}
+#' {\emph{Statistics in Medicine}, 39(5): 617– 638} \doi{10.1002/sim.8434}
 #'
 #' Barbieri, M.M. and Berger, J.O. (2004). Optimal predictive model selection
 #' {\emph{Ann. Statist}, 32(3):870–897}

R/spinBayes-package.R

@@ -21,42 +21,41 @@ NULL
 #' predict() takes a BVCfit object and returns the predicted values for new observations.
 #'
 #' @references
-#' Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., Wu, C. (2020) Semiparametric Bayesian variable selection for gene-environment interactions.
-#' {\emph{Statistics in Medicine}, 39(5): 617– 638} \url{https://doi.org/10.1002/sim.8434}
+#' Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., Wu, C. (2020). Semiparametric Bayesian variable selection
+#' for gene-environment interactions. \emph{Statistics in Medicine}, 39(5): 617–638. \doi{10.1002/sim.8434}.
 #'
-#' Zhou, F., Ren, J., Lu, X., Ma, S. and Wu, C. (2020). Gene–Environment Interaction: a Variable Selection Perspective.
-#' {\emph{Epistasis. Methods in Molecular Biology.} Humana Press (Accepted)} \url{https://arxiv.org/abs/2003.02930}
+#' Zhou, F., Ren, J., Lu, X., Ma, S., and Wu, C. (2021). Gene-Environment Interaction: A Variable Selection Perspective.
+#' \emph{Methods in Molecular Biology}, 2212:191-223. \doi{10.1007/978-1-0716-0947-7_13}. PMID: 33733358.
 #'
-#' Wu, C., Jiang, Y., Ren, J., Cui, Y., Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.
-#' {\emph{Statistics in Medicine}, 37:437–456} \url{https://doi.org/10.1002/sim.7518}
+#' Wu, C., Jiang, Y., Ren, J., Cui, Y., Ma, S. (2018). Dissecting gene-environment interactions: A penalized robust approach
+#' accounting for hierarchical structures. \emph{Statistics in Medicine}, 37:437–456. \doi{10.1002/sim.7518}.
 #'
-#' Wu, C., Zhong, P.-S., and Cui, Y. (2018). Additive varying–coefficient model for nonlinear gene–
-#' environment interactions.
-#' {\emph{Statistical Applications in Genetics and Molecular Biology}, 17(2)}
+#' Wu, C., Zhong, P.-S., and Cui, Y. (2018). Additive varying–coefficient model for nonlinear gene–environment interactions.
+#' \emph{Statistical Applications in Genetics and Molecular Biology}, 17(2). \doi{10.1515/sagmb-2017-0008}.
 #'
-#' Jiang, Y., Huang, Y., Du, Y., Zhao, Y., Ren, J., Ma, S., Wu, C. (2017). Identification of prognostic genes and pathways in lung adenocarcinoma using a Bayesian Approach.
-#' \emph{Cancer Informatics, 1(7)}
+#' Jiang, Y., Huang, Y., Du, Y., Zhao, Y., Ren, J., Ma, S., Wu, C. (2017). Identification of prognostic genes and pathways in
+#' lung adenocarcinoma using a Bayesian Approach. \emph{Cancer Informatics}, 1(7).
 #'
-#' Wu, C., Shi, X., Cui, Y. and Ma, S. (2015). A penalized robust semiparametric approach for gene-environment interactions.
-#' {\emph{Statistics in Medicine}, 34 (30): 4016–4030} \url{https://doi.org/10.1002/sim.6609}
+#' Wu, C., Shi, X., Cui, Y., and Ma, S. (2015). A penalized robust semiparametric approach for gene-environment interactions.
+#' \emph{Statistics in Medicine}, 34(30): 4016–4030. \doi{10.1002/sim.6609}.
 #'
 #' Wu, C., and Ma, S. (2015). A selective review of robust variable selection with applications in bioinformatics.
-#' {\emph{Briefings in Bioinformatics}, 16(5), 873–883} \url{https://doi.org/10.1093/bib/bbu046}
+#' \emph{Briefings in Bioinformatics}, 16(5), 873–883. \doi{10.1093/bib/bbu046}.
 #'
-#' Wu, C., Cui, Y., and Ma, S. (2014). Integrative analysis of gene–environment interactions under a multi–response partially linear varying coefficient model.
-#' {\emph{Statistics in Medicine}, 33(28), 4988–4998} \url{https://doi.org/10.1002/sim.6287}
+#' Wu, C., Cui, Y., and Ma, S. (2014). Integrative analysis of gene–environment interactions under a multi–response partially
+#' linear varying coefficient model. \emph{Statistics in Medicine}, 33(28), 4988–4998. \doi{10.1002/sim.6287}.
 #'
-#' Wu, C. and Cui, Y. (2013). Boosting signals in gene–based association studies via efficient SNP selection.
-#' {\emph{Briefings in Bioinformatics}, 15(2):279–291} \url{https://doi.org/10.1093/bib/bbs087}
+#' Wu, C., and Cui, Y. (2013). Boosting signals in gene–based association studies via efficient SNP selection.
+#' \emph{Briefings in Bioinformatics}, 15(2):279–291. \doi{10.1093/bib/bbs087}.
 #'
-#' Wu, C. and Cui, Y. (2013). A novel method for identifying nonlinear gene–environment interactions in case–control association studies.
-#' {\emph{Human Genetics}, 132(12):1413–1425} \url{https://doi.org/10.1007/s00439-013-1350-z}
+#' Wu, C., and Cui, Y. (2013). A novel method for identifying nonlinear gene–environment interactions in case–control
+#' association studies. \emph{Human Genetics}, 132(12):1413–1425. \doi{10.1007/s00439-013-1350-z}.
 #'
-#' Wu, C., Zhong, P.S. and Cui, Y. (2013). High dimensional variable selection for gene-environment interactions.
-#' {\emph{Technical Report. Michigan State University}}
+#' Wu, C., Zhong, P.S., and Cui, Y. (2013). High dimensional variable selection for gene-environment interactions.
+#' \emph{Technical Report. Michigan State University}.
 #'
 #' Wu, C., Li, S., and Cui, Y. (2012). Genetic Association Studies: An Information Content Perspective.
-#' {\emph{Current Genomics}, 13(7), 566–573} \url{https://doi.org/10.2174/138920212803251382}
+#' \emph{Current Genomics}, 13(7), 566–573. \doi{10.2174/138920212803251382}.
 #'
 #' @seealso \code{\link{BVCfit}}
 NULL

README.md

@@ -115,13 +115,13 @@ implemented in C++.
     pred$pmse
     # c(pred$y.pred)
 
-<!-- ## News -->
-<!-- ### regnet 0.3.0 [2018-5-21] -->
-<!-- * Two new, easy to use, integrated interfaces: cv.regnet() and regnet(). -->
-<!-- * New methods for continuous and survival responses. -->
-<!-- * The new "clv" argument allows the presence of clinical variables that are not subject to penalty in the X matrix. -->
-<!-- ### regnet 0.2.0 [2017-10-14] -->
-<!-- * Provides c++ implementation for coordinate descent algorithms. This update significantly increases the speed of cross-validation functions in this package. -->
+## News
+
+### spinBayes 0.2.0 \[2024-2-21\]
+
+- Added a generic function plot() for plotting identified varying
+  effects.
+- Updated the documentation.
 
 ## Methods