Merge branch 'dev'

.gitignore

@@ -8,3 +8,5 @@ src/*.dll
 #man/*.Rd
 *.tar.gz
 inst/doc
+*.pdf
+

.travis.yml

@@ -22,8 +22,6 @@ r:
 # BiocInstaller required for 'graph' package from Bioconductor
 bioc_required: true
 
-# Install up-to-date dependencies from GitHub
-r_github_packages:
-    - itsrainingdata/sparsebnUtils
-    - itsrainingdata/ccdrAlgorithm
-    - gujyjean/discretecdAlgorithm
+# Set CXX1X for R-devel, as R-devel does not detect CXX1X support for gcc 4.6.3
+before_install:
+- if [[ "$TRAVIS_R_VERSION_STRING" = 'devel' ]]; then mkdir ~/.R && echo 'CXX1X=g++ -std=c++0x -g -O2 -fPIC' > ~/.R/Makevars; fi

DESCRIPTION

@@ -1,15 +1,15 @@
 Package: sparsebn
 Title: Learning Sparse Bayesian Networks from High-Dimensional Data
-Version: 0.0.1
-Date: 2016-08-12
+Version: 0.0.2
+Date: 2016-11-28
 Authors@R: person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre"))
 Maintainer: Bryon Aragam <sparsebn@gmail.com>
 Description: Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization. Designed to incorporate mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
 Depends:
     R (>= 3.2.3),
-    sparsebnUtils,
-    ccdrAlgorithm,
-    discretecdAlgorithm
+    sparsebnUtils (>= 0.0.3),
+    ccdrAlgorithm (>= 0.0.2),
+    discretecdAlgorithm (>= 0.0.2)
 Suggests:
     knitr,
     rmarkdown,

NAMESPACE

@@ -1,8 +1,15 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(plotDAG,edgeList)
+S3method(plotDAG,sparsebnFit)
+S3method(plotDAG,sparsebnPath)
 export(estimate.covariance)
 export(estimate.dag)
 export(estimate.precision)
+export(plotDAG)
 import(ccdrAlgorithm)
 import(discretecdAlgorithm)
 import(sparsebnUtils)
+importFrom(grDevices,gray)
+importFrom(grDevices,rgb)
+importFrom(graphics,plot)

NEWS.md

@@ -0,0 +1,16 @@
+# sparsebn 0.0.2
+
+## Features
+* Added a `NEWS.md` file to track changes to the package
+* Added plotDAG to provide convenient default for plotting large graphs
+* `estimate.dag` now takes an optional logical argument `adaptive`: If `TRUE`, then an adaptive version of the CD algorithm will be run for discrete data. This argument is ignored for continuous data.
+* Vignette updated and re-written
+
+## Bug fixes
+
+* Cytometry data has been fixed to correct some errors in the network structure; now correctly reflects the network learned in Sachs et al. (2005)
+
+# sparsebn 0.0.1
+
+* Initial stable release
+

R/sparsebn-main.R

@@ -40,6 +40,7 @@
 #' @param weight.scale (CD only) A postitive number to scale weight matrix.
 #' @param convLb (CD only) Small positive number used in Hessian approximation.
 #' @param upperbound (CD only) A large positive value used to truncate the adaptive weights. A -1 value indicates that there is no truncation.
+#' @param adaptive (CD only) \code{TRUE / FALSE}, if \code{TRUE} the adaptive algorithm will be run.
 #' @param verbose \code{TRUE / FALSE} whether or not to print out progress and summary reports.
 #'
 #' @return A \code{\link[sparsebnUtils]{sparsebnPath}} object.
@@ -49,7 +50,7 @@
 #' \dontrun{
 #' # Estimate a DAG from the cytometry data
 #' data(cytometryContinuous)
-#' dat <- sparsebnData(cytometryContinuous$data, type = "d", ivn = cytometryContinuous$ivn)
+#' dat <- sparsebnData(cytometryContinuous$data, type = "c", ivn = cytometryContinuous$ivn)
 #' estimate.dag(dat)
 #' }
 #'
@@ -64,6 +65,7 @@ estimate.dag <- function(data,
                          weight.scale = 1.0,
                          convLb = 0.01,
                          upperbound = 100.0,
+                         adaptive = FALSE,
                          verbose = FALSE
 ){
     pp <- ncol(data$data)
@@ -107,7 +109,8 @@ estimate.dag <- function(data,
                                     error.tol = error.tol,
                                     convLb = convLb,
                                     weight.scale = weight.scale,
-                                    upperbound = upperbound)
+                                    upperbound = upperbound,
+                                    adaptive = adaptive)
     }
 }
 
@@ -132,7 +135,7 @@ estimate.dag <- function(data,
 #'
 #' \dontrun{
 #' data(cytometryContinuous)
-#' dat <- sparsebnData(cytometryContinuous$data, type = "d", ivn = cytometryContinuous$ivn)
+#' dat <- sparsebnData(cytometryContinuous$data, type = "c", ivn = cytometryContinuous$ivn)
 #' estimate.covariance(dat) # estimate covariance
 #' estimate.precision(dat)  # estimate precision
 #' }

R/sparsebn-plotting.R

@@ -0,0 +1,83 @@
+#
+#  sparsebn-plotting.R
+#  sparsebn
+#
+#  Created by Bryon Aragam (local) on 10/2/16.
+#  Copyright (c) 2016 Bryon Aragam. All rights reserved.
+#
+
+#
+# PACKAGE SPARSEBN: Default methods for plotting DAGs
+#
+#   CONTENTS:
+#       plotDAG
+#       plotDAG.edgeList
+#       plotDAG.sparsebnFit
+#       plotDAG.sparsebnPath
+#
+
+#' Plot a DAG
+#'
+#' Using some sensible defaults for large graphs, plot a DAG object. Uses \code{\link[igraph]{igraph}}
+#' package by default.
+#'
+#' This method is not intended for customization. For more control over the output, use
+#' \code{\link{plot}} and see \code{\link{setPlotPackage}} for plotting only and/or
+#' \code{\link{setGraphPackage}} for even more control. These methods grants the user the full
+#' feature set of the selected package.
+#'
+#' @param x An \code{\link{edgeList}}, \code{\link{sparsebnFit}}, or \code{\link{sparsebnPath}} object.
+#' @param ... Additional arguments to \code{\link{plot}}.
+#'
+#' @export
+plotDAG <- function(x){
+    ### Must use igraph for the default method
+    current_plot_pkg <- getPlotPackage()
+    if(current_plot_pkg != "igraph"){
+        err_msg <- sprintf("This method requires that the 'igraph' package be set as your plotting package. You are currently set to use the '%s' package instead. Please set this packge to be 'igraph', or use the default plot() method from the %s package instead. See ?setPlotPackage for more details.", current_plot_pkg, current_plot_pkg)
+        stop(err_msg)
+    }
+
+    UseMethod("plotDAG", x)
+}
+
+#' @export
+plotDAG.edgeList <- function(x, ...){
+
+    #
+    # Note that the defaults used here are not the same as for sparsebnPath!
+    #
+    plot(x,
+         vertex.size = 4,
+         vertex.label = NA,
+         vertex.label.color = gray(0),
+         vertex.color = rgb(1,0,0),
+         edge.width = 1,
+         edge.color = gray(0),
+         edge.arrow.size = 0.5,
+         ...)
+}
+
+#' @export
+plotDAG.sparsebnFit <- function(x, ...){
+    plotDAG(x$edges, ...)
+}
+
+#' @export
+plotDAG.sparsebnPath <- function(x, ...){
+
+    #
+    # This method does not delegate directly to sparsebnFit because
+    #  plotting a grid of graphs requires a different visual layout.
+    #
+
+    plot(x,
+         vertex.size = 4,
+         vertex.label = NA,
+         vertex.label.color = gray(0),
+         vertex.color = rgb(1,0,0),
+         edge.width = 0.5,
+         edge.color = gray(0),
+         edge.arrow.size = 0.15,
+         ...)
+}

R/zzz.R

@@ -7,10 +7,12 @@
 #
 
 #' @import sparsebnUtils ccdrAlgorithm discretecdAlgorithm
+#' @importFrom grDevices gray rgb
+#' @importFrom graphics plot
 
 .onAttach <- function(libname, pkgname){
     msg <- paste0("\n",
-                  "sparsebn v0.0.1, Copyright (c) 2016\n",
+                  "sparsebn v0.0.2, Copyright (c) 2016\n",
                   "\tBryon Aragam, University of California, Los Angeles\n",
                   "\tJiaying Gu, University of California, Los Angeles\n",
                   "\tDacheng Zhang, University of California, Los Angeles\n",

README.Rmd

@@ -15,6 +15,8 @@ knitr::opts_chunk$set(
 # sparsebn
 
 [![Travis-CI Build Status](https://travis-ci.org/itsrainingdata/sparsebn.svg?branch=master)](https://travis-ci.org/itsrainingdata/sparsebn)
+[![](http://www.r-pkg.org/badges/version/sparsebn)](http://www.r-pkg.org/pkg/sparsebn)
+[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/sparsebn)](http://www.r-pkg.org/pkg/sparsebn)
 
 Methods for learning sparse Bayesian networks and other graphical models from high-dimensional data via sparse regularization. Designed to handle:
 

README.md

@@ -1,7 +1,7 @@
 sparsebn
 ========
 
-[![Travis-CI Build Status](https://travis-ci.org/itsrainingdata/sparsebn.svg?branch=master)](https://travis-ci.org/itsrainingdata/sparsebn)
+[![Travis-CI Build Status](https://travis-ci.org/itsrainingdata/sparsebn.svg?branch=master)](https://travis-ci.org/itsrainingdata/sparsebn) [![](http://www.r-pkg.org/badges/version/sparsebn)](http://www.r-pkg.org/pkg/sparsebn) [![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/sparsebn)](http://www.r-pkg.org/pkg/sparsebn)
 
 Methods for learning sparse Bayesian networks and other graphical models from high-dimensional data via sparse regularization. Designed to handle:
 

cran-comments.md

@@ -1,33 +1,19 @@
 ## Test environments
-* local OS X install, R 3.3.1
-* ubuntu 12.04 (travis-ci), R 3.3.1
+* local OS X install, R 3.3.2
+* ubuntu 12.04.5 (travis-ci), R 3.3.2 (oldrel, devel, and release)
 * win-builder (devel and release)
 
 ## R CMD check results
-There were no ERRORs or WARNINGs.
+There were no ERRORs, WARNINGs, or NOTEs.
 
-There was 1 NOTE:
+## CRAN notes
 
-* checking CRAN incoming feasibility ... NOTE
-Maintainer: ‘Bryon Aragam <sparsebn@gmail.com>’
-
-New submission
-
-This is the first version of this package that has been submitted to CRAN.
-
-## Re-submission notes
-- I get
-
-Reading CITATION file fails with
-  $ operator is invalid for atomic vectors
-when package is not installed.
-
-Fixed by replacing packageDecription with meta as described in Section 1.9 of R-exts.
+The current warnings listed for v0.0.1 at https://cran.rstudio.com/web/checks/check_results_sparsebn.html are a result of recent updates to the dependencies for this submission. The current submission remedies all of these issues.
 
 ## Dependencies
 
 CHECK has been run on all dependencies and passed.
 
 ## Reverse dependencies
 
-This is a new release, so there are no reverse dependencies.
+There are no reverse dependencies.