Clearing WARNINGS from CRAN checks

.Rbuildignore

@@ -0,0 +1,4 @@
+^cran-comments\.md$
+^.*\.Rproj$
+^\.Rproj\.user$
+^sgd\.Rproj$

.gitignore

@@ -3,3 +3,5 @@
 *.Rdata
 *.so
 *.o
+*.Rproj
+.Rproj.user

DESCRIPTION

@@ -3,13 +3,14 @@ Type: Package
 Title: Stochastic Gradient Descent for Scalable Estimation
 Version: 1.1.1
 Authors@R: c(
-    person("Dustin", "Tran", email = "[email protected]", role = c("aut", "cre")),
+    person("Junhyung Lyle", "Kim", email = "[email protected]", role = c("cre", "aut")),
+    person("Dustin", "Tran", role = "aut"),
     person("Panos", "Toulis", role = "aut"),
     person("Tian", "Lian", role = "ctb"),
     person("Ye", "Kuang", role = "ctb"),
     person("Edoardo", "Airoldi", role = "ctb")
     )
-Maintainer: Dustin Tran <[email protected]>
+Maintainer: Junhyung Lyle Kim <[email protected]>
 Description: A fast and flexible set of tools for large scale estimation. It
     features many stochastic gradient methods, built-in models, visualization
     tools, automated hyperparameter tuning, model checking, interval estimation,
@@ -36,4 +37,4 @@ LinkingTo:
     RcppArmadillo
 LazyData: yes
 VignetteBuilder: R.rsp
-RoxygenNote: 6.1.0.9000
+RoxygenNote: 6.1.1

R/sgd.R

@@ -158,11 +158,12 @@
 #' Wei Xu. Towards optimal one pass large scale learning with averaged
 #' stochastic gradient descent. arXiv preprint arXiv:1107.2490, 2011.
 #'
+#'  # Dimensions
 #' @examples
 #' ## Linear regression
 #' set.seed(42)
 #' N <- 1e4
-#' d <- 10
+#' d <- 5
 #' X <- matrix(rnorm(N*d), ncol=d)
 #' theta <- rep(5, d+1)
 #' eps <- rnorm(N)
@@ -171,25 +172,12 @@
 #' sgd.theta <- sgd(y ~ ., data=dat, model="lm")
 #' sprintf("Mean squared error: %0.3f", mean((theta - as.numeric(sgd.theta$coefficients))^2))
 #'
-#' ## Wine quality (Cortez et al., 2009): Logistic regression
-#' set.seed(42)
-#' data("winequality")
-#' dat <- winequality
-#' dat$quality <- as.numeric(dat$quality > 5) # transform to binary
-#' test.set <- sample(1:nrow(dat), size=nrow(dat)/8, replace=FALSE)
-#' dat.test <- dat[test.set, ]
-#' dat <- dat[-test.set, ]
-#' sgd.theta <- sgd(quality ~ ., data=dat,
-#'                  model="glm", model.control=binomial(link="logit"),
-#'                  sgd.control=list(reltol=1e-5, npasses=200),
-#'                  lr.control=c(scale=1, gamma=1, alpha=30, c=1))
-#' sgd.theta
 #'
 #' @useDynLib sgd
 #' @import MASS
 #' @importFrom methods new
 #' @importFrom Rcpp evalCpp
-#' @importFrom stats gaussian is.empty.model model.matrix model.response rnorm
+#' @importFrom stats gaussian is.empty.model model.matrix model.response rnorm coef fitted predict
 
 ################################################################################
 # Classes

cran-comments.md

@@ -0,0 +1,24 @@
+## Test environments
+
+* local OS X install, R 3.5.0
+* win-builder (devel and release)
+
+
+## R CMD check results
+There were no ERRORs or WARNINGs.
+
+There was 1 NOTE:
+
+* checking CRAN incoming feasibility ... NOTE
+  Maintainer: ‘Junhyung Lyle Kim <[email protected]>’
+
+  New submission
+
+  Package was archived on CRAN
+
+  CRAN repository db overrides:
+    X-CRAN-Comment: Archived on 2018-01-23 as check problems were not
+      corrected in time.
+
+  -> This is the new submission after fixing the check problems. 
+

src/Makevars

@@ -1,2 +1,3 @@
 PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
-PKG_CPPFLAGS = -I.
+PKG_CPPFLAGS = -I../inst/include -DARMA_DONT_PRINT_OPENMP_WARNING -DARMA_DONT_PRINT_CXX11_WARNING
+CXX_STD = CXX11

src/Makevars.win

@@ -1,2 +1,3 @@
 PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
-PKG_CPPFLAGS = -I.
+PKG_CPPFLAGS = -I../inst/include -DARMA_DONT_PRINT_OPENMP_WARNING -DARMA_DONT_PRINT_CXX11_WARNING
+CXX_STD = CXX11

src/data/data_point.h

@@ -1,7 +1,7 @@
 #ifndef DATA_DATA_POINT_H
 #define DATA_DATA_POINT_H
 
-#include "basedef.h"
+#include "../basedef.h"
 
 struct data_point {
   /**

src/data/data_set.h

@@ -1,8 +1,8 @@
 #ifndef DATA_DATA_SET_H
 #define DATA_DATA_SET_H
 
-#include "basedef.h"
-#include "data/data_point.h"
+#include "../basedef.h"
+#include "data_point.h"
 
 // wrapper around R's RNG such that we get a uniform distribution over
 // [0,n) as required by the STL algorithm

src/learn-rate/base_learn_rate.h

@@ -1,8 +1,8 @@
 #ifndef LEARN_RATE_BASE_LEARN_RATE_H
 #define LEARN_RATE_BASE_LEARN_RATE_H
 
-#include "basedef.h"
-#include "learn-rate/learn_rate_value.h"
+#include "../basedef.h"
+#include "learn_rate_value.h"
 
 class base_learn_rate {
   /**

src/learn-rate/ddim_learn_rate.h

@@ -1,9 +1,9 @@
 #ifndef LEARN_RATE_DDIM_LEARN_RATE_H
 #define LEARN_RATE_DDIM_LEARN_RATE_H
 
-#include "basedef.h"
-#include "learn-rate/base_learn_rate.h"
-#include "learn-rate/learn_rate_value.h"
+#include "../basedef.h"
+#include "base_learn_rate.h"
+#include "learn_rate_value.h"
 
 class ddim_learn_rate : public base_learn_rate {
   /**

src/learn-rate/learn_rate_value.h

@@ -1,7 +1,7 @@
 #ifndef LEARN_RATE_LEARN_RATE_VALUE_H
 #define LEARN_RATE_LEARN_RATE_VALUE_H
 
-#include "basedef.h"
+#include "../basedef.h"
 
 class learn_rate_value {
   /**

src/learn-rate/onedim_eigen_learn_rate.h

@@ -1,9 +1,9 @@
 #ifndef LEARN_RATE_ONEDIM_EIGEN_LEARN_RATE_H
 #define LEARN_RATE_ONEDIM_EIGEN_LEARN_RATE_H
 
-#include "basedef.h"
-#include "learn-rate/base_learn_rate.h"
-#include "learn-rate/learn_rate_value.h"
+#include "../basedef.h"
+#include "base_learn_rate.h"
+#include "learn_rate_value.h"
 
 class onedim_eigen_learn_rate : public base_learn_rate {
   /**

src/learn-rate/onedim_learn_rate.h

@@ -1,9 +1,9 @@
 #ifndef LEARN_RATE_ONEDIM_LEARN_RATE_H
 #define LEARN_RATE_ONEDIM_LEARN_RATE_H
 
-#include "basedef.h"
-#include "learn-rate/base_learn_rate.h"
-#include "learn-rate/learn_rate_value.h"
+#include "../basedef.h"
+#include "base_learn_rate.h"
+#include "learn_rate_value.h"
 
 class onedim_learn_rate : public base_learn_rate {
   /**

src/model/base_model.h

@@ -1,8 +1,8 @@
 #ifndef MODEL_BASE_MODEL_H
 #define MODEL_BASE_MODEL_H
 
-#include "basedef.h"
-#include "data/data_point.h"
+#include "../basedef.h"
+#include "../data/data_point.h"
 
 class base_model {
   /**

src/model/cox_model.h

@@ -1,9 +1,9 @@
 #ifndef MODEL_COX_MODEL_H
 #define MODEL_COX_MODEL_H
 
-#include "basedef.h"
-#include "data/data_point.h"
-#include "model/base_model.h"
+#include "../basedef.h"
+#include "../data/data_point.h"
+#include "base_model.h"
 
 class cox_model : public base_model {
   /**

src/model/glm/glm_family.h

@@ -1,7 +1,7 @@
 #ifndef MODEL_GLM_FAMILY_H
 #define MODEL_GLM_FAMILY_H
 
-#include "basedef.h"
+#include "../../basedef.h"
 
 class base_family;
 class gaussian_family;

src/model/glm/glm_transfer.h

@@ -1,7 +1,7 @@
 #ifndef MODEL_GLM_TRANSFER_H
 #define MODEL_GLM_TRANSFER_H
 
-#include "basedef.h"
+#include "../../basedef.h"
 
 class base_transfer;
 class identity_transfer;

src/model/glm_model.h

@@ -1,11 +1,11 @@
 #ifndef MODEL_GLM_MODEL_H
 #define MODEL_GLM_MODEL_H
 
-#include "basedef.h"
-#include "data/data_point.h"
-#include "model/base_model.h"
-#include "model/glm/glm_family.h"
-#include "model/glm/glm_transfer.h"
+#include "../basedef.h"
+#include "../data/data_point.h"
+#include "base_model.h"
+#include "glm/glm_family.h"
+#include "glm/glm_transfer.h"
 
 class glm_model : public base_model {
   /**

src/model/gmm_model.h

@@ -1,9 +1,9 @@
 #ifndef MODEL_GMM_MODEL_H
 #define MODEL_GMM_MODEL_H
 
-#include "basedef.h"
-#include "data/data_point.h"
-#include "model/base_model.h"
+#include "../basedef.h"
+#include "../data/data_point.h"
+#include "base_model.h"
 
 class gmm_model : public base_model {
   /**

src/model/m-estimation/m_loss.h

@@ -1,7 +1,7 @@
 #ifndef MODEL_M_LOSS_H
 #define MODEL_M_LOSS_H
 
-#include "basedef.h"
+#include "../../basedef.h"
 
 class base_loss;
 class huber_loss;

src/model/m_model.h

@@ -1,10 +1,10 @@
 #ifndef MODEL_M_MODEL_H
 #define MODEL_M_MODEL_H
 
-#include "basedef.h"
-#include "data/data_point.h"
-#include "model/base_model.h"
-#include "model/m-estimation/m_loss.h"
+#include "../basedef.h"
+#include "../data/data_point.h"
+#include "base_model.h"
+#include "m-estimation/m_loss.h"
 
 class m_model : public base_model {
   /**

src/post-process/cox_post_process.h

@@ -1,9 +1,9 @@
 #ifndef POST_PROCESS_COX_POST_PROCESS_H
 #define POST_PROCESS_COX_POST_PROCESS_H
 
-#include "basedef.h"
-#include "data/data_set.h"
-#include "model/cox_model.h"
+#include "../basedef.h"
+#include "../data/data_set.h"
+#include "../model/cox_model.h"
 
 template <typename SGD>
 Rcpp::List post_process(const SGD& sgd, const data_set& data,

src/post-process/glm_post_process.h

@@ -1,9 +1,9 @@
 #ifndef POST_PROCESS_GLM_POST_PROCESS_H
 #define POST_PROCESS_GLM_POST_PROCESS_H
 
-#include "basedef.h"
-#include "data/data_set.h"
-#include "model/glm_model.h"
+#include "../basedef.h"
+#include "../data/data_set.h"
+#include "../model/glm_model.h"
 
 template <typename SGD>
 Rcpp::List post_process(const SGD& sgd, const data_set& data,

src/post-process/gmm_post_process.h

@@ -1,9 +1,9 @@
 #ifndef POST_PROCESS_GMM_POST_PROCESS_H
 #define POST_PROCESS_GMM_POST_PROCESS_H
 
-#include "basedef.h"
-#include "data/data_set.h"
-#include "model/gmm_model.h"
+#include "../basedef.h"
+#include "../data/data_set.h"
+#include "../model/gmm_model.h"
 
 // model.out: flag to include weighting matrix
 template <typename SGD>

src/post-process/m_post_process.h

@@ -1,9 +1,9 @@
 #ifndef POST_PROCESS_M_POST_PROCESS_H
 #define POST_PROCESS_M_POST_PROCESS_H
 
-#include "basedef.h"
-#include "data/data_set.h"
-#include "model/m_model.h"
+#include "../basedef.h"
+#include "../data/data_set.h"
+#include "../model/m_model.h"
 
 template <typename SGD>
 Rcpp::List post_process(const SGD& sgd, const data_set& data,

src/sgd/base_sgd.h

@@ -1,11 +1,11 @@
 #ifndef SGD_BASE_SGD_H
 #define SGD_BASE_SGD_H
 
-#include "basedef.h"
-#include "learn-rate/base_learn_rate.h"
-#include "learn-rate/onedim_learn_rate.h"
-#include "learn-rate/onedim_eigen_learn_rate.h"
-#include "learn-rate/ddim_learn_rate.h"
+#include "../basedef.h"
+#include "../learn-rate/base_learn_rate.h"
+#include "../learn-rate/onedim_learn_rate.h"
+#include "../learn-rate/onedim_eigen_learn_rate.h"
+#include "../learn-rate/ddim_learn_rate.h"
 
 class base_sgd {
   /**