update documentation and DESCRIPTION (#68)

DESCRIPTION

@@ -1,6 +1,6 @@
 Type: Package
 Package: mildsvm
-Title: Multiple-Instance Learning With Support Vector Machines
+Title: Multiple-Instance Learning with Support Vector Machines
 Version: 0.4.0
 Authors@R: c(
     person("Sean", "Kent", , "skent259@gmail.com", role = c("aut", "cre"),
@@ -20,7 +20,7 @@ Description: Weakly supervised (WS), multiple instance (MI) data lives in
     Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews
     et al. (2003)
     <https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>.
-    Many functions use the 'Gurobi' optimization backend to improve the
+    Many functions use the 'Gurobi' optimization back-end to improve the
     optimization problem speed; the 'gurobi' R package and associated
     software can be downloaded from <https://www.gurobi.com> after
     obtaining a license.

R/kfm_nystrom.R

@@ -44,7 +44,7 @@ validate_kfm_nystrom <- function(x) {
 #'
 #'   Kent, S., & Yu, M. (2022). Non-convex SVM for cancer diagnosis based on
 #'   morphologic features of tumor microenvironment *arXiv preprint*
-#'   [arXiv:2206.14704](http://arxiv.org/abs/2206.14704)
+#'   [arXiv:2206.14704](https://arxiv.org/abs/2206.14704)
 #'
 #' @examples
 #' df <- data.frame(

R/mior.R

@@ -71,8 +71,7 @@ validate_mior <- function(x) {
 #'
 #' @references Xiao, Y., Liu, B., & Hao, Z. (2017). Multiple-instance ordinal
 #'   regression. *IEEE Transactions on Neural Networks and Learning Systems*,
-#'   *29*(9), 4398-4413.
-#'   [doi:10.1109/TNNLS.2017.2766164](https://doi.org/10.1109/TNNLS.2017.2766164)
+#'   *29*(9), 4398-4413. \doi{10.1109/TNNLS.2017.2766164}
 #'
 #' @seealso [predict.misvm()] for prediction on new data.
 #'

R/mismm.R

@@ -88,7 +88,7 @@ validate_mismm <- function(x) {
 #'
 #' @references Kent, S., & Yu, M. (2022). Non-convex SVM for cancer diagnosis
 #'   based on morphologic features of tumor microenvironment *arXiv preprint*
-#'   [arXiv:2206.14704](http://arxiv.org/abs/2206.14704)
+#'   [arXiv:2206.14704](https://arxiv.org/abs/2206.14704)
 #'
 #' @seealso [predict.mismm()] for prediction on new data.
 #'

R/misvm.R

@@ -107,7 +107,7 @@ validate_misvm <- function(x) {
 #'
 #'   Kent, S., & Yu, M. (2022). Non-convex SVM for cancer diagnosis based on
 #'   morphologic features of tumor microenvironment *arXiv preprint*
-#'   [arXiv:2206.14704](http://arxiv.org/abs/2206.14704)
+#'   [arXiv:2206.14704](https://arxiv.org/abs/2206.14704)
 #'
 #' @seealso
 #' * [predict.misvm()] for prediction on new data.

R/svor_exc.R

@@ -54,7 +54,7 @@ validate_svor_exc <- function(x) {
 #'
 #' @references Chu, W., & Keerthi, S. S. (2007). Support vector ordinal
 #'   regression. *Neural computation*, *19*(3), 792-815.
-#'   [doi:10.1162/neco.2007.19.3.792](https://doi.org/10.1162/neco.2007.19.3.792)
+#'   \doi{10.1162/neco.2007.19.3.792}
 #'
 #' @seealso [predict.svor_exc()] for prediction on new data.
 #'