solvers: Various solvers for SLOPE

# #' Bodyfat
# #'
# #' The response (`y`) corresponds to
# #' estimates of percentage of body fat from application of
# #' Siri's 1956 equation to measurements of underwater weighing, as well as
# #' age, weight, height, and a variety of
# #' body circumference measurements.
# #'
# #' @format A list with two items representing 252 observations from
# #'   14 variables
# #' \describe{
# #'   \item{age}{age (years)}
# #'   \item{weight}{weight (lbs)}
# #'   \item{height}{height (inches)}
# #'   \item{neck}{neck circumference (cm)}
# #'   \item{chest}{chest circumference (cm)}
# #'   \item{abdomen}{abdomen circumference (cm)}
# #'   \item{hip}{hip circumference (cm)}
# #'   \item{thigh}{thigh circumference (cm)}
# #'   \item{knee}{knee circumference (cm)}
# #'   \item{ankle}{ankle circumference (cm)}
# #'   \item{biceps}{biceps circumference (cm)}
# #'   \item{forearm}{forearm circumference (cm)}
# #'   \item{wrist}{wrist circumference (cm)}
# #' }
# #' @source http://lib.stat.cmu.edu/datasets/bodyfat
# #' @source https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html
# "bodyfat"

# #' Abalone
# #'
# #' This data set contains observations of abalones, the common
# #' name for any of a group of sea snails. The goal is to predict the
# #' age of an individual abalone given physical measurements such as
# #' sex, weight, and height.
# #'
# #' Only a stratified sample of 211 rows of the original data set are used here.
# #'
# #' @format A list with two items representing 211 observations from
# #'   9 variables
# #' \describe{
# #'   \item{sex}{sex of abalone, 1 for female}
# #'   \item{infant}{indicates that the person is an infant}
# #'   \item{length}{longest shell measurement in mm}
# #'   \item{diameter}{perpendicular to length in mm}
# #'   \item{height}{height in mm including meat in shell}
# #'   \item{weight_whole}{weight of entire abalone}
# #'   \item{weight_shucked}{weight of meat}
# #'   \item{weight_viscera}{weight of viscera}
# #'   \item{weight_shell}{weight of shell}
# #'   \item{rings}{rings. +1.5 gives the age in years}
# #' }
# #' @source Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
# #'   Statistics and Probability Letters, 33 (1997) 291-297.
# "abalone"

# #' Heart disease
# #'
# #' Diagnostic attributes of patients classified as having heart disease or not.
# #'
# #' @section Preprocessing:
# #' The original dataset contained 13 variables. The nominal of these were
# #' dummycoded, removing the first category. No precise information regarding
# #' variables `chest_pain`, `thal` and `ecg` could be found, which explains
# #' their obscure definitions here.
# #'
# #' @format 270 observations from 17 variables represented as a list consisting
# #' of a binary factor response vector `y`,
# #' with levels 'absence' and 'presence' indicating the absence or presence of
# #' heart disease and `x`: a sparse feature matrix of class 'dgCMatrix' with the
# #' following variables:
# #' \describe{
# #'   \item{age}{age}
# #'   \item{bp}{diastolic blood pressure}
# #'   \item{chol}{serum cholesterol in mg/dl}
# #'   \item{hr}{maximum heart rate achieved}
# #'   \item{old_peak}{ST depression induced by exercise relative to rest}
# #'   \item{vessels}{the number of major blood vessels (0 to 3) that were
# #'                  colored by fluoroscopy}
# #'   \item{sex}{sex of the participant: 0 for male, 1 for female}
# #'   \item{angina}{a dummy variable indicating whether the person suffered
# #'                 angina-pectoris during exercise}
# #'   \item{glucose_high}{indicates a fasting blood sugar over 120 mg/dl}
# #'   \item{cp_typical}{typical angina}
# #'   \item{cp_atypical}{atypical angina}
# #'   \item{cp_nonanginal}{non-anginal pain}
# #'   \item{ecg_abnormal}{indicates a ST-T wave abnormality
# #'                       (T wave inversions and/or ST elevation or depression of
# #'                       > 0.05 mV)}
# #'   \item{ecg_estes}{probable or definite left ventricular hypertrophy by
# #'                    Estes' criteria}
# #'   \item{slope_flat}{a flat ST curve during peak exercise}
# #'   \item{slope_downsloping}{a downwards-sloping ST curve during peak exercise}
# #'   \item{thal_reversible}{reversible defect}
# #'   \item{thal_fixed}{fixed defect}
# #' }
# #' @source Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository
# #'   <http://archive.ics.uci.edu/ml>. Irvine, CA: University of California,
# #'   School of Information and Computer Science.
# #' @source <https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#heart>
# "heart"

# #' Wine cultivars
# #'
# #' A data set of results from chemical analysis of wines grown in Italy
# #' from three different cultivars.
# #'
# #' @format 178 observations from 13 variables represented as a list consisting
# #' of a categorical response vector `y`
# #' with three levels: *A*, *B*, and *C* representing different
# #' cultivars of wine as well as `x`: a sparse feature matrix of class
# #' 'dgCMatrix' with the following variables:
# #' \describe{
# #'   \item{alcohol}{alcoholic content}
# #'   \item{malic}{malic acid}
# #'   \item{ash}{ash}
# #'   \item{alcalinity}{alcalinity of ash}
# #'   \item{magnesium}{magnemium}
# #'   \item{phenols}{total phenols}
# #'   \item{flavanoids}{flavanoids}
# #'   \item{nonflavanoids}{nonflavanoid phenols}
# #'   \item{proanthocyanins}{proanthocyanins}
# #'   \item{color}{color intensity}
# #'   \item{hue}{hue}
# #'   \item{dilution}{OD280/OD315 of diluted wines}
# #'   \item{proline}{proline}
# #' }
# #'
# #' @source Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository
# #'   <http://archive.ics.uci.edu/ml>. Irvine, CA: University of California,
# #'   School of Information and Computer Science.
# #' @source <https://raw.githubusercontent.com/hadley/rminds/master/1-data/wine.csv>
# #' @source <https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#wine>
# "wine"

# #' Student performance
# #'
# #' A data set of the attributes of 382 students in secondary education
# #' collected from two schools. The goal is to predict the
# #' grade in math and Portugese at the end of the third period. See the
# #' cited sources for additional information.
# #'
# #' @section Preprocessing:
# #' All of the grade-specific predictors were dropped from the data set.
# #' (Note that it is not clear from the source why some of these predictors are
# #' specific to each grade, such as which parent is the student's guardian.)
# #' The categorical variables were dummy-coded. Only the final grades (G3)
# #' were kept as dependent variables, whilst the
# #' first and second period grades were dropped.
# #'
# #' @format 382 observations from 13 variables represented as a list consisting
# #' of a binary factor response matrix `y` with two responses: `portugese` and
# #' `math` for the final scores in period three for the respective subjects.
# #' The list also contains `x`: a sparse feature matrix of class
# #' 'dgCMatrix' with the following variables:
# #' \describe{
# #'   \item{school_ms}{student's primary school, 1 for Mousinho da Silveira and 0
# #'         for Gabriel Pereira}
# #'   \item{sex}{sex of student, 1 for male}
# #'   \item{age}{age of student}
# #'   \item{urban}{urban (1) or rural (0) home address}
# #'   \item{large_family}{whether the family size is larger than 3}
# #'   \item{cohabitation}{whether parents live together}
# #'   \item{Medu}{mother's level of education (ordered)}
# #'   \item{Fedu}{fathers's level of education (ordered)}
# #'   \item{Mjob_health}{whether the mother was employed in health care}
# #'   \item{Mjob_other}{whether the mother was employed as something other than
# #'         the specified job roles}
# #'   \item{Mjob_services}{whether the mother was employed in the service sector}
# #'   \item{Mjob_teacher}{whether the mother was employed as a teacher}
# #'   \item{Fjob_health}{whether the father was employed in health care}
# #'   \item{Fjob_other}{whether the father was employed as something other than
# #'         the specified job roles}
# #'   \item{Fjob_services}{whether the father was employed in the service sector}
# #'   \item{Fjob_teacher}{whether the father was employed as a teacher}
# #'   \item{reason_home}{school chosen for being close to home}
# #'   \item{reason_other}{school chosen for another reason}
# #'   \item{reason_rep}{school chosen for its reputation}
# #'   \item{nursery}{whether the student attended nursery school}
# #'   \item{internet}{Pwhether the student has internet access at home}
# #' }
# #'
# #' @source P. Cortez and A. Silva. Using Data Mining to Predict Secondary School
# #'   Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th
# #'   FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto,
# #'   Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
# #'   <http://www3.dsi.uminho.pt/pcortez/student.pdf>
# #' @source Dua, D. and Karra Taniskidou, E. (2017). UCI Machine Learning Repository
# #'  <http://archive.ics.uci.edu/ml>. Irvine, CA: University of California,
# #'  School of Information and Computer Science.
# "student"


# #' Binary Covtype 
# #'
# #' @section Preprocessing:
# #' Data has been preprocesses so as to have a binary response variable
# "binary_covtype"

# #' golub
# #'
# #' Gene expression data set. Binary response.
# #'
# #' @source https://statweb.stanford.edu/~tibs/strong/
# "golub"

# #' dorothea
# #'
# #' Binary response data set.
# #'
# #' @section UCI abstract: DOROTHEA is a drug discovery dataset. Chemical
# #' compounds represented
# #' by structural molecular features must be classified as active (binding
# #' to thrombin) or inactive. This is one of 5 datasets of the NIPS 2003
# #' feature selection challenge.
# #'
# #' @source https://archive.ics.uci.edu/ml/datasets/Dorothea
# #' @source https://statweb.stanford.edu/~tibs/strong/
# "dorothea"

# #' gisette
# #'
# #' Binary response data set.
# #'
# #' @section UCI abstract: GISETTE is a handwritten digit recognition problem.
# #' The problem is to separate the highly confusable digits '4' and '9'. This
# #' dataset is one of five datasets of the NIPS 2003 feature selection challenge.
# #'
# #' @source https://statweb.stanford.edu/~tibs/strong/
# #' @source https://archive.ics.uci.edu/ml/datasets/Gisette
# "gisette"

# #' cpusmall
# #'
# #' Regression data set from the LIBSVM database
# #'
# #' @source http://www.cs.toronto.edu/~delve/data/datasets.html
# #' @source https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html
# "cpusmall"