Title: Locally Scaled Density Based Clustering
Author: Fella Ulandari and Robert Kurniawan
Description: Implementation of Locally Scaled Density Based Clustering (LSDBC) algorithm proposed by Bicici and Yuret (2007) <doi:10.1007/978-3-540-71618-1_82>. This package also contains some supporting functions such as betaCV() function and get_spectral() function.

Title: Fixed effect panel data regression
Description: Fit a least-squares dummy variable regression model

Title: Least square dummy variable regression
Description: Fit a least square dummy variable regression

Title: Solving Least Squares Problems under Equality/Inequality
person("Yong", "Wang", email = "yongwang@auckland.ac.nz", role = c("aut","cre")),
person("Charles L.", "Lawson", role = "aut"),
person("Richard J.", "Hanson", role = "aut"))
Description: It contains functions that solve least squares linear
regression problems under linear equality/inequality
constraints. It is developed based on the 'Fortran' program of
Lawson and Hanson (1974, 1995), which is public domain and

Title: Solving Least Squares or Quadratic Programming Problems under
constraints. Functions for solving quadratic programming
problems are also available, which transform such problems
into least squares ones first. It is developed based on the
'Fortran' program of Lawson and Hanson (1974, 1995), which is
X-CRAN-Comment: Orphaned and corrected on 2020-05-06 as check problems

Description: Linear multiple output using sparse group lasso
LinkingTo: sglOptim, Rcpp, RcppProgress, RcppArmadillo, BH
Collate: 'lsgl_fit.R' 'lsgl_cv.R' 'lsgl_predict.R' 'lsgl_config.R'

Description: Linear multiple output using sparse group lasso. The algorithm finds the sparse group lasso penalized maximum likelihood estimator. This result in feature and parameter selection, and parameter estimation. Use of multiple processors for cross validation and subsampling is supported through OpenMP. Development version is on github.
Depends: R (>= 3.0.0), sglOptim (== 1.2.0), Matrix

Description: Linear multiple output using sparse group lasso. The algorithm
finds the sparse group lasso penalized maximum likelihood estimator. This result
in feature and parameter selection, and parameter estimation. Use of multiple
processors for cross validation and subsampling is supported through OpenMP.
Depends: R (>= 3.0.0), sglOptim (== 1.2.2), Matrix