Steffen Kriewald, Till Fluschnik, Dominik Reusser, Diego Rybski
Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for matrix, raster and single coordinates on a plain (euclidean distance) or on a sphere (great-circle or orthodromic distance).

Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance).

Optimal Subset Cardinality Regression (OSCAR) Models Using the
L0-Pseudonorm
c(
person(given="Teemu Daniel", family="Laajala", role=c("aut", "cre"), email="teelaa@utu.fi", comment = c(ORCID = "0000-0002-7016-7354")),
person(given="Kaisa", family="Joki", role=c("aut"), email="kjjoki@utu.fi"),
person(given="Anni", family="Halkola", role=c("aut"), email="ansuha@utu.fi"))
Optimal Subset Cardinality Regression (OSCAR) models offer
regularized linear regression using the L0-pseudonorm, conventionally
known as the number of non-zero coefficients. The package estimates an
optimal subset of features using the L0-penalization via
cross-validation, bootstrapping and visual diagnostics. Effective
Fortran implementations are offered along the package for finding
optima for the DC-decomposition, which is used for transforming the
discrete L0-regularized optimization problem into a continuous
non-convex optimization task. These optimization modules include DBDC
('Double Bundle method for nonsmooth DC optimization' as described in
Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited
Memory Bundle Method for large-scale nonsmooth optimization' as
in Haarala et al. (2004) <doi:10.1080/10556780410001689225>).
Multiple regression model families are supported: Cox, logistic,
and Gaussian.
graphics, grDevices, hamlet, Matrix, methods, stats, survival,
utils
Teemu Daniel Laajala [aut, cre]
(<https://orcid.org/0000-0002-7016-7354>),
Kaisa Joki [aut],
Anni Halkola [aut]