Title: Design, Planning and Analysis of Observational Studies
Author: Sebastien Haneuse, Takumi Saegusa, Nilanjan Chaterjee, Norman Breslow
Description: A suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011)<doi:10.18637/jss.v043.i11>.
Packaged: 2020-11-15 20:30:21 UTC; sebastienhaneuse

Author: Steffen Kriewald, Till Fluschnik, Dominik Reusser, Diego Rybski
Description: 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).

Description: 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).

Title: Optimal Subset Cardinality Regression (OSCAR) Models Using the
person(given="Teemu Daniel", family="Laajala", role=c("aut", "cre"), email="teelaa@utu.fi", comment = c(ORCID = "0000-0002-7016-7354")),
Description: 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
Memory Bundle Method for large-scale nonsmooth optimization' as
Multiple regression model families are supported: Cox, logistic,
Imports: graphics, grDevices, hamlet, Matrix, methods, stats, survival,
Suggests: ePCR, glmnet, knitr, pROC, rmarkdown