patterns. Includes Connectivity function, Cumulative
connectivity function and clustering function, plus the triplet

Description: Graph based clustering summaries for spatial point patterns. Includes Connectivity function,
Cumulative connectivity function and clustering function, plus the triplet intensity function T (and some other).

Title: Spatial Graph Based Clustering Summaries for Spatial Point
Cumulative connectivity function and clustering function, plus the triangle/triplet intensity function T.

Description: Graph based clustering summaries for spatial point patterns.
Includes Connectivity function, Cumulative connectivity function and clustering
function, plus the triangle/triplet intensity function T.

Title: Fit a GLM (or cox model) with a combination of lasso and group
Author: Noah Simon, Jerome Friedman, Trevor Hastie, and Rob Tibshirani
Description: Fit a regularized generalized linear model via penalized
maximum likelihood.  The model is fit for a path of values of
the penalty parameter. Fits linear, logistic and Cox models.

Title: Fit a GLM (or Cox Model) with a Combination of Lasso and Group
Packaged: 2018-12-21 00:28:03 UTC; noahsimon

Packaged: 2019-09-27 17:27:56 UTC; noahsimon

Package: SGP
Version: 0.0-1
Date: 2008-10-16
Title: Student Growth Percentile and Percentile Growth Projection/Trajectory Functions
Depends: R (>= 2.7.0), splines, quantreg
Author: Damian W. Betebenner <dbetebenner@nciea.org>,
with contributions from Jonathan Weeks, Jinnie Choi, Xin Wei, and Hi Shin Shim
Maintainer: Damian W. Betebenner <dbetebenner@nciea.org>
Description: Functions to calculate growth percentile and projections for students using large scale, longitudinal assessment data.
These norm referenced growth values are presently used in some state testing and accountability systems. The functions use
quantile regression techniques package to estimate the conditional density associated with each students achievement history.
Student Growth Projections (i.e., percentile growth trajectories) are calculated using the coefficient matrices derived from
the quantile regression analyses and help to identify what it will take for students to reach future achievement targets.
LazyLoad: Yes
LazyData: Yes
License: Creative Commons Attribution + ShareAlike (by-sa)
Packaged: Thu Oct 16 13:51:06 2008; damian