Author: Michal Czajkowski, Ryan Gill, Greg Rempala
Description: Fits and tests logistic joinpoint models.
Packaged: Wed Jun 13 04:26:11 2007; rsgill01

Packaged: Mon Jul 16 13:44:00 2007; stats

Packaged: Thu Feb 21 20:54:16 2008; rsgill01

Packaged: Wed May  7 11:31:58 2008; root

Title: Leveraging Learning to Automatically Manage Algorithms
Author: Lars Kotthoff, contributions by Barry Hurley
Description: Provides functionality to train and evaluate algorithm
Suggests: RWeka, FSelector, e1071, flexclust, testthat

Description: Provides functionality to train and evaluate algorithm selection models for portfolios.
Imports: rJava, parallelMap, ggplot2, checkmate, BBmisc, plyr,

Imports: rJava, parallelMap, ggplot2, checkmate

Imports: rJava, parallelMap, ggplot2, checkmate, BBmisc, plyr

Title: The L-Logistic Bayesian Regression
Authors@R: c(person("Sara", "Alexandre Fonsêca", role = "aut", email = "saralexandre@alu.ufc.br"),
person("Rosineide", "Fernando da Paz", role = c("aut", "cre"), email = "rfpaz2@gmail.com"),
person("Jorge Luís", "Bazán", role = "ctb"))
Maintainer: Rosineide Fernando da Paz <rfpaz2@gmail.com>
Description: R functions and data sets for the work Paz, R.F., Balakrishnan, N and Bazán, J.L. (2018). L-logistic regression models: Prior sensitivity analysis, robustness to outliers and applications. Brazilian Journal of Probability and Statistics, <https://www.imstat.org/wp-content/uploads/2018/05/BJPS397.pdf>.
Imports: llogistic, rstan, MCMCpack, MASS, coda, stats

Author: Holger Diedrich, Dr. Markus Abel (Department of Physics,
Description: LLE is a non-linear algorithm for mapping high-dimensional
data into a lower dimensional (intrinsic) space. This package
provides the main functions to performs the LLE alogrithm
including some enhancements like subset selection, calculation

Title: Local Log-linear Models for Capture-Recapture
Description: Applies local log-linear capture-recapture models (LLLMs) a for
closed populations, as described in the ongoing doctoral thesis work of
Zachary Kurtz in the department of statistics at Carnegie Mellon
University.  The method is relevant when there are 3-4 capture occasions,
with auxiliary covariates available for all capture occasions.  As part of
estimating the number of missing population units, the method estimates the
"rate of missingness" as it varies over the covariate space.  In addition,
user-friendly functions are provided to recreate (approximately) the method
of Zwane and Van der Heijden (2004), which applied the VGAM package in a
Imports: data.table, combinat, VGAM, plyr