Michal Czajkowski, Ryan Gill, Greg Rempala
Fits and tests logistic joinpoint models.

Leveraging Learning to Automatically Manage Algorithms
Lars Kotthoff, contributions by Barry Hurley
Provides functionality to train and evaluate algorithm
selection models for portfolios.
RWeka, FSelector, e1071, flexclust, testthat

Lars Kotthoff [aut,cre],
Bernd Bischl [aut],
Barry Hurley [ctb],
Talal Rahwan [ctb],
Damir Pulatov [ctb]
Provides functionality to train and evaluate algorithm selection models for portfolios.
rJava, parallelMap, ggplot2, checkmate, BBmisc, plyr,
data.table

Lars Kotthoff [aut, cre],
Barry Hurley [ctb],
Talal Rahwan [ctb]

Lars Kotthoff [aut,cre],
Bernd Bischl [aut],
Barry Hurley [ctb],
Talal Rahwan [ctb]

rJava, parallelMap, ggplot2, checkmate, BBmisc, plyr

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"))
Sara Alexandre Fonsêca [aut],
Rosineide Fernando da Paz [aut, cre],
Jorge Luís Bazán [ctb]
Rosineide Fernando da Paz <rfpaz2@gmail.com>
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>.
llogistic, rstan, MCMCpack, MASS, coda, stats

Holger Diedrich, Dr. Markus Abel (Department of Physics,
University Potsdam)
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
of the intrinsic dimension etc.

Local Log-linear Models for Capture-Recapture
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
way that is closely related to LLLMs.