Author: Ni Zhao [aut], Haoyu Zhang [aut, cre]
Packaged: 2018-09-13 21:00:37 UTC; haoyuzhang

Title: Forward-in-Time Simulation and Tallying of Pairwise
person(given = c("Eric", "C."), "Anderson", , "eric.anderson@noaa.gov", role = c("aut", "cre"),
Description: Provides an R wrapper around the program 'spip'
program for the simulation of pedigrees within age-structured populations
with user-specified life histories.  Also includes a variety
of functions to parse 'spip' output to compile information about
related pairs amongst simulated, sampled individuals, to assess
the feasibility and potential accuracy of close-kin mark-recapture (CKMR).
and vignettes are mirrored at <https://eriqande.github.io/CKMRpop/index.html>
Imports: dplyr, ggforce, ggplot2, ggraph, igraph, magrittr, purrr, Rcpp
(>= 1.0.4), readr, stats, stringr, tibble, tidygraph, tidyr,
Author: Eric C. Anderson [aut, cre] (<https://orcid.org/0000-0003-1326-0840>)
Maintainer: Eric C. Anderson <eric.anderson@noaa.gov>

Title: Critical Line Algorithm in Pure R
Description: Implements 'Markovitz' Critical Line Algorithm ('CLA') for
classical mean-variance portfolio optimization.  Care has been taken for
correctness in light of previous buggy implementations.

Description: Implements 'Markovitz' Critical Line Algorithm ('CLA') for classical
mean-variance portfolio optimization, see Markovitz (1952) <doi:10.2307/2975974>.
Care has been taken for correctness in light of previous buggy implementations.

Imports: stats, grDevices, graphics, utils
Description: Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical
mean-variance portfolio optimization, see Markowitz (1952) <doi:10.2307/2975974>.

Suggests: fGarch, FRAPO, Matrix, sfsmisc

Title: CLAG: an unsupervised non hierarchical clustering algorithm
Author: Linda DIB, Raphael CHAMPEIMONT, Alessandra CARBONE
Description: CLAG (for CLusters AGgregation) is an unsupervised non
hierarchical clustering algorithm designed to cluster a large
variety of biological data and to provide a clustered matrix
and numerical values indicating cluster strength. CLAG
clusterizes correlation matrices for residues in protein
families, gene-expression and miRNA data related to various
cancer types, sets of species described by multidimensional
vectors of characters, binary matrices. It does not ask to all
data points to cluster and it converges yielding the same
result at each run. Its simplicity and speed allows it to run