Package: likeLTD
Title: Tools to Evaluate DNA Profile Evidence
Description: Tools to determine DNA profile Weight of Evidence.
For further information see the likeLTD guide provided,
or Balding, D.J. (2013) <DOI:10.1073/pnas.1219739110>.
Depends: R (>= 2.10), DEoptim, ggplot2, gtools, rtf
Suggests: svUnit, scales
Imports: gdata, tools, tcltk
Version: 6.1.0
Date: 2016-05-30
Author: David Balding, Adrian Timpson, Christopher Steele, Mayeul d'Avezac, James Hetherington.
Maintainer: Christopher Steele <c.steele.11@ucl.ac.uk>
License: GPL-3
URL: https://sites.google.com/site/baldingstatisticalgenetics/
NeedsCompilation: yes
Packaged: 2016-05-30 14:20:37 UTC; csteele
Repository: CRAN
Date/Publication: 2016-05-30 16:58:13
Package: IBHM
Type: Package
Title: Approximation using the IBHM method
Version: 1.1-11
Date: 2014-01-18
Author: Pawel Zawistowski
Maintainer: Pawel Zawistowski <dratewka@gmail.com>
Description: Implementation of an incremental model construction method called IBHM which
stands for Incrementally Built Heterogeneous Model. The method is designed for solving
real number approximation problems in a highly automated fashion.
License: GPL (>= 2)
Depends: R (>= 2.15.2), compiler, DEoptim (>= 2.2-1), cmaes (>= 1.0-11), Rcpp (>= 0.10.3), methods (>= 3.0.1)
LinkingTo: Rcpp
RcppModules: evaluator
Packaged: 2014-01-18 22:41:46 UTC; pz
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-01-19 00:29:25
Package: EcoHydRology
Version: 0.4.12
Title: A community modeling foundation for Eco-Hydrology.
Author: Fuka DR, Walter MT, Archibald JA, Steenhuis TS, and Easton ZM
Maintainer: Daniel Fuka <drf28@cornell.edu>
Depends: R (>= 2.10), operators, topmodel, DEoptim, XML
Description: This package provides a flexible foundation for scientists,
engineers, and policy makers to base teaching exercises as well as for
more applied use to model complex eco-hydrological interactions.
License: GPL-2
Repository: CRAN
Date/Publication: 2014-04-04 08:09:55
KeepSource: TRUE
Packaged: 2014-04-03 23:55:45 UTC; dan
NeedsCompilation: no
Package: galts
Type: Package
Title: Genetic algorithms and C-steps based LTS (Least Trimmed Squares)
estimation
Version: 1.3
Date: 2013-02-06
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman@istanbul.edu.tr>
Description: This package includes the ga.lts function that estimates
LTS (Least Trimmed Squares) parameters using genetic algorithms
and C-steps. ga.lts() constructs a genetic algorithm to form a
basic subset and iterates C-steps as defined in Rousseeuw and
van-Driessen (2006) to calculate the cost value of the LTS
criterion. OLS(Ordinary Least Squares) regression is known to
be sensitive to outliers. A single outlying observation can
change the values of estimated parameters. LTS is a resistant
estimator even the number of outliers is up to half of the
data. This package is for estimating the LTS parameters with
lower bias and variance in a reasonable time. Version 1.3
included the function medmad for fast outlier detection in
linear regression.
Depends: genalg, DEoptim
Repository: CRAN
License: GPL
LazyLoad: yes
Packaged: 2013-02-06 20:25:43 UTC; hako
Date/Publication: 2013-02-07 09:27:39
Package: selectMeta
Type: Package
Title: Estimation of Weight Functions in Meta Analysis
Version: 1.0.8
Date: 2015-07-03
Author: Kaspar Rufibach <kaspar.rufibach@gmail.com>
Maintainer: Kaspar Rufibach <kaspar.rufibach@gmail.com>
Depends: DEoptim (>= 2.0-6)
Imports: graphics, stats
Description: Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
License: GPL (>= 2)
URL: http://www.kasparrufibach.ch
Packaged: 2015-07-03 09:52:15 UTC; rufibach
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-07-03 12:51:10
Package: quickpsy
Type: Package
Title: Fits Psychometric Functions for Multiple Groups
Version: 0.1.3
Authors@R: c(
person("Linares", "Daniel", email = "danilinares@gmail.com", role = c("aut","cre")),
person("López-Moliner", "Joan", email = "j.lopezmoliner@gmail.com", role = "aut"))
URL: http://dlinares.org/quickpsy.html
Description: Quickly fits and plots psychometric functions (normal, logistic,
Weibull or any or any function defined by the user) for multiple groups.
Depends: R (>= 3.1.2), DEoptim, dplyr, ggplot2
Imports: MPDiR
Encoding: UTF-8
License: MIT + file LICENSE
LazyData: true
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-06-13 14:47:37 UTC; daniellinares
Author: Linares Daniel [aut, cre],
López-Moliner Joan [aut]
Maintainer: Linares Daniel <danilinares@gmail.com>
Repository: CRAN
Date/Publication: 2016-06-13 16:57:55
providing 
.