Fri, 24 Nov 2017

Package tscount updated to version 1.4.1 with previous version 1.4.0 dated 2017-09-30

Title: Analysis of Count Time Series
Description: Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
Author: Tobias Liboschik [aut, cre], Roland Fried [aut], Konstantinos Fokianos [aut], Philipp Probst [aut], Jonathan Rathjens [ctb]
Maintainer: Tobias Liboschik <liboschik@statistik.tu-dortmund.de>

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Package TrajDataMining updated to version 0.1.4 with previous version 0.1.3 dated 2017-11-23

Title: Trajectories Data Mining
Description: Contains a set of methods for trajectory data preparation, such as filtering, compressing and clustering, and for trajectory pattern discovery.
Author: Diego Monteiro [aut, cre], Karine Reis [ctb], Carlos Alberto [ctb], Felipe Carvalho [ctb]
Maintainer: Diego Monteiro <dvm1607@gmail.com>

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Package ThresholdROC updated to version 2.6 with previous version 2.5 dated 2017-08-01

Title: Threshold Estimation
Description: Point and interval estimations of optimum thresholds for continuous diagnostic tests (two- and three- state settings).
Author: Sara Perez-Jaume, Natalia Pallares, Konstantina Skaltsa
Maintainer: Sara Perez-Jaume <spjaume@gmail.com>

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Package tgram updated to version 0.2-3 with previous version 0.2-2 dated 2013-07-24

Title: Compute and Plot Tracheidograms
Description: Functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.
Author: Marcelino de la Cruz and Lucia DeSoto
Maintainer: Marcelino de la Cruz <marcelino.delacruz@urjc.es>

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Package SNFtool updated to version 2.2.1 with previous version 2.2 dated 2014-09-08

Title: Similarity Network Fusion
Description: Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Author: Bo Wang, Aziz Mezlini, Feyyaz Demir, Marc Fiume, Zhuowen Tu, Michael Brudno, Benjamin Haibe-Kains, Anna Goldenberg
Maintainer: Daniel Cole <goldenberglab@gmail.com>

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Package Rramas updated to version 0.1-5 with previous version 0.1-4 dated 2014-01-28

Title: Matrix Population Models
Description: Imitating the 'Ramas' (c) software, analyzes and predicts from matrix population models, (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.
Author: Marcelino de la Cruz
Maintainer: Marcelino de la Cruz <marcelino.delacruz@urjc.es>

Diff between Rramas versions 0.1-4 dated 2014-01-28 and 0.1-5 dated 2017-11-24

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Package rmcfs updated to version 1.2.7 with previous version 1.2.6 dated 2017-07-03

Title: The MCFS-ID Algorithm for Feature Selection and Interdependency Discovery
Description: MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M.Draminski, A.Rada-Iglesias, S.Enroth, C.Wadelius, J. Koronacki, J.Komorowski (2008) <doi:10.1093/bioinformatics/btm486>.
Author: Michal Draminski [aut, cre], Jacek Koronacki [aut], Julian Zubek [ctb]
Maintainer: Michal Draminski <michal.draminski@ipipan.waw.pl>

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Package Rdimtools updated to version 0.1.3 with previous version 0.1.2 dated 2017-11-13

Title: Dimension Reduction and Estimation Methods
Description: We provide a rich collection of linear and nonlinear dimension reduction techniques implemented using 'RcppArmadillo'. The question on what we should use as the target dimension is addressed by intrinsic dimension estimation methods introduced as well. For more details on dimensionality techniques, see the paper by Ma and Zhu (2013) <doi:10.1111/j.1751-5823.2012.00182.x> if you are interested in statistical approach, or Engel, Huttenberger, and Hamann (2012) <doi:10.4230/OASIcs.VLUDS.2011.135> for a broader cross-disciplinary overview.
Author: Kisung You [aut, cre], Donggun Kim [ctb], Yoon Mo Jung [ctb]
Maintainer: Kisung You <kyou@nd.edu>

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Package inferference updated to version 1.0.0 with previous version 0.5.4 dated 2017-09-25

Title: Methods for Causal Inference with Interference
Description: Provides methods for estimating causal effects in the presence of interference described in B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.
Author: Bradley Saul
Maintainer: Bradley Saul <bradleysaul@gmail.com>

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Package ecespa updated to version 1.1-9 with previous version 1.1-8 dated 2015-07-04

Title: Functions for Spatial Point Pattern Analysis
Description: Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on 'spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) <doi:10.1111/j.0906-7590.2008.05299.x> and Olano et al. (2009) <doi:10.1051/forest:2008074>.
Author: Marcelino de la Cruz Rot, with contributions of Philip M. Dixon and Jose M. Blanco-Moreno
Maintainer: Marcelino de la Cruz Rot <marcelino.delacruz@urjc.es>

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Package dixon updated to version 0.0-6 with previous version 0.0-5 dated 2013-03-13

Title: Nearest Neighbour Contingency Table Analysis
Description: Function to test spatial segregation and association based in contingency table analysis of nearest neighbour counts following Dixon (2002) <doi:10.1080/11956860.2002.11682700>. Some 'Fortran' code has been included to the original dixon2002() function of the 'ecespa' package to improve speed.
Author: Marcelino de la Cruz Rot and Philip M. Dixon
Maintainer: Marcelino de la Cruz Rot <marcelino.delacruz@urjc.es>

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Package venn updated to version 1.5 with previous version 1.2 dated 2016-05-10

Title: Draw Venn Diagrams
Description: Draws and displays Venn diagrams up to 7 sets, and any Boolean union of set intersections.
Author: Adrian Dusa [aut, cre, cph]
Maintainer: Adrian Dusa <dusa.adrian@unibuc.ro>

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Package replicatedpp2w updated to version 0.1-2 with previous version 0.1-1 dated 2015-04-29

Title: Two-Way ANOVA-Like Method to Analyze Replicated Point Patterns
Description: Test for effects of both individual factors and their interaction on replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.
Author: Marcelino de la Cruz Rot
Maintainer: Marcelino de la Cruz <marcelino.delacruz@urjc.es>

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Package unitizer updated to version 1.4.5 with previous version 1.4.4 dated 2017-09-01

Title: Interactive R Unit Tests
Description: Simplifies regression tests by comparing objects produced by test code with earlier versions of those same objects. If objects are unchanged the tests pass, otherwise execution stops with error details. If in interactive mode, tests can be reviewed through the provided interactive environment.
Author: Brodie Gaslam [aut, cre]
Maintainer: Brodie Gaslam <brodie.gaslam@yahoo.com>

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Package tmaptools updated to version 1.2-2 with previous version 1.2-1 dated 2017-05-11

Title: Thematic Map Tools
Description: Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps.
Author: Martijn Tennekes [aut, cre]
Maintainer: Martijn Tennekes <mtennekes@gmail.com>

Diff between tmaptools versions 1.2-1 dated 2017-05-11 and 1.2-2 dated 2017-11-24

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Package reticulate updated to version 1.3.1 with previous version 1.3 dated 2017-11-16

Title: R Interface to Python
Description: R interface to Python modules, classes, and functions. When calling into Python R data types are automatically converted to their equivalent Python types. When values are returned from Python to R they are converted back to R types. Compatible with all versions of Python >= 2.7.
Author: JJ Allaire [aut, cre], Kevin Ushey [ctb], RStudio [cph, fnd], Yuan Tang [aut, cph], Dirk Eddelbuettel [ctb, cph], Bryan Lewis [ctb, cph], Marcus Geelnard [aut, cph] (TinyThread library, http://tinythreadpp.bitsnbites.eu/)
Maintainer: JJ Allaire <jj@rstudio.com>

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Package RcppThread updated to version 0.1.2 with previous version 0.1.1 dated 2017-11-07

Title: R-Friendly Threading in C++
Description: Provides a C++11-style thread class and thread pool that can safely be interrupted from R.
Author: Thomas Nagler [aut, cre]
Maintainer: Thomas Nagler <mail@tnagler.com>

Diff between RcppThread versions 0.1.1 dated 2017-11-07 and 0.1.2 dated 2017-11-24

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Package LAM updated to version 0.1-22 with previous version 0.0-17 dated 2017-05-11

Title: Some Latent Variable Models
Description: Contains some procedures for latent variable modelling with a particular focus on multilevel data. The LAM package contains mean and covariance structure modelling for multivariate normally distributed data ('mlnormal'), a general Metropolis-Hastings algorithm ('amh') and penalized maximum likelihood estimation ('pmle').
Author: Alexander Robitzsch [aut,cre]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

Diff between LAM versions 0.0-17 dated 2017-05-11 and 0.1-22 dated 2017-11-24

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Package covTestR updated to version 0.1.1 with previous version 0.1.0 dated 2017-11-22

Title: Covariance Matrix Tests
Description: Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance matrix testing described by Schott (2007) <doi:10.1016/j.csda.2007.03.004> and high-dimensional one-sample tests of covariance matrix structure described by Fisher, et al. (2010) <doi:10.1016/j.jmva.2010.07.004>. Covariance matrix tests use C++ to speed performance and allow larger data sets.
Author: Ben Barnard [aut, cre], Dean Young [aut]
Maintainer: Ben Barnard <ben_barnard@outlook.com>

Diff between covTestR versions 0.1.0 dated 2017-11-22 and 0.1.1 dated 2017-11-24

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More information about covTestR at CRAN
Permanent link

Package cctools updated to version 0.1.1 with previous version 0.1.0 dated 2017-04-26

Title: Tools for the Continuous Convolution Trick in Nonparametric Estimation
Description: Implements the uniform scaled beta distribution and the continuous convolution kernel density estimator.
Author: Thomas Nagler [aut, cre]
Maintainer: Thomas Nagler <thomas.nagler@tum.de>

Diff between cctools versions 0.1.0 dated 2017-04-26 and 0.1.1 dated 2017-11-24

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Package STARTS updated to version 0.2-10 with previous version 0.1-51 dated 2017-08-27

Title: Functions for the STARTS Model
Description: Contains functions for estimating the STARTS model of Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>, <DOI:10.1037/10409-008>.
Author: Alexander Robitzsch [aut,cre], Oliver Luedtke [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

Diff between STARTS versions 0.1-51 dated 2017-08-27 and 0.2-10 dated 2017-11-24

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New package gpHist with initial version 0.1
Package: gpHist
Type: Package
Title: Gaussian Process with Histogram Intersection Kernel
Version: 0.1
Date: 2017-11-24
Author: Dennis Becker
Maintainer: Dennis Becker <dbecker@leuphana.de>
Description: Provides an implementation of a Gaussian process regression with a histogram intersection kernel (HIK) and utilizes approximations to speed up learning and prediction. In contrast to a squared exponential kernel, an HIK provides advantages such as linear memory and learning time requirements. However, the HIK only provides a piecewise-linear approximation of the function. Furthermore, the number of estimated eigenvalues is reduced. The eigenvalues and vectors are required for the approximation of the log-likelihood function as well as the approximation of the predicted variance of new samples. This package provides approximations for a single eigenvalue as well as multiple. Further information of the variance and log-likelihood approximation, as well as the Gaussian process with HIK, can be found in the paper by Rodner et al. (2016) <doi:10.1007/s11263-016-0929-y>.
License: GPL (>= 2)
LazyLoad: yes
NeedsCompilation: yes
Packaged: 2017-11-24 13:19:29 UTC; dennis
Repository: CRAN
Date/Publication: 2017-11-24 14:03:57 UTC

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New package fmriqa with initial version 0.1.0
Package: fmriqa
Type: Package
Title: Functional MRI Quality Assurance Routines
Version: 0.1.0
Date: 2017-11-22
Authors@R: person("Martin", "Wilson", email = "martin@pipegrep.co.uk", role = c("cre","aut"))
Description: Methods for performing fMRI quality assurance (QA) measurements of test objects. Heavily based on the fBIRN procedures detailed by Friedman and Glover (2006) <doi:10.1002/jmri.20583>.
BugReports: https://github.com/martin3141/fmriqa/issues
License: GPL-3
LazyData: true
RoxygenNote: 6.0.1
Imports: viridisLite, RNifti, ggplot2, reshape2, gridExtra, grid, tidyr, optparse, tcltk, RcppEigen, pracma
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2017-11-24 09:40:04 UTC; wilsonmp
Author: Martin Wilson [cre, aut]
Maintainer: Martin Wilson <martin@pipegrep.co.uk>
Repository: CRAN
Date/Publication: 2017-11-24 13:51:41 UTC

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Package fDMA updated to version 2.2.1 with previous version 2.2 dated 2017-11-15

Title: Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes
Description: Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.
Author: Krzysztof Drachal [aut, cre] (Faculty of Economic Sciences, University of Warsaw, Poland)
Maintainer: Krzysztof Drachal <kdrachal@wne.uw.edu.pl>

Diff between fDMA versions 2.2 dated 2017-11-15 and 2.2.1 dated 2017-11-24

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Package Aoptbdtvc updated to version 0.0.2 with previous version 0.0.1 dated 2017-11-22

Title: A-Optimal Block Designs for Comparing Test Treatments with Controls
Description: A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.
Author: Baidya Nath Mandal [aut, cre], Sukanta Dash [aut], Rajender Parsad [aut]
Maintainer: Baidya Nath Mandal <mandal.stat@gmail.com>

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Package vdmR updated to version 0.2.5 with previous version 0.2.4 dated 2017-06-23

Title: Visual Data Mining Tools
Description: This provides web-based visual data-mining tools by adding interactive functions to 'ggplot2' graphics. Brushing and linking between the multiple plots is one of the main feature of this package. Currently scatter plots, histograms, parallel coordinate plots and choropleth maps are supported.
Author: Tomokazu Fujino
Maintainer: Tomokazu Fujino <fujino@fwu.ac.jp>

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Package tmap updated to version 1.11 with previous version 1.10 dated 2017-05-11

Title: Thematic Maps
Description: Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
Author: Martijn Tennekes [aut, cre], Joel Gombin [ctb], Sebastian Jeworutzki [ctb], Kent Russell [ctb], Richard Zijdeman [ctb], John Clouse [ctb]
Maintainer: Martijn Tennekes <mtennekes@gmail.com>

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Package qle updated to version 0.16-6 with previous version 0.16-5 dated 2017-11-16

Title: Simulation-Based Quasi-Likelihood Estimation
Description: A simulation-based quasi-likelihood method (Baaske, M., Ballani, F., v.d. Boogaart, K.G. (2014) <doi:10.5566/ias.v33.p107-119>) for parameter estimation of parametric statistical models for which closed-form representations of distributional characteristics are unavailable and can only be obtained by computationally intensive simulations of the model.
Author: Markus Baaske [aut, cre, cph], K. Gerald van den Boogaart [ths]
Maintainer: Markus Baaske <markus.baaske@uni-jena.de>

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Package loa updated to version 0.2.43.1 with previous version 0.2.38 dated 2016-03-01

Title: Lattice Options and Add-Ins
Description: Various plots and functions that make use of the lattice/trellis plotting framework. The plots, which include loaPlot(), GoogleMap() and trianglePlot(), use panelPal(), a function that extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel and panel-to-key synchronization/management.
Author: Karl Ropkins
Maintainer: Karl Ropkins <k.ropkins@its.leeds.ac.uk>

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Package ANN2 updated to version 1.4 with previous version 1.3 dated 2017-11-16

Title: Artificial Neural Networks for Anomaly Detection
Description: Training of general classification and regression neural networks using gradient descent. Special features include a function for training replicator neural networks and a function for training autoencoders. Multiple activation and cost functions (including Huber and pseudo-Huber) are supported, as well as L1 and L2 regularization, momentum, early stopping and the possibility to specify a learning rate schedule. The package contains a vectorized gradient descent implementation which facilitates faster training through batch learning.
Author: Bart Lammers
Maintainer: Bart Lammers <bart.f.lammers@gmail.com>

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Package lime updated to version 0.3.1 with previous version 0.3.0 dated 2017-09-15

Title: Local Interpretable Model-Agnostic Explanations
Description: When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) <arXiv:1602.04938>.
Author: Thomas Lin Pedersen [cre, aut], Michaƫl Benesty [aut]
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>

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Package galts updated to version 1.3.1 with previous version 1.3 dated 2013-02-07

Title: Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares) Estimation
Description: 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 includes the function medmad for fast outlier detection in linear regression.
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman@istanbul.edu.tr>

Diff between galts versions 1.3 dated 2013-02-07 and 1.3.1 dated 2017-11-24

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New package SteinerNet with initial version 2.0
Package: SteinerNet
Version: 2.0
Date: 2017-11-23
Title: Steiner Tree Approach for Graph Analysis
License: GPL-3
Author: Afshin Sadeghi
Maintainer: Afshin Sadeghi <sadeghi.afshin@gmail.com>
Depends: R (>= 3.1)
Imports: igraph, limma, RBGL, graph, grDevices, stats, utils, graphics
Description: A set of graph functions to find Steiner trees on graphs. It provides tools for analysing Steiner tree application on networks. It has applications in biological pathway network analysis (Sadeghi 2013) <doi:10.1186/1471-2105-14-144>.
Packaged: 2017-11-23 18:08:11 UTC; afshin
Repository: CRAN
Date/Publication: 2017-11-24 09:16:38 UTC
NeedsCompilation: no
X-CRAN-Comment: Archived on 2013-09-07: it was never updated from igraph0 to igraph despite several reminders.

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Package DLMtool updated to version 5.0.1 with previous version 5.0 dated 2017-10-24

Title: Data-Limited Methods Toolkit
Description: Development, simulation testing, and implementation of management procedures for data-limited fisheries (see Carruthers et al (2014) <doi:10.1016/j.fishres.2013.12.014>).
Author: Tom Carruthers [aut, cre], Adrian Hordyk [aut]
Maintainer: Tom Carruthers <t.carruthers@fisheries.ubc.ca>

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 DLMtool-5.0.1/DLMtool/data/FlatE_NDom.RData            |binary
 DLMtool-5.0.1/DLMtool/data/FleetDescription.RData      |binary
 DLMtool-5.0.1/DLMtool/data/Generic_DecE.RData          |binary
 DLMtool-5.0.1/DLMtool/data/Generic_FlatE.RData         |binary
 DLMtool-5.0.1/DLMtool/data/Generic_IncE.RData          |binary
 DLMtool-5.0.1/DLMtool/data/Generic_fleet.RData         |binary
 DLMtool-5.0.1/DLMtool/data/Generic_obs.RData           |binary
 DLMtool-5.0.1/DLMtool/data/Gulf_blue_tilefish.RData    |binary
 DLMtool-5.0.1/DLMtool/data/Herring.RData               |binary
 DLMtool-5.0.1/DLMtool/data/ImpDescription.RData        |binary
 DLMtool-5.0.1/DLMtool/data/Imprecise_Biased.RData      |binary
 DLMtool-5.0.1/DLMtool/data/Imprecise_Unbiased.RData    |binary
 DLMtool-5.0.1/DLMtool/data/IncE_HDom.RData             |binary
 DLMtool-5.0.1/DLMtool/data/IncE_NDom.RData             |binary
 DLMtool-5.0.1/DLMtool/data/Low_Effort_Non_Target.RData |binary
 DLMtool-5.0.1/DLMtool/data/Mackerel.RData              |binary
 DLMtool-5.0.1/DLMtool/data/OMDescription.RData         |binary
 DLMtool-5.0.1/DLMtool/data/ObsDescription.RData        |binary
 DLMtool-5.0.1/DLMtool/data/Overages.RData              |binary
 DLMtool-5.0.1/DLMtool/data/Perfect_Imp.RData           |binary
 DLMtool-5.0.1/DLMtool/data/Perfect_Info.RData          |binary
 DLMtool-5.0.1/DLMtool/data/Porgy.RData                 |binary
 DLMtool-5.0.1/DLMtool/data/Precise_Biased.RData        |binary
 DLMtool-5.0.1/DLMtool/data/Precise_Unbiased.RData      |binary
 DLMtool-5.0.1/DLMtool/data/Red_snapper.RData           |binary
 DLMtool-5.0.1/DLMtool/data/Rockfish.RData              |binary
 DLMtool-5.0.1/DLMtool/data/Simulation_1.RData          |binary
 DLMtool-5.0.1/DLMtool/data/Snapper.RData               |binary
 DLMtool-5.0.1/DLMtool/data/Sole.RData                  |binary
 DLMtool-5.0.1/DLMtool/data/StockDescription.RData      |binary
 DLMtool-5.0.1/DLMtool/data/Target_All_Fish.RData       |binary
 DLMtool-5.0.1/DLMtool/data/Targeting_Small_Fish.RData  |binary
 DLMtool-5.0.1/DLMtool/data/Toothfish.RData             |binary
 DLMtool-5.0.1/DLMtool/data/ourReefFish.RData           |binary
 DLMtool-5.0.1/DLMtool/data/testOM.RData                |binary
 DLMtool-5.0.1/DLMtool/inst/Example_Chile_Hake.rmd      |only
 DLMtool-5.0.1/DLMtool/inst/Example_Chile_Hake.xlsx     |binary
 DLMtool-5.0.1/DLMtool/man/OM-class.Rd                  |   10 
 DLMtool-5.0.1/DLMtool/man/OMDescription.Rd             |    2 
 DLMtool-5.0.1/DLMtool/man/OMdoc.Rd                     |    2 
 DLMtool-5.0.1/DLMtool/man/OMinit.Rd                    |   10 
 DLMtool-5.0.1/DLMtool/man/SRAsim.Rd                    |   17 
 DLMtool-5.0.1/DLMtool/man/SampleStockPars.Rd           |    4 
 DLMtool-5.0.1/DLMtool/man/StochasticSRA.Rd             |   23 
 DLMtool-5.0.1/DLMtool/man/StochasticSRAcpp.Rd          |only
 DLMtool-5.0.1/DLMtool/man/Stock-class.Rd               |    2 
 DLMtool-5.0.1/DLMtool/man/StockDescription.Rd          |    2 
 DLMtool-5.0.1/DLMtool/src/LSRA_opt_cpp.cpp             |    1 
 DLMtool-5.0/DLMtool/inst/Example_Chile_hake_source.rmd |only
 DLMtool-5.0/DLMtool/man/StochasticSRA2.Rd              |only
 82 files changed, 597 insertions(+), 334 deletions(-)

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