Tue, 13 Feb 2018

Package PUlasso updated to version 2.2 with previous version 2.1 dated 2018-01-03

Title: High-Dimensional Variable Selection with Presence-Only Data
Description: Efficient algorithm for solving PU (Positive and Unlabelled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing via 'OpenMP' are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2017) <arXiv:1711.08129>.
Author: Hyebin Song [aut, cre], Garvesh Raskutti [aut]
Maintainer: Hyebin Song <hsong56@wisc.edu>

Diff between PUlasso versions 2.1 dated 2018-01-03 and 2.2 dated 2018-02-13

 DESCRIPTION                    |   14 +--
 MD5                            |   36 ++++----
 NAMESPACE                      |    1 
 R/RcppExports.R                |   12 --
 R/cv.grpPUlasso.R              |  167 +----------------------------------------
 R/deviances.R                  |   28 ------
 R/grpPUlasso.R                 |  113 ---------------------------
 inst/doc/PUlasso-vignette.R    |    7 -
 inst/doc/PUlasso-vignette.Rmd  |   15 ---
 inst/doc/PUlasso-vignette.html |   40 +++------
 man/cv.grpPUlasso.Rd           |    2 
 man/grpPUlasso.Rd              |    2 
 src/RcppExports.cpp            |   70 -----------------
 src/cppWrappers.cpp            |   95 +----------------------
 src/cv_LUfit.cpp               |   10 +-
 src/groupLasso.cpp             |   16 +++
 src/groupLasso.h               |    1 
 tests/testthat/test.R          |   70 +++++++++++++----
 vignettes/PUlasso-vignette.Rmd |   15 ---
 19 files changed, 138 insertions(+), 576 deletions(-)

More information about PUlasso at CRAN
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Package IATscores updated to version 0.2.1 with previous version 0.1-2 dated 2015-05-19

Title: Implicit Association Test Scores Using Robust Statistics
Description: Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).
Author: Giulio Costantini
Maintainer: Giulio Costantini <costantinigiulio@gmail.com>

Diff between IATscores versions 0.1-2 dated 2015-05-19 and 0.2.1 dated 2018-02-13

 DESCRIPTION              |   18 ++---
 MD5                      |   27 ++++----
 NAMESPACE                |   18 +++--
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 R/RobustScores.R         |   55 +++++++++++++++-
 R/SplitHalf.R            |   48 ++++++++++++++
 R/TestRetest.R           |   51 ++++++++++++++-
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 man/Pretreatment.Rd      |   53 +++++++++++++++
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 man/SplitHalf.Rd         |  137 ++++++++++++++++++++++++++++++++++++++++-
 man/TestRetest.Rd        |  130 ++++++++++++++++++++++++++++++++++++++
 man/Tgraph.Rd            |    3 
 16 files changed, 677 insertions(+), 64 deletions(-)

More information about IATscores at CRAN
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Package neldermead updated to version 1.0-11 with previous version 1.0-10 dated 2015-01-11

Title: R Port of the 'Scilab' Neldermead Module
Description: Provides several direct search optimization algorithms based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed shape simplex), the variable shape simplex method of Nelder and Mead (unconstrained optimization with a variable shape simplex made), and Box's complex method (constrained optimization with a variable shape simplex).
Author: Sebastien Bihorel, Michael Baudin (author of the original module)
Maintainer: Sebastien Bihorel <sb.pmlab@gmail.com>

Diff between neldermead versions 1.0-10 dated 2015-01-11 and 1.0-11 dated 2018-02-13

 Changelog                        |    5 +
 DESCRIPTION                      |   12 ++--
 MD5                              |  104 +++++++++++++++++++--------------------
 NAMESPACE                        |    1 
 R/boxlinesearch.R                |    2 
 R/costf.transposex.R             |    2 
 R/fmin.gridsearch.R              |    2 
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 54 files changed, 115 insertions(+), 107 deletions(-)

More information about neldermead at CRAN
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New package umx with initial version 2.0.2
Package: umx
Version: 2.0.2
Date: 2018-01-29
Title: Structural Equation and Twin Modeling in R
Authors@R: c(person("Timothy C", "Bates", role = c("aut", "cre"), email = "timothy.c.bates@gmail.com"))
Maintainer: Timothy C Bates <timothy.c.bates@gmail.com>
License: GPL-3
Encoding: UTF-8
URL: http://github.com/tbates/umx
Description: Quickly create, run, and report structural equation and twin models. See '?umx' to learn more.
Depends: R (>= 3.2.0), OpenMx (>= 2.7.9)
Imports: cowplot, ggplot2, DiagrammeR, knitr, MASS, Matrix, mvtnorm, nlme, numDeriv, polycor, R2HTML, RCurl, sfsmisc, xtable, methods, utils
Suggests: cocor, devtools, gdata, Hmisc, testthat, rmarkdown
BugReports: http://github.com/tbates/umx/issues
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-02-13 22:25:03 UTC; tim
Author: Timothy C Bates [aut, cre]
Repository: CRAN
Date/Publication: 2018-02-13 23:40:21 UTC

More information about umx at CRAN
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Package SIS updated to version 0.8-6 with previous version 0.8-4 dated 2017-04-21

Title: Sure Independence Screening
Description: Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) and all of its variants in generalized linear models and the Cox proportional hazards model.
Author: Jianqing Fan, Yang Feng, Diego Franco Saldana, Richard Samworth, Yichao Wu
Maintainer: Yang Feng <yang.feng@columbia.edu>

Diff between SIS versions 0.8-4 dated 2017-04-21 and 0.8-6 dated 2018-02-13

 DESCRIPTION        |    9 ++++-----
 MD5                |   16 ++++++++--------
 R/SIS.R            |   37 +++++++++++++++++++------------------
 R/predict.SIS.R    |    7 ++++---
 R/standardize.R    |    4 ++--
 inst/CITATION      |   29 ++++++++++++++++++-----------
 man/SIS.Rd         |   37 +++++++++++++++++++------------------
 man/predict.SIS.Rd |    7 ++++---
 man/standardize.Rd |    4 ++--
 9 files changed, 80 insertions(+), 70 deletions(-)

More information about SIS at CRAN
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Package NetworkToolbox updated to version 1.0.0 with previous version 0.0.1.1 dated 2017-12-20

Title: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Description: Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershogoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Author: Alexander Christensen
Maintainer: Alexander Christensen <alexpaulchristensen@gmail.com>

Diff between NetworkToolbox versions 0.0.1.1 dated 2017-12-20 and 1.0.0 dated 2018-02-13

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 NetworkToolbox-1.0.0/NetworkToolbox/R/NetworkToolbox--master.R | 3846 +++++++++-
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 52 files changed, 3848 insertions(+), 492 deletions(-)

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Package merDeriv updated to version 0.1-3 with previous version 0.1-2 dated 2017-08-01

Title: Case-Wise and Cluster-Wise Derivatives for Mixed Effects Models
Description: Compute analytic case-wise and cluster-wise derivatives for mixed effects models with respect to fixed effect parameters, random effect (co)variances, and residual variances.
Author: Ting Wang [aut, cre], Edgar Merkle [aut]
Maintainer: Ting Wang <twb8d@mail.missouri.edu>

Diff between merDeriv versions 0.1-2 dated 2017-08-01 and 0.1-3 dated 2018-02-13

 DESCRIPTION      |    9 +++++----
 MD5              |    4 ++--
 R/vcov.lmerMod.R |   13 ++++++++++---
 3 files changed, 17 insertions(+), 9 deletions(-)

More information about merDeriv at CRAN
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Package epinet updated to version 2.1.8 with previous version 2.1.7 dated 2016-02-21

Title: Epidemic/Network-Related Tools
Description: A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
Author: Chris Groendyke [aut, cre], David Welch [aut], David Hunter [ctb]
Maintainer: Chris Groendyke <cgroendyke@gmail.com>

Diff between epinet versions 2.1.7 dated 2016-02-21 and 2.1.8 dated 2018-02-13

 DESCRIPTION            |    8 ++++----
 MD5                    |   26 ++++++++++++++------------
 NAMESPACE              |    2 +-
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 man/Epinet.Rd          |    4 +++-
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 15 files changed, 43 insertions(+), 20 deletions(-)

More information about epinet at CRAN
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Package BH updated to version 1.66.0-1 with previous version 1.65.0-1 dated 2017-08-24

Title: Boost C++ Header Files
Description: Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking. This package aims to provide the most useful subset of Boost libraries for template use among CRAN package. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. As of release 1.65.0-1, the following Boost libraries are included: 'algorithm' 'align' 'any' 'atomic' 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config' 'container' 'date'_'time' 'detail' 'dynamic_bitset' 'exception' 'filesystem' 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap' 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams' 'iterator' 'math' 'move' 'mpl' 'multiprcecision' 'numeric' 'pending' 'phoenix' 'preprocessor' 'propery_tree' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort' 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'utility' 'uuid'.
Author: Dirk Eddelbuettel, John W. Emerson and Michael J. Kane
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between BH versions 1.65.0-1 dated 2017-08-24 and 1.66.0-1 dated 2018-02-13

 ChangeLog                                                                                 |   19 
 DESCRIPTION                                                                               |   23 
 MD5                                                                                       | 1730 +-
 README.md                                                                                 |   18 
 build/partial.rdb                                                                         |binary
 inst/NEWS.Rd                                                                              |    7 
 inst/include/boost/accumulators/accumulators_fwd.hpp                                      |    1 
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 inst/include/boost/accumulators/numeric/functional/valarray.hpp                           |   68 
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 inst/include/boost/archive/basic_archive.hpp                                              |   13 
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 inst/include/boost/asio.hpp                                                               |   30 
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 inst/include/boost/asio/basic_socket.hpp                                                  |  456 
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 inst/include/boost/asio/buffer.hpp                                                        | 2307 +--
 inst/include/boost/asio/buffered_read_stream.hpp                                          |   39 
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 inst/include/boost/asio/coroutine.hpp                                                     |   12 
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 inst/include/boost/asio/detail/timer_scheduler.hpp                                        |    2 
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 inst/include/boost/asio/detail/variadic_templates.hpp                                     |   98 
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 inst/include/boost/container/pmr/polymorphic_allocator.hpp                                |    8 
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 inst/include/boost/container/string.hpp                                                   |   82 
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 inst/include/boost/container/vector.hpp                                                   |   91 
 inst/include/boost/core/lightweight_test.hpp                                              |    6 
 inst/include/boost/cstdint.hpp                                                            |    7 
 inst/include/boost/date_time/adjust_functors.hpp                                          |   40 
 inst/include/boost/date_time/filetime_functions.hpp                                       |  102 
 inst/include/boost/date_time/gregorian/greg_day.hpp                                       |    6 
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 inst/include/boost/date_time/gregorian/greg_year.hpp                                      |    8 
 inst/include/boost/date_time/microsec_time_clock.hpp                                      |   41 
 inst/include/boost/date_time/posix_time/conversion.hpp                                    |    2 
 inst/include/boost/detail/bitmask.hpp                                                     |   29 
 inst/include/boost/detail/interlocked.hpp                                                 |   27 
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 inst/include/boost/detail/utf8_codecvt_facet.hpp                                          |   25 
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 inst/include/boost/detail/winapi/access_rights.hpp                                        |   84 
 inst/include/boost/detail/winapi/apc.hpp                                                  |   47 
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 inst/include/boost/detail/winapi/error_codes.hpp                                          | 2959 ----
 inst/include/boost/detail/winapi/error_handling.hpp                                       |  143 
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 inst/include/boost/detail/winapi/get_system_directory.hpp                                 |   63 
 inst/include/boost/detail/winapi/get_thread_times.hpp                                     |   55 
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 inst/include/boost/detail/winapi/waitable_timer.hpp                                       |  145 
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More information about BH at CRAN
Permanent link

Package rvg updated to version 0.1.8 with previous version 0.1.7 dated 2017-12-03

Title: R Graphics Devices for Vector Graphics Output
Description: Vector Graphics devices for 'SVG', 'DrawingML' for Microsoft Word, PowerPoint and Excel. Functions extending package 'officer' are provided to embed 'DrawingML' graphics into 'Microsoft PowerPoint' presentations and 'Microsoft Excel' workbooks.
Author: David Gohel [aut, cre], Bob Rudis [ctb] (the javascript code used by function set_attr), Francois Brunetti [ctb] (clipping algorithms)
Maintainer: David Gohel <david.gohel@ardata.fr>

Diff between rvg versions 0.1.7 dated 2017-12-03 and 0.1.8 dated 2018-02-13

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More information about rvg at CRAN
Permanent link

Package optimsimplex updated to version 1.0-7 with previous version 1.0-6 dated 2017-07-28

Title: R Port of the 'Scilab' Optimsimplex Module
Description: Provides a building block for optimization algorithms based on a simplex. The 'optimsimplex' package may be used in the following optimization methods: the simplex method of Spendley et al. (1962) <doi:10.1080/00401706.1962.10490033>, the method of Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, Box's algorithm for constrained optimization (1965) <doi:10.1093/comjnl/8.1.42>, the multi-dimensional search by Torczon (1989) <http://www.cs.wm.edu/~va/research/thesis.pdf>, etc...
Author: Sebastien Bihorel, Michael Baudin (author of the original module)
Maintainer: Sebastien Bihorel <sb.pmlab@gmail.com>

Diff between optimsimplex versions 1.0-6 dated 2017-07-28 and 1.0-7 dated 2018-02-13

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More information about optimsimplex at CRAN
Permanent link

Package MXM updated to version 1.3.2 with previous version 1.3.1 dated 2017-11-30

Title: Feature Selection (Including Multiple Solutions) and Bayesian Networks
Description: Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. Reference: Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets, Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>.
Author: Michail Tsagris [aut, cre], Ioannis Tsamardinos [aut, cph], Vincenzo Lagani [aut, cph], Giorgos Athineou [aut], Giorgos Borboudakis [ctb], Anna Roumpelaki [ctb]
Maintainer: Michail Tsagris <mtsagris@csd.uoc.gr>

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 MXM-1.3.2/MXM/R/ebic.univregs.R                  |only
 MXM-1.3.2/MXM/R/ebic.wr.bsreg.R                  |    2 
 MXM-1.3.2/MXM/R/ebic.zip.bsreg.R                 |    2 
 MXM-1.3.2/MXM/R/ebicScore.R                      |only
 MXM-1.3.2/MXM/R/fbed.beta.R                      |   25 
 MXM-1.3.2/MXM/R/fbed.clogit.R                    |   48 -
 MXM-1.3.2/MXM/R/fbed.cr.R                        |   29 
 MXM-1.3.2/MXM/R/fbed.gee.reg.R                   |only
 MXM-1.3.2/MXM/R/fbed.geeglm.R                    |only
 MXM-1.3.2/MXM/R/fbed.geeglm.reps.R               |only
 MXM-1.3.2/MXM/R/fbed.geelm.R                     |only
 MXM-1.3.2/MXM/R/fbed.geelm.reps.R                |only
 MXM-1.3.2/MXM/R/fbed.glm.R                       |  207 +++---
 MXM-1.3.2/MXM/R/fbed.glm2.R                      |only
 MXM-1.3.2/MXM/R/fbed.glmm.R                      |   34 -
 MXM-1.3.2/MXM/R/fbed.glmm.reg.R                  |   61 +
 MXM-1.3.2/MXM/R/fbed.glmm.reps.R                 |only
 MXM-1.3.2/MXM/R/fbed.lm.R                        |   45 -
 MXM-1.3.2/MXM/R/fbed.lmm.R                       |   68 +-
 MXM-1.3.2/MXM/R/fbed.lmm.reps.R                  |only
 MXM-1.3.2/MXM/R/fbed.mmreg.R                     |   34 -
 MXM-1.3.2/MXM/R/fbed.multinom.R                  |   34 -
 MXM-1.3.2/MXM/R/fbed.nb.R                        |   32 -
 MXM-1.3.2/MXM/R/fbed.ordgee.R                    |only
 MXM-1.3.2/MXM/R/fbed.ordgee.reps.R               |only
 MXM-1.3.2/MXM/R/fbed.ordinal.R                   |   35 -
 MXM-1.3.2/MXM/R/fbed.reg.R                       |  129 ++--
 MXM-1.3.2/MXM/R/fbed.tobit.R                     |   34 -
 MXM-1.3.2/MXM/R/fbed.wr.R                        |   33 -
 MXM-1.3.2/MXM/R/fbed.zip.R                       |   24 
 MXM-1.3.2/MXM/R/fbedreg.bic.R                    |only
 MXM-1.3.2/MXM/R/fs.reg.R                         |   72 --
 MXM-1.3.2/MXM/R/fs.reg_2.R                       |   56 -
 MXM-1.3.2/MXM/R/gSquare.R                        |   14 
 MXM-1.3.2/MXM/R/gammabsreg.R                     |   91 --
 MXM-1.3.2/MXM/R/gammafsreg.R                     |  210 +-----
 MXM-1.3.2/MXM/R/gammafsreg_2.R                   |   34 -
 MXM-1.3.2/MXM/R/glm.bsreg.R                      |  462 ++++----------
 MXM-1.3.2/MXM/R/glm.fsreg.R                      |  548 +----------------
 MXM-1.3.2/MXM/R/glm.fsreg_2.R                    |  433 +------------
 MXM-1.3.2/MXM/R/glmm.bsreg.R                     |   14 
 MXM-1.3.2/MXM/R/glmm.ci.mm.R                     |   13 
 MXM-1.3.2/MXM/R/glmm.gee.univregs.R              |only
 MXM-1.3.2/MXM/R/glmm.reps.bsreg.R                |only
 MXM-1.3.2/MXM/R/group.mvbetas.R                  |only
 MXM-1.3.2/MXM/R/iamb.R                           |    9 
 MXM-1.3.2/MXM/R/iamb.bs.R                        |   40 -
 MXM-1.3.2/MXM/R/iamb.gammabs.R                   |   11 
 MXM-1.3.2/MXM/R/iamb.glmbs.R                     |   14 
 MXM-1.3.2/MXM/R/iamb.normlogbs.R                 |   10 
 MXM-1.3.2/MXM/R/iamb.tobitbs.R                   |    8 
 MXM-1.3.2/MXM/R/ida.R                            |only
 MXM-1.3.2/MXM/R/identifyTheEquivalent.R          |    7 
 MXM-1.3.2/MXM/R/identifyTheEquivalent.gee.R      |only
 MXM-1.3.2/MXM/R/identifyTheEquivalent.ma.R       |    8 
 MXM-1.3.2/MXM/R/identifyTheEquivalent.temporal.R |    6 
 MXM-1.3.2/MXM/R/internaliamb.binombs.R           |   47 -
 MXM-1.3.2/MXM/R/internaliamb.bs.R                |    2 
 MXM-1.3.2/MXM/R/internaliamb.gammabs.R           |   57 -
 MXM-1.3.2/MXM/R/internaliamb.lmbs.R              |  107 ---
 MXM-1.3.2/MXM/R/internaliamb.mmbs.R              |only
 MXM-1.3.2/MXM/R/internaliamb.normlogbs.R         |   57 -
 MXM-1.3.2/MXM/R/internaliamb.poisbs.R            |   46 -
 MXM-1.3.2/MXM/R/internaliamb.tobitbs.R           |    2 
 MXM-1.3.2/MXM/R/is.dag.R                         |    6 
 MXM-1.3.2/MXM/R/kfbed.gee.reg.R                  |only
 MXM-1.3.2/MXM/R/kfbed.glmm.reg.R                 |only
 MXM-1.3.2/MXM/R/kfbed.reg.R                      |only
 MXM-1.3.2/MXM/R/lm.fsreg.R                       |  257 +-------
 MXM-1.3.2/MXM/R/lm.fsreg_2.R                     |  280 ++------
 MXM-1.3.2/MXM/R/lmm.bsreg.R                      |    3 
 MXM-1.3.2/MXM/R/lmm.reps.bsreg.R                 |only
 MXM-1.3.2/MXM/R/local.mmhc.skel.R                |    6 
 MXM-1.3.2/MXM/R/ma.mmpc.R                        |  415 -------------
 MXM-1.3.2/MXM/R/ma.ses.R                         |   36 -
 MXM-1.3.2/MXM/R/mammpc.output_script.R           |   12 
 MXM-1.3.2/MXM/R/mases.output_script.R            |   12 
 MXM-1.3.2/MXM/R/max_min_assoc.R                  |    8 
 MXM-1.3.2/MXM/R/max_min_assoc.gee.R              |only
 MXM-1.3.2/MXM/R/max_min_assoc.ma.R               |    6 
 MXM-1.3.2/MXM/R/max_min_assoc.temporal.R         |    6 
 MXM-1.3.2/MXM/R/mb.R                             |    5 
 MXM-1.3.2/MXM/R/min_assoc.R                      |   10 
 MXM-1.3.2/MXM/R/min_assoc.gee.R                  |only
 MXM-1.3.2/MXM/R/min_assoc.ma.R                   |    8 
 MXM-1.3.2/MXM/R/min_assoc.temporal.R             |    8 
 MXM-1.3.2/MXM/R/mm.bsreg.R                       |only
 MXM-1.3.2/MXM/R/mm.fsreg.R                       |only
 MXM-1.3.2/MXM/R/mm.fsreg_2.R                     |only
 MXM-1.3.2/MXM/R/mmhc.skel.R                      |   11 
 MXM-1.3.2/MXM/R/mmmb.R                           |    6 
 MXM-1.3.2/MXM/R/mmpc.gee.model.R                 |only
 MXM-1.3.2/MXM/R/mmpc.model.R                     |  128 +---
 MXM-1.3.2/MXM/R/mmpc.or.R                        |    6 
 MXM-1.3.2/MXM/R/mmpc.path.R                      |    6 
 MXM-1.3.2/MXM/R/mmpc.temporal.model.R            |   56 -
 MXM-1.3.2/MXM/R/mmpc.timeclass.model.R           |only
 MXM-1.3.2/MXM/R/mmpcbackphase.R                  |   30 
 MXM-1.3.2/MXM/R/nei.R                            |    5 
 MXM-1.3.2/MXM/R/normlog.bsreg.R                  |   92 --
 MXM-1.3.2/MXM/R/normlog.fsreg.R                  |  406 +++++-------
 MXM-1.3.2/MXM/R/normlog.fsreg_2.R                |   37 -
 MXM-1.3.2/MXM/R/omp.R                            |only
 MXM-1.3.2/MXM/R/omp2.R                           |only
 MXM-1.3.2/MXM/R/ordinal.reg.R                    |   71 +-
 MXM-1.3.2/MXM/R/pc.or.R                          |    2 
 MXM-1.3.2/MXM/R/pc.skel.R                        |    2 
 MXM-1.3.2/MXM/R/perm.IdentifyEquivalence.R       |   12 
 MXM-1.3.2/MXM/R/perm.Internalmmpc.R              |   19 
 MXM-1.3.2/MXM/R/perm.Internalses.R               |   43 -
 MXM-1.3.2/MXM/R/perm.apply_ideq.R                |    6 
 MXM-1.3.2/MXM/R/perm.betaregs.R                  |    2 
 MXM-1.3.2/MXM/R/perm.identifyTheEquivalent.R     |    6 
 MXM-1.3.2/MXM/R/perm.max_min_assoc.R             |    7 
 MXM-1.3.2/MXM/R/perm.min_assoc.R                 |    8 
 MXM-1.3.2/MXM/R/perm.mmpc.R                      |   85 --
 MXM-1.3.2/MXM/R/perm.ses.R                       |   74 --
 MXM-1.3.2/MXM/R/perm.univariateScore.R           |    9 
 MXM-1.3.2/MXM/R/perm.univregs.R                  |   22 
 MXM-1.3.2/MXM/R/perm.zipregs.R                   |    2 
 MXM-1.3.2/MXM/R/permBeta.R                       |   63 --
 MXM-1.3.2/MXM/R/permBinom.R                      |   37 -
 MXM-1.3.2/MXM/R/permCR.R                         |   24 
 MXM-1.3.2/MXM/R/permClogit.R                     |   34 -
 MXM-1.3.2/MXM/R/permDcor.R                       |   80 --
 MXM-1.3.2/MXM/R/permER.R                         |   24 
 MXM-1.3.2/MXM/R/permFisher.R                     |  145 +---
 MXM-1.3.2/MXM/R/permGamma.R                      |   39 -
 MXM-1.3.2/MXM/R/permIGreg.R                      |   61 -
 MXM-1.3.2/MXM/R/permLogistic.R                   |  199 +-----
 MXM-1.3.2/MXM/R/permMMFisher.R                   |only
 MXM-1.3.2/MXM/R/permMMreg.R                      |only
 MXM-1.3.2/MXM/R/permMVreg.R                      |   40 -
 MXM-1.3.2/MXM/R/permMultinom.R                   |only
 MXM-1.3.2/MXM/R/permNB.R                         |   42 -
 MXM-1.3.2/MXM/R/permNormLog.R                    |   42 -
 MXM-1.3.2/MXM/R/permOrdinal.R                    |only
 MXM-1.3.2/MXM/R/permPois.R                       |   41 -
 MXM-1.3.2/MXM/R/permRQ.R                         |   39 -
 MXM-1.3.2/MXM/R/permReg.R                        |  122 +--
 MXM-1.3.2/MXM/R/permTobit.R                      |   20 
 MXM-1.3.2/MXM/R/permWR.R                         |   28 
 MXM-1.3.2/MXM/R/permZIP.R                        |   56 -
 MXM-1.3.2/MXM/R/permcor.R                        |   19 
 MXM-1.3.2/MXM/R/permcorrels.R                    |    2 
 MXM-1.3.2/MXM/R/permgSquare.R                    |   20 
 MXM-1.3.2/MXM/R/quasibinom.bsreg.R               |only
 MXM-1.3.2/MXM/R/quasibinom.fsreg.R               |only
 MXM-1.3.2/MXM/R/quasibinom.fsreg_2.R             |only
 MXM-1.3.2/MXM/R/quasipois.bsreg.R                |only
 MXM-1.3.2/MXM/R/quasipois.fsreg.R                |only
 MXM-1.3.2/MXM/R/quasipois.fsreg_2.R              |only
 MXM-1.3.2/MXM/R/reg.fit.R                        |   33 -
 MXM-1.3.2/MXM/R/rint.reg.R                       |only
 MXM-1.3.2/MXM/R/rint.regs.R                      |   66 +-
 MXM-1.3.2/MXM/R/score.univregs.R                 |   12 
 MXM-1.3.2/MXM/R/ses.gee.model.R                  |only
 MXM-1.3.2/MXM/R/ses.model.R                      |  335 ++++------
 MXM-1.3.2/MXM/R/ses.temporal.model.R             |only
 MXM-1.3.2/MXM/R/ses.timeclass.model.R            |only
 MXM-1.3.2/MXM/R/tc.plot.R                        |    6 
 MXM-1.3.2/MXM/R/testIndBeta.R                    |   70 --
 MXM-1.3.2/MXM/R/testIndBinom.R                   |   27 
 MXM-1.3.2/MXM/R/testIndClogit.R                  |   15 
 MXM-1.3.2/MXM/R/testIndFisher.R                  |  149 +---
 MXM-1.3.2/MXM/R/testIndGEEGamma.R                |only
 MXM-1.3.2/MXM/R/testIndGEELogistic.R             |only
 MXM-1.3.2/MXM/R/testIndGEENormLog.R              |only
 MXM-1.3.2/MXM/R/testIndGEEOrdinal.R              |only
 MXM-1.3.2/MXM/R/testIndGEEPois.R                 |only
 MXM-1.3.2/MXM/R/testIndGEEReg.R                  |only
 MXM-1.3.2/MXM/R/testIndGLMMLogistic.R            |only
 MXM-1.3.2/MXM/R/testIndGLMMPois.R                |only
 MXM-1.3.2/MXM/R/testIndGLMMReg.R                 |only
 MXM-1.3.2/MXM/R/testIndGamma.R                   |   38 -
 MXM-1.3.2/MXM/R/testIndIGreg.R                   |   72 --
 MXM-1.3.2/MXM/R/testIndLMM.R                     |only
 MXM-1.3.2/MXM/R/testIndLogistic.R                |   94 ---
 MXM-1.3.2/MXM/R/testIndMMFisher.R                |only
 MXM-1.3.2/MXM/R/testIndMMReg.R                   |only
 MXM-1.3.2/MXM/R/testIndMVreg.R                   |   33 -
 MXM-1.3.2/MXM/R/testIndMultinom.R                |only
 MXM-1.3.2/MXM/R/testIndNB.R                      |   32 -
 MXM-1.3.2/MXM/R/testIndNormLog.R                 |   31 
 MXM-1.3.2/MXM/R/testIndOrdinal.R                 |only
 MXM-1.3.2/MXM/R/testIndPois.R                    |   39 -
 MXM-1.3.2/MXM/R/testIndQBinom.R                  |only
 MXM-1.3.2/MXM/R/testIndQPois.R                   |only
 MXM-1.3.2/MXM/R/testIndRQ.R                      |   50 -
 MXM-1.3.2/MXM/R/testIndReg.R                     |   65 --
 MXM-1.3.2/MXM/R/testIndSpearman.R                |  122 +--
 MXM-1.3.2/MXM/R/testIndTimeLogistic.R            |only
 MXM-1.3.2/MXM/R/testIndTimeMultinom.R            |only
 MXM-1.3.2/MXM/R/testIndTobit.R                   |   12 
 MXM-1.3.2/MXM/R/testIndZIP.R                     |   45 -
 MXM-1.3.2/MXM/R/tobit.bsreg.R                    |   57 -
 MXM-1.3.2/MXM/R/tobit.fsreg.R                    |   19 
 MXM-1.3.2/MXM/R/tobit.fsreg_2.R                  |    3 
 MXM-1.3.2/MXM/R/triangles.search.R               |only
 MXM-1.3.2/MXM/R/univariateScore.R                |    7 
 MXM-1.3.2/MXM/R/univariateScore.gee.R            |only
 MXM-1.3.2/MXM/R/univariateScore.ma.R             |   14 
 MXM-1.3.2/MXM/R/univariateScore.temporal.R       |   16 
 MXM-1.3.2/MXM/R/univariateScore.timeclass.R      |only
 MXM-1.3.2/MXM/R/univregs.R                       |  169 ++---
 MXM-1.3.2/MXM/R/wald.Internalmmpc.R              |   38 -
 MXM-1.3.2/MXM/R/wald.Internalses.R               |   40 -
 MXM-1.3.2/MXM/R/wald.mmpc.R                      |   48 -
 MXM-1.3.2/MXM/R/wald.ses.R                       |   46 -
 MXM-1.3.2/MXM/R/wald.univariateScore.R           |    9 
 MXM-1.3.2/MXM/R/wald.univregs.R                  |   13 
 MXM-1.3.2/MXM/R/waldBeta.R                       |   25 
 MXM-1.3.2/MXM/R/waldBinom.R                      |   24 
 MXM-1.3.2/MXM/R/waldCR.R                         |   12 
 MXM-1.3.2/MXM/R/waldER.R                         |   17 
 MXM-1.3.2/MXM/R/waldGamma.R                      |   20 
 MXM-1.3.2/MXM/R/waldIGreg.R                      |   34 -
 MXM-1.3.2/MXM/R/waldLogistic.R                   |only
 MXM-1.3.2/MXM/R/waldMMreg.R                      |   24 
 MXM-1.3.2/MXM/R/waldNB.R                         |   23 
 MXM-1.3.2/MXM/R/waldNormLog.R                    |   20 
 MXM-1.3.2/MXM/R/waldPois.R                       |   24 
 MXM-1.3.2/MXM/R/waldQBinom.R                     |only
 MXM-1.3.2/MXM/R/waldQPois.R                      |only
 MXM-1.3.2/MXM/R/waldTobit.R                      |   14 
 MXM-1.3.2/MXM/R/waldWR.R                         |   17 
 MXM-1.3.2/MXM/R/waldZIP.R                        |   34 -
 MXM-1.3.2/MXM/R/waldmmpc.model.R                 |   69 --
 MXM-1.3.2/MXM/R/waldmmpc.path.R                  |    6 
 MXM-1.3.2/MXM/R/waldses.model.R                  |  199 ++----
 MXM-1.3.2/MXM/R/wr.fsreg.R                       |only
 MXM-1.3.2/MXM/R/wr.fsreg_2.R                     |only
 MXM-1.3.2/MXM/R/zip.fsreg_2.R                    |    1 
 MXM-1.3.2/MXM/R/zipwei.R                         |    3 
 MXM-1.3.2/MXM/inst/doc/article.pdf               |binary
 MXM-1.3.2/MXM/inst/doc/guide.ltx                 |  175 ++++-
 MXM-1.3.2/MXM/inst/doc/guide.pdf                 |binary
 MXM-1.3.2/MXM/man/MMPC.gee.output-class.Rd       |only
 MXM-1.3.2/MXM/man/MMPC.temporal.output-class.Rd  |    3 
 MXM-1.3.2/MXM/man/MMPCoutput-class.Rd            |    5 
 MXM-1.3.2/MXM/man/MXM-internal.Rd                |   91 ++
 MXM-1.3.2/MXM/man/MXM-package.Rd                 |   21 
 MXM-1.3.2/MXM/man/MXMCondIndTests.Rd             |   67 +-
 MXM-1.3.2/MXM/man/SES.Rd                         |   38 -
 MXM-1.3.2/MXM/man/SES.gee.output-class.Rd        |only
 MXM-1.3.2/MXM/man/SES.temporal.Rd                |   67 +-
 MXM-1.3.2/MXM/man/SES.temporal.output-class.Rd   |    3 
 MXM-1.3.2/MXM/man/SES.timeclass.Rd               |only
 MXM-1.3.2/MXM/man/SESoutput-class.Rd             |    5 
 MXM-1.3.2/MXM/man/auc.Rd                         |    4 
 MXM-1.3.2/MXM/man/bic.fsreg.Rd                   |   14 
 MXM-1.3.2/MXM/man/bic.glm.fsreg.Rd               |   27 
 MXM-1.3.2/MXM/man/bs.reg.Rd                      |    8 
 MXM-1.3.2/MXM/man/censIndCR.Rd                   |   79 +-
 MXM-1.3.2/MXM/man/certificate.of.exclusion.Rd    |only
 MXM-1.3.2/MXM/man/ci.mm.Rd                       |    7 
 MXM-1.3.2/MXM/man/condi.Rd                       |    2 
 MXM-1.3.2/MXM/man/corfs.network.Rd               |    2 
 MXM-1.3.2/MXM/man/cv.ses.Rd                      |   96 +--
 MXM-1.3.2/MXM/man/ebic.bsreg.Rd                  |   14 
 MXM-1.3.2/MXM/man/ebic.glmm.bsreg.Rd             |    8 
 MXM-1.3.2/MXM/man/fbed.gee.reg.Rd                |only
 MXM-1.3.2/MXM/man/fbed.glmm.reg.Rd               |   50 +
 MXM-1.3.2/MXM/man/fbed.reg.Rd                    |   54 +
 MXM-1.3.2/MXM/man/fbedreg.bic.Rd                 |only
 MXM-1.3.2/MXM/man/fs.reg.Rd                      |   29 
 MXM-1.3.2/MXM/man/gSquare.Rd                     |   26 
 MXM-1.3.2/MXM/man/glm.bsreg.Rd                   |   17 
 MXM-1.3.2/MXM/man/glm.fsreg.Rd                   |   15 
 MXM-1.3.2/MXM/man/glmm.bsreg.Rd                  |    8 
 MXM-1.3.2/MXM/man/glmm.ci.mm.Rd                  |    9 
 MXM-1.3.2/MXM/man/group.mvbetas.Rd               |only
 MXM-1.3.2/MXM/man/iamb.Rd                        |    6 
 MXM-1.3.2/MXM/man/iamb.bs.Rd                     |   13 
 MXM-1.3.2/MXM/man/ida.Rd                         |only
 MXM-1.3.2/MXM/man/lm.fsreg.Rd                    |   17 
 MXM-1.3.2/MXM/man/local.mmhc.skel.Rd             |    7 
 MXM-1.3.2/MXM/man/ma.ses.Rd                      |   20 
 MXM-1.3.2/MXM/man/mammpc.output-class.Rd         |    5 
 MXM-1.3.2/MXM/man/mases.output-class.Rd          |    5 
 MXM-1.3.2/MXM/man/mb.Rd                          |   11 
 MXM-1.3.2/MXM/man/mmhc.skel.Rd                   |    6 
 MXM-1.3.2/MXM/man/mmmb.Rd                        |   14 
 MXM-1.3.2/MXM/man/mmpc.or.Rd                     |   11 
 MXM-1.3.2/MXM/man/mmpc.path.Rd                   |   21 
 MXM-1.3.2/MXM/man/mmpc.temporal.model.Rd         |   43 +
 MXM-1.3.2/MXM/man/mmpc.timeclass.model.Rd        |only
 MXM-1.3.2/MXM/man/mmpcbackphase.Rd               |   12 
 MXM-1.3.2/MXM/man/nei.Rd                         |   10 
 MXM-1.3.2/MXM/man/omp.Rd                         |only
 MXM-1.3.2/MXM/man/pc.or.Rd                       |   10 
 MXM-1.3.2/MXM/man/pc.skel.Rd                     |   13 
 MXM-1.3.2/MXM/man/permcor.Rd                     |   17 
 MXM-1.3.2/MXM/man/plotnetwork.Rd                 |    2 
 MXM-1.3.2/MXM/man/rdag.Rd                        |    7 
 MXM-1.3.2/MXM/man/reg.fit.Rd                     |   13 
 MXM-1.3.2/MXM/man/ses.model.Rd                   |   10 
 MXM-1.3.2/MXM/man/shd.Rd                         |    6 
 MXM-1.3.2/MXM/man/tc.plot.Rd                     |    7 
 MXM-1.3.2/MXM/man/testIndBeta.Rd                 |   53 -
 MXM-1.3.2/MXM/man/testIndBinom.Rd                |   49 -
 MXM-1.3.2/MXM/man/testIndClogit.Rd               |   18 
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More information about MXM at CRAN
Permanent link

Package mixture updated to version 1.5 with previous version 1.4 dated 2015-03-10

Title: Finite Gaussian Mixture Models for Clustering and Classification
Description: An implementation of all 14 Gaussian parsimonious clustering models (GPCMs) for model-based clustering and model-based classification.
Author: Ryan P. Browne, Aisha ElSherbiny and Paul D. McNicholas
Maintainer: Ryan Browne <rbrowne@math.mcmaster.ca>

Diff between mixture versions 1.4 dated 2015-03-10 and 1.5 dated 2018-02-13

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More information about mixture at CRAN
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Package memery updated to version 0.4.2 with previous version 0.4.1 dated 2018-02-12

Title: Internet Memes for Data Analysts
Description: Generates internet memes that optionally include a superimposed inset plot and other atypical features, combining the visual impact of an attention-grabbing meme with graphic results of data analysis. The package differs from related packages that focus on imitating and reproducing standard memes. Some packages do this by interfacing with online meme generators whereas others achieve this natively. This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites. It reads images directly from local files or via URL and meme generation is done by the package. While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on allowing for non-standard meme layouts and hybrids of memes mixed with graphs. While this package can be used to make basic memes like an online meme generator would produce, it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting foreground graphs of data analysis results. The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes rather than restricting to the standard top and/or bottom text placement. This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels. The package offers a selection of templates for graph placement and appearance with respect to the underlying meme. Graph templates also permit additional template-specific customization. Animated gif support is provided but this is optional and functional only if the 'magick' package is installed. 'magick' is not required unless gif functionality is desired.
Author: Matthew Leonawicz [aut, cre]
Maintainer: Matthew Leonawicz <mfleonawicz@alaska.edu>

Diff between memery versions 0.4.1 dated 2018-02-12 and 0.4.2 dated 2018-02-13

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More information about memery at CRAN
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Package heavy updated to version 0.38.19 with previous version 0.38.1 dated 2017-04-04

Title: Robust Estimation Using Heavy-Tailed Distributions
Description: Functions to perform robust estimation considering heavy-tailed distributions. Currently, the package includes routines for linear regression, linear mixed-effect models, multivariate location and scatter estimation, multivariate regression, penalized splines, random variate generation and some support functions.
Author: Felipe Osorio
Maintainer: Felipe Osorio <felipe.osorios@usm.cl>

Diff between heavy versions 0.38.1 dated 2017-04-04 and 0.38.19 dated 2018-02-13

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More information about heavy at CRAN
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Package getTBinR updated to version 0.5.2 with previous version 0.5.1 dated 2018-01-26

Title: Get WHO Tuberculosis Data
Description: Facilitates easy import of analysis ready World Health Organisation Tuberculosis data and provides plotting functions for exploratory data analysis.
Author: Sam Abbott [aut, cre]
Maintainer: Sam Abbott <contact@samabbott.co.uk>

Diff between getTBinR versions 0.5.1 dated 2018-01-26 and 0.5.2 dated 2018-02-13

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Package phonics updated to version 1.0.0 with previous version 0.7.5 dated 2017-08-25

Title: Phonetic Spelling Algorithms
Description: Provides a collection of phonetic algorithms including Soundex, Metaphone, NYSIIS, Caverphone, and others.
Author: James P. Howard, II [aut, cre], Oliver Keyes [ctb]
Maintainer: "James P. Howard, II" <jh@jameshoward.us>

Diff between phonics versions 0.7.5 dated 2017-08-25 and 1.0.0 dated 2018-02-13

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Package enveomics.R updated to version 1.1.7 with previous version 1.1.6 dated 2017-08-29

Title: Various Utilities for Microbial Genomics and Metagenomics
Description: A collection of functions for microbial ecology and other applications of genomics and metagenomics. Companion package for the Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016 <DOI:10.7287/peerj.preprints.1900v1>).
Author: Luis M. Rodriguez-R [aut, cre]
Maintainer: Luis M. Rodriguez-R <lmrodriguezr@gmail.com>

Diff between enveomics.R versions 1.1.6 dated 2017-08-29 and 1.1.7 dated 2018-02-13

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Package tuber updated to version 0.9.4 with previous version 0.9.3 dated 2018-02-02

Title: Client for the YouTube API
Description: Get comments posted on YouTube videos, information on how many times a video has been liked, search for videos with particular content, and much more. You can also scrape captions from a few videos. To learn more about the YouTube API, see <https://developers.google.com/youtube/v3/>.
Author: Gaurav Sood [aut, cre], Kate Lyons [ctb]
Maintainer: Gaurav Sood <gsood07@gmail.com>

Diff between tuber versions 0.9.3 dated 2018-02-02 and 0.9.4 dated 2018-02-13

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More information about tuber at CRAN
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Package polymapR updated to version 1.0.13 with previous version 1.0.12 dated 2017-12-14

Title: Linkage Analysis in Outcrossing Polyploids
Description: Creation of linkage maps in polyploid species from marker dosage scores of an F1 cross from two heterozygous parents. Currently works for autotriploid, autotetraploid and autohexaploid species, as well as segmental allotetraploids. Methods are described in a preprint manuscript of Bourke et al. (2017) <doi:10.1101/228817>.
Author: Peter Bourke [aut, cre], Geert van Geest [aut]
Maintainer: Peter Bourke <pbourkey@gmail.com>

Diff between polymapR versions 1.0.12 dated 2017-12-14 and 1.0.13 dated 2018-02-13

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Package EDMeasure updated to version 1.1.0 with previous version 1.0.0 dated 2018-02-12

Title: Dependence Measures via Energy Statistics
Description: Implementations of (1) mutual dependence measures and mutual independence tests in Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>; (2) independent component analysis methods based on mutual dependence measures in Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532> and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>; (3) conditional mean dependence measures and conditional mean independence tests in Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012> and Park, T., et al. (2015) <doi:10.1214/15-EJS1047>.
Author: Ze Jin [aut, cre], Shun Yao [aut], David S. Matteson [aut], Xiaofeng Shao [aut]
Maintainer: Ze Jin <zj58@cornell.edu>

Diff between EDMeasure versions 1.0.0 dated 2018-02-12 and 1.1.0 dated 2018-02-13

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Package mopa updated to version 1.0.1 with previous version 1.0.0 dated 2017-06-06

Title: Species Distribution MOdeling with Pseudo-Absences
Description: Tools for transferable species distribution modeling and pseudo-absence data generation allowing the straightforward design of relatively complex experiments with multiple factors affecting the uncertainty (variability) of SDM outputs (pseudo-absence sample, climate projection, modeling algorithm, etc.), and the quantification of the contribution of different factors to the final variability following the method described in Deque el al. (2010) <doi:10.1007/s00382-011-1053-x>. Multiple methods for pseudo-absence data generation can be applied, including the novel Three-step method as described in Iturbide et al. (2015) <doi:10.1016/j.ecolmodel.2015.05.018>. Additionally, a function for niche overlap calculation is provided, considering the metrics described in Warren et al. (2008) <10.1111/j.1558-5646.2008.00482.x> and in Pianka (1973) <10.1146/annurev.es.04.110173.000413>.
Author: Maialen Iturbide [aut, cre], Joaquin Bedia [ctb], SantanderMetGroup http://meteo.unican.es [ctb]
Maintainer: Maialen Iturbide <miturbide@ifca.unican.es>

Diff between mopa versions 1.0.0 dated 2017-06-06 and 1.0.1 dated 2018-02-13

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Package Mediana updated to version 1.0.6 with previous version 1.0.5 dated 2017-05-25

Title: Clinical Trial Simulations
Description: Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.
Author: Gautier Paux, Alex Dmitrienko.
Maintainer: Gautier Paux <gautier@paux.fr>

Diff between Mediana versions 1.0.5 dated 2017-05-25 and 1.0.6 dated 2018-02-13

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Package RmecabKo updated to version 0.1.6.2 with previous version 0.1.6 dated 2017-10-06

Title: An 'Rcpp' Interface for Eunjeon Project
Description: An 'Rcpp' interface for Eunjeon project <http://eunjeon.blogspot.com/>. The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library, and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct. This package provides part-of-speech tagging and tokenization function for Korean text.
Author: Junhewk Kim
Maintainer: Junhewk Kim <junhewk.kim@gmail.com>

Diff between RmecabKo versions 0.1.6 dated 2017-10-06 and 0.1.6.2 dated 2018-02-13

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New package RGF with initial version 1.0.0
Package: RGF
Type: Package
Title: Regularized Greedy Forest
Version: 1.0.0
Date: 2018-02-12
Authors@R: c( person("Lampros", "Mouselimis", email = "mouselimislampros@gmail.com", role = c("aut", "cre")), person("Ryosuke", "Fukatani", role = "cph", comment = "Author of the python wrapper of the 'Regularized Greedy Forest' machine learning algorithm"), person("Tong", "Zhang", role = "cph", comment = "Author of the 'Regularized Greedy Forest' and of the Multi-core implementation of Regularized Greedy Forest machine learning algorithm"), person("Rie", "Johnson", role = "cph", comment = "Author of the 'Regularized Greedy Forest' machine learning algorithm") )
Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>
Description: Regularized Greedy Forest wrapper of the 'Regularized Greedy Forest' <https://github.com/fukatani/rgf_python> 'python' package, which also includes a Multi-core implementation (FastRGF) <https://github.com/baidu/fast_rgf>.
License: GPL-3
Copyright: inst/COPYRIGHTS
SystemRequirements: Python (>= 2.7), rgf_python, fast_rgf, scikit-learn (>= 0.19.0), scipy ( >= 1.0.0), numpy ( >= 1.14.0). Detailed installation instructions for each operating system can be found in the README file.
Depends: R(>= 3.2.3)
Imports: reticulate, R6, Matrix
Suggests: testthat, covr, knitr, rmarkdown
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-02-12 18:48:02 UTC; lampros
Author: Lampros Mouselimis [aut, cre], Ryosuke Fukatani [cph] (Author of the python wrapper of the 'Regularized Greedy Forest' machine learning algorithm), Tong Zhang [cph] (Author of the 'Regularized Greedy Forest' and of the Multi-core implementation of Regularized Greedy Forest machine learning algorithm), Rie Johnson [cph] (Author of the 'Regularized Greedy Forest' machine learning algorithm)
Repository: CRAN
Date/Publication: 2018-02-13 16:37:32 UTC

More information about RGF at CRAN
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Package GENEAclassify updated to version 1.4.9 with previous version 1.4.8 dated 2018-01-16

Title: Segmentation and Classification of Accelerometer Data
Description: Segmentation and classification procedures for data from the 'Activinsights GENEActiv' <https://www.activinsights.com/products/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.
Author: Chris Campbell [aut], Aimee Gott [aut], Joss Langford [aut], Charles Sweetland [aut, cre], Activinsights Ltd [cph]
Maintainer: Charles Sweetland <charles@sweetland-solutions.co.uk>

Diff between GENEAclassify versions 1.4.8 dated 2018-01-16 and 1.4.9 dated 2018-02-13

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Package autothresholdr updated to version 1.1.2 with previous version 1.1.1 dated 2018-01-28

Title: An R Port of the 'ImageJ' Plugin 'Auto Threshold'
Description: Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. See <http://imagej.net/Auto_Threshold> and Landini et al. (2017) <DOI:10.1111/jmi.12474>.
Author: Rory Nolan [aut, cre, trl], Luis Alvarez [ctb], Sergi Padilla-Parra [ctb, ths], Gabriel Landini [ctb, cph]
Maintainer: Rory Nolan <rorynoolan@gmail.com>

Diff between autothresholdr versions 1.1.1 dated 2018-01-28 and 1.1.2 dated 2018-02-13

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Package nproc updated to version 2.1.1 with previous version 2.0.9 dated 2017-09-17

Title: Neyman-Pearson (NP) Classification Algorithms and NP Receiver Operating Characteristic (NP-ROC) Curves
Description: In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers.
Author: Yang Feng, Jingyi Jessica Li and Xin Tong
Maintainer: Yang Feng <yang.feng@columbia.edu>

Diff between nproc versions 2.0.9 dated 2017-09-17 and 2.1.1 dated 2018-02-13

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Permanent link

Package edgarWebR updated to version 0.3.1 with previous version 0.3.0 dated 2017-12-22

Title: SEC Filings Access
Description: A set of methods to access and parse live filing information from the U.S. Securities and Exchange Commission (SEC - <https://sec.gov>) including company and fund filings along with all associated metadata.
Author: Micah J Waldstein [aut, cre]
Maintainer: Micah J Waldstein <micah@waldste.in>

Diff between edgarWebR versions 0.3.0 dated 2017-12-22 and 0.3.1 dated 2018-02-13

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Package Rilostat updated to version 0.2.1 with previous version 0.2 dated 2017-09-12

Title: ILO Open Data via Ilostat Bulk Download Facility or SDMX Web Service
Description: Tools to download data from the ilostat database <http://www.ilo.org/ilostat> together with search and manipulation utilities.
Author: ILO Department of Statistics [cph, fnd], David Bescond [aut, cre], Rosina Gammarano [ctr]
Maintainer: David Bescond <bescond@ilo.org>

Diff between Rilostat versions 0.2 dated 2017-09-12 and 0.2.1 dated 2018-02-13

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More information about Rilostat at CRAN
Permanent link

Package tmaptools updated to version 1.2-3 with previous version 1.2-2 dated 2017-11-24

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 1.2-2 dated 2017-11-24 and 1.2-3 dated 2018-02-13

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Package tmap updated to version 1.11-1 with previous version 1.11 dated 2017-11-24

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>

Diff between tmap versions 1.11 dated 2017-11-24 and 1.11-1 dated 2018-02-13

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New package future.callr with initial version 0.2.0
Package: future.callr
Version: 0.2.0
Depends: R (>= 3.2.0), future (>= 1.7.0)
Imports: callr (>= 2.0.2)
Suggests: future.apply, listenv, markdown, R.rsp
VignetteBuilder: R.rsp
Title: A Future API for Parallel Processing using 'callr'
Authors@R: c(person("Henrik", "Bengtsson", role=c("aut", "cre", "cph"), email = "henrikb@braju.com"))
Description: Implementation of the Future API on top of the 'callr' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine. Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession'), the 'callr' backend provided here can run more than 125 parallel R processes.
License: LGPL (>= 2.1)
LazyLoad: TRUE
URL: https://github.com/HenrikBengtsson/future.callr
BugReports: https://github.com/HenrikBengtsson/future.callr/issues
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-02-13 04:09:23 UTC; hb
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>
Repository: CRAN
Date/Publication: 2018-02-13 12:56:10 UTC

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Package jtools updated to version 0.9.4 with previous version 0.9.3 dated 2018-01-28

Title: Analysis and Presentation of Social Scientific Data
Description: This is a collection of tools that the author (Jacob) has written for the purpose of more efficiently understanding and sharing the results of (primarily) regression analyses. There are a number of functions focused specifically on the interpretation and presentation of interactions in linear models. Just about everything supports models from the survey package.
Author: Jacob A. Long [aut, cre] (<https://orcid.org/0000-0002-1582-6214>)
Maintainer: Jacob A. Long <long.1377@osu.edu>

Diff between jtools versions 0.9.3 dated 2018-01-28 and 0.9.4 dated 2018-02-13

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Permanent link

Package inferr updated to version 0.3.0 with previous version 0.2.0 dated 2017-12-11

Title: Inferential Statistics
Description: Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of statistical tests provided in 'stats' package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.
Author: Aravind Hebbali [aut, cre]
Maintainer: Aravind Hebbali <hebbali.aravind@gmail.com>

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Package pdmod updated to version 1.0.1 with previous version 1.0 dated 2014-03-28

Title: Proximal/Distal Modeling Framework for Pavlovian Conditioning Phenomena
Description: Fits a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.
Author: Chloe Bracis
Maintainer: Chloe Bracis <cbracis@uw.edu>

Diff between pdmod versions 1.0 dated 2014-03-28 and 1.0.1 dated 2018-02-13

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New package huxtable with initial version 2.0.2
Package: huxtable
Type: Package
Title: Simply Create LaTeX and HTML Tables
Version: 2.0.2
Author: David Hugh-Jones [aut, cre]
Maintainer: David Hugh-Jones <davidhughjones@gmail.com>
Description: Creates HTML and LaTeX tables. Provides similar functionality to 'xtable', but does more, with a simpler interface. Allows export to Microsoft Word or PowerPoint using the 'officer' package. Includes a 'huxreg' function for creation of regression tables, and 'quick_*' one-liner commands to print data as Word, PDF or HTML.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: testthat, devtools, broom, ggplot2, officer, flextable, psych, nnet, covr, lme4, crayon
RoxygenNote: 6.0.1
URL: https://hughjonesd.github.io/huxtable
BugReports: https://github.com/hughjonesd/huxtable/issues
Imports: knitr, rmarkdown, htmltools, lazyeval, utils, dplyr, tibble, glue, assertthat, stringr
VignetteBuilder: knitr
Authors@R: person(given = "David", family = "Hugh-Jones", role = c("aut", "cre"), email = "davidhughjones@gmail.com")
NeedsCompilation: no
Packaged: 2018-02-08 21:19:34 UTC; david
Repository: CRAN
Date/Publication: 2018-02-08 22:36:35

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New package fishdata with initial version 0.1.3
Package: fishdata
Type: Package
Title: A Small Collection of Fish Population Datasets
Version: 0.1.3
Authors@R: person("Conor", "Neilson", email = "condwanaland@gmail.com", role = c("aut", "cre"))
Maintainer: Conor Neilson <condwanaland@gmail.com>
Description: A collection of four datasets based around the population dynamics of migratory fish. Datasets contain both basic size information on a per fish basis, as well as otolith data that contains a per day record of fish growth history. All data in this package was collected by the author, from 2015-2016, in the Wellington region of New Zealand.
License: GPL-3
Depends: R (>= 2.10)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown, dplyr, magrittr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-02-13 04:16:26 UTC; apple
Author: Conor Neilson [aut, cre]
Repository: CRAN
Date/Publication: 2018-02-13 10:09:12 UTC

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Package semtree updated to version 0.9.12 with previous version 0.9.11 dated 2017-12-14

Title: Recursive Partitioning for Structural Equation Models
Description: SEM Trees and SEM Forests -- an extension of model-based decision trees and forests to Structural Equation Models (SEM). SEM trees hierarchically split empirical data into homogeneous groups sharing similar data patterns with respect to a SEM by recursively selecting optimal predictors of these differences. SEM forests are an extension of SEM trees. They are ensembles of SEM trees each built on a random sample of the original data. By aggregating over a forest, we obtain measures of variable importance that are more robust than measures from single trees.
Author: Andreas M. Brandmaier [aut, cre], John J. Prindle [aut]
Maintainer: Andreas M. Brandmaier <andy@brandmaier.de>

Diff between semtree versions 0.9.11 dated 2017-12-14 and 0.9.12 dated 2018-02-13

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Package ncmeta updated to version 0.0.2 with previous version 0.0.1 dated 2017-12-01

Title: Straightforward 'NetCDF' Metadata
Description: Extract metadata from 'NetCDF' data sources, these can be files, file handles or servers. This package leverages and extends the lower level functions of the 'RNetCDF' package providing a consistent set of functions that all return data frames. We introduce named concepts of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library itself with only the named concepts of 'variables', 'dimensions' and 'attributes'. 'ncmeta' provides a required framework for the in-development 'tidync' project <https://github.com/hypertidy/tidync>.
Author: Michael Sumner [aut, cre], Tomas Remenyi [ctb], Ben Raymond [ctb]
Maintainer: Michael Sumner <mdsumner@gmail.com>

Diff between ncmeta versions 0.0.1 dated 2017-12-01 and 0.0.2 dated 2018-02-13

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New package MLML2R with initial version 0.1.0
Package: MLML2R
Type: Package
Title: Maximum Likelihood Estimation of DNA Methylation and Hydroxymethylation Levels
Version: 0.1.0
Author: Samara Kiihl [aut, cre], Maria Jose Martinez-Garrido [aut], Arce Domingo-Relloso [aut], Jose Bermudez [aut], Maria Tellez-Plaza [aut]
Maintainer: Samara Kiihl <samara@ime.unicamp.br>
Description: Maximum likelihood estimates (MLE) of the levels of 5-mC and 5-hmC in the DNA using information from BS-conversion, TAB-conversion, and oxBS-conversion methods. One can use information from all three methods or any combination of two of them. Estimates are based on Binomial model by Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and Kiihl et al. (2018, to appear).
License: MIT + file LICENSE
Encoding: UTF-8
VignetteBuilder: knitr
LazyData: true
biocViews: Software, MethylationArray, Epigenetics, DNAMethylation, Microarray, TwoChannel, OneChannel
RoxygenNote: 6.0.1
Suggests: minfi, BiocStyle, microbenchmark, GEOquery, knitr, rmarkdown
BugReports: https://github.com/samarafk/MLML2R/issues
NeedsCompilation: no
Packaged: 2018-02-13 02:55:09 UTC; samara
Repository: CRAN
Date/Publication: 2018-02-13 09:40:10 UTC

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New package librarysnapshot with initial version 0.1.2
Package: librarysnapshot
Date: 2018-02-12
Version: 0.1.2
Type: Package
Title: Library Snapshot for Packages and Dependencies in Use by Current Session
Authors@R: c( person( "Peter", "Meissner", role = c("aut", "cre"), email = "retep.meissner@gmail.com" ) )
Maintainer: Peter Meissner <retep.meissner@gmail.com>
Description: Generate a local library copy with relevant packages. All packages currently found within the search path - except base packages - will be copied to the directory provided and can be used later on with the .libPaths() function.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Imports: utils
URL: https://github.com/petermeissner/librarysnapshot
BugReports: https://github.com/petermeissner/librarysnapshot/issues
Suggests: covr, testthat
NeedsCompilation: no
Packaged: 2018-02-12 20:43:54 UTC; peter
Author: Peter Meissner [aut, cre]
Repository: CRAN
Date/Publication: 2018-02-13 09:34:24 UTC

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New package cRegulome with initial version 0.1.0
Package: cRegulome
Type: Package
Version: 0.1.0
Title: Obtain and Visualize Regulome-Gene Expression Correlations in Cancer
Description: Builds a 'SQLite' database file of pre-calculated transcription factor/microRNA-gene correlations (co-expression) in cancer from the Cistrome Cancer Liu et al. (2011) <doi:10.1186/gb-2011-12-8-r83> and 'miRCancerdb' databases (in press). Provides custom classes and functions to query, tidy and plot the correlation data.
License: GPL-3
Authors@R: person("Mahmoud", "Ahmed", email = "mahmoud.s.fahmy@students.kasralainy.edu.eg", role = c("aut", "cre"))
URL: https://github.com/MahShaaban/cRegulome
BugReports: https://github.com/MahShaaban/cRegulome/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Depends: R(>= 2.10.0)
Imports: DBI, graphics, httr, RSQLite, UpSetR, VennDiagram, magrittr, dplyr, ggplot2, ggridges, tidyr, reshape2, purrr, grid, R.utils
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, testthat, covr, dbplyr, clusterProfiler, igraph, org.Hs.eg.db, readxl, AnnotationDbi, BiocInstaller
NeedsCompilation: no
Packaged: 2018-02-13 02:07:08 UTC; rstudio
Author: Mahmoud Ahmed [aut, cre]
Maintainer: Mahmoud Ahmed <mahmoud.s.fahmy@students.kasralainy.edu.eg>
Repository: CRAN
Date/Publication: 2018-02-13 09:44:49 UTC

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New package clusteredinterference with initial version 1.0.0
Package: clusteredinterference
Type: Package
Date: 2018-02-12
Title: Causal Effects from Observational Studies with Clustered Interference
Version: 1.0.0
Authors@R: c(person("Brian G.", "Barkley", email = "BarkleyBG@outlook.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1787-4735")), person("Bradley", "Saul", role = c("ctb")))
Author: Brian G. Barkley [aut, cre] (<https://orcid.org/0000-0003-1787-4735>), Bradley Saul [ctb]
Maintainer: Brian G. Barkley <BarkleyBG@outlook.com>
Description: Estimating causal effects from observational studies assuming clustered (or partial) interference. These inverse probability-weighted estimators target new estimands arising from population-level treatment policies. The estimands and estimators are introduced in Barkley et al. (2017) <arXiv:1711.04834>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Depends: R (>= 3.2)
Imports: Formula (>= 1.1-2), cubature (>= 1.1-2), lme4 (>= 1.1-10), numDeriv (>= 2014.2-1), rootSolve (>= 1.6.6)
Suggests: testthat, rprojroot, knitr, rmarkdown, covr
VignetteBuilder: knitr
URL: http://github.com/BarkleyBG/clusteredinterference
BugReports: http://github.com/BarkleyBG/clusteredinterference/issues
NeedsCompilation: no
Packaged: 2018-02-12 20:07:21 UTC; Brian G. Barkley
Repository: CRAN
Date/Publication: 2018-02-13 09:24:28 UTC

More information about clusteredinterference at CRAN
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