Thu, 14 Jun 2018

Package TrendInTrend updated to version 1.1.2 with previous version 1.0.2 dated 2017-08-22

Title: Odds Ratio Estimation and Power Calculation for the Trend in Trend Model
Description: Estimation of causal odds ratio and power calculation given trends in exposure prevalence and outcome frequencies of stratified data.
Author: Xinyao Ji and Ashkan Ertefaie
Maintainer: Ashkan Ertefaie <ashkan_ertefaie@urmc.rochester.edu>

Diff between TrendInTrend versions 1.0.2 dated 2017-08-22 and 1.1.2 dated 2018-06-14

 DESCRIPTION      |    8 ++++----
 MD5              |   10 +++++-----
 R/TrendInTrend.R |   28 +++++++++++++++-------------
 man/OR.Rd        |   16 +++++++++-------
 man/ttdetect.Rd  |    5 ++++-
 man/ttpower.Rd   |    4 ++++
 6 files changed, 41 insertions(+), 30 deletions(-)

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Permanent link

New package lidR with initial version 1.5.1
Package: lidR
Type: Package
Title: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
Version: 1.5.1
Date: 2018-06-14
Authors@R: c( person("Jean-Romain", "Roussel", email = "jean-romain.roussel.1@ulaval.ca", role = c("aut", "cre", "cph")), person("David", "Auty", email = "", role = c("aut", "ctb"), comment = "Reviews the documentation"), person("Florian", "De Boissieu", email = "", role = ("ctb"), comment = "Fixed bugs and improved catalog features"), person("Andrew", "Sánchez Meador", email = "", role = ("ctb"), comment = "Implemented lassnags"))
Description: Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.
URL: https://github.com/Jean-Romain/lidR
BugReports: https://github.com/Jean-Romain/lidR/issues
License: GPL-3
Depends: R (>= 3.1.0),methods
Imports: data.table, future, gdalUtils, geometry, grDevices, gstat, lazyeval, mapview, mapedit, memoise, RANN, raster, Rcpp, rgeos, rgl, rlas (>= 1.1.10), settings, sp, stats, tools, utils
Suggests: rgdal, testthat, EBImage, hexbin
LazyData: true
RoxygenNote: 6.0.1
LinkingTo: Rcpp
Encoding: UTF-8
ByteCompile: true
biocViews:
Collate: 'RcppExports.R' 'catalog_apply.r' 'catalog_clip.r' 'catalog_index.r' 'catalog_laxindex.r' 'catalog_makecluster.r' 'catalog_query.r' 'catalog_reshape.r' 'catalog_select.r' 'class-lasheader.r' 'class-las.r' 'class-lascatalog.r' 'class-lascluster.r' 'constant.R' 'deprecated.r' 'grid_canopy.r' 'grid_catalog.r' 'grid_density.r' 'grid_hexametrics.r' 'grid_metrics.r' 'grid_metrics3d.r' 'grid_terrain.r' 'grid_tincanopy.r' 'lasaggreagte.r' 'lascheck.r' 'lasclassify.r' 'lasclip.r' 'lascolor.r' 'lasfilter.r' 'lasfilterdecimate.r' 'lasfiltersurfacepoints.r' 'lasground.r' 'lasindentify.r' 'lasmetrics.r' 'lasnormalize.r' 'lasroi.r' 'lassmooth.r' 'lassnags.r' 'lastrees.r' 'lastrees_dalponte.r' 'lastrees_li.r' 'lastrees_li2.r' 'lastrees_silva.r' 'lastrees_watershed.r' 'lasupdateheader.r' 'lidRError.r' 'metrics.r' 'metrics_canopy_roughness.r' 'mutatebyref.r' 'options.r' 'plot.catalog.r' 'plot.las.r' 'plot.lashexametrics.r' 'plot.lasmetrics.r' 'plot.lasmetrics3d.r' 'plot3d.r' 'readLAS.r' 'subcircled.r' 'tree_detection.r' 'tree_metrics.r' 'utils_colors.r' 'utils_geometry.r' 'utils_interpolations.r' 'utils_misc.r' 'utils_projection.r' 'utils_spatial.r' 'utils_typecast.r' 'writeLAS.r' 'zzz.r'
NeedsCompilation: yes
Packaged: 2018-06-14 21:40:31 UTC; jr
Author: Jean-Romain Roussel [aut, cre, cph], David Auty [aut, ctb] (Reviews the documentation), Florian De Boissieu [ctb] (Fixed bugs and improved catalog features), Andrew Sánchez Meador [ctb] (Implemented lassnags)
Maintainer: Jean-Romain Roussel <jean-romain.roussel.1@ulaval.ca>
Repository: CRAN
Date/Publication: 2018-06-14 22:24:14 UTC

More information about lidR at CRAN
Permanent link

Package radiant.basics updated to version 0.9.5 with previous version 0.8.0 dated 2017-04-27

Title: Basics Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Basics menu includes interfaces for probability calculation, central limit theorem simulation, comparing means and proportions, goodness-of-fit testing, cross-tabs, and correlation. The application extends the functionality in radiant.data.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant.basics versions 0.8.0 dated 2017-04-27 and 0.9.5 dated 2018-06-14

 radiant.basics-0.8.0/radiant.basics/inst/app/tools/analysis/clt.R                                    |only
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 radiant.basics-0.8.0/radiant.basics/tests/urls                                                       |only
 radiant.basics-0.9.5/radiant.basics/DESCRIPTION                                                      |   26 
 radiant.basics-0.9.5/radiant.basics/MD5                                                              |  229 
 radiant.basics-0.9.5/radiant.basics/NAMESPACE                                                        |   29 
 radiant.basics-0.9.5/radiant.basics/NEWS.md                                                          |   71 
 radiant.basics-0.9.5/radiant.basics/R/aaa.R                                                          |   19 
 radiant.basics-0.9.5/radiant.basics/R/clt.R                                                          |only
 radiant.basics-0.9.5/radiant.basics/R/compare_means.R                                                |  399 -
 radiant.basics-0.9.5/radiant.basics/R/compare_props.R                                                |  356 -
 radiant.basics-0.9.5/radiant.basics/R/correlation.R                                                  |  234 
 radiant.basics-0.9.5/radiant.basics/R/cross_tabs.R                                                   |  526 -
 radiant.basics-0.9.5/radiant.basics/R/goodness.R                                                     |  325 
 radiant.basics-0.9.5/radiant.basics/R/prob_calc.R                                                    | 3334 +++++-----
 radiant.basics-0.9.5/radiant.basics/R/radiant.R                                                      |   40 
 radiant.basics-0.9.5/radiant.basics/R/single_mean.R                                                  |  277 
 radiant.basics-0.9.5/radiant.basics/R/single_prop.R                                                  |  287 
 radiant.basics-0.9.5/radiant.basics/README.md                                                        |   70 
 radiant.basics-0.9.5/radiant.basics/inst/app/global.R                                                |    4 
 radiant.basics-0.9.5/radiant.basics/inst/app/help.R                                                  |   28 
 radiant.basics-0.9.5/radiant.basics/inst/app/init.R                                                  |   47 
 radiant.basics-0.9.5/radiant.basics/inst/app/server.R                                                |   23 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/clt_ui.R                                 |only
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/compare_means_ui.R                       |  210 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/compare_props_ui.R                       |  205 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/correlation_ui.R                         |  190 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/cross_tabs_ui.R                          |  152 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/goodness_ui.R                            |  150 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/prob_calc_ui.R                           |  861 +-
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/single_mean_ui.R                         |  180 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/analysis/single_prop_ui.R                         |  183 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/clt.md                                       |    4 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/compare_means.md                             |    7 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/compare_props.md                             |    6 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/correlation.md                               |   12 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/cross_tabs.md                                |   10 
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 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/prob_calc.Rmd                                |    8 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/prob_calc.md                                 |    8 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/single_mean.md                               |    8 
 radiant.basics-0.9.5/radiant.basics/inst/app/tools/help/single_prop.md                               |    7 
 radiant.basics-0.9.5/radiant.basics/inst/app/ui.R                                                    |   16 
 radiant.basics-0.9.5/radiant.basics/inst/app/www/js/run_return.js                                    |   48 
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 radiant.basics-0.9.5/radiant.basics/man/correlation.Rd                                               |   16 
 radiant.basics-0.9.5/radiant.basics/man/cross_tabs.Rd                                                |   12 
 radiant.basics-0.9.5/radiant.basics/man/goodness.Rd                                                  |   13 
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 radiant.basics-0.9.5/radiant.basics/man/plot.prob_chisq.Rd                                           |    7 
 radiant.basics-0.9.5/radiant.basics/man/plot.prob_disc.Rd                                            |    8 
 radiant.basics-0.9.5/radiant.basics/man/plot.prob_expo.Rd                                            |    7 
 radiant.basics-0.9.5/radiant.basics/man/plot.prob_fdist.Rd                                           |    7 
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 radiant.basics-0.9.5/radiant.basics/man/plot.prob_tdist.Rd                                           |    7 
 radiant.basics-0.9.5/radiant.basics/man/plot.prob_unif.Rd                                            |    7 
 radiant.basics-0.9.5/radiant.basics/man/plot.single_mean.Rd                                          |    4 
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 radiant.basics-0.9.5/radiant.basics/man/prob_tdist.Rd                                                |   15 
 radiant.basics-0.9.5/radiant.basics/man/prob_unif.Rd                                                 |    6 
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 radiant.basics-0.9.5/radiant.basics/man/summary.prob_chisq.Rd                                        |   11 
 radiant.basics-0.9.5/radiant.basics/man/summary.prob_disc.Rd                                         |    8 
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 radiant.basics-0.9.5/radiant.basics/man/summary.prob_tdist.Rd                                        |   11 
 radiant.basics-0.9.5/radiant.basics/man/summary.prob_unif.Rd                                         |   11 
 radiant.basics-0.9.5/radiant.basics/man/summary.single_mean.Rd                                       |    4 
 radiant.basics-0.9.5/radiant.basics/man/summary.single_prop.Rd                                       |    4 
 radiant.basics-0.9.5/radiant.basics/tests/testthat/test_stats.R                                      |   80 
 123 files changed, 5193 insertions(+), 3874 deletions(-)

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Package PReMiuM updated to version 3.1.8 with previous version 3.1.7 dated 2018-04-01

Title: Dirichlet Process Bayesian Clustering, Profile Regression
Description: Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
Author: David I. Hastie, Silvia Liverani <liveranis@gmail.com> and Sylvia Richardson with contributions from Aurore J. Lavigne, Lucy Leigh, Lamiae Azizi, Xi Liu, Ruizhu Huang, Austin Gratton, Wei Jing
Maintainer: Silvia Liverani <liveranis@gmail.com>

Diff between PReMiuM versions 3.1.7 dated 2018-04-01 and 3.1.8 dated 2018-06-14

 ChangeLog                           |    9 
 DESCRIPTION                         |   12 
 MD5                                 |   44 
 NAMESPACE                           |    1 
 R/postProcess.R                     | 6192 ++++++++++++++++++------------------
 man/PReMiuM-package.Rd              |    4 
 man/margModelPosterior.Rd           |    2 
 man/profRegr.Rd                     |    4 
 man/setHyperparams.Rd               |    7 
 man/simBenchmark.Rd                 |only
 src/PReMiuM.cpp                     |    9 
 src/include/PReMiuMData.h           |    2 
 src/include/PReMiuMIO.h             |  117 
 src/include/PReMiuMModel.h          |   91 
 src/include/PReMiuMOptions.h        |   15 
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 31 files changed, 3481 insertions(+), 3128 deletions(-)

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Package polspline updated to version 1.1.13 with previous version 1.1.12 dated 2015-07-14

Title: Polynomial Spline Routines
Description: Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
Author: Charles Kooperberg <clk@fredhutch.org>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between polspline versions 1.1.12 dated 2015-07-14 and 1.1.13 dated 2018-06-14

 DESCRIPTION                 |    8 +--
 MD5                         |   23 ++++----
 NAMESPACE                   |    2 
 R/polspline.R               |    4 +
 src/allpack.f               |   19 ++++---
 src/hareall.c               |    8 +--
 src/heftall.c               |    9 +--
 src/lsdall.c                |  113 ++++++++++++++++++++++++++------------------
 src/lspecall.c              |   19 ++++---
 src/nlsd.c                  |   49 +++++++++++--------
 src/polyall.c               |    4 +
 src/polymars.c              |   61 +++--------------------
 src/registerDynamicSymbol.c |only
 13 files changed, 158 insertions(+), 161 deletions(-)

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New package MODISTools with initial version 1.0.0
Package: MODISTools
Title: Interface to the 'MODIS Land Products Subsets' Web Services
Version: 1.0.0
Authors@R: person("Hufkens","Koen", email="koen.hufkens@gmail.com", role=c("aut", "cre"), comment = c(ORCID = "0000-0002-5070-8109"))
Description: Programmatic interface to the 'MODIS Land Products Subsets' web services (<https://modis.ornl.gov/data/modis_webservice.html>). Allows for easy downloads of 'MODIS' time series directly to your R workspace or your computer.
URL: https://github.com/khufkens/MODISTools
BugReports: https://github.com/khufkens/MODISTools/issues
Depends: R (>= 3.4)
Imports: httr, utils, tidyr, jsonlite
License: AGPL-3
LazyData: true
ByteCompile: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown, covr, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-14 17:41:59 UTC; khufkens
Author: Hufkens Koen [aut, cre] (<https://orcid.org/0000-0002-5070-8109>)
Maintainer: Hufkens Koen <koen.hufkens@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-14 21:58:16 UTC

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New package mixor with initial version 1.0.4
Package: mixor
Type: Package
Title: Mixed-Effects Ordinal Regression Analysis
Version: 1.0.4
Date: 2018-06-13
Author: Kellie J. Archer, Donald Hedeker, Rachel Nordgren, Robert D. Gibbons
Maintainer: Kellie J. Archer <archer.43@osu.edu>
Description: Provides the function 'mixor' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
License: GPL (>= 2)
Depends: R (>= 2.10), survival
BuildResaveData: best
Biarch: yes
NeedsCompilation: yes
LazyLoad: true
Packaged: 2018-06-14 16:09:02 UTC; kjarcher
Repository: CRAN
Date/Publication: 2018-06-14 21:17:56 UTC

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Package LogicReg updated to version 1.5.10 with previous version 1.5.9 dated 2016-08-30

Title: Logic Regression
Description: Routines for fitting Logic Regression models.
Author: Charles Kooperberg <clk@fredhutch.org> and Ingo Ruczinski <ingo@jhu.edu>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between LogicReg versions 1.5.9 dated 2016-08-30 and 1.5.10 dated 2018-06-14

 DESCRIPTION                 |    8 ++++----
 MD5                         |   10 ++++++----
 NAMESPACE                   |    2 +-
 man/LogicReg-internal.Rd    |only
 man/logreg.Rd               |   10 +++++-----
 src/registerDynamicSymbol.c |only
 src/slogic.f                |   18 ++++++++++++++++++
 7 files changed, 34 insertions(+), 14 deletions(-)

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New package eseis with initial version 0.4.0
Package: eseis
Type: Package
Title: Environmental Seismology Toolbox
Version: 0.4.0
Date: 2018-06-14
Authors@R: c( person("Michael", "Dietze", role = c("cre", "aut", "trl"), email = "mdietze@gfz-potsdam.de"), person("Christoph", "Burow", role = c("ctb")), person("Sophie", "Lagarde", role = c("ctb", "trl")))
Maintainer: Michael Dietze <mdietze@gfz-potsdam.de>
Description: Environmental seismology is a scientific field that studies the seismic signals, emitted by Earth surface processes. This package provides all relevant functions to read/write seismic data files, prepare, analyse and visualise seismic data, and generate reports of the processing history.
License: GPL-3
Depends: R (>= 3.4.0)
LinkingTo: Rcpp (>= 0.12.5)
Imports: sp, multitaper, raster, rgdal, caTools, signal, fftw, matrixStats, methods, IRISSeismic, XML, rmarkdown, Rcpp (>= 0.12.5)
Suggests: plot3D, rgl
SystemRequirements: gipptools dataselect
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-06-14 21:26:51 UTC; mdietze
Author: Michael Dietze [cre, aut, trl], Christoph Burow [ctb], Sophie Lagarde [ctb, trl]
Repository: CRAN
Date/Publication: 2018-06-14 23:39:08

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Package docxtools updated to version 0.1.2 with previous version 0.1.1 dated 2017-03-12

Title: Tools for R Markdown to Docx Documents
Description: A set of helper functions for using R Markdown to create documents in docx format, especially documents for use in a classroom or workshop setting.
Author: Richard Layton [aut, cre]
Maintainer: Richard Layton <graphdoctor@gmail.com>

Diff between docxtools versions 0.1.1 dated 2017-03-12 and 0.1.2 dated 2018-06-14

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New package coxed with initial version 0.1.1
Package: coxed
Type: Package
Title: Duration-Based Quantities of Interest for the Cox Proportional Hazards Model
Version: 0.1.1
Depends: R (>= 2.13.0), rms, survival, mgcv
Authors@R: c( person("Kropko,", "Jonathan", email = "jkropko@virginia.edu", role = c("aut", "cre")), person("Harden,", "Jeffrey J.", email = "jeff.harden@nd.edu", role = c("aut")))
Description: Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model.
License: GPL-2
Encoding: UTF-8
URL: https://github.com/jkropko/coxed
LazyData: true
Imports: PermAlgo, dplyr, tidyr, ggplot2, gridExtra, utils
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-14 18:38:31 UTC; jk8sd
Author: Kropko, Jonathan [aut, cre], Harden, Jeffrey J. [aut]
Maintainer: "Kropko, Jonathan" <jkropko@virginia.edu>
Repository: CRAN
Date/Publication: 2018-06-14 21:39:48 UTC

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Package BHTSpack updated to version 0.3 with previous version 0.2 dated 2018-06-12

Title: Bayesian Multi-Plate High-Throughput Screening of Compounds
Description: Can be used for joint identification of candidate compound hits from multiple assays, in drug discovery. This package implements the framework of I. D. Shterev, D. B. Dunson, C. Chan and G. D. Sempowski. "Bayesian Multi-Plate High-Throughput Screening of Compounds", Scientific Reports (to appear). This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
Author: Ivo D. Shterev [aut, cre], David B. Dunson [aut], Cliburn Chan [aut], Gregory D. Sempowski [aut]
Maintainer: Ivo D. Shterev <i.shterev@duke.edu>

Diff between BHTSpack versions 0.2 dated 2018-06-12 and 0.3 dated 2018-06-14

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Package logspline updated to version 2.1.11 with previous version 2.1.10 dated 2018-06-02

Title: Routines for Logspline Density Estimation
Description: Contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e. the Kooperberg and Stone (1992) <DOI:10.2307/1390786> algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997) <DOI:10.1214/aos/1031594728>.
Author: Charles Kooperberg <clk@fredhutch.org>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between logspline versions 2.1.10 dated 2018-06-02 and 2.1.11 dated 2018-06-14

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New package jeek with initial version 1.0.0
Package: jeek
Type: Package
Date: 2018-06-15
Title: A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Version: 1.0.0
Authors@R: c(person("Beilun", "Wang", role = c("aut", "cre"), email = "bw4mw@virginia.edu"), person("Yanjun", "Qi", role = "aut", email = "yanjun@virginia.edu"))
Author: Beilun Wang [aut, cre], Yanjun Qi [aut]
Maintainer: Beilun Wang <bw4mw@virginia.edu>
Description: Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeekDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) <arXiv:1806.00548>.
Depends: R (>= 3.0.0), lpSolve, pcaPP, igraph
Suggests: parallel
License: GPL-2
Encoding: UTF-8
URL: https://github.com/QData/jeek
BugReports: https://github.com/QData/jeek
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-13 14:53:50 UTC; beilunwang
Repository: CRAN
Date/Publication: 2018-06-14 19:24:50 UTC

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New package genesysr with initial version 0.9.1
Package: genesysr
Version: 0.9.1
Title: Genesys PGR Client
Description: Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>). Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.
Authors@R: c(person(family = "Global Crop Diversity Trust", role = c("cph")), person("Matija", "Obreza", email = "matija.obreza@croptrust.org", role = c("aut", "cre")), person("Nora", "Castaneda", email = "nora.castaneda@croptrust.org", role = c("ctb")))
Maintainer: Matija Obreza <matija.obreza@croptrust.org>
Depends: R (>= 3.1.0)
Imports: httr, jsonlite
License: Apache License 2.0
RoxygenNote: 6.0.1
URL: https://gitlab.croptrust.org/genesys-pgr/genesysr
BugReports: https://gitlab.croptrust.org/genesys-pgr/genesysr/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-12 19:12:29 UTC; mobreza
Author: Global Crop Diversity Trust [cph], Matija Obreza [aut, cre], Nora Castaneda [ctb]
Repository: CRAN
Date/Publication: 2018-06-14 19:24:54 UTC

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New package varjmcm with initial version 0.1.0
Package: varjmcm
Type: Package
Title: Estimations for the Covariance of Estimated Parameters in Joint Mean-Covariance Models
Version: 0.1.0
Authors@R: c(person("Naimin", "Jing", email = "naimin.jing@temple.edu", role = c("aut", "cre")), person("Hexin", "Bai", role = "aut"), person("Tong", "Wang", role = "aut"), person("Cheng Yong", "Tang", role = "aut"))
Description: The goal of the package is to equip the 'jmcm' package (current version 0.1.8.0) with estimations of the covariance of estimated parameters. Two methods are provided. The first method is to use the inverse of estimated Fisher's information matrix, see M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) <doi:10.1214/aos/1176351062> for reference.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: jmcm
Imports: expm, MASS, stats, Matrix
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-12 16:19:38 UTC; jnm
Author: Naimin Jing [aut, cre], Hexin Bai [aut], Tong Wang [aut], Cheng Yong Tang [aut]
Maintainer: Naimin Jing <naimin.jing@temple.edu>
Repository: CRAN
Date/Publication: 2018-06-14 19:00:00 UTC

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New package stockR with initial version 1.0.68
Package: stockR
Title: Identifying Stocks in Genetic Data
Version: 1.0.68
Authors@R: c(person("Scott D.", "Foster", role = c("aut", "cre"), email = "scott.foster@csiro.au"))
Author: Scott D. Foster [aut, cre]
Description: Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after find good starting values via a K-means clustering of the genetic data. Details can be found in Foster, Feutry, Grewe, Berry, Hui, Davies (in press) Reliably Discriminating Stock Structure with Genetic Markers: Mixture Models with Robust and Fast Computation. Molecular Ecology Resources.
Maintainer: Scott D. Foster <scott.foster@csiro.au>
License: GPL (>= 2)
Imports: stats, gtools, parallel
Suggests: knitr
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2018-06-12 01:02:44 UTC; fos085
Depends: R (>= 2.10)
Repository: CRAN
Date/Publication: 2018-06-14 18:32:57 UTC

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Package splines2 updated to version 0.2.8 with previous version 0.2.7 dated 2017-12-01

Title: Regression Spline Functions and Classes
Description: Constructs B-splines and its integral, monotone splines (M-splines) and its integral (I-splines), convex splines (C-splines), and their derivatives of given order. Piecewise constant basis is allowed for B-splines and M-splines. See De Boor (1978) <doi:10.1002/zamm.19800600129>, Ramsay (1988) <doi:10.1214/ss/1177012761>, and Meyer (2008) <doi:10.1214/08-AOAS167> for more information.
Author: Wenjie Wang [aut, cre], Jun Yan [aut]
Maintainer: Wenjie Wang <wenjie.2.wang@uconn.edu>

Diff between splines2 versions 0.2.7 dated 2017-12-01 and 0.2.8 dated 2018-06-14

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Package smacof updated to version 1.10-8 with previous version 1.10-5 dated 2018-03-20

Title: Multidimensional Scaling
Description: Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, inverse MDS, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Author: Patrick Mair [aut, cre], Jan De Leeuw [aut], Ingwer Borg [ctb], Patrick J. F. Groenen [ctb]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

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Package semds updated to version 0.9-4 with previous version 0.9-3 dated 2018-05-26

Title: Structural Equation Multidimensional Scaling
Description: Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within an SEM framework while the objects are represented in a low-dimensional space as in MDS.
Author: Patrick Mair [aut, cre], Jose Fernando Vera [aut]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

Diff between semds versions 0.9-3 dated 2018-05-26 and 0.9-4 dated 2018-06-14

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New package nparMD with initial version 0.1.0
Package: nparMD
Type: Package
Title: Nonparametric Analysis of Multivariate Data in Factorial Designs
Version: 0.1.0
Depends: R (>= 3.1.0)
Imports: matrixStats, matrixcalc, MASS, gtools, Formula, methods, stats
Author: Maximilian Kiefel and Arne C. Bathke
Maintainer: Maximilian Kiefel <physik210@hotmail.com>
Description: Analysis of multivariate data with two-way completely randomized factorial design. The analysis is based on fully nonparametric, rank-based methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda, Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The package offers two functions performing the analysis, one for small and the other for large sample sizes. The underlying methodology is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8>.
License: GPL-2 | GPL-3
Encoding: UTF-8
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-11 13:57:59 UTC; Maxi
Repository: CRAN
Date/Publication: 2018-06-14 18:33:00 UTC

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New package morpheus with initial version 0.2-0
Package: morpheus
Title: Estimate Parameters of Mixtures of Logistic Regressions
Description: Mixture of logistic regressions parameters (H)estimation with (U)spectral methods. The main methods take d-dimensional inputs and a vector of binary outputs, and return parameters according to the GLMs mixture model (General Linear Model). For more details see chapter 3 in the PhD thesis of Mor-Absa Loum: <http://www.theses.fr/s156435>, available here <https://www.math.u-psud.fr/~loum/IMG/pdf/these.compressed-2.pdf>.
Version: 0.2-0
Author: Benjamin Auder <Benjamin.Auder@u-psud.fr> [aut,cre], Mor-Absa Loum <Mor-Absa.Loum@u-psud.fr> [aut]
Maintainer: Benjamin Auder <Benjamin.Auder@u-psud.fr>
Depends: R (>= 3.0.0),
Imports: MASS, jointDiag, methods, pracma
Suggests: devtools, flexmix, parallel, testthat, roxygen2, tensor, nloptr
License: MIT + file LICENSE
RoxygenNote: 5.0.1
Collate: 'utils.R' 'A_NAMESPACE.R' 'computeMu.R' 'multiRun.R' 'optimParams.R' 'plot.R' 'sampleIO.R'
NeedsCompilation: yes
Packaged: 2018-06-12 12:24:17 UTC; auder
Repository: CRAN
Date/Publication: 2018-06-14 18:48:14 UTC

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New package mase with initial version 0.1.1
Package: mase
Type: Package
Title: Model-Assisted Survey Estimators
Version: 0.1.1
Date: 2018-06-06
Authors@R: c(person("Kelly", "McConville", role = c("aut", "cre", "cph"), email = "mcconville@reed.edu"), person("Becky", "Tang", role = "aut"), person("George", "Zhu", role = "aut"), person("Sida", "Li", role = "ctb"), person("Shirley", "Chueng", role = "ctb"), person("Daniell", "Toth", role = c("ctb", "cph"), comment = "Author and copyright holder of treeDesignMatrix helper function") )
Maintainer: Kelly McConville <mcconville@reed.edu>
Description: A set of model-assisted survey estimators and corresponding variance estimators for single stage, unequal probability, without replacement sampling designs. All of the estimators can be written as a generalized regression estimator with the Horvitz-Thompson, ratio, post-stratified, and regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6). Two of the estimators employ a statistical learning model as the assisting model: the elastic net regression estimator, which is an extension of the lasso regression estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>. The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the bootstrap variance estimator is presented in Mashreghi et al. (2016) <doi:10.1214/16-SS113>.
License: GPL-2
LazyData: TRUE
Imports: MASS, glmnet, Matrix, foreach, survey, dplyr, magrittr, rpms, boot, stats, Rdpack
Suggests: roxygen2, testthat
Depends: R (>= 3.1)
Collate: 'gregt.R' 'varMase.R' 'GREG.R' 'gregElasticNett.R' 'gregElasticNet.R' 'gregTree.R' 'gregtreet.R' 'htt.R' 'horvitzThompson.R' 'logisticGregElasticNett.R' 'logisticGregt.R' 'postStratt.R' 'postStrat.R' 'ratioEstimatort.R' 'ratioEstimator.R' 'treeDesignMatrix.R'
RoxygenNote: 6.0.1
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2018-06-11 19:21:21 UTC; kswatmac
Author: Kelly McConville [aut, cre, cph], Becky Tang [aut], George Zhu [aut], Sida Li [ctb], Shirley Chueng [ctb], Daniell Toth [ctb, cph] (Author and copyright holder of treeDesignMatrix helper function)
Repository: CRAN
Date/Publication: 2018-06-14 18:33:03 UTC

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New package liger with initial version 0.1
Package: liger
Type: Package
Title: Lightweight Iterative Geneset Enrichment
Version: 0.1
Authors@R: c(person("Jean", "Fan", role = c("aut", "cre"), email = "jeanfan@fas.harvard.edu", comment = c(ORCID = "0000-0002-0212-5451")), person("Peter", "Kharchenko", role = "aut", email = "Peter_Kharchenko@hms.harvard.edu"))
Description: Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. The original algorithm is detailed in Subramanian et al. with 'Java' implementations available through the Broad Institute (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>). The 'liger' package provides a lightweight R implementation of this enrichment test on a list of values (Fan et al., 2017 <doi:10.5281/zenodo.887386>). Given a list of values, such as p-values or log-fold changes derived from differential expression analysis or other analyses comparing biological states, this package enables you to test a priori defined set of genes for enrichment to enable interpretability of highly significant or high fold-change genes.
License: GPL-3 | file LICENSE
LazyData: TRUE
Depends: R (>= 2.10)
Imports: graphics, stats, Rcpp, matrixStats, parallel
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/JEFworks/liger
BugReports: https://github.com/JEFworks/liger/issues
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-06-12 13:38:26 UTC; Jean
Author: Jean Fan [aut, cre] (<https://orcid.org/0000-0002-0212-5451>), Peter Kharchenko [aut]
Maintainer: Jean Fan <jeanfan@fas.harvard.edu>
Repository: CRAN
Date/Publication: 2018-06-14 18:33:07 UTC

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New package IPCAPS with initial version 1.1.5
Package: IPCAPS
Type: Package
Title: Iterative Pruning to Capture Population Structure
Version: 1.1.5
Authors@R: c(person(given = "Kridsadakorn", family = "Chaichoompu", email = "kridsadakorn@biostatgen.org", role = c("aut", "cre")), person(given = "Kristel", family = 'Van Steen', role = "aut"), person(given = "Fentaw", family = "Abegaz", role = "aut"), person(given = "Sissades", family = "Tongsima", role = "aut"), person(given = "Philip", family = "Shaw", role = "aut"), person(given = "Anavaj", family = "Sakuntabhai", role = "aut"), person(given = "Luisa", family = "Pereira", role = "aut"))
Description: An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
Depends: R (>= 3.2.4.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Imports: stats,utils,graphics,grDevices,MASS,Matrix,expm,KRIS,fpc,LPCM,apcluster,Rmixmod
Suggests: testthat
BugReports: https://gitlab.com/kris.ccp/ipcaps/issues
URL: https://gitlab.com/kris.ccp/ipcaps
Collate: 'parallelization.R' 'check.stopping.R' 'clustering.mode.R' 'clustering.R' 'data.R' 'export.groups.R' 'get.node.info.R' 'ipcaps-package.R' 'process.each.node.R' 'output.template.R' 'save.html.R' 'save.eigenplots.html.R' 'save.plots.label.html.R' 'save.plots.cluster.html.R' 'save.plots.R' 'postprocess.R' 'preprocess.R' 'ipcaps.R' 'top.discriminator.R'
NeedsCompilation: no
Packaged: 2018-06-11 13:05:39 UTC; kridsadakorn
Author: Kridsadakorn Chaichoompu [aut, cre], Kristel Van Steen [aut], Fentaw Abegaz [aut], Sissades Tongsima [aut], Philip Shaw [aut], Anavaj Sakuntabhai [aut], Luisa Pereira [aut]
Maintainer: Kridsadakorn Chaichoompu <kridsadakorn@biostatgen.org>
Repository: CRAN
Date/Publication: 2018-06-14 18:01:51 UTC

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New package INDperform with initial version 0.1.0
Type: Package
Package: INDperform
Version: 0.1.0
Title: Evaluation of Indicator Performances for Assessing Ecosystem States
Authors@R: c( person("Saskia A.", "Otto", email = "saskia.a.otto@gmail.com", role = c("aut", "cre")), person("Rene", "Plonus", role = "aut"), person("Steffen", "Funk", role = "aut"), person("Alexander", "Keth", role = "aut") )
Description: An implementation of the 7-step approach suggested by Otto et al. (2018) <doi:10.1016/j.ecolind.2017.05.045> to validate ecological state indicators and to select a suite of complimentary and well performing indicators. This suite can be then used to assess the current state of the system in comparison to a reference period. However, the tools in this package are very generic and can be used to test any type of indicator (e.g. social or economic indicators).
URL: https://github.com/saskiaotto/INDperform
BugReports: https://github.com/SaskiaAOtto/INDperform/issues
Encoding: UTF-8
Depends: R(>= 3.3)
Imports: cowplot (>= 0.7.0), dplyr (>= 0.5.0), grDevices (>= 1.8-17), ggplot2 (>= 2.2.1), htmlwidgets (>= 0.8), jsonlite (>= 1.4), lazyeval (>= 0.2.0), magrittr (>= 1.5), mgcv (>= 1.8-17), nlme (>= 3.1-131), purrr (>= 0.2.2), RColorBrewer (>= 1.1-2), rhandsontable (>= 0.3.4), shiny (>= 1.0.1), stringr (>= 1.2.0), tibble (>= 1.3.0), tidyr (>= 0.6.1), vdiffr (>= 0.1.1), vegan (>= 2.4-3)
Suggests: doParallel (>= 1.0.11), ggdendro (>= 0.1-20), gridExtra (>= 2.2.1), knitr (>= 1.16), parallel (>= 3.3.1), pbapply (>= 1.3-3), testthat (>= 1.0.2), tripack (>= 1.3-8)
License: GPL
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-12 18:19:08 UTC; saskiaotto
Author: Saskia A. Otto [aut, cre], Rene Plonus [aut], Steffen Funk [aut], Alexander Keth [aut]
Maintainer: Saskia A. Otto <saskia.a.otto@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-14 19:00:07 UTC

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New package GFORCE with initial version 0.1.2
Package: GFORCE
Type: Package
Title: Clustering and Inference Procedures for High-Dimensional Latent Variable Models
Version: 0.1.2
Authors@R: person("Carson", "Eisenach", email = "eisenach@princeton.edu", role = c("aut", "cre"))
Description: A complete suite of computationally efficient methods for high dimensional clustering and inference problems in G-Latent Models (a type of Latent Variable Gaussian graphical model). The main feature is the FORCE (First-Order, Certifiable, Efficient) clustering algorithm which is a fast solver for a semi-definite programming (SDP) relaxation of the K-means problem. For certain types of graphical models (G-Latent Models), with high probability the algorithm not only finds the optimal clustering, but produces a certificate of having done so. This certificate, however, is model independent and so can also be used to certify data clustering problems. The 'GFORCE' package also contains implementations of inferential procedures for G-Latent graphical models using n-fold cross validation. Also included are native code implementations of other popular clustering methods such as Lloyd's algorithm with kmeans++ initialization and complete linkage hierarchical clustering. The FORCE method is due to Eisenach and Liu (2017) <arxiv:1806.00530>.
License: GPL-2
LazyData: TRUE
RoxygenNote: 6.0.1
Imports: MASS,lpSolve,stats
Suggests: testthat
NeedsCompilation: yes
Packaged: 2018-06-12 16:17:02 UTC; carson
Author: Carson Eisenach [aut, cre]
Maintainer: Carson Eisenach <eisenach@princeton.edu>
Repository: CRAN
Date/Publication: 2018-06-14 19:00:12 UTC

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New package DiPhiSeq with initial version 0.1.0
Package: DiPhiSeq
Type: Package
Title: Robust Tests for Differential Dispersion and Differential Expression in RNA-Sequencing Data
Version: 0.1.0
Authors@R: c(person("Jun", "Li", email = "jun.li@nd.edu", role = c("aut", "cre")), person("Alicia T.", "Lamere", role = c("aut")))
Description: Implements the algorithm described in Jun Li and Alicia T. Lamere, "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes" (Unpublished). Detects not only genes that show different average expressions ("differential expression", DE), but also genes that show different diversities of expressions in different groups ("differentially dispersed", DD). DD genes can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise.
Depends: R (>= 3.1.0)
Imports: stats (>= 3.1.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-12 15:15:40 UTC; jun
Author: Jun Li [aut, cre], Alicia T. Lamere [aut]
Maintainer: Jun Li <jun.li@nd.edu>
Repository: CRAN
Date/Publication: 2018-06-14 19:00:17 UTC

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New package corTest with initial version 0.9.6
Package: corTest
Type: Package
Title: Robust Tests for Equal Correlation
Version: 0.9.6
Author: Danyang Yu, Weiliang Qiu
Maintainer: Danyang Yu <danyangyu@hnu.edu.cn>
Description: There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The difference between 6 methods is the difference of U which are proportion to different types of correlation. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the passage New Statistical Methods for Constructing Robust Differential Correlation Networks (expected to be public).
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
Imports: MASS, graphics, stats
NeedsCompilation: no
Packaged: 2018-06-12 12:38:37 UTC; yudan
Repository: CRAN
Date/Publication: 2018-06-14 18:37:57 UTC

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Package breakDown updated to version 0.1.6 with previous version 0.1.5 dated 2018-04-06

Title: Model Agnostic Explainers for Individual Predictions
Description: Model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. Break Down Plot presents variable contributions in a concise graphical way. This package work for binary classifiers and general regression models.
Author: Przemyslaw Biecek [aut, cre], Aleksandra Grudziaz [ctb]
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>

Diff between breakDown versions 0.1.5 dated 2018-04-06 and 0.1.6 dated 2018-06-14

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Package Arothron updated to version 1.0.1 with previous version 1.0.0 dated 2018-04-03

Title: Geometric Morphometrics Analyses
Description: Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2018 <doi:10.1002/ajpa.23493>), and functions to import and export the coordinates of landmarks and 3D paths into 'landmarkAscii' and 'am' format files.
Author: Antonio Profico, Alessio Veneziano, Marina Melchionna, Pasquale Raia
Maintainer: Antonio Profico <antonio.profico@uniroma1.it>

Diff between Arothron versions 1.0.0 dated 2018-04-03 and 1.0.1 dated 2018-06-14

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New package jaccard with initial version 0.1.0
Package: jaccard
Type: Package
Title: Test Similarity Between Binary Data using Jaccard/Tanimoto Coefficients
Version: 0.1.0
Date: 2018-06-06
Author: Neo Christopher Chung <nchchung@gmail.com>, Błażej Miasojedow <bmiasojedow@gmail.com>, Michał Startek <M.Startek@mimuw.edu.pl>, Anna Gambin <aniag@mimuw.edu.pl>
Maintainer: Neo Christopher Chung <nchchung@gmail.com>
Description: Calculate statistical significance of Jaccard/Tanimoto similarity coefficients for binary data.
biocViews:
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 0.12.6), qvalue, dplyr, magrittr
LinkingTo: Rcpp
NeedsCompilation: yes
SystemRequirements: C++11
RoxygenNote: 6.0.1
Packaged: 2018-06-10 01:52:22 UTC; nc
Repository: CRAN
Date/Publication: 2018-06-14 17:53:00 UTC

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Package tuckerR.mmgg updated to version 1.5.1 with previous version 1.5.0 dated 2017-02-23

Title: Three-Mode Principal Components Analysis
Description: Performs Three-Mode Principal Components Analysis, which carries out Tucker Models.
Author: Marta Marticorena [aut], Gustavo Gimenez [cre], Cecilia Gonzalez [ctb], Sergio Bramardi [aut]
Maintainer: Gustavo Gimenez <gustavo.gimenez@faea.uncoma.edu.ar>

Diff between tuckerR.mmgg versions 1.5.0 dated 2017-02-23 and 1.5.1 dated 2018-06-14

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Package radiant.design updated to version 0.9.5 with previous version 0.8.0 dated 2017-04-27

Title: Design Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Design menu includes interfaces for design of experiments, sampling, and sample size calculation. The application extends the functionality in radiant.data.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant.design versions 0.8.0 dated 2017-04-27 and 0.9.5 dated 2018-06-14

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Package tensorflow updated to version 1.8 with previous version 1.5 dated 2018-01-17

Title: R Interface to 'TensorFlow'
Description: Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
Author: JJ Allaire [aut, cre], RStudio [cph, fnd], Yuan Tang [aut, cph] (<https://orcid.org/0000-0001-5243-233X>), Dirk Eddelbuettel [ctb, cph], Nick Golding [ctb, cph], Tomasz Kalinowski [ctb, cph], Google Inc. [ctb, cph] (Examples and Tutorials)
Maintainer: JJ Allaire <jj@rstudio.com>

Diff between tensorflow versions 1.5 dated 2018-01-17 and 1.8 dated 2018-06-14

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Package RcppAlgos updated to version 2.0.2 with previous version 2.0.1 dated 2018-06-10

Title: Tools for Combinatorics and Computational Mathematics
Description: Provides optimized functions implemented in C++ with 'Rcpp' for solving problems in combinatorics and computational mathematics. There are combination/permutations function that are both flexible as well as efficient with respect to speed and memory. There are optional constraint parameters that when utilized, generate all combinations/ permutations of a vector meeting specific criteria (e.g. finding all combinations such that the sum is less than a bound). These functions are capable of generating specific combinations/permutations (e.g. retrieve only the 50th lexicographical permutation of 5 elements choose 3) by utilizing the bounds arguments. This sets up nicely for parallelization and allows for fast generation of combinations/ permutations beyond 2^31 - 1 results as chunks are generated independently. All combinatorial functions are capable of handling multisets as well. Additionally, there are several highly efficient number theoretic functions that are useful for problems common in computational mathematics. These include various sieving functions that can quickly generate the following: prime numbers, number of coprime elements, number of divisors, prime factorizations, and complete factorizations. Some of these functions make use of the fast integer division library 'libdivide' by <http://ridiculousfish.com>. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is capable of generating all primes less than a billion in just over 1 second. It can also quickly generate prime numbers over a range (e.g. primeSieve(10^13, 10^13+10^9)). There are stand-alone vectorized functions for general factoring (e.g. all divisors of number), primality testing, as well as prime factoring via Pollard's rho algorithm. Finally, there is a prime counting function that implements a simple variation of Legendre's formula based on the algorithm by Kim Walisch. It is capable of returning the number of primes below a trillion in under 0.5 seconds.
Author: Joseph Wood
Maintainer: Joseph Wood <jwood000@gmail.com>

Diff between RcppAlgos versions 2.0.1 dated 2018-06-10 and 2.0.2 dated 2018-06-14

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Package jpndistrict updated to version 0.3.2 with previous version 0.3.1 dated 2018-05-01

Title: Create Japanese Administration Area and Office Maps
Description: Utilizing the data that Japanese administration area provided by the National Land Numerical Information download service (<http://nlftp.mlit.go.jp/ksj/index.html>). This package provide map data is based on the Digital Map 25000 (Map Image) published by Geospatial Information Authority of Japan (Approval No.603FY2017 information usage <http://www.gsi.go.jp>).
Author: Shinya Uryu [aut, cre] (<https://orcid.org/0000-0002-0493-6186>), Geospatial Information Authority of Japan [dtc] (This package data sets, National Land numerical information by the Geographical Survey Institute with the approval of Geographical Survey Institute Head (Approval No.603FY2017 information usage))
Maintainer: Shinya Uryu <suika1127@gmail.com>

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Package CGPfunctions updated to version 0.4 with previous version 0.3 dated 2018-04-19

Title: Powell Miscellaneous Functions for Teaching and Learning Statistics
Description: Miscellaneous functions useful for teaching statistics as well as actually practicing the art. They typically are not “new” methods but rather wrappers around either base R or other packages. Currently contains: 'Plot2WayANOVA' which as the name implies conducts a 2 way ANOVA and plots the results using 'ggplot2'. 'neweta' which is a helper function that appends the results of a Type II eta squared calculation onto a classic ANOVA table. Mode which finds the modal value in a vector of data. 'SeeDist' which wraps around 'ggplot2' to provide visualizations of univariate data.
Author: Chuck Powell [aut, cre]
Maintainer: Chuck Powell <ibecav@gmail.com>

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Package progress updated to version 1.2.0 with previous version 1.1.2 dated 2016-12-14

Title: Terminal Progress Bars
Description: Configurable Progress bars, they may include percentage, elapsed time, and/or the estimated completion time. They work in terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the 'macOS' 'R.app'. The package also provides a 'C++' 'API', that works with or without 'Rcpp'.
Author: Gábor Csárdi [aut, cre], Rich FitzJohn [aut]
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

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Package lue updated to version 0.2.1 with previous version 0.2.0 dated 2018-05-14

Title: Light Use Efficiency Model to Estimate Biomass and YIELD with and Without Vapour Pressure Deficit
Description: Contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.
Author: Maninder Singh Dhillon [aut,cre], Thorsten Dahms [ctb], Leon Nill [ctb]
Maintainer: Maninder Singh Dhillon <manidhillon1989@gmail.com>

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Package compositions updated to version 1.40-2 with previous version 1.40-1 dated 2014-06-07

Title: Compositional Data Analysis
Description: Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
Author: K. Gerald van den Boogaart <boogaart@hzdr.de>, Raimon Tolosana-Delgado, Matevz Bren
Maintainer: K. Gerald van den Boogaart <support@boogaart.de>

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Package wnl updated to version 0.4.1 with previous version 0.4.0 dated 2018-04-02

Title: Minimization Tool for Pharmacokinetic-Pharmacodynamic Data Analysis
Description: This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.
Author: Kyun-Seop Bae [aut]
Maintainer: Kyun-Seop Bae <k@acr.kr>

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Package lingtypology updated to version 1.0.13 with previous version 1.0.12 dated 2018-03-10

Title: Linguistic Typology and Mapping
Description: Provides R with the Glottolog database <http://glottolog.org> and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project <http://clld.org/>, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages <https://ropensci.github.io/lingtypology/> and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website.
Author: George Moroz
Maintainer: George Moroz <agricolamz@gmail.com>

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Package qpcR updated to version 1.4-1 with previous version 1.4-0 dated 2014-09-15

Title: Modelling and Analysis of Real-Time PCR Data
Description: Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
Author: Andrej-Nikolai Spiess <a.spiess@uke.uni-hamburg.de>
Maintainer: Andrej-Nikolai Spiess <a.spiess@uke.uni-hamburg.de>

Diff between qpcR versions 1.4-0 dated 2014-09-15 and 1.4-1 dated 2018-06-14

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Package visNetwork updated to version 2.0.4 with previous version 2.0.3 dated 2018-01-09

Title: Network Visualization using 'vis.js' Library
Description: Provides an R interface to the 'vis.js' JavaScript charting library. It allows an interactive visualization of networks.
Author: Almende B.V. [aut, cph] (vis.js library in htmlwidgets/lib, http://visjs.org, http://www.almende.com/home), Benoit Thieurmel [aut, cre] (R interface), Titouan Robert [aut, ctb]
Maintainer: Benoit Thieurmel <benoit.thieurmel@datastorm.fr>

Diff between visNetwork versions 2.0.3 dated 2018-01-09 and 2.0.4 dated 2018-06-14

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Package MVN updated to version 5.5 with previous version 5.4 dated 2018-06-09

Title: Multivariate Normality Tests
Description: Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests (Korkmaz et al, (2014), <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).
Author: Selcuk Korkmaz [aut, cre], Dincer Goksuluk [aut], Gokmen Zararsiz [aut]
Maintainer: Selcuk Korkmaz <selcukorkmaz@gmail.com>

Diff between MVN versions 5.4 dated 2018-06-09 and 5.5 dated 2018-06-14

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Package TPD updated to version 1.0.0 with previous version 0.1.2 dated 2017-11-09

Title: Methods for Measuring Functional Diversity Based on Trait Probability Density
Description: Tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.
Author: Carlos P. Carmona <perezcarmonacarlos@gmail.com>
Maintainer: Carlos P. Carmona <perezcarmonacarlos@gmail.com>

Diff between TPD versions 0.1.2 dated 2017-11-09 and 1.0.0 dated 2018-06-14

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Package nimble updated to version 0.6-11 with previous version 0.6-10 dated 2018-03-25

Title: MCMC, Particle Filtering, and Programmable Hierarchical Modeling
Description: A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <http://r-nimble.org>.
Author: Perry de Valpine, Christopher Paciorek, Daniel Turek, Cliff Anderson-Bergman, Nick Michaud, Fritz Obermeyer, Duncan Temple Lang, and see AUTHORS file for additional contributors.
Maintainer: Christopher Paciorek <paciorek@stat.berkeley.edu>

Diff between nimble versions 0.6-10 dated 2018-03-25 and 0.6-11 dated 2018-06-14

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More information about nimble at CRAN
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Package ggfan updated to version 0.1.2 with previous version 0.1.1 dated 2017-11-22

Title: Summarise a Distribution Through Coloured Intervals
Description: Implements the functionality of the 'fanplot' package as 'geoms' for 'ggplot2'. Designed for summarising MCMC samples from a posterior distribution, where a visualisation is desired for several values of a continuous covariate. Increasing posterior intervals of the sampled quantity are mapped to a continuous colour scale.
Author: Jason Hilton [aut, cre]
Maintainer: Jason Hilton <jason_hilton@yahoo.com>

Diff between ggfan versions 0.1.1 dated 2017-11-22 and 0.1.2 dated 2018-06-14

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Package ref (with last version 0.99) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2013-12-05 0.99
2012-08-09 0.98
2009-10-11 0.97
2009-04-15 0.96
2007-10-22 0.95
2007-10-21 0.94-1
2007-08-25 0.94
2006-04-27 0.92

Permanent link
Package tree.bins updated to version 0.1.1 with previous version 0.1.0 dated 2018-02-26

Title: Recategorization of Factor Variables by Decision Tree Leaves
Description: Provides users the ability to categorize categorical variables dependent on a response variable. It creates a decision tree by using one of the categorical variables (class factor) and the selected response variable. The decision tree is created from the rpart() function from the 'rpart' package. The rules from the leaves of the decision tree are extracted, and used to recategorize the appropriate categorical variable (predictor). This step is performed for each of the categorical variables that is fed into the data component of the function. Only variables containing more than 2 factor levels will be considered in the function. The final output generates a data set containing the recategorized variables or a list containing a mapping table for each of the candidate variables. For more details see T. Hastie et al (2009, ISBN: 978-0-387-84857-0).
Author: Piro Polo [aut, cre]
Maintainer: Piro Polo <piropolo98@gmail.com>

Diff between tree.bins versions 0.1.0 dated 2018-02-26 and 0.1.1 dated 2018-06-14

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Package testit updated to version 0.8 with previous version 0.7 dated 2017-05-22

Title: A Simple Package for Testing R Packages
Description: Provides two convenience functions assert() and test_pkg() to facilitate testing R packages.
Author: Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>)
Maintainer: Yihui Xie <xie@yihui.name>

Diff between testit versions 0.7 dated 2017-05-22 and 0.8 dated 2018-06-14

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Package spatstat.utils updated to version 1.8-2 with previous version 1.8-0 dated 2017-11-20

Title: Utility Functions for 'spatstat'
Description: Contains utility functions for the 'spatstat' package which may also be useful for other purposes.
Author: Adrian Baddeley [aut, cre], Rolf Turner [aut], Ege Rubak [aut]
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat.utils versions 1.8-0 dated 2017-11-20 and 1.8-2 dated 2018-06-14

 DESCRIPTION               |    8 ++---
 MD5                       |   14 ++++-----
 R/resolve.defaults.R      |    9 ++++--
 R/tapplysum.R             |    8 ++++-
 R/utilseq.R               |   65 +++++++++++++++++++++-------------------------
 inst/doc/packagesizes.txt |    2 -
 man/macros/defns.Rd       |    2 -
 man/tapplysum.Rd          |    3 +-
 8 files changed, 59 insertions(+), 52 deletions(-)

More information about spatstat.utils at CRAN
Permanent link

Package polyclip updated to version 1.9-0 with previous version 1.8-7 dated 2018-05-16

Title: Polygon Clipping
Description: R port of Angus Johnson's open source library Clipper. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Author: Angus Johnson [aut] (C++ original, http://www.angusj.com/delphi/clipper.php), Adrian Baddeley [aut, trl, cre], Kurt Hornik [ctb], Brian D. Ripley [ctb], Elliott Sales de Andrade [ctb]
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between polyclip versions 1.8-7 dated 2018-05-16 and 1.9-0 dated 2018-06-14

 DESCRIPTION  |   10 ++++++----
 MD5          |   11 ++++++-----
 NAMESPACE    |    4 +---
 NEWS         |only
 R/clipper.R  |   34 +++++++++++++++++++++-------------
 configure    |   18 +++++++++---------
 configure.ac |    2 +-
 7 files changed, 44 insertions(+), 35 deletions(-)

More information about polyclip at CRAN
Permanent link

Package expss updated to version 0.8.7 with previous version 0.8.6 dated 2018-01-24

Title: Tables with Labels and Some Useful Functions from Spreadsheets and 'SPSS' Statistics
Description: Package provides tabulation functions with support for 'SPSS'-style labels, multiple / nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in 'knitr', R notebooks, 'Shiny' and 'Jupyter' notebooks. Proper methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package offers useful functions for data processing in marketing research / social surveys - popular data transformation functions from 'SPSS' Statistics ('RECODE', 'COUNT', 'COMPUTE', 'DO IF', etc.) and 'Excel' ('COUNTIF', 'VLOOKUP', etc.). Package is intended to help people to move data processing from 'Excel'/'SPSS' to R.
Author: Gregory Demin [aut, cre]
Maintainer: Gregory Demin <gdemin@gmail.com>

Diff between expss versions 0.8.6 dated 2018-01-24 and 0.8.7 dated 2018-06-14

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 expss-0.8.7/expss/R/if_val.R                                                 | 1112 
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 expss-0.8.7/expss/R/weighted_stats.R                                         |  785 
 expss-0.8.7/expss/R/where.R                                                  |  204 
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 expss-0.8.7/expss/R/zzz_experimental.R                                       |  532 
 expss-0.8.7/expss/build/vignette.rds                                         |binary
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 expss-0.8.7/expss/inst/doc/tables-with-labels.html                           |11677 +++++-----
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 expss-0.8.7/expss/tests/testthat/test_significance_means.R                   |  778 
 expss-0.8.7/expss/tests/testthat/test_sort.R                                 |  246 
 expss-0.8.7/expss/tests/testthat/test_split_labels.R                         |  696 
 expss-0.8.7/expss/tests/testthat/test_split_separate.R                       |  211 
 expss-0.8.7/expss/tests/testthat/test_subst.R                                |   80 
 expss-0.8.7/expss/tests/testthat/test_sum.R                                  |  220 
 expss-0.8.7/expss/tests/testthat/test_sum_if.R                               |  264 
 expss-0.8.7/expss/tests/testthat/test_sum_if_excel_examples.R                |  676 
 expss-0.8.7/expss/tests/testthat/test_tab_sort.R                             |  324 
 expss-0.8.7/expss/tests/testthat/test_utils.R                                |  110 
 expss-0.8.7/expss/tests/testthat/test_values2labels.R                        |  100 
 expss-0.8.7/expss/tests/testthat/test_vec_ops.R                              |  260 
 expss-0.8.7/expss/tests/testthat/test_vlookup.R                              |  531 
 expss-0.8.7/expss/tests/testthat/test_weighted_stats.R                       | 1087 
 expss-0.8.7/expss/tests/testthat/test_where.R                                |  326 
 expss-0.8.7/expss/tests/testthat/test_window_fun.R                           |   90 
 expss-0.8.7/expss/tests/testthat/test_with_data.table.R                      |  281 
 expss-0.8.7/expss/vignettes/labels-support.Rmd                               |  128 
 expss-0.8.7/expss/vignettes/tables-with-labels.Rmd                           | 1573 -
 253 files changed, 48550 insertions(+), 47729 deletions(-)

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