Wed, 06 Feb 2019

New package CascadeData with initial version 1.2
Package: CascadeData
Type: Package
Title: Experimental Data of Cascade Experiments in Genomics
Version: 1.2
Date: 2019-02-06
Depends: R (>= 2.10)
Imports:
Suggests:
Authors@R: c( person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-0837-8281")), person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy-bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-4615-1512")), person(given = "Laurent", family= "Vallat", role = c("ctb"), email = "vallat@unistra.fr"), person(given = "Nicolas", family= "Jung", role = c("ctb"), email = "nicolas.jung@unistra.fr"))
Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Laurent Vallat [ctb], Nicolas Jung [ctb]
Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr>
Description: These experimental expression data (5 leukemic 'CLL' B-lymphocyte of aggressive form from 'GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the 'Cascade' one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
License: GPL (>= 2)
Encoding: UTF-8
RoxygenNote: 6.1.1
URL: http://www-irma.u-strasbg.fr/~fbertran/, https://github.com/fbertran/CascadeData
BugReports: https://github.com/fbertran/CascadeData/issues
NeedsCompilation: no
Packaged: 2019-02-06 18:51:03 UTC; fbertran
Repository: CRAN
Date/Publication: 2019-02-07 00:03:21 UTC

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Package unifed updated to version 1.0.1 with previous version 1.0 dated 2019-01-30

Title: The Unifed Distribution
Description: Probability functions and family for glm() of the unifed distribution (Quijano Xacur, 2018; <arXiv:1812.00251>).
Author: Oscar Alberto Quijano Xacur [aut,cre]
Maintainer: Oscar Alberto Quijano Xacur <oscar.quijano@use.startmail.com>

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New package POD with initial version 0.99.0
Package: POD
Type: Package
Title: Probability of Detection for Qualitative PCR Methods
Version: 0.99.0
Date: 2019-02-06
Author: Markus Boenn (State Office for Consumer Protection Saxony-Anhalt, Germany)
Maintainer: Markus Boenn <markus.boenn.sf@gmail.com>
Description: This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified. This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' <http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015) <doi:10.1007/s00769-015-1112-9>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-06 06:34:18 UTC; boenn
Repository: CRAN
Date/Publication: 2019-02-06 23:53:27 UTC

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Package pcmabc updated to version 1.0.2 with previous version 1.0.1 dated 2019-02-05

Title: Approximate Bayesian Computations for Phylogenetic Comparative Methods
Description: Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2018) <arXiv:1812.03715>.
Author: Krzysztof Bartoszek <krzbar@protonmail.ch>, Pietro Lio'
Maintainer: Krzysztof Bartoszek <krzbar@protonmail.ch>

Diff between pcmabc versions 1.0.1 dated 2019-02-05 and 1.0.2 dated 2019-02-06

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New package insurancerating with initial version 0.4.0
Package: insurancerating
Type: Package
Title: Analytic Insurance Rating Techniques
Version: 0.4.0
Author: Martin Haringa
Maintainer: Martin Haringa <mtharinga@gmail.com>
Description: Methods for insurance rating. It provides a data driven strategy for the construction of insurance tariff classes. This strategy is based on the work by Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>. The package also adds functionality showing additional lines for the reference categories in the levels of the coefficients in the output of a generalized linear regression analysis. In addition it implements a procedure determining the level of a factor with the largest exposure, and thereafter changing the base level of the factor to this level.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: classInt, ggplot2, mgcv, rpart
Depends: R (>= 3.3)
Suggests: testthat
NeedsCompilation: no
Packaged: 2019-02-06 16:24:27 UTC; haringa
Repository: CRAN
Date/Publication: 2019-02-06 23:11:02 UTC

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Package GWSDAT updated to version 3.0.3 with previous version 3.0.1 dated 2018-06-19

Title: GroundWater Spatiotemporal Data Analysis Tool (GWSDAT)
Description: Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.
Author: Wayne W Jones <wayne.w.jones@shell.com>, Ludger Evers <ludger.evers@glasgow.ac.uk>, Andrej Aderhold <andrej.aderhold@protonmail.com>
Maintainer: Ludger Evers <ludger.evers@glasgow.ac.uk>

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Package futility updated to version 0.3 with previous version 0.2 dated 2017-11-22

Title: Interim Analysis of Operational Futility in Randomized Trials with Time-to-Event Endpoints and Fixed Follow-Up
Description: Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results.
Author: Yingying Zhuang [aut], Michal Juraska [aut, cre], Doug Grove [ctb], Peter Gilbert [ctb], Alexander Luedtke [ctb], Sanne Roels [ctb], An Vandebosch [ctb]
Maintainer: Michal Juraska <mjuraska@fredhutch.org>

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Package ecmwfr updated to version 1.1.0 with previous version 1.0.1 dated 2019-01-19

Title: Interface to 'ECMWF' and 'CDS' Data Web Services
Description: Programmatic interface to the European Centre for Medium-Range Weather Forecasts public and restricted dataset web services (ECMWF; <https://www.ecmwf.int/>) as well as Copernicus's Climate Data Store (CDS; <https://cds.climate.copernicus.eu>). Allows for easy downloads of weather forecasts and climate data directly to your R work space or your computer.
Author: Koen Hufkens [aut, cre], Reto Stauffer [ctb] (<https://orcid.org/0000-0002-3798-5507>)
Maintainer: Koen Hufkens <koen.hufkens@gmail.com>

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New package bain with initial version 0.2.0
Package: bain
Type: Package
Date: 2019-01-23
Title: Bayes Factors for Informative Hypotheses
Version: 0.2.0
Authors@R: c( person(c("Xin"), "Gu", role = c("aut"), email = "guxin57@hotmail.com"), person(c("Herbert"), "Hoijtink", role = c("aut"), email = "h.hoijtink@uu.nl"), person(c("Joris"), "Mulder", role = c("aut"), email = "jomulder@gmail.com"), person(c("Caspar", "J"), "van Lissa", role = c("aut", "cre"), email = "c.j.vanlissa@uu.nl"), person(c("Jeff"), "Jones", role = "ctb"), person(c("Niels"), "Waller", role = "ctb"), person("The R Core Team", role = "cph") )
Description: Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. S3 methods are available for specific types of lm() models, namely ANOVA, ANCOVA, and multiple regression, and for the t_test(). The statistical underpinnings are described in Hoijtink, Mulder, van Lissa, and Gu, (2018) <doi:10.31234/osf.io/v3shc>, Gu, Mulder, and Hoijtink, (2018) <doi:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder, (2018) <doi:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel, (2018) <doi:10.1037/met0000187>.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
URL: https://informative-hypotheses.sites.uu.nl/software/bain/
NeedsCompilation: yes
RoxygenNote: 6.1.1
Depends: R (>= 3.0.0), stats
Suggests: MASS, testthat, knitr, rmarkdown
VignetteBuilder: knitr
Packaged: 2019-02-02 13:55:32 UTC; Lissa102
Author: Xin Gu [aut], Herbert Hoijtink [aut], Joris Mulder [aut], Caspar J van Lissa [aut, cre], Jeff Jones [ctb], Niels Waller [ctb], The R Core Team [cph]
Maintainer: Caspar J van Lissa <c.j.vanlissa@uu.nl>
Repository: CRAN
Date/Publication: 2019-02-06 23:25:00 UTC

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Package rerf updated to version 2.0.3 with previous version 2.0.2 dated 2018-12-04

Title: Randomer Forest
Description: R-RerF (aka Randomer Forest (RerF) or Random Projection Forests) is an algorithm developed by Tomita (2016) <arXiv:1506.03410v2> which is similar to Random Forest - Random Combination (Forest-RC) developed by Breiman (2001) <doi:10.1023/A:1010933404324>. Random Forests create axis-parallel, or orthogonal trees. That is, the feature space is recursively split along directions parallel to the axes of the feature space. Thus, in cases in which the classes seem inseparable along any single dimension, Random Forests may be suboptimal. To address this, Breiman also proposed and characterized Forest-RC, which uses linear combinations of coordinates rather than individual coordinates, to split along. This package, 'rerf', implements RerF which is similar to Forest-RC. The difference between the two algorithms is where the random linear combinations occur: Forest-RC combines features at the per tree level whereas RerF takes linear combinations of coordinates at every node in the tree.
Author: Jesse Patsolic [ctb, cre], Benjamin Falk [ctb], Jaewon Chung [ctb], James Browne [aut], Tyler Tomita [aut], Joshua Vogelstein [ths]
Maintainer: Jesse Patsolic <software@neurodata.io>

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Package rstap updated to version 1.0.3 with previous version 1.0.2 dated 2019-01-02

Title: Spatial Temporal Aggregated Predictor Models via 'stan'
Description: Estimates previously compiled stap regression models using the 'rstan' package. Users specify models via a custom R syntax with a formula and data.frame plus additional arguments for priors.
Author: Adam Peterson [aut, cre]
Maintainer: Adam Peterson <atpvyc@umich.edu>

Diff between rstap versions 1.0.2 dated 2019-01-02 and 1.0.3 dated 2019-02-06

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Package EFAutilities updated to version 2.0.0 with previous version 1.2.3 dated 2018-10-09

Title: Utility Functions for Exploratory Factor Analysis
Description: A number of utility function for exploratory factor analysis are included in this package. In particular, it computes standard errors for parameter estimates and factor correlations under a variety of conditions.
Author: Guangjian Zhang, Ge Jiang, Minami Hattori, Lauren Trichtinger
Maintainer: Guangjian Zhang <gzhang3@nd.edu>

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Package cmsaf updated to version 1.9.5 with previous version 1.9.4 dated 2018-08-06

Title: Tools for CM SAF NetCDF Data
Description: The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable observing of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<http://www.cmsaf.eu>). It is a joint cooperation of seven National Meteorological and Hydrological Services. The 'cmsaf' R-package provides a collection of R-operators for the analysis and manipulation of CM SAF NetCDF formatted data. The 'cmsaf' R-package is tested for CM SAF NetCDF data, which are structured in three-dimensional arrays (longitude, latitude, time) on a rectangular grid. Layered CM SAF data have to be converted with the provided levbox_mergetime() function. The 'cmsaf' R-package operators have only minor checks for deviations from the recommended data structure, and give only few specific error messages. Scripts for an easy application of the 'cmsaf' package operators and the preparation and visualization of CM SAF NetCDF data are provided at the CM SAF webpage (<http://www.cmsaf.eu/EN/Products/Tools/Tools_node.html>).
Author: Steffen Kothe <Steffen.Kothe@dwd.de>
Maintainer: Steffen Kothe <Steffen.Kothe@dwd.de>

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Package aire.zmvm updated to version 0.8.1 with previous version 0.8.0 dated 2018-10-30

Title: Download Mexico City Pollution, Wind, and Temperature Data
Description: Tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See <http://aire.cdmx.gob.mx/> for more information.
Author: Diego Valle-Jones [aut, cre]
Maintainer: Diego Valle-Jones <diego@diegovalle.net>

Diff between aire.zmvm versions 0.8.0 dated 2018-10-30 and 0.8.1 dated 2019-02-06

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Package PTAk updated to version 1.3-34 with previous version 1.2-12 dated 2015-07-20

Title: Principal Tensor Analysis on k Modes
Description: A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.
Author: Leibovici & Sabatier (1998) <doi:10.1016/S0024-3795(97)81516-9>, Didier G. Leibovici (2010)<doi:10.18637/jss.v034.i10>
Maintainer: Didier G. Leibovici <GeotRYcs@gmail.com>

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Package mccmeiv updated to version 2.0 with previous version 1.1 dated 2018-09-12

Title: Estimating Parameters for a Matched Case Control Design with a Mismeasured Exposure using Instrumental Variables
Description: Applying the methodology from Manuel et al., estimates for the parameters for matched case control data with a mismeasured exposure are calculated through the use of user supplied instrumental variables (Submitted).
Author: Chris M Manuel, Samiran Sinha, and Suojin Wang
Maintainer: Chris M Manuel <cmmanuel@stat.tamu.edu>

Diff between mccmeiv versions 1.1 dated 2018-09-12 and 2.0 dated 2019-02-06

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Package lucid updated to version 1.7 with previous version 1.6 dated 2018-08-24

Title: Printing Floating Point Numbers in a Human-Friendly Format
Description: Print vectors (and data frames) of floating point numbers using a non-scientific format optimized for human readers. Vectors of numbers are rounded using significant digits, aligned at the decimal point, and all zeros trailing the decimal point are dropped. See: Wright (2016). Lucid: An R Package for Pretty-Printing Floating Point Numbers. In JSM Proceedings, Statistical Computing Section. Alexandria, VA: American Statistical Association. 2270-2279.
Author: Kevin Wright [aut, cre] (<https://orcid.org/0000-0002-0617-8673>)
Maintainer: Kevin Wright <kw.stat@gmail.com>

Diff between lucid versions 1.6 dated 2018-08-24 and 1.7 dated 2019-02-06

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New package geosample with initial version 0.2.1
Package: geosample
Type: Package
Title: Construction of Geostatistical Sampling Designs
Version: 0.2.1
Author: Michael G Chipeta, Peter J Diggle
Maintainer: Michael G Chipeta <mgchipeta@mlw.mw>
Imports: splancs, pdist, graphics, stats
Depends: R (>= 3.0.0), sf, sp
Description: Functions for constructing sampling designs, including spatially random, inhibitory (simple or with close pairs), both discrete and continuous, and adaptive designs. For details on the methods, see the following references: Chipeta et al. (2016) <doi:10.1016/j.spasta.2015.12.004> and Chipeta et al. (2016) <doi:10.1002/env.2425>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: geoR, dplyr, PrevMap, rmarkdown, testthat, viridisLite, raster, knitr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-02 12:15:45 UTC; admin
Repository: CRAN
Date/Publication: 2019-02-06 16:23:22 UTC

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New package tsfeatures with initial version 1.0.0
Package: tsfeatures
Title: Time Series Feature Extraction
Version: 1.0.0
Authors@R: c( person("Rob", "Hyndman", email = "Rob.Hyndman@monash.edu", role = c("aut","cre"), comment = c(ORCID = "0000-0002-2140-5352")), person("Yanfei", "Kang", role = "aut", comment = c(ORCID = "0000-0001-8769-6650")), person("Pablo", "Montero-Manso", email="p.montero.manso@udc.es", role="aut"), person("Thiyanga", "Talagala", role = "aut", comment=c(ORCID = "0000-0002-0656-9789")), person("Earo", "Wang", role = "aut", comment=c(ORCID = "0000-0001-6448-5260")), person("Yangzhuoran", "Yang", email = "Fin.Yang@monash.edu", role = "aut"), person("Souhaib", "Ben Taieb", role = "ctb"), person("Cao", "Hanqing", role="ctb"), person("D K", "Lake", email="dlake@virginia.edu", role="ctb"), person("Nikolay", "Laptev", role="ctb"), person("J R", "Moorman", role="ctb"), person("Mitchell", "O'Hara-Wild", role="ctb", comment=c(ORCID = "0000-0001-6729-7695")))
Description: Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
Depends: R (>= 3.2.3)
Imports: ForeCA, fracdiff, forecast (>= 8.3), purrr, RcppRoll (>= 0.2.2), stats, tibble, tseries, urca, future, furrr
Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr, dplyr, Mcomp, GGally
License: GPL-3
LazyData: true
ByteCompile: true
URL: https://pkg.robjhyndman.com/tsfeatures/
BugReports: https://github.com/robjhyndman/tsfeatures/issues/
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-02-02 01:26:37 UTC; hyndman
Author: Rob Hyndman [aut, cre] (<https://orcid.org/0000-0002-2140-5352>), Yanfei Kang [aut] (<https://orcid.org/0000-0001-8769-6650>), Pablo Montero-Manso [aut], Thiyanga Talagala [aut] (<https://orcid.org/0000-0002-0656-9789>), Earo Wang [aut] (<https://orcid.org/0000-0001-6448-5260>), Yangzhuoran Yang [aut], Souhaib Ben Taieb [ctb], Cao Hanqing [ctb], D K Lake [ctb], Nikolay Laptev [ctb], J R Moorman [ctb], Mitchell O'Hara-Wild [ctb] (<https://orcid.org/0000-0001-6729-7695>)
Maintainer: Rob Hyndman <Rob.Hyndman@monash.edu>
Repository: CRAN
Date/Publication: 2019-02-06 15:50:03 UTC

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Package totalcensus updated to version 0.6.1 with previous version 0.5.1 dated 2018-12-08

Title: Extract Decennial Census and American Community Survey Data
Description: Download summary files from Census Bureau <https://www2.census.gov/> and extract data, in particular high resolution data at block, block group, and tract level, from decennial census and American Community Survey 1-year and 5-year estimates.
Author: Guanglai Li
Maintainer: Guanglai Li <liguanglai@gmail.com>

Diff between totalcensus versions 0.5.1 dated 2018-12-08 and 0.6.1 dated 2019-02-06

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New package Repliscope with initial version 1.0.0
Package: Repliscope
Type: Package
Language: en-GB
Title: Replication Timing Profiling using DNA Copy Number
Version: 1.0.0
Author: Dzmitry G Batrakou
Maintainer: Dzmitry G Batrakou <d.batrakou@gmail.com>
Description: Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 2.10), ggplot2, shiny
Imports: stats, utils, grDevices, colourpicker
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-01 19:22:56 UTC; dzmitry
Repository: CRAN
Date/Publication: 2019-02-06 15:53:37 UTC

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New package mstknnclust with initial version 0.1.0
Package: mstknnclust
Type: Package
Title: MST-kNN Clustering Algorithm
Version: 0.1.0
Date: 2019-01-25
Authors@R: c(person("Jorge Parraga-Alava", email = "jorge.parraga@usach.cl", role = c("aut", "cre")), person("Pablo Moscato", email = "pablo.moscato@newcastle.edu.au", role=c("aut")), person("Mario Inostroza-Ponta", email = "mario.inostroza@usach.cl", role = c("aut")))
Author: Jorge Parraga-Alava [aut, cre], Pablo Moscato [aut], Mario Inostroza-Ponta [aut]
Maintainer: Jorge Parraga-Alava <jorge.parraga@usach.cl>
Description: Implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.
Depends: R (>= 3.2.5)
License: GPL-2
Encoding: UTF-8
Imports: igraph, amap
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2019-02-01 19:12:01 UTC; jorgeklz
Repository: CRAN
Date/Publication: 2019-02-06 15:50:09 UTC

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Package mgcv updated to version 1.8-27 with previous version 1.8-26 dated 2018-11-21

Title: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
Description: Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.
Author: Simon Wood <simon.wood@r-project.org>
Maintainer: Simon Wood <simon.wood@r-project.org>

Diff between mgcv versions 1.8-26 dated 2018-11-21 and 1.8-27 dated 2019-02-06

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New package IPPP with initial version 1.0
Package: IPPP
Type: Package
Title: Inhomogeneous Poisson Point Processes
Version: 1.0
Date: 2019-01-13
Author: Niklas Hohmann
Maintainer: Niklas Hohmann <Niklas.Hohmann@fau.de>
Description: Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.
License: CC BY 4.0
Depends: stats
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-01 18:44:31 UTC; nick
Repository: CRAN
Date/Publication: 2019-02-06 15:53:41 UTC

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Package brglm2 updated to version 0.5.0 with previous version 0.1.8 dated 2018-04-16

Title: Bias Reduction in Generalized Linear Models
Description: Estimation and inference from generalized linear models based on various methods for bias reduction. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2016) <arXiv:1604.04768>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <http://www.jstor.org/stable/2345592>. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation). 'brglm2' also provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in binomial response generalized linear models.
Author: Ioannis Kosmidis [aut, cre], Kjell Konis [ctb], Euloge Clovis Kenne Pagui [ctb], Nicola Sartori [ctb]
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>

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New package bayfoxr with initial version 0.0.1
Package: bayfoxr
Title: Global Bayesian Foraminifera Core Top Calibration
Version: 0.0.1
Authors@R: person("Steven", "Malevich", email = "malevich@email.arizona.edu", role = c("aut", "cre"))
Description: A Bayesian, global planktic foraminifera core top calibration to modern sea-surface temperatures. Includes four calibration models, considering species-specific calibration parameters and seasonality.
URL: https://github.com/brews/bayfoxr/
BugReports: https://github.com/brews/bayfoxr/issues
Depends: R (>= 3.4)
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Suggests: testthat, knitr, rmarkdown
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-01 21:15:18 UTC; sbm
Author: Steven Malevich [aut, cre]
Maintainer: Steven Malevich <malevich@email.arizona.edu>
Repository: CRAN
Date/Publication: 2019-02-06 15:53:33 UTC

More information about bayfoxr at CRAN
Permanent link

Package promote updated to version 1.1.1 with previous version 1.1.0 dated 2018-11-07

Title: Client for the 'Alteryx Promote' API
Description: Deploy, maintain, and invoke predictive models using the 'Alteryx Promote' REST API. 'Alteryx Promote' is available at the URL: <https://www.alteryx.com/products/alteryx-promote>.
Author: Paul E. Promote <promotedev@alteryx.com>
Maintainer: Paul E. Promote <promotedev@alteryx.com>

Diff between promote versions 1.1.0 dated 2018-11-07 and 1.1.1 dated 2019-02-06

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Package metap updated to version 1.1 with previous version 1.0 dated 2018-07-25

Title: Meta-Analysis of Significance Values
Description: The canonical way to perform meta-analysis involves using effect sizes. When they are not available this package provides a number of methods for meta-analysis of significance values including the methods of Edgington, Fisher, Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results; and a routine for graphical display.
Author: Michael Dewey
Maintainer: Michael Dewey <lists@dewey.myzen.co.uk>

Diff between metap versions 1.0 dated 2018-07-25 and 1.1 dated 2019-02-06

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Package testDriveR updated to version 0.5.1 with previous version 0.4.0 dated 2018-08-31

Title: Teaching Data for Statistics and Data Science
Description: Provides data sets for teaching statistics and data science courses. It includes a sample of data from John Edmund Kerrich's famous coinflip experiment. These are data that I use for teaching SOC 4015 / SOC 5050 at Saint Louis University (SLU). The package also contains an R Markdown template with the required formatting for assignments in my courses SOC 4015, SOC 4650, SOC 5050, and SOC 5650 at SLU.
Author: Christopher Prener [aut, cre] (<https://orcid.org/0000-0002-4310-9888>)
Maintainer: Christopher Prener <chris.prener@slu.edu>

Diff between testDriveR versions 0.4.0 dated 2018-08-31 and 0.5.1 dated 2019-02-06

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Package repr updated to version 0.19.2 with previous version 0.19.1 dated 2019-01-02

Title: Serializable Representations
Description: String and binary representations of objects for several formats / mime types.
Author: Philipp Angerer [aut, cre] (<https://orcid.org/0000-0002-0369-2888>), Thomas Kluyver [aut], Jan Schulz [aut], abielr [ctb], Denilson Figueiredo de Sa [ctb], Jim Hester [ctb], karldw [ctb], Dave Foster [ctb], Carson Sievert [ctb]
Maintainer: Philipp Angerer <phil.angerer@gmail.com>

Diff between repr versions 0.19.1 dated 2019-01-02 and 0.19.2 dated 2019-02-06

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Package ProTrackR updated to version 0.3.6 with previous version 0.3.5 dated 2017-10-14

Title: Manipulate and Play 'ProTracker' Modules
Description: 'ProTracker' is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play 'ProTracker' module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of 'ProTracker' and the Commodore Amiga alive.
Author: Pepijn de Vries [aut, cre, dtc]
Maintainer: Pepijn de Vries <pepijn.devries@outlook.com>

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Package multivariance updated to version 2.0.0 with previous version 1.2.1 dated 2019-01-04

Title: Measuring Multivariate Dependence Using Distance Multivariance
Description: Distance multivariance is a measure of dependence which can be used to detect and quantify dependence. The necessary functions are implemented in this packages, and examples are given. For the theoretic background we refer to the papers: B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors: generalized distance covariance and Gaussian covariance. VMSTA, 2018, Vol. 5, No. 3, 353-383. <arXiv:1711.07778>. B. Böttcher, M. Keller-Ressel, R.L. Schilling, Distance multivariance: New dependence measures for random vectors. <arXiv:1711.07775>. B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using Distance Multivariance. <arXiv:1712.06532>. G. Berschneider, B. Böttcher, On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. <arXiv:1808.07280>.
Author: Björn Böttcher [aut, cre], Martin Keller-Ressel [ctb]
Maintainer: Björn Böttcher <bjoern.boettcher@tu-dresden.de>

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Package jomo updated to version 2.6-7 with previous version 2.6-6 dated 2019-01-22

Title: Multilevel Joint Modelling Multiple Imputation
Description: Similarly to Schafer's package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi: 10.1002/9781119942283>. Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
Author: Matteo Quartagno, James Carpenter
Maintainer: Matteo Quartagno <m.quartagno@ucl.ac.uk>

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Package GenomicTools updated to version 0.2.8 with previous version 0.2.6 dated 2017-07-11

Title: Collection of Tools for Genomic Data Analysis
Description: A loose collection of tools for the analysis of expression and genotype data, currently with the main focus on (e)QTL and MDR analysis.
Author: Daniel Fischer
Maintainer: Daniel Fischer <daniel.fischer@luke.fi>

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Package vortexR updated to version 1.1.6 with previous version 1.1.5 dated 2018-05-14

Title: Post Vortex Simulation Analysis
Description: Facilitate Post Vortex Simulation Analysis by offering tools to collate multiple Vortex (v10) output files into one R object, and analyse the collated output statistically. Vortex is a software for the development of individual-based model for population dynamic simulation (see <http://www.vortex10.org/Vortex10.aspx>).
Author: Carlo Pacioni [aut, cre], Florian W. Mayer [aut]
Maintainer: Carlo Pacioni <C.Pacioni@Murdoch.edu.au>

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Package pcLasso updated to version 1.1 with previous version 1.0 dated 2019-01-11

Title: Principal Components Lasso
Description: A method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' <arXiv:1810.04651>.
Author: Jerome Friedman, Kenneth Tay, Robert Tibshirani
Maintainer: Rob Tibshirani <tibs@stanford.edu>

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Package roll updated to version 1.1.2 with previous version 1.1.1 dated 2018-08-11

Title: Rolling Statistics
Description: Fast and efficient computation of rolling statistics for time-series data.
Author: Jason Foster
Maintainer: Jason Foster <jason.j.foster@gmail.com>

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Package ordinalForest updated to version 2.3-1 with previous version 2.3 dated 2019-01-24

Title: Ordinal Forests: Prediction and Variable Ranking with Ordinal Target Variables
Description: The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importances in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2019). The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2019) Ordinal Forests. Journal of Classification, <doi:10.1007/s00357-018-9302-x>.
Author: Roman Hornung
Maintainer: Roman Hornung <hornung@ibe.med.uni-muenchen.de>

Diff between ordinalForest versions 2.3 dated 2019-01-24 and 2.3-1 dated 2019-02-06

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Package myTAI updated to version 0.9.0 with previous version 0.8.0 dated 2018-05-23

Title: Evolutionary Transcriptomics
Description: Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. (2017) <doi:10.1093/bioinformatics/btx835>). The aim of this tool is to provide a transcriptome analysis environment for answering questions regarding the evolution of biological processes (Drost et al. (2016) <doi:10.1101/051565>).
Author: Hajk-Georg Drost [aut, cre] (<https://orcid.org/0000-0002-1567-306X>>>)
Maintainer: Hajk-Georg Drost <hgd23@cam.ac.uk>

Diff between myTAI versions 0.8.0 dated 2018-05-23 and 0.9.0 dated 2019-02-06

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New package mfx with initial version 1.2-2
Package: mfx
Type: Package
Title: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
Version: 1.2-2
Date: 2019-02-06
Authors@R: c( person( "Alan", "Fernihough", role = c("aut", "cre"), email = "alan.fernihough@gmail.com" ), person( "Arne", "Henningsen", role = "ctb", email = "arne.henningsen@gmail.com" ) )
Description: Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. Greene (2008, pp. 780-7) provides a textbook introduction to this topic.
License: GPL-2 | GPL-3
Depends: stats, sandwich, lmtest, MASS, betareg
NeedsCompilation: no
Packaged: 2019-02-06 07:08:01 UTC; gsl324
Author: Alan Fernihough [aut, cre], Arne Henningsen [ctb]
Maintainer: Alan Fernihough <alan.fernihough@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-06 11:20:07 UTC

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Package cNORM updated to version 1.1.5 with previous version 1.1.2 dated 2018-12-08

Title: Continuous Norming
Description: Conventional methods for producing standard scores in psychometrics or biometrics are often plagued with "jumps" or "gaps" (i.e., discontinuities) in norm tables and low confidence for assessing extreme scores. The continuous norming method introduced by A. Lenhard et al. (2016), <doi:10.1177/1073191116656437>, generates continuous test norm scores on the basis of the raw data from standardization samples, without requiring assumptions about the distribution of the raw data: Norm scores are directly established from raw data by modeling the latter ones as a function of both percentile scores and an explanatory variable (e.g., age). The method minimizes bias arising from sampling and measurement error, while handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost all of the variance in the original norm data sample.
Author: Wolfgang Lenhard [cre, aut] (<https://orcid.org/0000-0002-8184-6889>), Alexandra Lenhard [aut], Sebastian Gary [ctb]
Maintainer: Wolfgang Lenhard <wolfgang.lenhard@uni-wuerzburg.de>

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Package shar updated to version 0.2 with previous version 0.1 dated 2019-01-27

Title: Species-Habitat Associations
Description: Analyse species-habitat associations in R. Therefore, information about the location of the species is needed and about the environmental conditions. To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.
Author: Maximillian H.K. Hesselbarth [aut, cre] (<https://orcid.org/0000-0003-1125-9918>), Marco Sciaini [aut] (<https://orcid.org/0000-0002-3042-5435>)
Maintainer: Maximillian H.K. Hesselbarth <maximilian.hesselbarth@uni-goettingen.de>

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Package overlapptest updated to version 1.1 with previous version 1.0 dated 2018-05-30

Title: Test Overlapping of Polygons Against Random Rotation
Description: Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) <doi:10.13140/RG.2.2.12825.72801>.
Author: Marcelino de la Cruz Rot
Maintainer: Marcelino de la Cruz <marcelino.delacruz@urjc.es>

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Package GenomicTools.fileHandler updated to version 0.1.4 with previous version 0.1.2 dated 2018-08-16

Title: File Handlers for Genomic Data Analysis
Description: A collection of I/O tools for handling the most commonly used genomic datafiles, like fasta/-q, bed, gff, gtf, ped/map and vcf.
Author: Daniel Fischer
Maintainer: Daniel Fischer <daniel.fischer@luke.fi>

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Package GenEst updated to version 1.2.2 with previous version 1.2.1 dated 2018-12-19

Title: Generalized Mortality Estimator
Description: Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.
Author: Daniel Dalthorp [aut, cre], Juniper Simonis [aut], Lisa Madsen [aut], Manuela Huso [aut], Paul Rabie [aut], Jeffrey Mintz [aut], Robert Wolpert [aut], Jared Studyvin [aut], Franzi Korner-Nievergelt [aut]
Maintainer: Daniel Dalthorp <ddalthorp@usgs.gov>

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Package Surrogate updated to version 1.2 with previous version 1.1 dated 2018-10-18

Title: Evaluation of Surrogate Endpoints in Clinical Trials
Description: In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
Author: Wim Van der Elst, Paul Meyvisch, Alvaro Florez Poveda, Ariel Alonso, Hannah M. Ensor, Christopher J. Weir & Geert Molenberghs
Maintainer: Wim Van der Elst <Wim.vanderelst@gmail.com>

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