Sun, 28 Apr 2019

Package restatapi updated to version 0.1.6 with previous version 0.1.4 dated 2019-04-26

Title: Search and Retrieve Data from Eurostat Database
Description: Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX <https://sdmx.org>) Web Services (<https://ec.europa.eu/eurostat/web/sdmx-web-services/about-this-service>) to search and download data from the Eurostat database using the SDMX standard.
Author: Mátyás Mészáros [aut, cre]
Maintainer: Mátyás Mészáros <matyas.meszaros@ec.europa.eu>

Diff between restatapi versions 0.1.4 dated 2019-04-26 and 0.1.6 dated 2019-04-28

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New package liayson with initial version 1.0.2
Package: liayson
Type: Package
Title: Linking Singe-Cell Transcriptomes Atween Contemporary Subpopulation Genomes
Version: 1.0.2
Date: 2019-04-26
Author: Noemi Andor
Maintainer: Noemi Andor <liayson.scRNA.R@gmail.com>
Description: Given an RNA-seq derived cell-by-gene matrix and an DNA-seq derived copy number segmentation, LIAYSON predicts the number of clones present in a tumor, their size, the copy number profile of each clone and the clone membership of each single cell (Andor, N. & Lau, B., et al. (2018) <doi:10.1101/445932>).
License: GPL-2
URL: https://github.com/noemiandor/liayson, https://groups.google.com/d/forum/liayson
Depends: R (>= 3.0)
Imports: phangorn, RColorBrewer, ape, parallel, plyr, matlab, biomaRt, distances, arules, e1071, proxy, gplots, methods
Suggests: mclust, fpc, NbClust, modeest, pastecs, vegan
Packaged: 2019-04-27 22:17:10 UTC; noemi
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2019-04-28 21:40:04 UTC
RoxygenNote: 6.1.1

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Package splot updated to version 0.4.2 with previous version 0.4.1 dated 2019-01-18

Title: Split Plot
Description: Automates common plotting tasks to ease data exploration. Makes density plots (potentially overlaid on histograms), scatter plots with prediction lines, or bar or line plots with error bars. For each type, y, or x and y variables can be plotted at levels of other variables, all with minimal specification.
Author: Micah Iserman
Maintainer: Micah Iserman <micah.iserman@ttu.edu>

Diff between splot versions 0.4.1 dated 2019-01-18 and 0.4.2 dated 2019-04-28

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Package radtools updated to version 1.0.6 with previous version 1.0.5 dated 2019-04-16

Title: Utilities for Convenient Extraction of Medical Image Metadata
Description: A collection of utilities for navigating medical image data. The DICOM and NIfTI formats are supported. Functions provide simple interfaces to the data and metadata contained in these formats. A particular emphasis on metadata allows simple conversion of image metadata to familiar R data structures such as lists and data frames. Where possible, generic functions can silently process either DICOM or NIfTI data. Additionally, image data can be extracted and viewed.
Author: Pamela Russell [aut, cre]
Maintainer: Pamela Russell <pamela.russell@gmail.com>

Diff between radtools versions 1.0.5 dated 2019-04-16 and 1.0.6 dated 2019-04-28

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Package interactions updated to version 1.1.0 with previous version 1.0.0 dated 2019-02-18

Title: Comprehensive, User-Friendly Toolkit for Probing Interactions
Description: A suite of functions for conducting and interpreting analysis of statistical interaction in regression models that was formerly part of the 'jtools' package. Functionality includes visualization of two- and three-way interactions among continuous and/or categorical variables as well as calculation of "simple slopes" and Johnson-Neyman intervals. These capabilities are implemented for generalized linear models in addition to the standard linear regression context.
Author: Jacob A. Long [aut, cre] (<https://orcid.org/0000-0002-1582-6214>)
Maintainer: Jacob A. Long <long.1377@osu.edu>

Diff between interactions versions 1.0.0 dated 2019-02-18 and 1.1.0 dated 2019-04-28

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New package mbgraphic with initial version 1.0.1
Package: mbgraphic
Type: Package
Title: Measure Based Graphic Selection
Version: 1.0.1
Date: 2019-04-24
Author: Katrin Grimm
Maintainer: Katrin Grimm <katrin-grimm@web.de>
VignetteBuilder: knitr
Description: Measure based exploratory data analysis. Some of the functions call interactive apps programmed with the package 'shiny' to provide flexible selection options. Version 1.0.1 uses dcor() from 'energy' instead of wdcor() from 'extracat'.
Depends: R (>= 3.1)
Imports: scagnostics, mgcv, energy, diptest, hexbin,scales, dplyr, magrittr, shiny, seriation, ggplot2, gridExtra, GGally, Rcpp, stats, graphics, utils, grDevices
Suggests: knitr, rmarkdown, png
LinkingTo: Rcpp
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2019-04-28 09:42:40 UTC; Katrin
Repository: CRAN
Date/Publication: 2019-04-28 19:20:03 UTC

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Package markmyassignment updated to version 0.8.2 with previous version 0.8.1 dated 2018-01-17

Title: Automatic Marking of R Assignments
Description: Automatic marking of R assignments for students and teachers based on 'testthat' test suites.
Author: Mans Magnusson, Oscar Pettersson
Maintainer: Mans Magnusson <mons.magnusson@gmail.com>

Diff between markmyassignment versions 0.8.1 dated 2018-01-17 and 0.8.2 dated 2019-04-28

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Package localIV updated to version 0.2.0 with previous version 0.1.0 dated 2018-08-05

Title: Estimation of Marginal Treatment Effects using Local Instrumental Variables
Description: In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such as the average treatment effect (ATE) and the average treatment effect on the treated (ATT) (Heckman, Urzua, and Vytlacil 2006 <doi:10.1162/rest.88.3.389>). Given a treatment selection model and an outcome model, the function mte() estimates the MTE via a semiparametric local instrumental variables method (or via a normal selection model). The function eval_mte() evaluates MTE at any combination of covariates x and latent resistance u, and the function eval_mte_tilde() evaluates MTE projected onto the estimated propensity score (Zhou and Xie 2019 <https://www.journals.uchicago.edu/doi/abs/10.1086/702172>). The object returned by mte() can be used to estimate conventional parameters such as ATE, ATT, and ATU (via average()) or marginal policy-relevant treatment effects (via mprte()).
Author: Xiang Zhou [aut, cre]
Maintainer: Xiang Zhou <xiang_zhou@fas.harvard.edu>

Diff between localIV versions 0.1.0 dated 2018-08-05 and 0.2.0 dated 2019-04-28

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Package exactRankTests updated to version 0.8-30 with previous version 0.8-29 dated 2017-03-01

Title: Exact Distributions for Rank and Permutation Tests
Description: Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel.
Author: Torsten Hothorn [aut, cre], Kurt Hornik [aut]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between exactRankTests versions 0.8-29 dated 2017-03-01 and 0.8-30 dated 2019-04-28

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Package legocolors updated to version 0.2.0 with previous version 0.1.2 dated 2019-03-24

Title: Official Lego Color Palettes
Description: Provides a dataset containing several color naming conventions established by multiple sources, along with associated color metadata. The package also provides related helper functions for mapping among the different Lego color naming conventions and between Lego colors, hex colors, and 'R' color names, making it easy to convert any color palette to one based on existing Lego colors while keeping as close to the original color palette as possible. The functions use nearest color matching based on Euclidean distance in RGB space. Naming conventions for color mapping include those from 'BrickLink' (<https://www.bricklink.com>), 'The Lego Group' (<https://www.lego.com>), 'LDraw' (<https://www.ldraw.org/>), and 'Peeron' (<http://www.peeron.com/>).
Author: Matthew Leonawicz [aut, cre]
Maintainer: Matthew Leonawicz <matt_leonawicz@esource.com>

Diff between legocolors versions 0.1.2 dated 2019-03-24 and 0.2.0 dated 2019-04-28

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Package ipred updated to version 0.9-9 with previous version 0.9-8 dated 2018-11-05

Title: Improved Predictors
Description: Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.
Author: Andrea Peters [aut], Torsten Hothorn [aut, cre], Brian D. Ripley [ctb], Terry Therneau [ctb], Beth Atkinson [ctb]
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between ipred versions 0.9-8 dated 2018-11-05 and 0.9-9 dated 2019-04-28

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Package telegram.bot updated to version 2.3.1 with previous version 2.3.0 dated 2019-02-09

Title: Develop a 'Telegram Bot' with R
Description: Provides a pure interface for the 'Telegram Bot API' <http://core.telegram.org/bots/api>. In addition to the pure API implementation, it features a number of tools to make the development of 'Telegram' bots with R easy and straightforward, providing an easy-to-use interface that takes some work off the programmer.
Author: Ernest Benedito [aut, cre]
Maintainer: Ernest Benedito <ebeneditos@gmail.com>

Diff between telegram.bot versions 2.3.0 dated 2019-02-09 and 2.3.1 dated 2019-04-28

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Package pricesensitivitymeter updated to version 1.0.0 with previous version 0.4.1 dated 2018-10-21

Title: Van Westendorp Price Sensitivity Meter Analysis
Description: An implementation of the van Westendorp Price Sensitivity Meter in R, which is a survey-based approach to analyze consumer price preferences and sensitivity (van Westendorp 1976, isbn:9789283100386).
Author: Max Alletsee [aut, cre]
Maintainer: Max Alletsee <max.alletsee@gmail.com>

Diff between pricesensitivitymeter versions 0.4.1 dated 2018-10-21 and 1.0.0 dated 2019-04-28

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Package OptM updated to version 0.1.3 with previous version 0.1.1 dated 2019-01-08

Title: Estimating the Optimal Number of Migration Edges from 'Treemix'
Description: The popular population genetic software 'Treemix' by 'Pickrell and Pritchard' (2012) <DOI:10.1371/journal.pgen.1002967> estimates the number of migration edges on a population tree. However, it can be difficult to determine the number of migration edges to include. Previously, it was customary to stop adding migration edges when 99.8% of variation in the data was explained, but 'OptM' automates this process using an ad hoc statistic based on the second-order rate of change in the log likelihood. 'OptM' also has added functionality for various threshold modeling to compare with the ad hoc statistic.
Author: Robert Fitak [aut, cre]
Maintainer: Robert Fitak <rfitak9@gmail.com>

Diff between OptM versions 0.1.1 dated 2019-01-08 and 0.1.3 dated 2019-04-28

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More information about OptM at CRAN
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Package ggwordcloud updated to version 0.4.0 with previous version 0.3.0 dated 2018-10-24

Title: A Word Cloud Geom for 'ggplot2'
Description: Provides a word cloud text geom for 'ggplot2'. Texts are placed so that they do not overlap as in 'ggrepel'. The algorithm used is a variation around the one of 'wordcloud2.js'.
Author: Erwan Le Pennec [aut, cre], Kamil Slowikowski [aut]
Maintainer: Erwan Le Pennec <lepennec@gmail.com>

Diff between ggwordcloud versions 0.3.0 dated 2018-10-24 and 0.4.0 dated 2019-04-28

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Package tsibble updated to version 0.8.0 with previous version 0.7.0 dated 2019-02-18

Title: Tidy Temporal Data Frames and Tools
Description: Provides a 'tbl_ts' class (the 'tsibble') to store and manage temporal data in a data-centric format, which is built on top of the 'tibble'. The 'tsibble' aims at easily manipulating and analysing temporal data, including counting and filling in time gaps, aggregate over calendar periods, performing rolling window calculations, and etc.
Author: Earo Wang [aut, cre] (<https://orcid.org/0000-0001-6448-5260>), Di Cook [aut, ths] (<https://orcid.org/0000-0002-3813-7155>), Rob Hyndman [aut, ths] (<https://orcid.org/0000-0002-2140-5352>), Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>)
Maintainer: Earo Wang <earo.wang@gmail.com>

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Package NACHO (with last version 0.5.5) was removed from CRAN

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

2019-04-28 0.5.5

Permanent link
New package xrf with initial version 0.1.2
Package: xrf
Title: eXtreme RuleFit
Version: 0.1.2
Authors@R: person("Karl", "Holub", email = "karljholub@gmail.com", role = c("aut", "cre"))
Description: An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result is a model that learns similarly to a tree ensemble, while often offering improved interpretability and achieving improved scoring runtime in live applications. Several algorithms for reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to several million rows and support sparse representations to handle tens of thousands of dimensions.
URL: https://github.com/holub008/xrf
BugReports: https://github.com/holub008/xrf/issues
Depends: R (>= 3.1.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: Matrix, glmnet, xgboost (>= 0.71.2), dplyr, fuzzyjoin, rlang, methods
Suggests: testthat
NeedsCompilation: no
Packaged: 2019-04-27 17:29:48 UTC; kholub
Author: Karl Holub [aut, cre]
Maintainer: Karl Holub <karljholub@gmail.com>
Repository: CRAN
Date/Publication: 2019-04-28 08:40:03 UTC

More information about xrf at CRAN
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New package Routliers with initial version 0.0.0.1
Package: Routliers
Title: Robust Outliers Detection
Version: 0.0.0.1
Authors@R: c( person(given = "Olivier", family = "Klein", role = c("aut"), email = "Klein.Olivier@ulb.ac.be"), person(given = "Marie", family = "Delacre", role = c("aut", "cre"), email = "marie.delacre@ulb.ac.be"))
Description: Detecting outliers using robust methods, i.e. the Median Absolute Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, & Licata (2013) <doi:10.1016/j.jesp.2013.03.013> and the Mahalanobis-Minimum Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein, O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>. There is also the more known but less robust Mahalanobis distance method, only for comparison purposes.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 2.10)
BugReports: https://github.com/mdelacre/Routliers/issues
Suggests: knitr, rmarkdown, testthat
Imports: MASS, stats, graphics
NeedsCompilation: no
Packaged: 2019-04-27 18:57:09 UTC; Administrateur
Author: Olivier Klein [aut], Marie Delacre [aut, cre]
Maintainer: Marie Delacre <marie.delacre@ulb.ac.be>
Repository: CRAN
Date/Publication: 2019-04-28 08:50:02 UTC

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New package NACHO with initial version 0.5.5
Package: NACHO
Type: Package
Title: NanoString Quality Control Dashboard
Version: 0.5.5
Authors@R: c( person(given = "Mickaël", family = "Canouil", role = c("aut", "cre"), email = "mickael.canouil@cnrs.fr", comment = c(ORCID = "0000-0002-3396-4549") ), person(given = "Roderick", family = "Slieker", role = "aut", email = "R.C.Slieker@lumc.nl", comment = c(ORCID = "0000-0003-0961-9152") ), person(given = "Gerard", family = "Bouland", role = "aut", email = "G.A.Bouland@lumc.nl") )
Description: NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. 'NACHO' does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.
License: GPL-3 | file LICENSE
URL: https://mcanouil.github.io/NACHO
BugReports: https://github.com/mcanouil/NACHO/issues
Depends: R (>= 3.5.0)
Imports: tibble, dplyr, tidyr, purrr, shiny, scales, ggplot2, ggbeeswarm, ggrepel, ggpubr, gtools
Suggests: covr, Biobase, GEOquery, utils, sessioninfo, knitr, rmarkdown, testthat
LazyData: true
RoxygenNote: 6.1.1
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-04-23 14:46:39 UTC; mcanouil
Author: Mickaël Canouil [aut, cre] (<https://orcid.org/0000-0002-3396-4549>), Roderick Slieker [aut] (<https://orcid.org/0000-0003-0961-9152>), Gerard Bouland [aut]
Maintainer: Mickaël Canouil <mickael.canouil@cnrs.fr>
Repository: CRAN
Date/Publication: 2019-04-28 09:00:02 UTC

More information about NACHO at CRAN
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Package RNOmni updated to version 0.7.1 with previous version 0.6.0 dated 2019-01-12

Title: Rank Normal Transformation Omnibus Test
Description: Genetic association tests that use the rank-based inverse normal transformation (INT). These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not. Moreover, INT-based tests dominate standard linear regression in terms of power. INT-based tests may be classified into two types: tests that directly transform the phenotype (D-INT) and tests that transform phenotypic residuals (I-INT). Our omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach.
Author: Zachary McCaw [aut, cre]
Maintainer: Zachary McCaw <zmccaw@g.harvard.edu>

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New package creditmodel with initial version 1.0
Package: creditmodel
Version: 1.0
Date: 2019-04-05
Title: Build Binary Classification Models in One Integrated Offering
Authors@R: c(person(given = "Dongping", family = "Fan",email = "fdp@pku.edu.cn",role = c("aut", "cre")))
Maintainer: Dongping Fan <fdp@pku.edu.cn>
Description: Provides a toolkit for building predictive models in one integrated offering. Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyperparameters, data mining and visualization, model evaluation, strategy analysis etc. 'creditmodel' is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster.
Depends: R(>= 3.3.0)
Imports: data.table,dplyr,randomForest,xgboost,glmnet,gbm,gridExtra,ggplot2(>= 1.0.1),car,foreach,doParallel,ggcorrplot,pmml,XML,rpart, sqldf,stringr
Suggests: knitr,testthat
VignetteBuilder: knitr
URL: https://github.com/FanHansen/automodel
BugReports: https://github.com/FanHansen/automodel/issues
Encoding: UTF-8
ByteCompile: yes
LazyData: yes
LazyLoad: yes
License: AGPL-3
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-04-27 17:23:24 UTC; HANSEN
Author: Dongping Fan [aut, cre]
Repository: CRAN
Date/Publication: 2019-04-28 07:30:03 UTC

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New package CLUSTShiny with initial version 0.1.0
Package: CLUSTShiny
Type: Package
Title: Interactive Document for Working with Cluster Analysis
Version: 0.1.0
Author: Kartikeya Bolar
Maintainer: Kartikeya Bolar <kartikeya.bolar@tapmi.edu.in>
Description: An interactive document on the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/ClusterAnalysis/>.
License: GPL-2
Encoding: UTF-8
LazyData: TRUE
Depends: R (>= 3.0.3)
Imports: shiny,rmarkdown,klaR,MASS,dplyr,datasets,psycho,cluster
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2019-04-25 18:02:29 UTC; KARTIKEYA
Repository: CRAN
Date/Publication: 2019-04-28 07:10:06 UTC

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Package haploR (with last version 2.0.9) was removed from CRAN

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

2019-04-22 2.0.9
2019-01-18 2.0.7
2018-08-17 2.0.6
2018-07-17 2.0.5
2018-07-15 2.0.4
2018-07-01 2.0.3
2018-05-23 1.6.4
2017-10-31 1.6.2
2017-09-22 1.6.1
2017-08-22 1.6.0
2017-07-18 1.5.1
2017-06-12 1.4.6
2017-05-03 1.4.4
2017-02-09 1.4.2
2016-12-31 1.4.1

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Package PUlasso updated to version 3.2.3 with previous version 3.2.2 dated 2019-02-28

Title: High-Dimensional Variable Selection with Presence-Only Data
Description: Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <arXiv:1711.08129>.
Author: Hyebin Song [aut, cre], Garvesh Raskutti [aut]
Maintainer: Hyebin Song <hsong56@wisc.edu>

Diff between PUlasso versions 3.2.2 dated 2019-02-28 and 3.2.3 dated 2019-04-28

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 17 files changed, 439 insertions(+), 303 deletions(-)

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Package mombf updated to version 2.2.3 with previous version 2.2.2 dated 2019-04-25

Title: Bayesian Model Selection and Averaging for Non-Local and Local Priors
Description: Bayesian model selection and averaging for regression and mixtures for non-local and selected local priors.
Author: David Rossell, John D. Cook, Donatello Telesca, P. Roebuck
Maintainer: David Rossell <rosselldavid@gmail.com>

Diff between mombf versions 2.2.2 dated 2019-04-25 and 2.2.3 dated 2019-04-28

 mombf-2.2.2/mombf/cleanup      |only
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 mombf-2.2.3/mombf/src/Makevars |only
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Package greybox updated to version 0.5.1 with previous version 0.5.0 dated 2019-04-21

Title: Toolbox for Model Building and Forecasting
Description: Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK)
Maintainer: Ivan Svetunkov <ivan@svetunkov.ru>

Diff between greybox versions 0.5.0 dated 2019-04-21 and 0.5.1 dated 2019-04-28

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 43 files changed, 753 insertions(+), 485 deletions(-)

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Package rcongresso (with last version 0.4.6) was removed from CRAN

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

2018-05-25 0.4.6
2018-01-26 0.3.2
2017-12-13 0.3.1
2017-11-30 0.3.0
2017-11-03 0.2.1
2017-10-10 0.1.3

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