Tue, 14 Aug 2018

Package ohtadstats updated to version 1.2.1 with previous version 1.1.1 dated 2018-05-27

Title: Tomoka Ohta D Statistics
Description: Calculate's Tomoka Ohta's partitioning of linkage disequilibrium, deemed D-statistics, for pairs of loci. Beissinger et al. (2016) <doi:10.1038/hdy.2015.81>.
Author: Paul F. Petrowski <pfpetrowski@mail.missouri.edu> & Timothy M. Beissinger <timbeissinger@gmail.com>
Maintainer: Paul F. Petrowski <pfpetrowski@mail.missouri.edu>

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Package rplos updated to version 0.8.4 with previous version 0.8.2 dated 2018-07-19

Title: Interface to the Search API for 'PLoS' Journals
Description: A programmatic interface to the 'SOLR' based search API (<http://api.plos.org/>) provided by the Public Library of Science journals to search their articles. Functions are included for searching for articles, retrieving articles, making plots, doing 'faceted' searches, 'highlight' searches, and viewing results of 'highlighted' searches in a browser.
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>), Carl Boettiger [aut], Karthik Ram [aut]
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package KScorrect updated to version 1.2.4 with previous version 1.2.3 dated 2018-03-04

Title: Lilliefors-Corrected Kolmogorov-Smirnov Goodness-of-Fit Tests
Description: Implements the Lilliefors-corrected Kolmogorov-Smirnov test for use in goodness-of-fit tests, suitable when population parameters are unknown and must be estimated by sample statistics. P-values are estimated by simulation. Can be used with a variety of continuous distributions, including normal, lognormal, univariate mixtures of normals, uniform, loguniform, exponential, gamma, and Weibull distributions. Functions to generate random numbers and calculate density, distribution, and quantile functions are provided for use with the log uniform and mixture distributions.
Author: Phil Novack-Gottshall, Steve C. Wang
Maintainer: Phil Novack-Gottshall <pnovack-gottshall@ben.edu>

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Package JointAI updated to version 0.3.0 with previous version 0.2.0 dated 2018-07-05

Title: Joint Analysis and Imputation of Incomplete Data
Description: Provides joint analysis and imputation of linear regression models, generalized linear regression models or linear mixed models with incomplete (covariate) data in the Bayesian framework. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <http://mcmc-jags.sourceforge.net> with the help of the package 'rjags'. It also provides summary and plotting functions for the output.
Author: Nicole S. Erler [aut, cre] (<https://orcid.org/0000-0002-9370-6832>)
Maintainer: Nicole S. Erler <n.erler@erasmusmc.nl>

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Package MALDIquantForeign updated to version 0.11.5 with previous version 0.11.1 dated 2017-12-04

Title: Import/Export Routines for 'MALDIquant'
Description: Functions for reading (tab, csv, Bruker fid, Ciphergen XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and writing (tab, csv, mMass MSD, mzML, imzML) different file formats of mass spectrometry data into/from 'MALDIquant' objects.
Author: Sebastian Gibb [aut, cre] (<https://orcid.org/0000-0001-7406-4443>), Pietro Franceschi [ctb]
Maintainer: Sebastian Gibb <mail@sebastiangibb.de>

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Package Quandl updated to version 2.9.1 with previous version 2.8.0 dated 2016-04-23

Title: API Wrapper for Quandl.com
Description: Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to <https://www.quandl.com/docs/api>. For more help on the package itself go to <https://www.quandl.com/help/r>.
Author: Raymond McTaggart [aut, cre], Gergely Daroczi [aut], Clement Leung [aut], Quandl Inc. [cph]
Maintainer: Raymond McTaggart <ray@quandl.com>

Diff between Quandl versions 2.8.0 dated 2016-04-23 and 2.9.1 dated 2018-08-14

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Package pmmlTransformations updated to version 1.3.2 with previous version 1.3.1 dated 2017-02-27

Title: Transforms Input Data from a PMML Perspective
Description: Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.
Author: Tridivesh Jena, Wen Ching Lin, Dmitriy Bolotov
Maintainer: Tridivesh Jena <rpmmlsupport@softwareag.com>

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Package kerasformula updated to version 1.5.0 with previous version 1.0.0 dated 2018-05-06

Title: A High-Level R Interface for Neural Nets
Description: Adds a high-level interface for 'keras' neural nets. kms() fits neural net and accepts R formulas to aid data munging and hyperparameter selection. kms() can optionally accept a compiled keras_sequential_model() from 'keras'. kms() accepts a number of parameters (like loss and optimizer) and splits the data into (optionally sparse) test and training matrices. kms() facilitates setting advanced hyperparameters (e.g., regularization). kms() returns a single object with predictions, a confusion matrix, and function call details.
Author: Pete Mohanty [aut, cre]
Maintainer: Pete Mohanty <pete.mohanty@gmail.com>

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Package ggstatsplot updated to version 0.0.5 with previous version 0.0.4 dated 2018-07-05

Title: 'ggplot2' Based Plots with Statistical Details
Description: Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests (parametric, non-parametric, or robust) included in the plots themselves. It is targeted primarily at behavioral sciences community to provide a one-line code to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms) or categorical (pie charts) data.
Author: Indrajeet Patil [aut, cre] (<https://orcid.org/0000-0003-1995-6531>)
Maintainer: Indrajeet Patil <patilindrajeet.science@gmail.com>

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Package WeMix updated to version 2.0.0 with previous version 1.0.2 dated 2018-04-10

Title: Weighted Mixed-Effects Models, using Multilevel Pseudo Maximum Likelihood Estimation
Description: Run mixed-effects models that include weights at every level. The WeMix package fits a Weighted Mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. The model is fit using adaptive quadrature.
Author: Paul Bailey, Claire Kelley, Trang Nguyen, Huade Huo.
Maintainer: Claire Kelley <ckelley@air.org>

Diff between WeMix versions 1.0.2 dated 2018-04-10 and 2.0.0 dated 2018-08-14

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Package ProliferativeIndex updated to version 1.0.1 with previous version 1.0.0 dated 2017-02-16

Title: Calculates and Analyzes the Proliferative Index
Description: Provides functions for calculating and analyzing the proliferative index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, et al. bioRxiv, 2016 <doi:10.1101/063057>.
Author: Brittany Lasseigne [aut, cre], Ryne Ramaker [aut]
Maintainer: Brittany Lasseigne <brittany.lasseigne@gmail.com>

Diff between ProliferativeIndex versions 1.0.0 dated 2017-02-16 and 1.0.1 dated 2018-08-14

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Package rcompanion updated to version 2.0.0 with previous version 1.13.2 dated 2018-04-27

Title: Functions to Support Extension Education Program Evaluation
Description: Functions and datasets to support "Summary and Analysis of Extension Program Evaluation in R" and "An R Companion for the Handbook of Biological Statistics". Vignettes are available at <http://rcompanion.org>.
Author: Salvatore Mangiafico [aut, cre]
Maintainer: Salvatore Mangiafico <mangiafico@njaes.rutgers.edu>

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New package vapour with initial version 0.1.0
Package: vapour
Title: Lightweight Access to the 'Geospatial Data Abstraction Library' ('GDAL')
Version: 0.1.0
Authors@R: c(person("Michael", "Sumner", email = "mdsumner@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2471-7511")), person("Simon", "Wotherspoon", role = "ctb", comment = "figured out the mechanism for the resampling algorithm"), person("Mark", "Padgham", role = "ctb", comment = "helped get started :)"), person("Edzer", "Pebesma", role = "ctb", comment = "wrote allocate_attribute, copied here from sf"), person("Roger", "Bivand", role = "ctb", comment = "wrote configure.ac, copied here from rgdal"), person("Jim", "Hester", role = "ctb", comment = "wrote CollectorList.h, copied here from fs package"))
Description: Provides low-level access to 'GDAL' functionality for R packages. The aim is to minimize the level of interpretation put on the 'GDAL' facilities, to enable direct use of it for a variety of purposes. 'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats that presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats <http://gdal.org/>. Other available packages 'rgdal' and 'sf' also provide access to the 'GDAL' library, but neither can be used for these lower level tasks, and both do many other tasks.
Depends: R (>= 3.3.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp
Imports: Rcpp, utils
RoxygenNote: 6.0.1
Suggests: covr, dplyr, geojsonsf, testthat, knitr, rbenchmark, rmarkdown
SystemRequirements: GDAL (>= 2.0.0), PROJ.4 (>= 4.8.0)
VignetteBuilder: knitr
URL: https://github.com/hypertidy/vapour
BugReports: https://github.com/hypertidy/vapour/issues
NeedsCompilation: yes
Packaged: 2018-08-09 01:54:22 UTC; mdsumner
Author: Michael Sumner [aut, cre] (<https://orcid.org/0000-0002-2471-7511>), Simon Wotherspoon [ctb] (figured out the mechanism for the resampling algorithm), Mark Padgham [ctb] (helped get started :)), Edzer Pebesma [ctb] (wrote allocate_attribute, copied here from sf), Roger Bivand [ctb] (wrote configure.ac, copied here from rgdal), Jim Hester [ctb] (wrote CollectorList.h, copied here from fs package)
Maintainer: Michael Sumner <mdsumner@gmail.com>
Repository: CRAN
Date/Publication: 2018-08-14 15:30:03 UTC

More information about vapour at CRAN
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Package snakecase updated to version 0.9.2 with previous version 0.9.1 dated 2018-03-25

Title: Convert Strings into any Case
Description: A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.
Author: Malte Grosser [aut, cre]
Maintainer: Malte Grosser <malte.grosser@gmail.com>

Diff between snakecase versions 0.9.1 dated 2018-03-25 and 0.9.2 dated 2018-08-14

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Permanent link

Package Rdistance updated to version 2.1.1 with previous version 1.3.2 dated 2015-07-22

Title: Analyses for Distance-Sampling and Abundance Estimation
Description: Distance-sampling is a popular method for abundance estimation in ecology. This package contains functions and associated routines to analyze distance-sampling data collected on point or line transects. It accommodates line and point transect analyses in one routine that accepts a regression-like formula object. Abundance routines perform automated bootstrapping and automated detection-function selection. Overall (study area) and site-level (transect or point) abundance estimates are available. A large suite of classical, parametric detection functions are included. Some uncommon (e.g., Gamma, negative exponential) detection functions are also included and users can define their own custom detection functions if needed. One form of non-parametric smoothed distance function is included. The help files and vignettes have been vetted by multiple authors and tested in multiple workshop settings.
Author: c( person("Trent", "McDonald", email="tmcdonald@west-inc.com", role=c("cre","aut")), person("Ryan", "Nielson", email="rnielson@west-inc.com", role=c("aut")), person("Jason", "Carlisle", email="jcarlisle@west-inc.com", role=c("aut")), person("Aidan", "McDonald", email="aidan@mcdcentral.org", role=c("aut")), person("Ben", "Augustine", email="", role=c("ctb")), person("James", "Griswald", email="", role=c("ctb")), person("Joel", "Reynolds", email="", role=c("ctb")), person("Pham", "Quang", email="", role=c("ctb")), person("Earl", "Becker", email="", role=c("ctb")), person("Aaron", "Christ", email="", role=c("ctb")), person("Brook", "Russelland", email="", role=c("ctb")), person("Patrick", "McKann", email="", role=c("ctb")), person("Lacey", "Jeroue", email="", role=c("ctb")), person("Hoffman", "Abigail", email="", role=c("ctb")), person("Kleinsausser", "Michael", email="", role=c("ctb")) )
Maintainer: Trent McDonald <tmcdonald@west-inc.com>

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Permanent link

Package landscapemetrics updated to version 0.1.1 with previous version 0.1.0 dated 2018-08-11

Title: Landscape Metrics for Categorical Map Patterns
Description: Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.umass.edu/landeco/research/fragstats/fragstats.html>) and new ones from the current literature on landscape metrics. This package supports 'raster' spatial objects and takes RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer from the 'raster' package as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.
Author: Maximillian H.K. Hesselbarth [aut, cre] (<https://orcid.org/0000-0003-1125-9918>), Marco Sciaini [aut] (<https://orcid.org/0000-0002-3042-5435>), Jakub Nowosad [aut] (<https://orcid.org/0000-0002-1057-3721>), Laura J. Graham [ctb] (<https://orcid.org/0000-0002-1057-3721>), Kimberly A. With [ctb] (<https://orcid.org/0000-0001-5570-1515>), Jeffrey Hollister [ctb] (<https://orcid.org/0000-0002-3611-7281>), Jeremy VanDerWal [ctb] (Original author of underlying C code for get_patches() function), Florian Privé [ctb] (Original author of underlying C++ code for get_nearestneighbour() function)
Maintainer: Maximillian H.K. Hesselbarth <maximilian.hesselbarth@uni-goettingen.de>

Diff between landscapemetrics versions 0.1.0 dated 2018-08-11 and 0.1.1 dated 2018-08-14

 DESCRIPTION                                    |    7 
 MD5                                            |  102 ++---
 NAMESPACE                                      |    1 
 NEWS.md                                        |    6 
 R/data.R                                       |  330 ++++++++---------
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 52 files changed, 2448 insertions(+), 2413 deletions(-)

More information about landscapemetrics at CRAN
Permanent link

Package phreeqc updated to version 3.4.8 with previous version 3.3.10 dated 2017-01-28

Title: R Interface to Geochemical Modeling Software
Description: A geochemical modeling program developed by the US Geological Survey that is designed to perform a wide variety of aqueous geochemical calculations, including speciation, batch-reaction, one-dimensional reactive-transport, and inverse geochemical calculations.
Author: S.R. Charlton, D.L. Parkhurst, and C.A.J. Appelo, with contributions from D. Gillespie for Chipmunk BASIC and S.D. Cohen, A.C. Hindmarsh, R. Serban, D. Shumaker, and A.G. Taylor for CVODE/SUNDIALS
Maintainer: S.R. Charlton <charlton@usgs.gov>

Diff between phreeqc versions 3.3.10 dated 2017-01-28 and 3.4.8 dated 2018-08-14

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More information about phreeqc at CRAN
Permanent link

Package geonapi updated to version 0.2-0 with previous version 0.1-0 dated 2018-04-27

Title: 'GeoNetwork' API R Interface
Description: Provides an R interface to the 'GeoNetwork' API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a 'GeoNetwork' web-application and exposte it to OGC CSW.
Author: Emmanuel Blondel [aut, cre] (<https://orcid.org/0000-0002-5870-5762>)
Maintainer: Emmanuel Blondel <emmanuel.blondel1@gmail.com>

Diff between geonapi versions 0.1-0 dated 2018-04-27 and 0.2-0 dated 2018-08-14

 DESCRIPTION                     |    8 ++--
 MD5                             |   16 ++++-----
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 README.md                       |    6 ++-
 man/GNUtils.Rd                  |    2 -
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 tests/testthat/test_GNManager.R |    4 --
 9 files changed, 101 insertions(+), 65 deletions(-)

More information about geonapi at CRAN
Permanent link

Package rpsftm updated to version 1.2.3 with previous version 1.2.2 dated 2018-01-25

Title: Rank Preserving Structural Failure Time Models
Description: Implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.
Author: Simon Bond [aut, cre] (primary author of code, secondary author of vignette), Annabel Allison [aut] (primary author of vignette, secondary author of code)
Maintainer: Simon Bond <simon.bond@addenbrookes.nhs.uk>

Diff between rpsftm versions 1.2.2 dated 2018-01-25 and 1.2.3 dated 2018-08-14

 DESCRIPTION                                                       |   10 +-
 MD5                                                               |   26 ++---
 NEWS.md                                                           |    4 
 R/print.rpsftm.R                                                  |    2 
 R/summary.rpsftm.R                                                |    2 
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 man/est_eqn.Rd                                                    |    3 
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More information about rpsftm at CRAN
Permanent link

New package ZipRadius with initial version 1.0.1
Package: ZipRadius
Type: Package
Title: Creates a Data Frame of US Zip Codes in a Given Radius from a Given US Zip Code
Version: 1.0.1
Depends: R (>= 2.10)
Author: Stephen Ewing <stephenewing1337@gmail.com>
Maintainer: Stephen Ewing <stephenewing1337@gmail.com>
Imports: dplyr, geosphere, magrittr, testthat, rlang, ggplot2
Description: Generates a data frame of US zip codes and their distance to the given zip code when given a starting zip code and a radius in miles. Also includes functions for use with 'choroplethrZip' which are detailed in the vignette.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: FALSE
RoxygenNote: 6.1.0
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-08-14 11:28:21 UTC; Steve
Repository: CRAN
Date/Publication: 2018-08-14 12:10:07 UTC

More information about ZipRadius at CRAN
Permanent link

Package XGR updated to version 1.1.4 with previous version 1.1.3 dated 2018-04-13

Title: Exploring Genomic Relations for Enhanced Interpretation Through Enrichment, Similarity, Network and Annotation Analysis
Description: The central goal of XGR by Fang et al. (2016) <doi:10.1186/s13073-016-0384-y> is to provide a data interpretation system necessary to do "big data" science. It is designed to make a user-defined gene or SNP list (or genomic regions) more interpretable by comprehensively utilising ontology annotations and interaction networks to reveal relationships and enhance opportunities for biological discovery. XGR is unique in supporting a broad range of ontologies (including knowledge of biological and molecular functions, pathways, diseases and phenotypes - in both human and mouse) and different types of networks (including functional, physical and pathway interactions). There are two core functionalities of XGR. The first is to provide basic infrastructures for easy access to built-in ontologies and networks. The second is to support data interpretations via 1) enrichment analysis using either built-in or custom ontologies, 2) similarity analysis for calculating semantic similarity between genes (or SNPs) based on their ontology annotation profiles, 3) network analysis for identification of gene networks given a query list of (significant) genes, SNPs or genomic regions, and 4) annotation analysis for interpreting genomic regions using co-localised functional genomic annotations (such as open chromatin, epigenetic marks, TF binding sites and genomic segments) and using nearby gene annotations (by ontologies). Together with its web app, XGR aims to provide a user-friendly tool for exploring genomic relations at the gene, SNP and genomic region level.
Author: Hai Fang, Bogdan Knezevic, Katie L Burnham, Julian C Knight
Maintainer: Hai Fang <hfang@well.ox.ac.uk>

Diff between XGR versions 1.1.3 dated 2018-04-13 and 1.1.4 dated 2018-08-14

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 274 files changed, 1987 insertions(+), 4669 deletions(-)

More information about XGR at CRAN
Permanent link

Package usethis updated to version 1.4.0 with previous version 1.3.0 dated 2018-02-24

Title: Automate Package and Project Setup
Description: Automate package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp', 'RStudio' projects, and more.
Author: Hadley Wickham [aut, cre] (<https://orcid.org/0000-0003-4757-117X>), Jennifer Bryan [aut] (<https://orcid.org/0000-0002-6983-2759>), RStudio [cph, fnd]
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between usethis versions 1.3.0 dated 2018-02-24 and 1.4.0 dated 2018-08-14

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New package Rnmr1D with initial version 1.2.0
Package: Rnmr1D
Type: Package
Title: Perform the Complete Processing of a Set of Proton Nuclear Magnetic Resonance Spectra
Version: 1.2.0
Date: 2018-08-13
Authors@R: c( person("Daniel", "Jacob", email="daniel.jacob@inra.fr", role=c("cre", "aut"), comment=c(ORCID="0000-0002-6687-7169")), person("Catherine", "Deborde", email="catherine.deborde@inra.fr", role=c("ctb")), person("Marie", "Lefebvre", email="marie.lefebvre@inra.fr", role=c("ctb")))
Copyright: Institut National de la Recherche Agronomique (INRA)
URL: https://github.com/INRA/Rnmr1D
Maintainer: Daniel Jacob <daniel.jacob@inra.fr>
Description: Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.
Depends: R (>= 2.10)
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.7), base64enc (>= 0.1), MASS(>= 7.3), Matrix, methods, doParallel (>= 1.0.11), foreach (>= 1.4.4), igraph (>= 1.2.1), impute (>= 1.54.0), MassSpecWavelet (>= 1.46.0), ptw (>= 1.9), signal (>= 0.7), speaq (>= 2.3.3), XML (>= 3.98)
LinkingTo: Rcpp
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2018-08-14 06:21:07 UTC; djaco
Author: Daniel Jacob [cre, aut] (<https://orcid.org/0000-0002-6687-7169>), Catherine Deborde [ctb], Marie Lefebvre [ctb]
Repository: CRAN
Date/Publication: 2018-08-14 12:40:07 UTC

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Permanent link

Package ipred updated to version 0.9-7 with previous version 0.9-6 dated 2017-03-01

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 0.9-6 dated 2017-03-01 and 0.9-7 dated 2018-08-14

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New package epiflows with initial version 0.2.0
Package: epiflows
Title: Predicting Disease Spread from Flow Data
Version: 0.2.0
Authors@R: c(person("Pawel", "Piatkowski", email = "pawel.piatkowski@posteo.net", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0822-5592")), person("Paula", "Moraga", email = "p.e.moraga-serrano@lancaster.ac.uk", role = "aut", comment = c(ORCID = "0000-0001-5266-0201")), person("Isobel", "Blake", email = "isobel.blake@imperial.ac.uk", role = c("ctb", "dtc")), person("Thibaut", "Jombart", email = "thibautjombart@gmail.com", role = "aut"), person("VP", "Nagraj", email = "vpnagraj@virginia.edu", role = "aut"), person("Zhian N.", "Kamvar", email = "zkamvar@gmail.com", role = "aut", comment = c(ORCID = "0000-0003-1458-7108")), person("Salla E.", "Toikkanen", email = "Salla.e.toikkanen@gmail.com", role = "aut"))
Description: Provides functions and classes designed to handle and visualise epidemiological flows between locations. Also contains a statistical method for predicting disease spread from flow data initially described in Dorigatti et al. (2017) <doi:10.2807/1560-7917.ES.2017.22.28.30572>. This package is part of the RECON (<http://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
Imports: epicontacts, leaflet, ggmap, geosphere, ggplot2, tibble, sp, htmlwidgets, stats, htmltools, visNetwork
Suggests: testthat, roxygen2, knitr, outbreaks, vdiffr, curl, rmarkdown
RoxygenNote: 6.1.0
URL: https://www.repidemicsconsortium.org/epiflows
BugReports: https://github.com/reconhub/epiflows/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-08-14 07:13:32 UTC; jovyan
Author: Pawel Piatkowski [aut, cre] (<https://orcid.org/0000-0002-0822-5592>), Paula Moraga [aut] (<https://orcid.org/0000-0001-5266-0201>), Isobel Blake [ctb, dtc], Thibaut Jombart [aut], VP Nagraj [aut], Zhian N. Kamvar [aut] (<https://orcid.org/0000-0003-1458-7108>), Salla E. Toikkanen [aut]
Maintainer: Pawel Piatkowski <pawel.piatkowski@posteo.net>
Repository: CRAN
Date/Publication: 2018-08-14 12:40:03 UTC

More information about epiflows at CRAN
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Package BootValidation updated to version 0.1.65 with previous version 0.1.5 dated 2018-05-18

Title: Adjusting for Optimism in 'glmnet' Regression using Bootstrapping
Description: Main objective of a predictive model is to provide accurated predictions of a new observations. Unfortunately we don't know how well the model performs. In addition, at the current era of omic data where p >> n, is not reasonable applying internal validation using data-splitting. Under this background a good method to assessing model performance is applying internal bootstrap validation (Harrell Jr, Frank E (2015) <doi:10.1007/978-1-4757-3462-1>.) This package provides bootstrap validation for the linear, logistic, multinomial and cox 'glmnet' models as well as lm and glm models.
Author: Antonio Jose Canada Martinez
Maintainer: Antonio Jose Canada Martinez <ancamar2@gmail.com>

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More information about BootValidation at CRAN
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Package BalancedSampling updated to version 1.5.3 with previous version 1.5.2 dated 2016-07-04

Title: Balanced and Spatially Balanced Sampling
Description: Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method and spatially correlated Poisson sampling (for spatially balanced sampling) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included.
Author: Anton Grafström, Jonathan Lisic
Maintainer: Anton Grafström <anton.grafstrom@gmail.com>

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Package DescTools updated to version 0.99.25 with previous version 0.99.24 dated 2018-03-19

Title: Tools for Descriptive Statistics
Description: A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
Author: Andri Signorell. Includes R source code and/or documentation previously published by (in alphabetical order): Ken Aho, Andreas Alfons, Nanina Anderegg, Tomas Aragon, Antti Arppe, Adrian Baddeley, Kamil Barton, Ben Bolker, Frederico Caeiro, Stephane Champely, Daniel Chessel, Leanne Chhay, Clint Cummins, Michael Dewey, Harold C. Doran, Stephane Dray, Charles Dupont, Dirk Eddelbuettel, Jeff Enos, Claus Ekstrom, Martin Elff, Kamil Erguler, Richard W. Farebrother, John Fox, Romain Francois, Michael Friendly, Tal Galili, Matthias Gamer, Joseph L. Gastwirth, Yulia R. Gel, Juergen Gross, Gabor Grothendieck, Frank E. Harrell Jr, Richard Heiberger, Michael Hoehle, Christian W. Hoffmann, Torsten Hothorn, Markus Huerzeler, Wallace W. Hui, Pete Hurd, Rob J. Hyndman, Pablo J. Villacorta Iglesias, Christopher Jackson, Matthias Kohl, Mikko Korpela, Max Kuhn, Detlew Labes, Friederich Leisch, Jim Lemon, Dong Li, Martin Maechler, Arni Magnusson, Daniel Malter, George Marsaglia, John Marsaglia, Alina Matei, David Meyer, Weiwen Miao, Giovanni Millo, Yongyi Min, David Mitchell, Markus Naepflin, Daniel Navarro, Henric Nilsson, Klaus Nordhausen, Derek Ogle, Hong Ooi, Nick Parsons, Sandrine Pavoine, Tony Plate, Roland Rapold, William Revelle, Tyler Rinker, Brian D. Ripley, Caroline Rodriguez, Nathan Russell, Nick Sabbe, Venkatraman E. Seshan, Greg Snow, Michael Smithson, Karline Soetaert, Werner A. Stahel, Alec Stephenson, Mark Stevenson, Matthias Templ, Terry Therneau, Yves Tille, Adrian Trapletti, Joshua Ulrich, Kevin Ushey, Jeremy VanDerWal, Bill Venables, John Verzani, Gregory R. Warnes, Stefan Wellek, Hadley Wickham, Rand R. Wilcox, Peter Wolf, Daniel Wollschlaeger, Thomas Yee, Achim Zeileis
Maintainer: Andri Signorell <andri@signorell.net>

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Permanent link

New package BayesCTDesign with initial version 0.5.0
Package: BayesCTDesign
Type: Package
Title: Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data
Version: 0.5.0
Authors@R: c( person("Barry", "Eggleston", email = "beggleston@rti.org", role = c("cre", "aut")), person("Doug", "Wilson", email = "doug.roy.wilson@gmail.com", role = c("aut")), person("Becky", "McNeil", email = "rmcneil@rti.org", role = c("aut")), person("Joseph", "Ibrahim", email = "jibrahim@email.unc.edu", role = c("aut")), person("Diane", "Catellier", email = "dcatellier@rti.org", role = c("fnd", "rth", "aut")))
Description: A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
URL: http://github.com/begglest/BayesCTDesign
BugReports: http://github.com/begglest/BayesCTDesign/issues
Imports: eha (>= 2.5.1), ggplot2 (>= 2.2.1), survival (>= 2.41-3), reshape2 (>= 1.4.3), stats (>= 3.5.0)
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2018-08-13 15:33:35 UTC; beggleston
Author: Barry Eggleston [cre, aut], Doug Wilson [aut], Becky McNeil [aut], Joseph Ibrahim [aut], Diane Catellier [fnd, rth, aut]
Maintainer: Barry Eggleston <beggleston@rti.org>
Repository: CRAN
Date/Publication: 2018-08-14 12:00:25 UTC

More information about BayesCTDesign at CRAN
Permanent link

Package AMR updated to version 0.3.0 with previous version 0.2.0 dated 2018-05-06

Title: Antimicrobial Resistance Analysis
Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR) of microbial isolates, by using new S3 classes and applying EUCAST expert rules on antibiograms according to Leclercq (2013) <doi:10.1111/j.1469-0691.2011.03703.x>.
Author: Matthijs S. Berends [aut, cre] (<https://orcid.org/0000-0001-7620-1800>), Christian F. Luz [aut, rev] (<https://orcid.org/0000-0001-5809-5995>), Erwin E.A. Hassing [ctb], Corinna Glasner [ths] (<https://orcid.org/0000-0003-1241-1328>), Alex W. Friedrich [ths] (<https://orcid.org/0000-0003-4881-038X>), Bhanu Sinha [ths] (<https://orcid.org/0000-0003-1634-0010>)
Maintainer: Matthijs S. Berends <m.s.berends@umcg.nl>

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Package ProFit updated to version 1.2.3 with previous version 1.2.1 dated 2018-08-03

Title: Fit Projected 2D Profiles to Galaxy Images
Description: Get data / Define model / ??? / Profit! 'ProFit' is a Bayesian galaxy fitting tool that uses a fast 'C++' image generation library and a flexible interface to a large number of likelihood samplers.
Author: Aaron Robotham, Dan Taranu, Rodrigo Tobar
Maintainer: Aaron Robotham <aaron.robotham@uwa.edu.au>

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Package MANOVA.RM updated to version 0.3.1 with previous version 0.2.1 dated 2017-09-29

Title: Analysis of Multivariate Data and Repeated Measures Designs
Description: Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2018) <arXiv:1801.08002>.
Author: Sarah Friedrich, Frank Konietschke, Markus Pauly
Maintainer: Sarah Friedrich <sarah.friedrich@alumni.uni-ulm.de>

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New package touchard with initial version 1.0
Package: touchard
Type: Package
Title: Touchard Model and Regression
Description: Tools for analyzing count data with the Touchard model. It includes univariate estimation (ML and MM) and regression tools, following Matsushita et al. (2018) The Touchard distribution, Communications in Statistics - Theory and Methods <doi:10.1080/03610926.2018.1444177>.
Version: 1.0
Date: 2018-08-07
Author: Bernardo Andrade and Sandro Oliveira
Maintainer: Bernardo Andrade <bbandrade@unb.br>
License: GPL-2
Depends: R (>= 3.2.0)
Imports: MASS, nleqslv, numDeriv, plotrix
NeedsCompilation: no
Packaged: 2018-08-13 12:02:50 UTC; root
Repository: CRAN
Date/Publication: 2018-08-14 09:40:03 UTC

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New package SDALGCP with initial version 0.1.0
Package: SDALGCP
Title: Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data
Version: 0.1.0
Authors@R: c( person("Olatunji", "Johnson", email = "olatunjijohnson21111@gmail.com", role = c("aut", "cre")), person("Emanuele", "Giorgi", email = "e.giorgi@lancaster.ac.uk", role = "aut"), person("Peter", "Diggle", email = "p.diggle@lancaster.ac.uk", role = "aut"))
Description: Provides a computationally efficient discrete approximation to log-Gaussian Cox process model for spatially aggregated disease count data. It uses Monte Carlo Maximum Likelihood for model parameter estimation as proposed by Christensen (2004) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk.
Depends: R (>= 3.4.0)
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
Imports: pdist(>= 1.2), Matrix(>= 1.2.14), PrevMap(>= 1.4.1), raster(>= 2.6.7), sp(>= 1.2.7), spatstat(>= 1.55.1), splancs(>= 2.1.40), methods(>= 3.4.4), maptools(>= 0.9.2), plyr(>= 1.8.4), pbapply(>= 1.3.4), progress(>= 1.1.2)
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-08-13 09:35:51 UTC; johnsono
Author: Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut]
Maintainer: Olatunji Johnson <olatunjijohnson21111@gmail.com>
Repository: CRAN
Date/Publication: 2018-08-14 09:30:05 UTC

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New package rfacebookstat with initial version 1.8.3
Package: rfacebookstat
Type: Package
Title: Load Data from Facebook API Marketing
Version: 1.8.3
Date: 2018-08-08
Author: Alexey Seleznev <selesnow@gmail.com>
Maintainer: Alexey Seleznev <selesnow@gmail.com>
Description: Load data by campaigns, ads, ad sets and insights, ad account and business manager from Facebook Marketing API into R. For more details see official documents by Facebook Marketing API <https://developers.facebook.com/docs/marketing-apis/>.
Depends: R (>= 3.0.0)
BugReports: https://github.com/selesnow/rfacebookstat/issues
Imports: data.table, httr, RCurl, bitops, jsonlite, dplyr
URL: http://selesnow.github.io/rfacebookstat
License: GPL-2
NeedsCompilation: no
Packaged: 2018-08-13 09:35:40 UTC; Alsey
Repository: CRAN
Date/Publication: 2018-08-14 09:30:02 UTC

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New package MRPC with initial version 1.0.0
Package: MRPC
Type: Package
Title: Mendelian Randomization (MR) Based PC Algorithm
Version: 1.0.0
Author: Md Bahadur Badsha <mdbadsha@uidaho.edu> and Audrey Qiuyan Fu <audreyf@uidaho.edu>
Maintainer: Md Bahadur Badsha <mdbadsha@uidaho.edu>
Description: Estimate the causal graph using generalized Mendelian Randomization (MR) based PC (MRPC) Algorithm. See Badsha and Fu (2018) <doi:10.1101/171348>.
License: GPL (>= 2)
LazyData: TRUE
Imports: GGally,Hmisc,WGCNA,bnlearn,dynamicTreeCut,mice,network,pcalg,methods
Suggests: BiocGenerics,fastcluster,Rgraphviz,stats,scales,sna
NeedsCompilation: no
Packaged: 2018-08-12 21:33:14 UTC; mdbadsha
Repository: CRAN
Date/Publication: 2018-08-14 09:10:03 UTC

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New package GmAMisc with initial version 1.0.0
Package: GmAMisc
Title: Gianmarco Alberti Miscellaneous
Version: 1.0.0
Authors@R: person("Gianmarco", "Alberti", email = "gianmarcoalberti@gmail.com", role = c("aut", "cre"))
Description: Contains many functions useful for univariate outlier detection, permutation-based t-test, permutation-based chi-square test, visualization of residuals, and bootstrap Cramer's V, plotting of the results of the Mann-Whitney and Kruskall-Wallis test, calculation of Brainerd-Robinson similarity coefficient and subsequent clustering, validation of logistic regression models, optimism-corrected AUC, robust Bland-Altman plot, calculation of posterior probability for different chronological relationships between two Bayesian radiocarbon phases, point pattern analysis, landform classification, clustering of spatial features.
Depends: R (>= 3.4.0), maptools (>= 0.9-2)
Imports: caTools (>= 1.17.1), classInt (>= 0.2-3), cluster (>= 2.0.7-1), coin (>= 1.2-2), corrplot (>= 0.84), DescTools (>= 0.99.24), dismo (>= 1.1-4), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), graphics (>= 3.4.3), grDevices (>= 3.4.3), gridExtra (>= 2.3), Hmisc (>= 4.1-1), InPosition (>= 0.12.7), kimisc (>= 0.4), lsr (>= 0.5), methods (>= 3.4.3), plyr (>= 1.8.4), pROC (>= 1.12.1), raster (>= 2.6-7), RcmdrMisc (>= 1.0-10), rgdal (>= 1.3-3), rgeos (>= 0.3-28), sp (>= 1.3-1), spatialEco (>= 1.1-0), spatstat (>= 1.56-0), rworldmap (>= 1.3.6), shape (>= 1.4.4), stats (>= 3.4.3), utils (>= 3.4.3)
Suggests: archdata (>= 1.2), gdistance (>= 1.2-2), resample (>= 0.4)
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2018-08-12 17:11:57 UTC; gianmarcoalberti
Author: Gianmarco Alberti [aut, cre]
Maintainer: Gianmarco Alberti <gianmarcoalberti@gmail.com>
Repository: CRAN
Date/Publication: 2018-08-14 09:10:07 UTC

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New package fc with initial version 0.1.0
Package: fc
Type: Package
Title: Standard Evaluation-Based Multivariate Function Composition
Version: 0.1.0
Authors@R: c(person("Xiaofei (Susan)", "Wang", email="xiaofei.wang@yale.edu", role = c("aut", "cre")), person("Michael", "Kane", email="michael.kane@yale.edu", role="aut"))
Description: Provides a streamlined, standard evaluation-based approach to multivariate function composition. Allows for chaining commands via a forward-pipe operator, %>%.
Encoding: UTF-8
LazyData: true
URL: https://github.com/swang87/fc
BugReports: https://github.com/swang87/fc/issues
License: GPL-2
Imports: codetools
Suggests: magrittr, purrr
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2018-08-13 13:49:00 UTC; eastie
Author: Xiaofei (Susan) Wang [aut, cre], Michael Kane [aut]
Maintainer: Xiaofei (Susan) Wang <xiaofei.wang@yale.edu>
Repository: CRAN
Date/Publication: 2018-08-14 09:40:06 UTC

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New package DemoDecomp with initial version 1.0.1
Package: DemoDecomp
Type: Package
Title: Decompose Demographic Functions
Version: 1.0.1
Date: 2018-08-13
Author: Tim Riffe
Maintainer: Tim Riffe <tim.riffe@gmail.com>
Description: Two general demographic decomposition methods are offered: Pseudo-continuous decomposition proposed by Horiuchi, Wilmoth, and Pletcher (2008) <doi:10.1353/dem.0.0033> and stepwise replacement decomposition proposed by Andreev, Shkolnikov and Begun (2002) <doi:10.4054/DemRes.2002.7.14>.
License: GPL-3
LazyLoad: yes
RoxygenNote: 6.1.0
Suggests: covr
RdMacros: Rdpack
Imports: Rdpack
Depends: R (>= 2.10)
BugReports: https://github.com/timriffe/DemoDecomp/issues
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2018-08-13 09:19:15 UTC; tim
Repository: CRAN
Date/Publication: 2018-08-14 09:30:09 UTC

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Package aphid updated to version 1.2.0 with previous version 1.1.0 dated 2018-03-16

Title: Analysis with Profile Hidden Markov Models
Description: Designed for the development and application of hidden Markov models and profile HMMs for biological sequence analysis. Contains functions for multiple and pairwise sequence alignment, model construction and parameter optimization, file import/export, implementation of the forward, backward and Viterbi algorithms for conditional sequence probabilities, tree-based sequence weighting, and sequence simulation. Features a wide variety of potential applications including database searching, gene-finding and annotation, phylogenetic analysis and sequence classification. Based on the models and algorithms described in Durbin et al (1998, ISBN: 9780521629713).
Author: Shaun Wilkinson [aut, cre]
Maintainer: Shaun Wilkinson <shaunpwilkinson@gmail.com>

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Package powerlmm updated to version 0.4.0 with previous version 0.3.0 dated 2018-04-17

Title: Power Analysis for Longitudinal Multilevel Models
Description: Calculate power for the 'time x treatment' effect in two- and three-level multilevel longitudinal studies with missing data. Both the third-level factor (e.g. therapists, schools, or physicians), and the second-level factor (e.g. subjects), can be assigned random slopes. Studies with partially nested designs, unequal cluster sizes, unequal allocation to treatment arms, and different dropout patterns per treatment are supported. For all designs power can be calculated both analytically and via simulations. The analytical calculations extends the method described in Galbraith et al. (2002) <doi:10.1016/S0197-2456(02)00205-2>, to three-level models. Additionally, the simulation tools provides flexible ways to investigate bias, Type I errors and the consequences of model misspecification.
Author: Kristoffer Magnusson [aut, cre]
Maintainer: Kristoffer Magnusson <hello@kristoffer.email>

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Package LMfilteR updated to version 0.1.2 with previous version 0.1.1 dated 2017-10-12

Title: Filter Methods for Parameter Estimation in Linear Regression Models
Description: We present a method based on filtering algorithms to estimate the parameters of linear regressions, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods.
Author: Christian Llano Robayo [aut, cre], Nazrul Shaikh [aut]
Maintainer: Christian Llano Robayo <cxl985@miami.edu>

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Package cobalt updated to version 3.4.0 with previous version 3.3.0 dated 2018-06-24

Title: Covariate Balance Tables and Plots
Description: Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'WeightIt', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with longitudinal treatments.
Author: Noah Greifer [aut, cre]
Maintainer: Noah Greifer <noah.greifer@gmail.com>

Diff between cobalt versions 3.3.0 dated 2018-06-24 and 3.4.0 dated 2018-08-14

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 cobalt-3.4.0/cobalt/inst/doc/cobalt_A3_longitudinal_treat.Rmd  |   16 
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 cobalt-3.4.0/cobalt/vignettes/cobalt_A3_longitudinal_treat.Rmd |   16 
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