Wed, 17 Jan 2018

Package Bolstad updated to version 0.2-35 with previous version 0.2-34 dated 2017-03-30

Title: Functions for Elementary Bayesian Inference
Description: A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.
Author: James Curran [aut, cre]
Maintainer: James Curran <j.curran@auckland.ac.nz>

Diff between Bolstad versions 0.2-34 dated 2017-03-30 and 0.2-35 dated 2018-01-17

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Package PythonInR updated to version 0.1-4 with previous version 0.1-3 dated 2015-11-19

Title: Use 'Python' from Within 'R'
Description: Interact with 'Python' <https://www.python.org/> from within 'R'.
Author: Florian Schwendinger [aut, cre], Kimyen Truong [ctb]
Maintainer: Florian Schwendinger <FlorianSchwendinger@gmx.at>

Diff between PythonInR versions 0.1-3 dated 2015-11-19 and 0.1-4 dated 2018-01-17

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Package PMCMR updated to version 4.2 with previous version 4.1 dated 2016-01-06

Title: Calculate Pairwise Multiple Comparisons of Mean Rank Sums
Description: Note, that the 'PMCMR' package is superseded by the novel 'PMCMRplus' package. The PMCMRplus package contains all functions from 'PMCMR' and many more parametric and non-parametric multiple comparison procedures, one-factorial trend tests, as well as improved method functions, such as print, summary and plot. The 'PMCMR' package is no longer maintained, but kept for compatibility of reverse depending packages for some time.
Author: Thorsten Pohlert
Maintainer: Thorsten Pohlert <thorsten.pohlert@gmx.de>

Diff between PMCMR versions 4.1 dated 2016-01-06 and 4.2 dated 2018-01-17

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Package furniture updated to version 1.7.3 with previous version 1.7.2 dated 2017-11-10

Title: Furniture for Quantitative Scientists
Description: Contains three main functions (i.e., three pieces of furniture): table1() which produces a well-formatted table of descriptives common as Table 1 in research articles, tableC() which produces a well-formatted table of correlations, and washer() which is helpful in cleaning up the data. In addition, long() and wide(), built on the stats::reshape(), can help reformat the data. These furniture functions are designed to simplify common tasks in quantitative analysis. Other data summary and cleaning tools are also available.
Author: Tyson S. Barrett [aut, cre] (0000-0002-2137-1391), Emily Brignone [aut], Daniel J. Laxman [aut]
Maintainer: Tyson S. Barrett <t.barrett@aggiemail.usu.edu>

Diff between furniture versions 1.7.2 dated 2017-11-10 and 1.7.3 dated 2018-01-17

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Package PWFSLSmoke updated to version 1.0.10 with previous version 1.0.7 dated 2018-01-12

Title: Utilities for Working with Air Quality Monitoring Data
Description: Utilities for working with air quality monitoring data with a focus on small particulates (PM2.5) generated by wildfire smoke. Functions are provided for downloading available data from the United States 'EPA' <https://www.epa.gov/outdoor-air-quality-data> and it's 'AirNow' air quality site <https://www.airnow.gov>. Additional sources of PM2.5 data made accessible by the package include: 'AIRSIS' (password protected) <https://www.oceaneering.com/data-management/> and 'WRCC' <https://wrcc.dri.edu/cgi-bin/smoke.pl>. Pre-generated data compilations are provided by 'PWFSL' <https://www.fs.fed.us/pnw/pwfsl/>.
Author: Jonathan Callahan [aut, cre], Rohan Aras [aut], Zach Dingels [aut], Jon Hagg [aut], Jimin Kim [aut], Helen Miller [aut], Rex Thompson [aut], Alice Yang [aut]
Maintainer: Jonathan Callahan <jonathan.s.callahan@gmail.com>

Diff between PWFSLSmoke versions 1.0.7 dated 2018-01-12 and 1.0.10 dated 2018-01-17

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Package keras updated to version 2.1.3 with previous version 2.1.2 dated 2017-12-17

Title: R Interface to 'Keras'
Description: Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Author: JJ Allaire [aut, cre], François Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph], Daniel Falbel [ctb, cph], Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph]
Maintainer: JJ Allaire <jj@rstudio.com>

Diff between keras versions 2.1.2 dated 2017-12-17 and 2.1.3 dated 2018-01-17

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Package cobalt updated to version 3.2.0 with previous version 3.1.0 dated 2017-11-12

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', and 'WeightIt' 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.
Author: Noah Greifer [aut, cre]
Maintainer: Noah Greifer <noah.greifer@gmail.com>

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Package ClusterR updated to version 1.1.0 with previous version 1.0.9 dated 2017-11-30

Title: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans and K-Medoids Clustering
Description: Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions.
Author: Lampros Mouselimis <mouselimislampros@gmail.com>
Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>

Diff between ClusterR versions 1.0.9 dated 2017-11-30 and 1.1.0 dated 2018-01-17

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New package tidypredict with initial version 0.1.0
Package: tidypredict
Version: 0.1.0
Title: Run Predictions Inside the Database
Description: It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm() and randomForest() models.
Authors@R: person("Edgar", "Ruiz", email = "edgar@rstudio.com", role = c("aut", "cre"))
Depends: R (>= 3.1), dplyr(>= 0.7), rlang, purrr, tibble, tidyr
Suggests: dbplyr, testthat, randomForest, knitr, rmarkdown, nycflights13, RSQLite, methods, DBI
License: GPL-3
URL: http://tidypredict.netlify.com/
BugReports: https://github.com/edgararuiz/tidypredict/issues
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-01-17 14:49:50 UTC; edgar
Author: Edgar Ruiz [aut, cre]
Maintainer: Edgar Ruiz <edgar@rstudio.com>
Repository: CRAN
Date/Publication: 2018-01-17 16:48:58 UTC

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New package Miso with initial version 0.1.1
Package: Miso
Type: Package
Title: Multi-Isotope Labeling for Metabolomics Analysis
Version: 0.1.1
Author: Yonghui Dong
Maintainer: Yonghui Dong <yonghui.dong@gmail.com>
Description: An efficient approach for fishing out the dual (or multiple) isotope labeled analytes using dual labeling of metabolites for metabolome analysis (DLEMMA) approach, described in Liron (2009) <doi:10.1021/ac901495a>.
Depends: R (>= 2.10), S4Vectors(>= 0.14.4)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-01-17 14:13:04 UTC; Dong
Repository: CRAN
Date/Publication: 2018-01-17 16:45:35 UTC

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New package junctions with initial version 1.0
Package: junctions
Type: Package
Title: The Breakdown of Genomic Ancestry Blocks in Hybrid Lineages
Version: 1.0
Date: 2018-01-16
Authors@R: c(person("Thijs","Janzen", email = "thijsjanzen@gmail.com", role=c("aut","cre")), person("Agner","Fog",email="agner@agner.org", role=c("ctb")))
URL: https//github.com/thijsjanzen/junctions
Description: Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018) <doi:10.1101/058107>.
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.13)
LinkingTo: Rcpp
SystemRequirements: C++11
Suggests: testit, testthat, knitr
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2018-01-17 12:41:26 UTC; janzen
Author: Thijs Janzen [aut, cre], Agner Fog [ctb]
Maintainer: Thijs Janzen <thijsjanzen@gmail.com>
Repository: CRAN
Date/Publication: 2018-01-17 16:32:23 UTC

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New package IROmiss with initial version 1.0.0
Package: IROmiss
Type: Package
Title: Imputation Regularized Optimization Algorithm
Version: 1.0.0
Date: 2018-01-16
Authors@R: c(person("Bochao", "Jia", role = c("aut", "cre", "cph"), email = "jbc409@ufl.edu"), person("Faming", "Liang", role = c("ctb"), email = "fmliang@purdue.edu"))
Depends: R (>= 3.0.2)
Imports: mvtnorm, equSA, huge, ncvreg
Description: Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at <https://people.clas.ufl.edu/yeluo/files/ica10.pdf> for detail. The publication "An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond" will be appear on Journal of the Royal Statistical Society Series B soon.
License: GPL-2
LazyLoad: true
Packaged: 2018-01-17 14:13:13 UTC; jia97
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2018-01-17 16:35:16 UTC
RoxygenNote: 6.0.1
Author: Bochao Jia [aut, cre, cph], Faming Liang [ctb]
Maintainer: Bochao Jia <jbc409@ufl.edu>

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Package tensorflow updated to version 1.5 with previous version 1.4.3 dated 2017-12-18

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

Diff between tensorflow versions 1.4.3 dated 2017-12-18 and 1.5 dated 2018-01-17

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Package Rlda updated to version 0.2.3 with previous version 0.2.2 dated 2017-08-08

Title: Bayesian LDA for Mixed-Membership Clustering Analysis
Description: Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data (i.e., Multinomial, Bernoulli, and Binomial entries). Albuquerque, Valle and Li (2017) <doi:10.13140/RG.2.2.34599.96164>.
Author: Pedro Albuquerque and Denis Valle and Daijiang Li
Maintainer: Pedro Albuquerque <pedroa@unb.br>

Diff between Rlda versions 0.2.2 dated 2017-08-08 and 0.2.3 dated 2018-01-17

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Package interp updated to version 1.0-30 with previous version 1.0-29 dated 2017-06-17

Title: Interpolation Methods
Description: Bivariate data interpolation on regular and irregular grids, either linear or using splines are the main part of this package. It is intended to provide FOSS replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions. Currently the piecewise linear interpolation part of akima::interp (and also akima::interpp) is implemented in interp::interp, this corresponds to the call akima::interp(..., linear=TRUE) which is the default setting and covers most of akima::interp use cases in depending packages. A re-implementation of Akimas spline interpolation (akima::interp(..., linear=FALSE)) is currently under development and will complete this package in a later version. Estimators for partial derivatives are already available, these are a prerequisite for the spline interpolation. The basic part is currently a GPLed triangulation algorithm (sweep hull algorithm by David Sinclair) providing the starting point for the piecewise linear interpolator. As side effect this algorithm is also used to provide replacements for the basic functions of the tripack package which also suffer from the ACM restrictions. All functions are designed to be backward compatible with their akima / tripack counterparts.
Author: Albrecht Gebhardt [aut, cre, cph] (...), Roger Bivand [aut], David Sinclair [aut, cph]
Maintainer: Albrecht Gebhardt <albrecht.gebhardt@aau.at>

Diff between interp versions 1.0-29 dated 2017-06-17 and 1.0-30 dated 2018-01-17

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Package GGIR updated to version 1.5-16 with previous version 1.5-12 dated 2017-08-08

Title: Raw Accelerometer Data Analysis
Description: A tool to process and analyse data collected with wearable raw acceleration sensors as described in van Hees and colleagues (2014) <doi: 10.1152/japplphysiol.00421.2014> and (2015) <doi: 10.1371/journal.pone.0142533>. The package has been developed and tested for binary data from 'GENEActiv' <https://www.activinsights.com/> and GENEA devices (not for sale), .csv-export data from 'Actigraph' <http://actigraphcorp.com> devices, and .cwa and .wav-format data from 'Axivity' <https://axivity.com/product/ax3>. These devices are currently widely used in research on human daily physical activity.
Author: Vincent T van Hees [aut, cre], Zhou Fang [ctb], Jing Hua Zhao [ctb], Joe Heywood [ctb], Evgeny Mirkes [ctb], Severine Sabia [ctb], Jairo H Migueles [ctb]
Maintainer: Vincent T van Hees <vincentvanhees@gmail.com>

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Package GenSA updated to version 1.1.7 with previous version 1.1.6 dated 2016-02-15

Title: Generalized Simulated Annealing
Description: Performs search for global minimum of a very complex non-linear objective function with a very large number of optima.
Author: Sylvain Gubian, Yang Xiang, Brian Suomela, Julia Hoeng, PMP SA.
Maintainer: Sylvain Gubian <DL.RSupport@pmi.com>

Diff between GenSA versions 1.1.6 dated 2016-02-15 and 1.1.7 dated 2018-01-17

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Package datarobot updated to version 2.8.0 with previous version 2.7.1 dated 2017-09-20

Title: 'DataRobot' Predictive Modeling API
Description: For working with the 'DataRobot' predictive modeling platform's API <https://www.datarobot.com/>.
Author: Ron Pearson [aut], Zachary Deane-Mayer [aut], David Chudzicki [aut], Dallin Akagi [aut], Sergey Yurgenson [aut], Thakur Raj Anand [aut], Peter Hurford [aut]
Maintainer: Peter Hurford <api-maintainer@datarobot.com>

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Package npsf updated to version 0.3.1 with previous version 0.3.0 dated 2018-01-16

Title: Nonparametric and Stochastic Efficiency and Productivity Analysis
Description: Provides a variety of tools for nonparametric and parametric efficiency measurement.
Author: Oleg Badunenko [aut, cre], Yaryna Kolomiytseva [aut], Pavlo Mozharovskyi [aut]
Maintainer: Oleg Badunenko <oleg.badunenko@port.ac.uk>

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Package lslx updated to version 0.6.1 with previous version 0.6.0 dated 2017-11-03

Title: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
Description: Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) with lasso or minimax concave penalty (MCP).
Author: Po-Hsien Huang [cre, aut], Wen-Hsin Hu [aut]
Maintainer: Po-Hsien Huang <psyphh@gmail.com>

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Package idefix updated to version 0.2.4 with previous version 0.2.2 dated 2017-12-15

Title: Efficient Designs for Discrete Choice Experiments
Description: Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Crabbe M, Akinc D and Vandebroek M (2014) <doi:10.1016/j.trb.2013.11.008>.
Author: Frits Traets [aut, cre]
Maintainer: Frits Traets <frits.traets@kuleuven.be>

Diff between idefix versions 0.2.2 dated 2017-12-15 and 0.2.4 dated 2018-01-17

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New package FarmSelect with initial version 1.0.0
Package: FarmSelect
Title: Factor Adjusted Robust Model Selection
Version: 1.0.0
Authors@R: c(person("Koushiki", "Bose", email = "bose@princeton.edu", role = c("aut", "cre")), person("Yuan", "Ke", email = "keyuan_princeton@163.com", role = "aut"),person("Kaizheng", "Wang", email = "kzwang2015@gmail.com", role = "aut"))
Description: Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.
Depends: R (>= 3.3.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: graphics, stats, grDevices, utils, methods, Rcpp, glmnet, ncvreg
URL: https://kbose28.github.io/FarmSelect/
RoxygenNote: 6.0.1
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Packaged: 2018-01-16 15:21:11 UTC; koushiki
Author: Koushiki Bose [aut, cre], Yuan Ke [aut], Kaizheng Wang [aut]
Maintainer: Koushiki Bose <bose@princeton.edu>
Repository: CRAN
Date/Publication: 2018-01-17 12:56:37 UTC

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Package dLagM updated to version 1.0.2 with previous version 1.0.0 dated 2017-12-20

Title: Time Series Regression Models with Distributed Lag Models
Description: Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Baltagi (2011) <doi:10.1007/978-3-642-20059-5> for more information.
Author: Haydar Demirhan
Maintainer: Haydar Demirhan <haydar.demirhan@rmit.edu.au>

Diff between dLagM versions 1.0.0 dated 2017-12-20 and 1.0.2 dated 2018-01-17

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New package detpack with initial version 1.0.0
Package: detpack
Type: Package
Title: Density Estimation and Random Number Generation with Distribution Element Trees
Version: 1.0.0
Author: Daniel Meyer
Maintainer: Daniel Meyer <meyerda@ethz.ch>
Description: Density estimation for possibly large data sets and conditional/unconditional random number generation with distribution element trees. For more details on distribution element trees see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632>.
Imports: parallel, graphics, grDevices, stats
License: GPL-2
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-01-17 11:21:39 UTC; meyerda
Repository: CRAN
Date/Publication: 2018-01-17 12:23:49 UTC

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Package conting updated to version 1.6.1 with previous version 1.6 dated 2016-08-11

Title: Bayesian Analysis of Contingency Tables
Description: Bayesian analysis of complete and incomplete contingency tables.
Author: Antony M. Overstall
Maintainer: Antony M. Overstall <A.M.Overstall@soton.ac.uk>

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More information about conting at CRAN
Permanent link

New package sumFREGAT with initial version 1.0.0
Package: sumFREGAT
Title: Fast Region-Based Association Tests on Summary Statistics
Version: 1.0.0
Author: Nadezhda M. Belonogova <belon@bionet.nsc.ru> and Gulnara R. Svishcheva <gulsvi@bionet.nsc.ru> with contributions from: Seunggeun Lee (kernel functions), Pierre Lafaye de Micheaux ('davies' method), Thomas Lumley ('kuonen' method), and James O. Ramsay (functional data analysis functions)
Maintainer: Nadezhda M. Belonogova <belon@bionet.nsc.ru>
Depends: R (>= 3.0.0)
Imports: methods, Matrix, splines
Suggests: seqminer
Repository: CRAN
Description: An adaptation of classical region/gene-based association analysis techniques that uses summary statistics (P values and effect sizes) and correlations between genetic variants as input. It is a tool to perform the most common and efficient gene-based tests on the results of genome-wide association (meta-)analyses without having the original genotypes and phenotypes at hand.
License: GPL-3
LazyLoad: yes
NeedsCompilation: yes
Packaged: 2018-01-17 04:43:30 UTC; lima
Date/Publication: 2018-01-17 11:55:00 UTC

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New package effectR with initial version 1.0.0
Package: effectR
Title: Predicts Oomycete Effectors
Version: 1.0.0
Authors@R: c(person("Javier", "Tabima", email = "tabimaj@oregonstate.edu", role = c("aut", "cre")),person(c("Niklaus", "J."), "Grunwald", role = "ths", email = "grunwaln@science.oregonstate.edu"))
Description: Predicts cytoplasmic effector proteins using genomic data by searching for motifs of interest using regular expression searches and hidden Markov models (HMM) based in Haas et al. (2009) <doi:10.1038/nature08358>.
Depends: R (>= 3.4.0)
Imports: seqinr (>= 3.3.6), ggplot2 (>= 2.2.1), shiny, reshape2, utils, viridis
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-01-16 23:57:09 UTC; tabimaj
Author: Javier Tabima [aut, cre], Niklaus J. Grunwald [ths]
Maintainer: Javier Tabima <tabimaj@oregonstate.edu>
Repository: CRAN
Date/Publication: 2018-01-17 11:26:56 UTC

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Package markmyassignment updated to version 0.8.1 with previous version 0.6.1 dated 2016-08-15

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>

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Package ETLUtils updated to version 1.4 with previous version 1.3 dated 2015-05-18

Title: Utility Functions to Execute Standard Extract/Transform/Load Operations (using Package 'ff') on Large Data
Description: Provides functions to facilitate the use of the 'ff' package in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL', 'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to do fast left outer join merging based on 'match', factorisation of data and a basic function for re-coding vectors.
Author: Jan Wijffels
Maintainer: Jan Wijffels <jwijffels@bnosac.be>

Diff between ETLUtils versions 1.3 dated 2015-05-18 and 1.4 dated 2018-01-17

 DESCRIPTION             |   23 ++++++++-------
 MD5                     |   34 +++++++++++------------
 NAMESPACE               |    2 -
 NEWS                    |    8 +++++
 R/ffsql.R               |   70 ++++++++++++++++++++++++++++--------------------
 README.md               |   13 ++------
 man/ETLUtils-package.Rd |    5 +--
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New package pwrFDR with initial version 1.80
Package: pwrFDR
Version: 1.80
Title: FDR Power
Author: Grant Izmirlian Jr <izmirlig@mail.nih.gov>
Maintainer: Grant Izmirlian Jr <izmirlig@mail.nih.gov>
Imports: methods, stats
Description: This is a package for calculating power and sample size in multiple testing situations using the Benjamini-Hochberg (BH) false discovery rate (FDR) procedure. The package computes power and sample size in one of either two ways, using the average power or the lambda-power. See Izmirlian, G. (2018) <arXiv:1801.03989>.
License: GPL (>= 2)
LazyLoad: yes
NeedsCompilation: yes
Packaged: 2018-01-16 20:23:13 UTC; izmirlig
Repository: CRAN
Date/Publication: 2018-01-17 09:45:04 UTC

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New package PMCMRplus with initial version 1.0.0
Package: PMCMRplus
Type: Package
Title: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended
Version: 1.0.0
Date: 2018-01-15
Author: Thorsten Pohlert
Maintainer: Thorsten Pohlert <thorsten.pohlert@gmx.de>
Description: For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons tests (Tukey-Kramer test, Scheffe test, LSD-test) and many-to-one tests (Dunnett test) for normally distributed residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane's T2 test, Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals and heterogeneous variances are provided. Van der Waerden's normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks. Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included. In addition, a Friedman-test for one-way ANOVA with repeated measures on ranks (CRBD) and Skillings-Mack test for unbalanced CRBD is provided with consequent all-pairs tests (Nemenyi test, Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test). A trend can be tested with Pages's test. Durbin's test for a two-way balanced incomplete block design (BIBD) is given in this package as well as Gore's test for CRBD with multiple observations per cell is given. Outlier tests, Mandel's k- and h statistic as well as functions for Type I error and Power analysis as well as generic summary, print and plot methods are provided.
Depends: R (>= 3.0.0)
Imports: mvtnorm (>= 1.0), multcompView, gmp, Rmpfr, SuppDists, kSamples (>= 1.2.7), BWStest (>= 0.2.1), MASS
Suggests: xtable, graphics, knitr, rmarkdown, car, e1071, multcomp, pwr
SystemRequirements: gmp (>= 4.2.3), mpfr (>= 3.0.0) | file README
VignetteBuilder: knitr, rmarkdown
Classification/MSC-2010: 62J15, 62J10, 62G10, 62F03, 62G30
NeedsCompilation: yes
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
License: GPL (>= 3)
Packaged: 2018-01-16 21:08:47 UTC; thorsten
Repository: CRAN
Date/Publication: 2018-01-17 09:42:40 UTC

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New package BMRBr with initial version 0.1.0
Package: BMRBr
Type: Package
Title: 'BMRB' File Downloader
Version: 0.1.0
Authors@R: person("Xi", "Chen", email=c("billchenxi@gmail.com", "billchenxi@uky.edu"), role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7094-6748"))
Description: Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially those of biomacromolecules such as proteins. Biological Magnetic Resonance Data Bank ('BMRB') is a repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules. Currently, 'BMRB' offers an R package 'RBMRB' to fetch data, however, it doesn't easily offer individual data file downloading and storing in a local directory. When using 'RBMRB', the data will stored as an R object, which fundamentally hinders the NMR researches to access the rich information from raw data, for example, the metadata. Here, 'BMRBr' File Downloader ('BMRBr') offers a more fundamental, low level downloader, which will download original deposited .str format file. This type of file contains information such as entry title, authors, citation, protein sequences, and so on. Many factors affect NMR experiment outputs, such as temperature, resonance sensitivity and etc., approximately 40% of the entries in the 'BMRB' have chemical shift accuracy problems [1,2] Unfortunately, current reference correction methods are heavily dependent on the availability of assigned protein chemical shifts or protein structure. This is my current research project is going to solve, which will be included in the future release of the package. The current version of the package is sufficient and robust enough for downloading individual 'BMRB' data file from the 'BMRB' database <http://www.bmrb.wisc.edu>. The functionalities of this package includes but not limited: * To simplifies NMR researches by combine data downloading and results analysis together. * To allows NMR data reaches a broader audience that could utilize more than just chemical shifts but also metadata. * To offer reference corrected data for entries without assignment or structure information (future release). Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E. Ioannidis, J. Lin, et al., BioMagResBank, Nucl. Acids Res. 36 (2008) D402–8. <doi:10.1093/nar/gkm957>. [2] L. Wang, H.R. Eghbalnia, A. Bahrami, J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications, J. Biomol. NMR. 32 (2005) 13–22. <doi:10.1007/s10858-005-1717-0>.
URL: https://github.com/billchenxi/BMRBr
BugReports: https://github.com/billchenxi/BMRBr/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Depends: R (>= 3.1.0), xml2, rvest, utils
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-01-16 17:13:38 UTC; Bill
Author: Xi Chen [aut, cre] (0000-0001-7094-6748)
Maintainer: Xi Chen <billchenxi@gmail.com>
Repository: CRAN
Date/Publication: 2018-01-17 09:12:03 UTC

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New package bbw with initial version 0.1.3
Package: bbw
Type: Package
Title: Blocked Weighted Bootstrap
Version: 0.1.3
Authors@R: c( person("Mark", "Myatt", email = "mark@brixtonhealth.com", role = "aut"), person("Ernest", "Guevarra", email = "ernestgmd@gmail.com", role = c("ctb", "cre")))
Description: The blocked weighted bootstrap (BBW) is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. population-proportional sampling or PPS as used in Standardized Monitoring and Assessment of Relief and Transitions or SMART surveys) or posterior weighting (e.g. as used in rapid assessment method or RAM and simple spatial sampling method or S3M surveys). The method was developed by Accion Contra la Faim, Brixton Health, Concern Worldwide, Global Alliance for Improved Nutrition, UNICEF Sierra Leone, UNICEF Sudan and Valid International. It has been tested by the Centers for Disease Control (CDC) using infant and young child feeding (IYCF) data. See Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys.
Imports: car
Depends: R (>= 3.0.1)
Suggests: knitr, rmarkdown, testthat
License: AGPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
URL: https://github.com/validmeasures/bbw
BugReports: https://github.com/validmeasures/bbw/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-01-16 19:12:58 UTC; ernest
Author: Mark Myatt [aut], Ernest Guevarra [ctb, cre]
Maintainer: Ernest Guevarra <ernestgmd@gmail.com>
Repository: CRAN
Date/Publication: 2018-01-17 09:18:12 UTC

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Package mau updated to version 0.1.2 with previous version 0.1.0 dated 2017-07-18

Title: Decision Models with Multi Attribute Utility Theory
Description: Provides functions for the creation, evaluation and test of decision models based in Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities for a set of indexes, compute utilities and weights for the whole decision tree defining the decision model and simulate weights employing Dirichlet distributions under addition constraints in weights.
Author: Felipe Aguirre [ctb], Julio Andrade [ctb], Pedro Guarderas [aut, cre], Daniel Lagos [ctb], Andrés Lopez [ctb], Nelson Recalde [ctb], Edison Salazar [ctb]
Maintainer: Pedro Guarderas <pedro.felipe.guarderas@gmail.com>

Diff between mau versions 0.1.0 dated 2017-07-18 and 0.1.2 dated 2018-01-17

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Package fulltext updated to version 1.0.0 with previous version 0.1.8 dated 2016-07-23

Title: Full Text of 'Scholarly' Articles Across Many Data Sources
Description: Provides a single interface to many sources of full text 'scholarly' data, including 'Biomed Central', Public Library of Science, 'Pubmed Central', 'eLife', 'F1000Research', 'PeerJ', 'Pensoft', 'Hindawi', 'arXiv' 'preprints', and more. Functionality included for searching for articles, downloading full or partial text, downloading supplementary materials, converting to various data formats.
Author: Scott Chamberlain [aut, cre] (0000-0003-1444-9135), Will Pearse [ctb]
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

Diff between fulltext versions 0.1.8 dated 2016-07-23 and 1.0.0 dated 2018-01-17

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 113 files changed, 3797 insertions(+), 3177 deletions(-)

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