Thu, 09 Nov 2017

Package rODE updated to version 0.99.6 with previous version 0.99.5 dated 2017-07-13

Title: Ordinary Differential Equation (ODE) Solvers Written in R Using S4 Classes
Description: Show physics, math and engineering students how an ODE solver is made and how effective R classes can be for the construction of the equations that describe natural phenomena. Inspiration for this work comes from the book on "Computer Simulations in Physics" by Harvey Gould, Jan Tobochnik, and Wolfgang Christian. Book link: <http://www.compadre.org/osp/items/detail.cfm?ID=7375>.
Author: Alfonso R. Reyes [aut, cre]
Maintainer: Alfonso R. Reyes <alfonso.reyes@oilgainsanalytics.com>

Diff between rODE versions 0.99.5 dated 2017-07-13 and 0.99.6 dated 2017-11-09

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Package anocva updated to version 0.1.1 with previous version 0.1.0 dated 2016-12-28

Title: A Non-Parametric Statistical Test to Compare Clustering Structures
Description: Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering.
Author: Maciel C. Vidal [aut, cre], Andre Fujita [aut]
Maintainer: Maciel C. Vidal <calebe@ime.usp.br>

Diff between anocva versions 0.1.0 dated 2016-12-28 and 0.1.1 dated 2017-11-09

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Package skeleSim updated to version 0.9.7 with previous version 0.9.5 dated 2016-09-14

Title: Genetic Simulation Engine
Description: A shiny interface and supporting tools to guide users in choosing appropriate simulations, setting parameters, calculating summary genetic statistics, and organizing data output, all within the R environment. In addition to supporting existing forward and reverse-time simulators, new simulators can be integrated into the environment relatively easily.
Author: Eric Archer [aut], Michelle DePrenger-Levin [aut], Sean Hoban [aut], Libby Liggins [aut], Christian Parobek [aut], Allan Strand [aut, cre]
Maintainer: Allan Strand <stranda@gmail.com>

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Package sjlabelled updated to version 1.0.5 with previous version 1.0.4 dated 2017-10-08

Title: Labelled Data Utility Functions
Description: Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.
Author: Daniel Lüdecke <d.luedecke@uke.de>
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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Package KMgene updated to version 1.1 with previous version 1.0 dated 2017-09-26

Title: Gene-Based Association Analysis for Complex Traits
Description: Gene based association test between a group of SNPs and traits including familial (both continuous and binary) traits, multivariate continuous (both independent and familial) traits and longitudinal continuous traits. Part of methods were previously published in Maity et al (2012) <doi:10.1002/gepi.21663>, Chen et al (2013) <doi:10.1002/gepi.21703>, Yan et al (2015) <doi:10.1159/000375409>, Yan et al (2015) <doi:10.1534/genetics.115.178590> and Yan et al (2015) <doi:10.1159/000445057>.
Author: Qi Yan
Maintainer: Qi Yan <qiy17@pitt.edu>

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Package diffobj updated to version 0.1.7 with previous version 0.1.6 dated 2016-11-15

Title: Diffs for R Objects
Description: Generate a colorized diff of two R objects for an intuitive visualization of their differences.
Author: Brodie Gaslam [aut, cre], Michael B. Allen [ctb, cph] (Original C implementation of Myers Diff Algorithm)
Maintainer: Brodie Gaslam <brodie.gaslam@yahoo.com>

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More information about diffobj at CRAN
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Package cjoint updated to version 2.0.6 with previous version 2.0.5 dated 2017-08-03

Title: AMCE Estimator for Conjoint Experiments
Description: An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Author: Soubhik Barari, Elissa Berwick, Jens Hainmueller, Daniel Hopkins, Sean Liu, Anton Strezhnev, Teppei Yamamoto
Maintainer: Anton Strezhnev <astrezhnev@fas.harvard.edu>

Diff between cjoint versions 2.0.5 dated 2017-08-03 and 2.0.6 dated 2017-11-09

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New package WRSS with initial version 1.0
Package: WRSS
Type: Package
Title: Water Resources System Simulator
Depends: R (>= 3.0.0), graphics, stats, Hmisc
Version: 1.0
Date: 2017-11-09
Author: Rezgar Arabzadeh; Parisa Aberi; Kaveh Panaghi; Shahab Araghinejad; Majid Montaseri
Maintainer: Rezgar Arabzadeh <rezgararabzadeh@ut.ac.ir>
Description: Water resources system simulator is a tool for simulation and analysis of large-scale water resources systems. There are existing packages on CRAN or other R repositories for reservoir system simulation and optimization (e.g. 'RSSOP' and 'reservoir'). The 'reservoir' proposes an implementation of stochastic dynamic programming for single reservoir operation, while 'RSSOP' and 'WRSS' are just simulator platforms but with capability of large scale reservoir operation. Although both 'RSSOP' and 'WRSS' are simulators for reservoirs systems, 'WRSS' enables us to simulate some more types of hydro-structures (e.g. aquifers, diversions, and etc.). Moreover, 'WRSS' is implemented so that to detect and simulate complicated water resources layouts through codding system and is sophisticated enough to understand almost any water resources schemes within the basin scale, which is not considered in the previous packages.
License: GPL-3
Repository: CRAN
NeedsCompilation: no
Packaged: 2017-11-09 14:07:57 UTC; Rizgar
Date/Publication: 2017-11-09 18:39:18 UTC

More information about WRSS at CRAN
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Package SimMultiCorrData updated to version 0.2.1 with previous version 0.2.0 dated 2017-10-25

Title: Simulation of Correlated Data with Multiple Variable Types
Description: Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative Binomial) variables with a specified correlation matrix. It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order (<DOI:10.1007/BF02293811>) or Headrick's fifth-order (<DOI:10.1016/S0167-9473(02)00072-5>) polynomial transformation. Binary and ordinal variables are simulated using a modification of GenOrd's ordsample function. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, <DOI:10.1002/asmb.901>). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, <DOI:10.1002/asmb.2072>). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds.
Author: Allison Cynthia Fialkowski
Maintainer: Allison Cynthia Fialkowski <allijazz@uab.edu>

Diff between SimMultiCorrData versions 0.2.0 dated 2017-10-25 and 0.2.1 dated 2017-11-09

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More information about SimMultiCorrData at CRAN
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Package roccv updated to version 1.1 with previous version 1.0 dated 2016-06-29

Title: ROC for Cross Validation Results
Description: Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.
Author: Ben Sherwood [aut, cre]
Maintainer: Ben Sherwood <ben.sherwood@ku.edu>

Diff between roccv versions 1.0 dated 2016-06-29 and 1.1 dated 2017-11-09

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New package predkmeans with initial version 0.1.0
Package: predkmeans
Title: Covariate Adaptive Clustering
Version: 0.1.0
Author: Joshua Keller
Maintainer: Joshua Keller <jkelle46@jhu.edu>
Description: Implements the predictive k-means method for clustering observations, using a mixture of experts model to allow covariates to influence cluster centers. Motivated by air pollution epidemiology settings, where cluster membership needs to be predicted across space. Includes functions for predicting cluster membership using spatial splines and principal component analysis (PCA) scores using either multinomial logistic regression or support vector machines (SVMs). For method details see Keller et al. (2017) <doi:10.1214/16-AOAS992>.
Imports: Rcpp (>= 0.11.5), maxLik, e1071, mgcv
Suggests:
License: GPL-3 | file LICENSE
LinkingTo: Rcpp, RcppArmadillo
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2017-11-09 14:20:42 UTC; Josh
Repository: CRAN
Date/Publication: 2017-11-09 18:48:27 UTC

More information about predkmeans at CRAN
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New package bigtime with initial version 0.1.0
Package: bigtime
Type: Package
Title: Sparse Estimation of Large Time Series Models
Version: 0.1.0
Authors@R: c( person("Ines", "Wilms", email = "ines.wilms@kuleuven.be", role = c("cre","aut")), person("David S.", "Matteson", email = "matteson@cornell.edu", role = "aut"), person("Jacob", "Bien", email = "jbien@usc.edu", role = "aut"), person("Sumanta", "Basu", email = "sumbose@cornell.edu", role = "aut"))
Author: Ines Wilms [cre, aut], David S. Matteson [aut], Jacob Bien [aut], Sumanta Basu [aut]
Maintainer: Ines Wilms <ines.wilms@kuleuven.be>
Description: Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Bien and Matteson (2017) <arXiv:1412.5250v2> and Wilms, Basu, Bien and Matteson (2017) <arXiv:1707.09208>.
Depends: R (>= 3.1.0), methods
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Imports: MASS, zoo, lattice, Rcpp, stats, utils, grDevices, graphics, corrplot
SystemRequirements: C++11
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
URL: http://github.com/ineswilms/bigtime
NeedsCompilation: yes
Packaged: 2017-11-09 15:25:19 UTC; u0070413
Repository: CRAN
Date/Publication: 2017-11-09 18:45:41 UTC

More information about bigtime at CRAN
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Package plsVarSel updated to version 0.9.2 with previous version 0.9.1 dated 2016-10-12

Title: Variable Selection in Partial Least Squares
Description: Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.
Author: Kristian Hovde Liland, Tahir Mehmood, Solve Sæbø
Maintainer: Kristian Hovde Liland <kristian.liland@nmbu.no>

Diff between plsVarSel versions 0.9.1 dated 2016-10-12 and 0.9.2 dated 2017-11-09

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More information about plsVarSel at CRAN
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Package civis updated to version 1.1.0 with previous version 1.0.2 dated 2017-09-21

Title: R Client for the 'Civis data science API'
Description: A set of tools around common workflows and a convenient interface to make requests directly to the 'Civis data science API' <https://www.civisanalytics.com/platform/>.
Author: Patrick Miller [cre, aut], Keith Ingersoll [aut], Bill Lattner [ctb], Anh Le [ctb], Michelangelo D'Agostino [ctb], Sam Weiss [ctb], Danning Chen [ctb]
Maintainer: Patrick Miller <pmiller@civisanalytics.com>

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More information about civis at CRAN
Permanent link

Package tcgsaseq updated to version 1.6.3 with previous version 1.6.2 dated 2017-08-04

Title: Time-Course Gene Set Analysis for RNA-Seq Data
Description: Analyze RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with longitudinal data. Method is detailed in: Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604.
Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre]
Maintainer: Boris P. Hejblum <boris.hejblum@u-bordeaux.fr>

Diff between tcgsaseq versions 1.6.2 dated 2017-08-04 and 1.6.3 dated 2017-11-09

 DESCRIPTION          |   15 ++---
 MD5                  |   45 ++++++++-------
 NEWS.md              |    5 +
 R/TcGSAseq-package.R |   11 ++-
 R/baduel.R           |   13 ++--
 R/sp_weights.R       |   19 ++++--
 R/tcgsa_seq.R        |  144 ++++++++++++++++++++++++++++++++++++++-------------
 R/varseq.R           |   13 +++-
 R/vc_score.R         |   24 ++++++--
 R/vc_score_h.R       |   12 ++--
 R/vc_test_asym.R     |   72 ++++++++++++++++---------
 R/vc_test_perm.R     |   14 +++-
 README.md            |    4 -
 inst/CITATION        |   20 +++----
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 man/vc_score_h.Rd    |    4 +
 man/vc_test_asym.Rd  |   16 +++--
 man/vc_test_perm.Rd  |    5 +
 tests                |only
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Package rrpack updated to version 0.1-6 with previous version 0.1-5 dated 2017-06-22

Title: Reduced-Rank Regression
Description: Multivariate regression methodologies including reduced-rank regression (RRR), reduced-rank ridge regression (RRS), robust reduced-rank regression (R4), generalized/mixed-response reduced-rank regression (mRRR), row-sparse reduced-rank regression (SRRR), reduced-rank regression with a sparse singular value decomposition (RSSVD), and sparse and orthogonal factor regression (SOFAR).
Author: Kun Chen [aut, cre], Wenjie Wang [ctb], Jun Yan [ctb]
Maintainer: Kun Chen <kun.chen@uconn.edu>

Diff between rrpack versions 0.1-5 dated 2017-06-22 and 0.1-6 dated 2017-11-09

 DESCRIPTION         |    8 +++---
 MD5                 |   30 ++++++++++++------------
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 man/sofar.Rd        |    9 ++++---
 src/RcppExports.cpp |   32 +++++++++++++-------------
 src/rrpack_init.c   |   64 ++++++++++++++++++++++++++--------------------------
 src/srrr.cpp        |   22 +++++++++--------
 src/surr_fit.cpp    |   15 ++++++------
 16 files changed, 148 insertions(+), 125 deletions(-)

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Permanent link

New package nesRdata with initial version 0.1.0
Package: nesRdata
Title: National Eutrophication Survey Data
Version: 0.1.0
Authors@R: c( person("Joseph", "Stachelek", role = c("aut","cre"), email = "stachel2@msu.edu", comment = c(ORCID = "0000-0002-5924-2464")) )
URL: https://github.com/jsta/nesRdata
BugReports: https://github.com/jsta/nesRdata/issues
Description: Serves data from the United States Environmental Protection Agency (USEPA) National Eutrophication Survey <https://www.epa.gov/national-aquatic-resource-surveys>.
Depends: R (>= 3.4.0)
Imports: rappdirs, dplyr, purrr, readr, dataone
License: GPL
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-11-08 17:59:26 UTC; jose
Author: Joseph Stachelek [aut, cre] (0000-0002-5924-2464)
Maintainer: Joseph Stachelek <stachel2@msu.edu>
Repository: CRAN
Date/Publication: 2017-11-09 12:57:52 UTC

More information about nesRdata at CRAN
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Package famSKATRC updated to version 1.1.0 with previous version 1.0.3 dated 2017-10-12

Title: Family Sequence Kernel Association Test for Rare and Common Variants
Description: FamSKAT-RC is a family-based association kernel test for both rare and common variants. This test is general and several special cases are known as other methods: famSKAT, which only focuses on rare variants in family-based data, SKAT, which focuses on rare variants in population-based data (unrelated individuals), and SKAT-RC, which focuses on both rare and common variants in population-based data. When one applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one applies famSKAT-RC and set the kinship matrix (fullkins) to the identity matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also include a small sample synthetic pedigree to demonstrate the method with. For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.
Author: Khalid B. Kunji [aut, cre], Mohamad Saad [aut]
Maintainer: Khalid B. Kunji <kkunji@hbku.edu.qa>

Diff between famSKATRC versions 1.0.3 dated 2017-10-12 and 1.1.0 dated 2017-11-09

 DESCRIPTION       |    6 +++---
 MD5               |    6 +++---
 R/famSKAT_RC.R    |   16 +++++++---------
 build/partial.rdb |binary
 4 files changed, 13 insertions(+), 15 deletions(-)

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New package wNNSel with initial version 0.1
Package: wNNSel
Type: Package
Title: Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables
Version: 0.1
Author: Shahla Faisal
Maintainer: Shahla Faisal <shahla_ramzan@yahoo.com>
Description: New tools for the imputation of missing values in high-dimensional data are introduced using the non-parametric nearest neighbor methods. It includes weighted nearest neighbor imputation methods that use specific distances for selected variables. It includes an automatic procedure of cross validation and does not require prespecified values of the tuning parameters. It can be used to impute missing values in high-dimensional data when the sample size is smaller than the number of predictors. For more information see Faisal and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.
Imports: stats
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2017-11-09 08:23:48 UTC; ramzan
Repository: CRAN
Date/Publication: 2017-11-09 11:32:18 UTC

More information about wNNSel at CRAN
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New package TPD with initial version 0.1.2
Package: TPD
Type: Package
Title: Methods for Measuring Functional Diversity Based on Trait Probability Density
Version: 0.1.2
Date: 2017-11-08
Author: Carlos P. Carmona <perezcarmonacarlos@gmail.com>
Maintainer: Carlos P. Carmona <perezcarmonacarlos@gmail.com>
Description: Tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.
License: GPL-3
LazyData: TRUE
Depends: ggplot2 (>= 1.0.0), ks (>= 1.9.2)
Imports: gridExtra (>= 0.9.1), mvtnorm (>= 0.8), graphics, stats
Suggests: knitr, testthat
VignetteBuilder: knitr
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-11-09 07:53:49 UTC; carlosperezcarmona
Repository: CRAN
Date/Publication: 2017-11-09 11:30:28 UTC

More information about TPD at CRAN
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New package svars with initial version 1.0.0
Package: svars
Type: Package
Title: Data-Driven Identification of SVAR Models
Version: 1.0.0
Date: 2017-11-08
Author: Alexander Lange [aut, cre], Bernhard Dalheimer [aut], Helmut Herwartz [aut], Simone Maxand [aut], Hannes Riebl [ctb]
Authors@R: c(person("Alexander", "Lange", role = c("aut", "cre"), email = "alexander.lange@uni-goettingen.de"), person("Bernhard", "Dalheimer", role = c("aut"), email = "bernhard.dalheimer@uni-goettingen.de"), person("Helmut", "Herwartz", role = c("aut"), email = "hherwartz@uni-goettingen.de"), person("Simone", "Maxand", role = c("aut"), email = "smaxand@uni-goettingen.de"), person("Hannes", "Riebl", role = c("ctb")))
Maintainer: Alexander Lange <alexander.lange@uni-goettingen.de>
Description: Implements data-driven identification methods for structural vector autoregressive (SVAR) models. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>).
Imports: vars, expm, reshape2, ggplot2, copula, clue, pbapply, steadyICA, tsDyn, DEoptim
License: GPL (>= 2)
LazyData: TRUE
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-11-09 09:03:31 UTC; alex
Repository: CRAN
Date/Publication: 2017-11-09 11:36:28 UTC

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New package nlmixr with initial version 0.9.0-1
Package: nlmixr
Type: Package
Title: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics
Depends: R (>= 3.2), nlme, RxODE
Imports: Rcpp (>= 0.12.3), brew, lattice, parallel, lbfgs, inline, dparser, methods, ggplot2, memoise, rex, minqa, Matrix, numDeriv, R.utils, mvtnorm, n1qn1
Suggests: knitr, rmarkdown, dplyr, data.table, lbfgsb3, testthat, madness, devtools, expm, matrixcalc, reshape2
Version: 0.9.0-1
Date: 2017-10-15
Authors@R: c(person("Matthew","Fidler", role = "aut", email = "matthew.fidler@gmail.com"), person("Yuan", "Xiong", role = "aut", email = "yuan.xiong@gmail.com"), person("Rik", "Schoemaker", role = "aut", email = "rik.schoemaker@occams.com"), person("Justin", "Wilkins", role = "aut", email = "justin.wilkins@occams.com"), person("Mirjam","Trame", role = "aut", email = "mirjam.trame@gmail.com"), person("Robert","Leary", role = "ctb"), person("Wenping", "Wang", role = c("aut", "cre"), email = "wwang8198@gmail.com"), person("Hadley","Wickham", role = "ctb"), person("Dirk","Eddelbuettel", role = "cph", email = "edd@debian.org"), person("David","Ardia",role = "cph"), person("Katharine","Mullen",role = "cph"), person("Sylvan","Elhay",role = "cph"), person("Jaroslav","Kautsky",role = "cph"), person("John","Burkardt",role = "cph"), person("Shanjie","Zhang",role = "cph"), person("Jianming","Jin",role = "cph") )
Description: Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'RxODE' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
License: GPL (>= 2)
NeedsCompilation: yes
LinkingTo: RcppArmadillo (>= 0.5.600.2.0), dparser(>= 0.1.7), Rcpp (>= 0.12.3), RcppEigen (>= 0.3.3.3.0), StanHeaders, BH
URL: https://github.com/nlmixrdevelopment/nlmixr
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 6.0.1
Packaged: 2017-11-08 18:36:37 UTC; annie
Author: Matthew Fidler [aut], Yuan Xiong [aut], Rik Schoemaker [aut], Justin Wilkins [aut], Mirjam Trame [aut], Robert Leary [ctb], Wenping Wang [aut, cre], Hadley Wickham [ctb], Dirk Eddelbuettel [cph], David Ardia [cph], Katharine Mullen [cph], Sylvan Elhay [cph], Jaroslav Kautsky [cph], John Burkardt [cph], Shanjie Zhang [cph], Jianming Jin [cph]
Maintainer: Wenping Wang <wwang8198@gmail.com>
Repository: CRAN
Date/Publication: 2017-11-09 11:54:55 UTC

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New package rWishart with initial version 0.1.0
Package: rWishart
Title: Random Wishart Matrix Generation
Version: 0.1.0
Authors@R: c(person("Ben", "Barnard", email = "ben_barnard@outlook.com", role = c("aut", "cre")), person("Dean", "Young", role = c("aut")))
Maintainer: Ben Barnard <ben_barnard@outlook.com>
Description: An expansion of R's 'stats' random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility modeling. Users can also use this package to create random covariance matrices.
Depends: R (>= 3.3)
Imports: Matrix, MASS, stats, lazyeval
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: covr, knitr, rmarkdown, testthat
URL: https://rwishart.bearstatistics.com
NeedsCompilation: no
Packaged: 2017-11-09 02:45:22 UTC; ben_barnard
Author: Ben Barnard [aut, cre], Dean Young [aut]
Repository: CRAN
Date/Publication: 2017-11-09 10:11:36 UTC

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New package epitab with initial version 0.2.0
Package: epitab
Type: Package
Title: Flexible Contingency Tables for Epidemiology
Version: 0.2.0
Author: Stuart Lacy
Maintainer: Stuart Lacy <stuart.lacy@york.ac.uk>
Description: Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: 'HTML', 'LaTeX', and 'Excel'.
URL: https://github.com/stulacy/epitab
License: GPL-2
Imports: kableExtra, knitr, MASS, survival, xml2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: dplyr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2017-11-08 23:28:18 UTC; stuart
Repository: CRAN
Date/Publication: 2017-11-09 10:09:30 UTC

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Package devtools updated to version 1.13.4 with previous version 1.13.3 dated 2017-08-02

Title: Tools to Make Developing R Packages Easier
Description: Collection of package development tools.
Author: Hadley Wickham [aut, cre], Winston Chang [aut], RStudio [cph], R Core team [ctb] (Some namespace and vignette code extracted from base R)
Maintainer: Hadley Wickham <hadley@rstudio.com>

Diff between devtools versions 1.13.3 dated 2017-08-02 and 1.13.4 dated 2017-11-09

 DESCRIPTION                                    |    8 ++++----
 MD5                                            |   20 ++++++++++----------
 NAMESPACE                                      |    1 -
 NEWS.md                                        |   11 +++++++----
 R/utils.r                                      |    4 ++++
 R/zzz.r                                        |    2 +-
 build/vignette.rds                             |binary
 inst/doc/dependencies.html                     |    4 ++--
 tests/testthat/test-load.r                     |    8 ++++----
 tests/testthat/testDllRcpp/R/RcppExports.R     |    6 +-----
 tests/testthat/testDllRcpp/src/RcppExports.cpp |   14 +++-----------
 11 files changed, 36 insertions(+), 42 deletions(-)

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New package MOST with initial version 0.1.0
Package: MOST
Type: Package
Title: Multiphase Optimization Strategy
Version: 0.1.0
Authors@R: c( person("Linda","Collins", email = "LMcollins@psu.edu", role = "aut"), person("Liying","Huang", email = "lxh37@psu.edu", role = c("aut","cre")), person("John","Dziak", email = "jjd264@psu.edu", role ="aut"))
Depends: R (>= 2.15.0)
Copyright: The Pennsylvania State University
Description: Provides functions similar to the 'SAS' macros previously provided to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826> and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers which outline practical benefits and challenges of factorial and fractional factorial experiments for scientists interested in developing biological and/or behavioral interventions, especially in the context of the multiphase optimization strategy (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>. The package currently contains three functions. First, RelativeCosts1() draws a graph of the relative cost of complete and reduced factorial designs versus other alternatives. Second, RandomAssignmentGenerator() returns a dataframe which contains a list of random numbers that can be used to conveniently assign participants to conditions in an experiment with many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect size, or required sample size of a factorial or fractional factorial experiment, for main effects or interactions, given several possible choices of effect size metric, and allowing pretests and clustering.
License: GPL (>= 2)
LazyData: TRUE
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2017-11-08 20:48:26 UTC; admin_lxh37
Author: Linda Collins [aut], Liying Huang [aut, cre], John Dziak [aut]
Maintainer: Liying Huang <lxh37@psu.edu>
Repository: CRAN
Date/Publication: 2017-11-09 09:07:08 UTC

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New package memery with initial version 0.3.0
Package: memery
Version: 0.3.0
Title: Internet Memes for Data Analysts
Description: Generates internet memes that optionally include a superimposed inset plot and other atypical features, combining the visual impact of an attention-grabbing meme with graphic results of data analysis. The package differs from related packages that focus on imitating and reproducing standard memes. Some packages do this by interfacing with online meme generators whereas others achieve this natively. This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites. It reads images directly from local files or via URL and meme generation is done by the package. While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on allowing for non-standard meme layouts and hybrids of memes mixed with graphs. While this package can be used to make basic memes like an online meme generator would produce, it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting foreground graphs of data analysis results. The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes rather than restricting to the standard top and/or bottom text placement. This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels. The package offers a selection of templates for graph placement and appearance with respect to the underlying meme. Graph templates also permit additional template-specific customization.
Authors@R: person("Matthew", "Leonawicz", email = "mfleonawicz@alaska.edu", role = c("aut", "cre"))
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
ByteCompile: true
URL: https://github.com/leonawicz/memery
BugReports: https://github.com/leonawicz/memery/issues
Suggests: testthat, knitr, rmarkdown, covr
VignetteBuilder: knitr
Depends: showtext,
Imports: sysfonts, grid, png, jpeg, Cairo, ggplot2, cowplot, magrittr, dplyr, purrr
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-11-08 22:11:43 UTC; Matt
Author: Matthew Leonawicz [aut, cre]
Maintainer: Matthew Leonawicz <mfleonawicz@alaska.edu>
Repository: CRAN
Date/Publication: 2017-11-09 09:47:05 UTC

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