Tue, 26 Feb 2019

Package spdynmod updated to version 1.1.5 with previous version 1.1.4 dated 2019-02-20

Title: Spatio-Dynamic Wetland Plant Communities Model
Description: A spatio-dynamic modelling package that focuses on three characteristic wetland plant communities in a semiarid Mediterranean wetland in response to hydrological pressures from the catchment. The package includes the data on watershed hydrological pressure and the initial raster maps of plant communities but also allows for random initial distribution of plant communities. For more detailed info see: Martinez-Lopez et al. (2015) <doi:10.1016/j.ecolmodel.2014.11.024>.
Author: Javier Martinez-Lopez <javi.martinez.lopez@gmail.com>, Babak Naimi <naimi.b@gmail.com>, Julia Martinez-Fernandez <juliamf@um.es>
Maintainer: Javier Martinez-Lopez <javi.martinez.lopez@gmail.com>

Diff between spdynmod versions 1.1.4 dated 2019-02-20 and 1.1.5 dated 2019-02-26

 DESCRIPTION                 |   12 +++++-------
 MD5                         |    8 ++++----
 NAMESPACE                   |    1 +
 R/mc_dynmodv2_7_functions.R |   16 ++++++++--------
 inst/doc/isem2013_jml.pdf   |binary
 5 files changed, 18 insertions(+), 19 deletions(-)

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Package SDLfilter updated to version 1.2.1 with previous version 1.2.0 dated 2019-02-18

Title: Filtering Satellite-Derived Locations
Description: Functions to filter GPS and/or Argos locations. The provided filters remove temporal and spatial duplicates, fixes located at a given height from estimated high tide line, and locations with high error as proposed in Shimada et al. (2012) <doi:10.3354/meps09747> and Shimada et al. (2016) <doi:10.1007/s00227-015-2771-0>.
Author: Takahiro Shimada
Maintainer: Takahiro Shimada <taka.shimada@gmail.com>

Diff between SDLfilter versions 1.2.0 dated 2019-02-18 and 1.2.1 dated 2019-02-26

 DESCRIPTION    |   10 +++++-----
 MD5            |   12 ++++++------
 NEWS           |    3 +++
 NEWS.md        |    5 +++++
 R/plotMap.R    |    6 +++---
 README.md      |    2 +-
 man/plotMap.Rd |    2 +-
 7 files changed, 24 insertions(+), 16 deletions(-)

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Package stochvol updated to version 2.0.1 with previous version 2.0.0 dated 2019-02-01

Title: Efficient Bayesian Inference for Stochastic Volatility (SV) Models
Description: Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002>; the most common use cases are described in Kastner (2016) <doi:10.18637/jss.v069.i05>. Also incorporates SV with leverage.
Author: Gregor Kastner [aut] (<https://orcid.org/0000-0002-8237-8271>), Darjus Hosszejni [aut, cre] (<https://orcid.org/0000-0002-3803-691X>)
Maintainer: Darjus Hosszejni <darjus.hosszejni@wu.ac.at>

Diff between stochvol versions 2.0.0 dated 2019-02-01 and 2.0.1 dated 2019-02-26

 DESCRIPTION              |    6 ++--
 MD5                      |   45 +++++++++++++++---------------
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 NEWS                     |    6 ++++
 R/plotting.R             |   69 +++++++++++++++++++++++++++++++++++++++++++++--
 R/stochvol-package.R     |    4 +-
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 src/theta-utils.cpp      |   27 +++++-------------
 src/theta-utils.h        |   13 +-------
 src/update_functions.cpp |    9 ++++--
 src/update_functions.h   |    3 +-
 24 files changed, 243 insertions(+), 133 deletions(-)

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Package MPN updated to version 0.2.0 with previous version 0.1.0 dated 2018-09-16

Title: Most Probable Number for Serial Dilutions
Description: Calculates the Most Probable Number for serial dilutions to quantify the concentration (density) of microbes in a laboratory sample (described in Jarvis (2010) <doi:10.1111/j.1365-2672.2010.04792.x>).
Author: Martine Ferguson [aut] (original R code), John Ihrie [cre, aut]
Maintainer: John Ihrie <John.Ihrie@fda.hhs.gov>

Diff between MPN versions 0.1.0 dated 2018-09-16 and 0.2.0 dated 2019-02-26

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 tests/testthat/test_mpn.R  |  136 +++++++++++++++++++++++++++++++++++++++++++--
 vignettes/MPN-vignette.Rmd |   50 ++++++++++++----
 14 files changed, 520 insertions(+), 123 deletions(-)

More information about MPN at CRAN
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Package list updated to version 9.1 with previous version 9.0 dated 2018-11-06

Title: Statistical Methods for the Item Count Technique and List Experiment
Description: Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, Blair and Imai (2012) <doi:10.1093/pan/mpr048>, Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>, Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.
Author: Graeme Blair [aut, cre], Winston Chou [aut], Kosuke Imai [aut], Bethany Park [ctb], Alexander Coppock [ctb]
Maintainer: Graeme Blair <graeme.blair@ucla.edu>

Diff between list versions 9.0 dated 2018-11-06 and 9.1 dated 2019-02-26

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 DESCRIPTION                 |    8 
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 R/ictreg.R                  |  429 ++++++++++++++++++++++++++++++++++++--------
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More information about list at CRAN
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Package DynTxRegime updated to version 4.0 with previous version 3.2 dated 2018-02-12

Title: Methods for Estimating Optimal Dynamic Treatment Regimes
Description: Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators.
Author: S. T. Holloway, E. B. Laber, K. A. Linn, B. Zhang, M. Davidian, and A. A. Tsiatis
Maintainer: Shannon T. Holloway <sthollow@ncsu.edu>

Diff between DynTxRegime versions 3.2 dated 2018-02-12 and 4.0 dated 2019-02-26

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 DynTxRegime-3.2/DynTxRegime/man/internal_newTxInfoNoSubsets.Rd                      |only
 DynTxRegime-3.2/DynTxRegime/man/internal_newTxInfoWithSubsets.Rd                    |only
 DynTxRegime-3.2/DynTxRegime/man/internal_newTypedSimpleFit.Rd                       |only
 DynTxRegime-3.2/DynTxRegime/man/iqLearnFS.Rd                                        |only
 DynTxRegime-3.2/DynTxRegime/man/iqLearnFSC.Rd                                       |only
 DynTxRegime-3.2/DynTxRegime/man/iqLearnSS.Rd                                        |only
 DynTxRegime-3.2/DynTxRegime/man/iqLearnVar.Rd                                       |only
 DynTxRegime-3.2/DynTxRegime/man/methods-Qstep.Rd                                    |only
 DynTxRegime-3.2/DynTxRegime/man/methods-classif.Rd                                  |only
 DynTxRegime-3.2/DynTxRegime/man/methods-coef.Rd                                     |only
 DynTxRegime-3.2/DynTxRegime/man/methods-cvInfo.Rd                                   |only
 DynTxRegime-3.2/DynTxRegime/man/methods-estimator.Rd                                |only
 DynTxRegime-3.2/DynTxRegime/man/methods-fitObject.Rd                                |only
 DynTxRegime-3.2/DynTxRegime/man/methods-fittedCont.Rd                               |only
 DynTxRegime-3.2/DynTxRegime/man/methods-fittedMain.Rd                               |only
 DynTxRegime-3.2/DynTxRegime/man/methods-genetic.Rd                                  |only
 DynTxRegime-3.2/DynTxRegime/man/methods-optTx.Rd                                    |only
 DynTxRegime-3.2/DynTxRegime/man/methods-optimObj.Rd                                 |only
 DynTxRegime-3.2/DynTxRegime/man/methods-outcome.Rd                                  |only
 DynTxRegime-3.2/DynTxRegime/man/methods-plot.Rd                                     |only
 DynTxRegime-3.2/DynTxRegime/man/methods-propen.Rd                                   |only
 DynTxRegime-3.2/DynTxRegime/man/methods-qqPlot.Rd                                   |only
 DynTxRegime-3.2/DynTxRegime/man/methods-regimeCoef.Rd                               |only
 DynTxRegime-3.2/DynTxRegime/man/methods-residuals.Rd                                |only
 DynTxRegime-3.2/DynTxRegime/man/methods-show.Rd                                     |only
 DynTxRegime-3.2/DynTxRegime/man/methods-stdDev.Rd                                   |only
 DynTxRegime-3.2/DynTxRegime/man/methods-summary.Rd                                  |only
 DynTxRegime-3.2/DynTxRegime/man/plugInValue.Rd                                      |only
 DynTxRegime-4.0/DynTxRegime/DESCRIPTION                                             |   47 
 DynTxRegime-4.0/DynTxRegime/MD5                                                     |  588 +++++----
 DynTxRegime-4.0/DynTxRegime/NAMESPACE                                               |  107 -
 DynTxRegime-4.0/DynTxRegime/R/A_DecisionPointList.R                                 |only
 DynTxRegime-4.0/DynTxRegime/R/A_DynTxRegime.R                                       |only
 DynTxRegime-4.0/DynTxRegime/R/A_List.R                                              |only
 DynTxRegime-4.0/DynTxRegime/R/A_ModelObjSubset.R                                    |only
 DynTxRegime-4.0/DynTxRegime/R/A_ModelObj_DecisionPointList.R                        |only
 DynTxRegime-4.0/DynTxRegime/R/A_ModelObj_SubsetList.R                               |only
 DynTxRegime-4.0/DynTxRegime/R/A_OptimalInfo.R                                       |only
 DynTxRegime-4.0/DynTxRegime/R/A_OptimalObj.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/A_SubsetList.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/A_generics.R                                          |only
 DynTxRegime-4.0/DynTxRegime/R/A_newModelObjSubset.R                                 |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxInfoBasic.R                                       |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxInfoFactor.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxInfoInteger.R                                     |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxInfoList.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxInfoNoSubsets.R                                   |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxInfoWithSubsets.R                                 |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxObj.R                                             |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxSubset.R                                          |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxSubsetFactor.R                                    |only
 DynTxRegime-4.0/DynTxRegime/R/B_TxSubsetInteger.R                                   |only
 DynTxRegime-4.0/DynTxRegime/R/C_TypedFit.R                                          |only
 DynTxRegime-4.0/DynTxRegime/R/C_TypedFitObj.R                                       |only
 DynTxRegime-4.0/DynTxRegime/R/C_TypedFit_SubsetList.R                               |only
 DynTxRegime-4.0/DynTxRegime/R/C_TypedFit_fSet.R                                     |only
 DynTxRegime-4.0/DynTxRegime/R/D_OutcomeIterateFit.R                                 |only
 DynTxRegime-4.0/DynTxRegime/R/D_OutcomeNoFit.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/D_OutcomeObj.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/D_OutcomeSimpleFit.R                                  |only
 DynTxRegime-4.0/DynTxRegime/R/D_OutcomeSimpleFit_SubsetList.R                       |only
 DynTxRegime-4.0/DynTxRegime/R/D_OutcomeSimpleFit_fSet.R                             |only
 DynTxRegime-4.0/DynTxRegime/R/D_newModel.R                                          |only
 DynTxRegime-4.0/DynTxRegime/R/E_class_IQLearnFS.R                                   |only
 DynTxRegime-4.0/DynTxRegime/R/E_class_IQLearnFS_C.R                                 |only
 DynTxRegime-4.0/DynTxRegime/R/E_class_IQLearnFS_ME.R                                |only
 DynTxRegime-4.0/DynTxRegime/R/E_class_IQLearnFS_VHet.R                              |only
 DynTxRegime-4.0/DynTxRegime/R/E_class_IQLearnSS.R                                   |only
 DynTxRegime-4.0/DynTxRegime/R/E_class_QLearn.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/E_iqLearnFSC.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/E_iqLearnFSM.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/E_iqLearnFSV.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/E_iqLearnSS.R                                         |only
 DynTxRegime-4.0/DynTxRegime/R/E_qLearn.R                                            |only
 DynTxRegime-4.0/DynTxRegime/R/F_PropensityFit.R                                     |only
 DynTxRegime-4.0/DynTxRegime/R/F_PropensityFit_SubsetList.R                          |only
 DynTxRegime-4.0/DynTxRegime/R/F_PropensityFit_fSet.R                                |only
 DynTxRegime-4.0/DynTxRegime/R/F_PropensityObj.R                                     |only
 DynTxRegime-4.0/DynTxRegime/R/G_Regime.R                                            |only
 DynTxRegime-4.0/DynTxRegime/R/G_RegimeObj.R                                         |only
 DynTxRegime-4.0/DynTxRegime/R/H_class_OptimalSeq.R                                  |only
 DynTxRegime-4.0/DynTxRegime/R/H_class_OptimalSeqCoarsened.R                         |only
 DynTxRegime-4.0/DynTxRegime/R/H_class_OptimalSeqMissing.R                           |only
 DynTxRegime-4.0/DynTxRegime/R/H_optimalSeq.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/I_ClassificationFit.R                                 |only
 DynTxRegime-4.0/DynTxRegime/R/I_ClassificationFit_SubsetList.R                      |only
 DynTxRegime-4.0/DynTxRegime/R/I_ClassificationFit_fSet.R                            |only
 DynTxRegime-4.0/DynTxRegime/R/I_ClassificationObj.R                                 |only
 DynTxRegime-4.0/DynTxRegime/R/J_class_OptimalClass.R                                |only
 DynTxRegime-4.0/DynTxRegime/R/J_optimalClass.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/K_Kernel.R                                            |only
 DynTxRegime-4.0/DynTxRegime/R/K_KernelObj.R                                         |only
 DynTxRegime-4.0/DynTxRegime/R/K_LinearKernel.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/K_MultiRadialKernel.R                                 |only
 DynTxRegime-4.0/DynTxRegime/R/K_PolyKernel.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/K_RadialKernel.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/L_ExpSurrogate.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/L_HingeSurrogate.R                                    |only
 DynTxRegime-4.0/DynTxRegime/R/L_HuberHingeSurrogate.R                               |only
 DynTxRegime-4.0/DynTxRegime/R/L_LogitSurrogate.R                                    |only
 DynTxRegime-4.0/DynTxRegime/R/L_SmoothRampSurrogate.R                               |only
 DynTxRegime-4.0/DynTxRegime/R/L_SqHingeSurrogate.R                                  |only
 DynTxRegime-4.0/DynTxRegime/R/L_Surrogate.R                                         |only
 DynTxRegime-4.0/DynTxRegime/R/M_MethodObject.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/M_OptimBasic.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/M_OptimKernel.R                                       |only
 DynTxRegime-4.0/DynTxRegime/R/M_OptimObj.R                                          |only
 DynTxRegime-4.0/DynTxRegime/R/N_CVBasic.R                                           |only
 DynTxRegime-4.0/DynTxRegime/R/N_CVInfo.R                                            |only
 DynTxRegime-4.0/DynTxRegime/R/N_CVInfo2Par.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/N_CVInfoLambda.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/N_CVInfoObj.R                                         |only
 DynTxRegime-4.0/DynTxRegime/R/N_CVInfokParam.R                                      |only
 DynTxRegime-4.0/DynTxRegime/R/N_OptimStep.R                                         |only
 DynTxRegime-4.0/DynTxRegime/R/O_Learning.R                                          |only
 DynTxRegime-4.0/DynTxRegime/R/O_LearningMulti.R                                     |only
 DynTxRegime-4.0/DynTxRegime/R/O_LearningObject.R                                    |only
 DynTxRegime-4.0/DynTxRegime/R/P_class_.owl.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/P_class_OWL.R                                         |only
 DynTxRegime-4.0/DynTxRegime/R/P_owl.R                                               |only
 DynTxRegime-4.0/DynTxRegime/R/Q_class_.rwl.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/Q_class_RWL.R                                         |only
 DynTxRegime-4.0/DynTxRegime/R/Q_rwl.R                                               |only
 DynTxRegime-4.0/DynTxRegime/R/R_bowl.R                                              |only
 DynTxRegime-4.0/DynTxRegime/R/R_class_BOWL.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/R_class_BOWLBasic.R                                   |only
 DynTxRegime-4.0/DynTxRegime/R/S_class_.earl.R                                       |only
 DynTxRegime-4.0/DynTxRegime/R/S_class_EARL.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/S_earl.R                                              |only
 DynTxRegime-4.0/DynTxRegime/R/checkInputs.R                                         |  398 +-----
 DynTxRegime-4.0/DynTxRegime/R/internalTest.R                                        |only
 DynTxRegime-4.0/DynTxRegime/R/titleIt.R                                             |    2 
 DynTxRegime-4.0/DynTxRegime/man/BOWL-class.Rd                                       |   98 -
 DynTxRegime-4.0/DynTxRegime/man/BOWL-methods.Rd                                     |only
 DynTxRegime-4.0/DynTxRegime/man/BOWLBasic-class.Rd                                  |only
 DynTxRegime-4.0/DynTxRegime/man/BOWLBasic-methods.Rd                                |only
 DynTxRegime-4.0/DynTxRegime/man/BOWLObj-class.Rd                                    |   30 
 DynTxRegime-4.0/DynTxRegime/man/CVBasic-class.Rd                                    |only
 DynTxRegime-4.0/DynTxRegime/man/CVInfo-class.Rd                                     |   55 
 DynTxRegime-4.0/DynTxRegime/man/CVInfo-methods.Rd                                   |only
 DynTxRegime-4.0/DynTxRegime/man/CVInfo2Par-class.Rd                                 |   49 
 DynTxRegime-4.0/DynTxRegime/man/CVInfo2Par-methods.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/CVInfoLambda-class.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/CVInfoLambda-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/CVInfoObj-class.Rd                                  |only
 DynTxRegime-4.0/DynTxRegime/man/CVInfoObj-methods.Rd                                |only
 DynTxRegime-4.0/DynTxRegime/man/CVInfokParam-class.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/CVInfokParam-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/Call.Rd                                             |only
 DynTxRegime-4.0/DynTxRegime/man/ClassificationFit-class.Rd                          |only
 DynTxRegime-4.0/DynTxRegime/man/ClassificationFit-methods.Rd                        |only
 DynTxRegime-4.0/DynTxRegime/man/ClassificationFit_SubsetList-class.Rd               |only
 DynTxRegime-4.0/DynTxRegime/man/ClassificationFit_SubsetList-methods.Rd             |only
 DynTxRegime-4.0/DynTxRegime/man/ClassificationFit_fSet-class.Rd                     |only
 DynTxRegime-4.0/DynTxRegime/man/ClassificationFit_fSet-methods.Rd                   |only
 DynTxRegime-4.0/DynTxRegime/man/ClassificationObj-class.Rd                          |only
 DynTxRegime-4.0/DynTxRegime/man/ClassificationObj-methods.Rd                        |only
 DynTxRegime-4.0/DynTxRegime/man/DTRstep.Rd                                          |only
 DynTxRegime-4.0/DynTxRegime/man/DecisionPointList-class.Rd                          |   70 -
 DynTxRegime-4.0/DynTxRegime/man/DecisionPointList-methods.Rd                        |only
 DynTxRegime-4.0/DynTxRegime/man/DynTxRegime-class.Rd                                |  140 --
 DynTxRegime-4.0/DynTxRegime/man/DynTxRegime-internal-api.Rd                         |  650 +++++++---
 DynTxRegime-4.0/DynTxRegime/man/DynTxRegime-methods.Rd                              |only
 DynTxRegime-4.0/DynTxRegime/man/EARL-class.Rd                                       |  119 -
 DynTxRegime-4.0/DynTxRegime/man/EARL-methods.Rd                                     |only
 DynTxRegime-4.0/DynTxRegime/man/ExpSurrogate-class.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/ExpSurrogate-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/HingeSurrogate-class.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/HingeSurrogate-methods.Rd                           |only
 DynTxRegime-4.0/DynTxRegime/man/HuberHingeSurrogate-class.Rd                        |only
 DynTxRegime-4.0/DynTxRegime/man/HuberHingeSurrogate-methods.Rd                      |only
 DynTxRegime-4.0/DynTxRegime/man/IQLearnFS-class.Rd                                  |   22 
 DynTxRegime-4.0/DynTxRegime/man/IQLearnFS-methods.Rd                                |only
 DynTxRegime-4.0/DynTxRegime/man/IQLearnFS_C-class.Rd                                |  100 -
 DynTxRegime-4.0/DynTxRegime/man/IQLearnFS_C-methods.Rd                              |only
 DynTxRegime-4.0/DynTxRegime/man/IQLearnFS_ME-class.Rd                               |   83 -
 DynTxRegime-4.0/DynTxRegime/man/IQLearnFS_ME-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/IQLearnFS_VHet-class.Rd                             |   98 -
 DynTxRegime-4.0/DynTxRegime/man/IQLearnFS_VHet-methods.Rd                           |only
 DynTxRegime-4.0/DynTxRegime/man/IQLearnSS-class.Rd                                  |  102 -
 DynTxRegime-4.0/DynTxRegime/man/IQLearnSS-methods.Rd                                |only
 DynTxRegime-4.0/DynTxRegime/man/Kernel-class.Rd                                     |only
 DynTxRegime-4.0/DynTxRegime/man/Kernel-methods.Rd                                   |only
 DynTxRegime-4.0/DynTxRegime/man/KernelObj-class.Rd                                  |only
 DynTxRegime-4.0/DynTxRegime/man/KernelObj-methods.Rd                                |only
 DynTxRegime-4.0/DynTxRegime/man/Learning-class.Rd                                   |only
 DynTxRegime-4.0/DynTxRegime/man/Learning-methods.Rd                                 |only
 DynTxRegime-4.0/DynTxRegime/man/LearningMulti-class.Rd                              |only
 DynTxRegime-4.0/DynTxRegime/man/LearningMulti-methods.Rd                            |only
 DynTxRegime-4.0/DynTxRegime/man/LearningObject-class.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/LearningObject-methods.Rd                           |only
 DynTxRegime-4.0/DynTxRegime/man/LinearKernel-class.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/LinearKernel-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/List-class.Rd                                       |   50 
 DynTxRegime-4.0/DynTxRegime/man/LogitSurrogate-class.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/LogitSurrogate-methods.Rd                           |only
 DynTxRegime-4.0/DynTxRegime/man/MethodObject-class.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/MethodObject-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/ModelObjSubset-class.Rd                             |   66 -
 DynTxRegime-4.0/DynTxRegime/man/ModelObjSubset-methods.Rd                           |only
 DynTxRegime-4.0/DynTxRegime/man/ModelObj_DecisionPointList-class.Rd                 |   40 
 DynTxRegime-4.0/DynTxRegime/man/ModelObj_SubsetList-class.Rd                        |   39 
 DynTxRegime-4.0/DynTxRegime/man/MultiRadialKernel-class.Rd                          |only
 DynTxRegime-4.0/DynTxRegime/man/MultiRadialKernel-methods.Rd                        |only
 DynTxRegime-4.0/DynTxRegime/man/OWL-class.Rd                                        |  133 +-
 DynTxRegime-4.0/DynTxRegime/man/OWL-methods.Rd                                      |only
 DynTxRegime-4.0/DynTxRegime/man/OptimBasic-class.Rd                                 |   60 
 DynTxRegime-4.0/DynTxRegime/man/OptimBasic-methods.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/OptimKernel-class.Rd                                |   67 -
 DynTxRegime-4.0/DynTxRegime/man/OptimKernel-methods.Rd                              |only
 DynTxRegime-4.0/DynTxRegime/man/OptimObj-class.Rd                                   |only
 DynTxRegime-4.0/DynTxRegime/man/OptimObj-methods.Rd                                 |only
 DynTxRegime-4.0/DynTxRegime/man/OptimStep-class.Rd                                  |only
 DynTxRegime-4.0/DynTxRegime/man/OptimStep-methods.Rd                                |only
 DynTxRegime-4.0/DynTxRegime/man/OptimStep.Rd                                        |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalClass-class.Rd                               |   77 -
 DynTxRegime-4.0/DynTxRegime/man/OptimalClass-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalClassObj-class.Rd                            |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalInfo-class.Rd                                |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalInfo-methods.Rd                              |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalObj-class.Rd                                 |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalObj-methods.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalSeq-class.Rd                                 |   84 +
 DynTxRegime-4.0/DynTxRegime/man/OptimalSeq-methods.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalSeqCoarsened-class.Rd                        |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalSeqCoarsened-methods.Rd                      |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalSeqMissing-class.Rd                          |only
 DynTxRegime-4.0/DynTxRegime/man/OptimalSeqMissing-methods.Rd                        |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeIterateFit-class.Rd                          |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeIterateFit-methods.Rd                        |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeNoFit-class.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeNoFit-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeObj-class.Rd                                 |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeObj-methods.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeSimpleFit-class.Rd                           |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeSimpleFit-methods.Rd                         |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeSimpleFit_SubsetList-class.Rd                |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeSimpleFit_SubsetList-methods.Rd              |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeSimpleFit_fSet-class.Rd                      |only
 DynTxRegime-4.0/DynTxRegime/man/OutcomeSimpleFit_fSet-methods.Rd                    |only
 DynTxRegime-4.0/DynTxRegime/man/PolyKernel-class.Rd                                 |only
 DynTxRegime-4.0/DynTxRegime/man/PolyKernel-methods.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/PropensityFit-class.Rd                              |   58 
 DynTxRegime-4.0/DynTxRegime/man/PropensityFit-methods.Rd                            |only
 DynTxRegime-4.0/DynTxRegime/man/PropensityFit_SubsetList-class.Rd                   |   52 
 DynTxRegime-4.0/DynTxRegime/man/PropensityFit_SubsetList-methods.Rd                 |only
 DynTxRegime-4.0/DynTxRegime/man/PropensityFit_fSet-class.Rd                         |   52 
 DynTxRegime-4.0/DynTxRegime/man/PropensityFit_fSet-methods.Rd                       |only
 DynTxRegime-4.0/DynTxRegime/man/PropensityObj-class.Rd                              |only
 DynTxRegime-4.0/DynTxRegime/man/PropensityObj-methods.Rd                            |only
 DynTxRegime-4.0/DynTxRegime/man/QLearn-class.Rd                                     |   84 -
 DynTxRegime-4.0/DynTxRegime/man/QLearnObj-class.Rd                                  |only
 DynTxRegime-4.0/DynTxRegime/man/RWL-class.Rd                                        |  133 +-
 DynTxRegime-4.0/DynTxRegime/man/RWL-methods.Rd                                      |only
 DynTxRegime-4.0/DynTxRegime/man/RadialKernel-class.Rd                               |only
 DynTxRegime-4.0/DynTxRegime/man/RadialKernel-methods.Rd                             |only
 DynTxRegime-4.0/DynTxRegime/man/Regime-class.Rd                                     |   96 -
 DynTxRegime-4.0/DynTxRegime/man/Regime-methods.Rd                                   |only
 DynTxRegime-4.0/DynTxRegime/man/RegimeObj-class.Rd                                  |only
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More information about DynTxRegime at CRAN
Permanent link

Package WriteXLS updated to version 4.1.0 with previous version 4.0.0 dated 2015-12-07

Title: Cross-Platform Perl Based R Function to Create Excel 2003 (XLS) and Excel 2007 (XLSX) Files
Description: Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX) files from one or more data frames. Each data frame will be written to a separate named worksheet in the Excel spreadsheet. The worksheet name will be the name of the data frame it contains or can be specified by the user.
Author: Marc Schwartz <marc_schwartz@me.com> and various authors for Perl modules listed in each .pm file.
Maintainer: Marc Schwartz <marc_schwartz@me.com>

Diff between WriteXLS versions 4.0.0 dated 2015-12-07 and 4.1.0 dated 2019-02-26

 DESCRIPTION                                          |    8 
 MD5                                                  |   79 
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More information about WriteXLS at CRAN
Permanent link

Package rgbif updated to version 1.2.0 with previous version 1.1.0 dated 2018-10-19

Title: Interface to the Global 'Biodiversity' Information Facility API
Description: A programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility ('GBIF'; <https://www.gbif.org/developer/summary>). 'GBIF' is a database of species occurrence records from sources all over the globe. 'rgbif' includes functions for searching for taxonomic names, retrieving information on data providers, getting species occurrence records, getting counts of occurrence records, and using the 'GBIF' tile map service to make 'rasters' summarizing huge amounts of data.
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>), Vijay Barve [ctb] (<https://orcid.org/0000-0002-4852-2567>), Peter Desmet [ctb] (<https://orcid.org/0000-0002-8442-8025>), Laurens Geffert [ctb], Dan Mcglinn [ctb] (<https://orcid.org/0000-0003-2359-3526>), Damiano Oldoni [ctb] (<https://orcid.org/0000-0003-3445-7562>), Karthik Ram [ctb] (<https://orcid.org/0000-0002-0233-1757>)
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package RAppArmor updated to version 3.0 with previous version 2.0.2 dated 2016-05-17

Title: Bindings to AppArmor and Security Related Linux Tools
Description: Bindings to kernel methods for enforcing security restrictions. AppArmor can apply mandatory access control (MAC) policies on a given task (process) via security profiles with detailed ACL definitions. In addition this package implements bindings for setting process resource limits (rlimit), uid, gid, affinity and priority. The high level R function 'eval.secure' builds on these methods to perform dynamic sandboxing: it evaluates a single R expression within a temporary fork which acts as a sandbox by enforcing fine grained restrictions without affecting the main R process. A portable version of this function is now available in the 'unix' package.
Author: Jeroen Ooms
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between RAppArmor versions 2.0.2 dated 2016-05-17 and 3.0 dated 2019-02-26

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More information about RAppArmor at CRAN
Permanent link

Package quanteda updated to version 1.4.1 with previous version 1.4.0 dated 2019-01-30

Title: Quantitative Analysis of Textual Data
Description: A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and ngrams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
Author: Kenneth Benoit [cre, aut, cph] (<https://orcid.org/0000-0002-0797-564X>), Kohei Watanabe [aut] (<https://orcid.org/0000-0001-6519-5265>), Haiyan Wang [aut] (<https://orcid.org/0000-0003-4992-4311>), Paul Nulty [aut] (<https://orcid.org/0000-0002-7214-4666>), Adam Obeng [aut] (<https://orcid.org/0000-0002-2906-4775>), Stefan Müller [aut] (<https://orcid.org/0000-0002-6315-4125>), Akitaka Matsuo [aut] (<https://orcid.org/0000-0002-3323-6330>), Jiong Wei Lua [aut], Patrick O. Perry [aut] (<https://orcid.org/0000-0001-7460-127X>), Jouni Kuha [aut] (<https://orcid.org/0000-0002-1156-8465>), Benjamin Lauderdale [aut] (<https://orcid.org/0000-0003-3090-0969>), William Lowe [aut] (<https://orcid.org/0000-0002-1549-6163>), Christian Müller [ctb], Lori Young [dtc] (Lexicoder Sentiment Dictionary 2015), Stuart Soroka [dtc] (Lexicoder Sentiment Dictionary 2015), Ian Fellows [cph] (authored wordcloud C source code (modified)), European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)
Maintainer: Kenneth Benoit <kbenoit@lse.ac.uk>

Diff between quanteda versions 1.4.0 dated 2019-01-30 and 1.4.1 dated 2019-02-26

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 34 files changed, 546 insertions(+), 366 deletions(-)

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New package pivmet with initial version 0.1.1
Package: pivmet
Type: Package
Title: Pivotal Methods for Bayesian Relabelling and k-Means Clustering
Version: 0.1.1
Date: 2019-02-26
Author: Leonardo Egidi[aut, cre], Roberta Pappadà[aut], Francesco Pauli[aut], Nicola Torelli[aut]
Maintainer: Leonardo Egidi <legidi@units.it>
License: GPL-2
Description: Collection of pivotal algorithms for: relabelling the MCMC chains in order to undo the label switching problem in Bayesian mixture models, as proposed in Egidi, Pappadà, Pauli and Torelli (2018a)<doi:10.1007/s11222-017-9774-2>; initializing the centers of the classical k-means algorithm in order to obtain a better clustering solution. For further details see Egidi, Pappadà, Pauli and Torelli (2018b)<ISBN:9788891910233>.
URL: https://github.com/leoegidi/pivmet
Encoding: UTF-8
LazyData: true
LazyLoad: yes
SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
Depends: bayesmix, rjags, runjags, mvtnorm, RcmdrMisc
Imports: cluster, mclust, MASS
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 6.1.0
BuildManual: yes
NeedsCompilation: no
Packaged: 2019-02-26 21:03:11 UTC; leoeg
Repository: CRAN
Date/Publication: 2019-02-26 21:20:17 UTC

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New package mDAG with initial version 1.0
Package: mDAG
Type: Package
Title: Inferring Causal Network from Mixed Observational Data Using a Directed Acyclic Graph
Version: 1.0
Date: 2019-2-25
Author: Wujuan Zhong, Li Dong
Maintainer: Wujuan Zhong <zhongwujuan@gmail.com>
Description: Learning a mixed directed acyclic graph based on both continuous and categorical data.
LazyData: true
Encoding: UTF-8
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.14), pcalg, mgm, bnlearn, methods, nnet
Depends: R (>= 2.10), logistf
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-02-26 20:06:10 UTC; Fox
Repository: CRAN
Date/Publication: 2019-02-26 22:00:03 UTC

More information about mDAG at CRAN
Permanent link

Package grpreg updated to version 3.2-1 with previous version 3.2-0 dated 2018-09-27

Title: Regularization Paths for Regression Models with Grouped Covariates
Description: Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge.
Author: Patrick Breheny [aut, cre] (<https://orcid.org/000-0002-0650-1119>), Yaohui Zeng [ctb]
Maintainer: Patrick Breheny <patrick-breheny@uiowa.edu>

Diff between grpreg versions 3.2-0 dated 2018-09-27 and 3.2-1 dated 2019-02-26

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More information about grpreg at CRAN
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Package english updated to version 1.2-1 with previous version 1.2-0 dated 2019-02-19

Title: Translate Integers into English
Description: Allow numbers to be presented in an English language version, one, two, three, ...
Author: John Fox, Bill Venables, Anthony Damico and Anne Pier Salverda
Maintainer: Bill Venables <Bill.Venables@gmail.com>

Diff between english versions 1.2-0 dated 2019-02-19 and 1.2-1 dated 2019-02-26

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More information about english at CRAN
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Package ECoL updated to version 0.2.0 with previous version 0.1.0 dated 2018-01-22

Title: Complexity Measures for Supervised Problems
Description: Provides measures to characterize the complexity of classification and regression problems based on aspects that quantify the linearity of the data, the presence of informative feature, the sparsity and dimensionality of the datasets. This package provides bug fixes, generalizations and implementations of many state of the art measures. The measures are described in the papers: Ho and Basu (2002) <doi:10.1109/34.990132> and Lorena et al. (2018) <doi:10.1007/s10994-017-5681-1>.
Author: Luis Garcia [aut, cre], Ana Lorena [aut, ctb]
Maintainer: Luis Garcia <lpfgarcia@icmc.usp.br>

Diff between ECoL versions 0.1.0 dated 2018-01-22 and 0.2.0 dated 2019-02-26

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Package concurve updated to version 1.0.4 with previous version 1.0.1 dated 2019-01-30

Title: Computes and Plots Confidence (Compatibility) Intervals, P-Values, and S-Values to Form Consonance Curves (Functions)
Description: Allows one to compute confidence (compatibility/consonance) intervals for various statistical tests along with their corresponding P-values and S-values. The intervals can be plotted to create consonance functions allowing one to see what effect sizes are compatible with the test model at various compatibility levels rather than being limited to one interval estimate such as 95%. These methods are discussed by Poole C. (1987) <doi:10.2105/AJPH.77.2.195>, Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>, Singh K, Xie M, Strawderman WE. (2007) <arXiv:0708.0976>, Rothman KJ, Greenland S, Lash TL. (2012, ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2018) <doi:10.7287/peerj.preprints.26857v4>, and Greenland S. (2018) <doi:10.1080/00031305.2018.1529625>.
Author: Zad Chow [aut, cre] (<https://orcid.org/0000-0003-1545-8199>), Andrew Vigotsky [aut] (<https://orcid.org/0000-0003-3166-0688>)
Maintainer: Zad Chow <zad@lesslikely.com>

Diff between concurve versions 1.0.1 dated 2019-01-30 and 1.0.4 dated 2019-02-26

 DESCRIPTION |   19 ++++++++++---------
 MD5         |    4 ++--
 README.md   |   48 +++++++++++++++++++++++++++++++++++++++++-------
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More information about concurve at CRAN
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Package canprot updated to version 0.1.2 with previous version 0.1.1 dated 2018-01-18

Title: Chemical Composition of Differential Protein Expression
Description: Datasets are collected here for differentially (up- and down-) expressed proteins identified in proteomic studies of cancer and in cell culture experiments. Tables of amino acid compositions of proteins are used for calculations of chemical composition, projected into selected basis species. Plotting functions are used to visualize the compositional differences and thermodynamic potentials for proteomic transformations.
Author: Jeffrey Dick
Maintainer: Jeffrey Dick <j3ffdick@gmail.com>

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Package Ohmage (with last version 2.11-4.1) was removed from CRAN

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

2019-01-31 2.11-4.1
2014-09-10 2.11-4

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Package Mobilize (with last version 2.16-4) was removed from CRAN

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

2014-09-12 2.16-4

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Package taxotools updated to version 0.0.9 with previous version 0.0.7 dated 2019-01-30

Title: Tools to Handle Taxonomic Lists
Description: Some tools to work with taxonomic name lists.
Author: Vijay Barve [aut, cre] (<https://orcid.org/0000-0002-4852-2567>)
Maintainer: Vijay Barve <vijay.barve@gmail.com>

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Package simstudy updated to version 0.1.12 with previous version 0.1.11 dated 2019-02-07

Title: Simulation of Study Data
Description: Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).
Author: Keith Goldfeld [aut, cre]
Maintainer: Keith Goldfeld <Keith.Goldfeld@nyumc.org>

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Package phenocamr updated to version 1.1.3 with previous version 1.1.1 dated 2018-08-22

Title: Facilitates 'PhenoCam' Data Access and Time Series Post-Processing
Description: Programmatic interface to the 'PhenoCam' web services (<http://phenocam.sr.unh.edu>). Allows for easy downloading of 'PhenoCam' data directly to your R workspace or your computer and provides post-processing routines for consistent and easy timeseries outlier detection, smoothing and estimation of phenological transition dates. Methods for this package are described in detail in Hufkens et. al (2018) <doi:10.1111/2041-210X.12970>.
Author: Hufkens Koen [aut, cre]
Maintainer: Hufkens Koen <koen.hufkens@gmail.com>

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Package namedCapture updated to version 2019.02.25 with previous version 2019.01.14 dated 2019-01-14

Title: Named Capture Regular Expressions
Description: User-friendly wrappers for named capture regular expressions.
Author: Toby Dylan Hocking
Maintainer: Toby Dylan Hocking <toby.hocking@r-project.org>

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Package modelObj updated to version 4.0 with previous version 3.0 dated 2017-05-12

Title: A Model Object Framework for Regression Analysis
Description: A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions. In this way, a user can easily go from linear to non-linear models within the same package.
Author: Shannon T. Holloway
Maintainer: Shannon T. Holloway <sthollow@ncsu.edu>

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Package fishtree updated to version 0.2.0 with previous version 0.1.0 dated 2018-11-30

Title: Interface to the Fish Tree of Life API
Description: An interface to the Fish Tree of Life API to download taxonomies, phylogenies, fossil calibrations, and diversification rate information for ray-finned fishes.
Author: Jonathan Chang [aut, cre] (<https://orcid.org/0000-0002-3811-1254>), Michael E Alfaro [ctb], Daniel L Rabosky [ctb], Stephen A Smith [ctb]
Maintainer: Jonathan Chang <jonathan.chang@ucla.edu>

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Package anipaths updated to version 0.9.7 with previous version 0.9.6 dated 2018-06-20

Title: Animation of Observed Trajectories Using Spline-Based Interpolation
Description: Animation of observed trajectories using spline-based interpolation (see for example, Buderman, F. E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), <doi:10.1111/2041-210X.12465> "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations.
Author: Henry Scharf
Maintainer: Henry Scharf <henry.scharf@colostate.edu>

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Package psychmeta updated to version 2.3.2 with previous version 2.3.1 dated 2019-02-15

Title: Psychometric Meta-Analysis Toolkit
Description: Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
Author: Jeffrey A. Dahlke [aut, cre], Brenton M. Wiernik [aut], Michael T. Brannick [ctb] (Testing), Jack Kostal [ctb] (Code for reshape_mat2dat function), Sean Potter [ctb] (Testing; Code for cumulative and leave1out plots), John Sakaluk [ctb] (Code for funnel and forest plots), Yuejia (Mandy) Teng [ctb] (Testing)
Maintainer: Jeffrey A. Dahlke <dahlk068@umn.edu>

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Package ProjectTemplate updated to version 0.9.0 with previous version 0.8.2 dated 2018-04-20

Title: Automates the Creation of New Statistical Analysis Projects
Description: Provides functions to automatically build a directory structure for a new R project. Using this structure, 'ProjectTemplate' automates data loading, preprocessing, library importing and unit testing.
Author: Aleksandar Blagotic [ctb], Diego Valle-Jones [ctb], Jeffrey Breen [ctb], Joakim Lundborg [ctb], John Myles White [aut, cph], Josh Bode [ctb], Kenton White [ctb, cre], Kirill Mueller [ctb], Matteo Redaelli [ctb], Noah Lorang [ctb], Patrick Schalk [ctb], Dominik Schneider [ctb]
Maintainer: Kenton White <jkentonwhite@gmail.com>

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More information about ProjectTemplate at CRAN
Permanent link

Package ncvreg updated to version 3.11-1 with previous version 3.11-0 dated 2018-09-13

Title: Regularization Paths for SCAD and MCP Penalized Regression Models
Description: Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided.
Author: Patrick Breheny [aut, cre] (<https://orcid.org/000-0002-0650-1119>)
Maintainer: Patrick Breheny <patrick-breheny@uiowa.edu>

Diff between ncvreg versions 3.11-0 dated 2018-09-13 and 3.11-1 dated 2019-02-26

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More information about ncvreg at CRAN
Permanent link

Package AICcmodavg updated to version 2.2-0 with previous version 2.1-1 dated 2017-06-19

Title: Model Selection and Multimodel Inference Based on (Q)AIC(c)
Description: Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs' and 'rjags' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.
Author: Marc J. Mazerolle <marc.mazerolle@sbf.ulaval.ca> and portions of code contributed by Dan Linden.
Maintainer: Marc J. Mazerolle <marc.mazerolle@sbf.ulaval.ca>

Diff between AICcmodavg versions 2.1-1 dated 2017-06-19 and 2.2-0 dated 2019-02-26

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Permanent link

Package SuperExactTest updated to version 1.0.6 with previous version 1.0.4 dated 2018-10-26

Title: Exact Test and Visualization of Multi-Set Intersections
Description: Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.
Author: Minghui Wang, Yongzhong Zhao and Bin Zhang
Maintainer: Minghui Wang <minghui.wang@mssm.edu>

Diff between SuperExactTest versions 1.0.4 dated 2018-10-26 and 1.0.6 dated 2019-02-26

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More information about SuperExactTest at CRAN
Permanent link

Package splashr updated to version 0.6.0 with previous version 0.4.1 dated 2018-01-15

Title: Tools to Work with the 'Splash' 'JavaScript' Rendering and Scraping Service
Description: 'Splash' <https://github.com/scrapinghub/splash> is a 'JavaScript' rendering service. It is a lightweight web browser with an 'HTTP' API, implemented in 'Python' using 'Twisted' and 'QT' and provides some of the core functionality of the 'RSelenium' or 'seleniumPipes' R packages in a lightweight footprint. Some of 'Splash' features include the ability to process multiple web pages in parallel; retrieving 'HTML' results and/or take screen shots; disabling images or use 'Adblock Plus' rules to make rendering faster; executing custom 'JavaScript' in page context; getting detailed rendering info in 'HAR' format.
Author: Bob Rudis [aut, cre] (<https://orcid.org/0000-0001-5670-2640>)
Maintainer: Bob Rudis <bob@rud.is>

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Package NetWeaver updated to version 0.0.6 with previous version 0.0.2 dated 2017-01-10

Title: Graphic Presentation of Complex Genomic and Network Data Analysis
Description: Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.
Author: Minghui Wang, Bin Zhang
Maintainer: Minghui Wang <m.h.wang@live.com>

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New package volleystat with initial version 0.1.0
Package: volleystat
Title: Detailed Statistics on Volleyball Matches
Version: 0.1.0
Authors@R: person("Viktor", "Bozhinov", email = "mail@viktor-bozhinov.de", role = c("aut", "cre"))
Maintainer: Viktor Bozhinov <mail@viktor-bozhinov.de>
Description: Volleyball match statistics of the German volleyball first division league (seasons 2013/2014 to 2017/2018). The data has been collected from the official volleyball first division homepage (www.volleyball-bundesliga.de) and contains information on teams, staff, sets, matches, and player-in-match statistics (extracted automatically from the official match reports).
URL: http://github.com/bozhinvi/volleystat
BugReports: https://github.com/bozhinvi/volleystat/issues
License: CC0
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 2.10)
Suggests: dplyr, tidyr, knitr, rmarkdown, ggplot2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-22 10:12:31 UTC; viktorbozhinov
Author: Viktor Bozhinov [aut, cre]
Repository: CRAN
Date/Publication: 2019-02-26 17:10:03 UTC

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Package simpleCache updated to version 0.4.1 with previous version 0.3.1 dated 2017-08-22

Title: Simply Caching R Objects
Description: Provides intuitive functions for caching R objects, encouraging reproducible, restartable, and distributed R analysis. The user selects a location to store caches, and then provides nothing more than a cache name and instructions (R code) for how to produce the R object. Also provides some advanced options like environment assignments, recreating or reloading caches, and cluster compute bindings (using the 'batchtools' package) making it flexible enough for use in large-scale data analysis projects.
Author: VP Nagraj [aut], Nathan Sheffield [aut, cre]
Maintainer: Nathan Sheffield <nathan@code.databio.org>

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Package opalr updated to version 1.0.1 with previous version 1.0.0 dated 2019-02-18

Title: 'Opal' Data Repository Client and 'DataSHIELD' Utils
Description: Data integration Web application for biobanks by 'OBiBa'. 'Opal' is the core database application for biobanks. Participant data, once collected from any data source, must be integrated and stored in a central data repository under a uniform model. 'Opal' is such a central repository. It can import, process, validate, query, analyze, report, and export data. 'Opal' is typically used in a research center to analyze the data acquired at assessment centres. Its ultimate purpose is to achieve seamless data-sharing among biobanks. This 'Opal' client allows to interact with 'Opal' web services and to perform operations on the R server side. 'DataSHIELD' administration tools are also provided.
Author: Yannick Marcon [aut, cre] (<https://orcid.org/0000-0003-0138-2023>), Amadou Gaye [ctb] (<https://orcid.org/0000-0002-1180-2792>), OBiBa group [cph]
Maintainer: Yannick Marcon <yannick.marcon@obiba.org>

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New package sievePH with initial version 1.0.0
Package: sievePH
Type: Package
Title: Sieve Analysis Methods for Proportional Hazards Models
Version: 1.0.0
Authors@R: c(person("Michal", "Juraska", email = "mjuraska@fredhutch.org", role = c("aut", "cre")), person("Stephanie", "Wu", email = "swu18@uw.edu", role = "ctb"))
Description: Implements semiparametric estimation and testing procedures for a continuous, possibly multivariate, mark-specific hazard ratio (treatment/placebo) of an event of interest in a randomized treatment efficacy trial with a time-to-event endpoint, as described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328 337, and in Juraska M and Gilbert PB (2015), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4): 606-25. The former considers continuous multivariate marks fully observed in all subjects who experience the event of interest, whereas the latter extends the previous work to allow multivariate marks that are subject to missingness-at-random. For models with missing marks, two estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases, and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the expected profile score vectors for subjects with missing marks. The augmented IPW estimator is doubly robust and recommended for use with incomplete mark data. The methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results.
URL: https://github.com/mjuraska/sievePH
BugReports: https://github.com/mjuraska/sievePH/issues
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: graphics, stats, survival
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-22 21:30:12 UTC; mjuraska
Author: Michal Juraska [aut, cre], Stephanie Wu [ctb]
Maintainer: Michal Juraska <mjuraska@fredhutch.org>
Repository: CRAN
Date/Publication: 2019-02-26 16:20:03 UTC

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Package lori updated to version 2.1.1 with previous version 2.0.0 dated 2019-02-19

Title: Low-Rank Interactions in Count Data with Covariates
Description: Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear model where main row and column effects are decomposed as regression terms on known covariates. A residual low-rank interaction term is also fitted. LORI returns estimates of covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure.
Author: Genevieve Robin [aut, cre]
Maintainer: Genevieve Robin <genevieve.robin@polytechnique.edu>

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Package hmeasure updated to version 1.0-2 with previous version 1.0-1 dated 2019-01-02

Title: The H-Measure and Other Scalar Classification Performance Metrics
Description: Classification performance metrics that are derived from the ROC curve of a classifier. The package includes the H-measure performance metric as described in <http://link.springer.com/article/10.1007/s10994-009-5119-5>, which computes the minimum total misclassification cost, integrating over any uncertainty about the relative misclassification costs, as per a user-defined prior. It also offers a one-stop-shop for other scalar metrics of performance, including sensitivity, specificity and many others, and also offers plotting tools for ROC curves and related statistics.
Author: Christoforos Anagnostopoulos <christoforos.anagnostopoulos06@imperial.ac.uk> and David J. Hand <d.j.hand@imperial.ac.uk>
Maintainer: Christoforos Anagnostopoulos <christoforos.anagnostopoulos06@imperial.ac.uk>

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Package forge updated to version 0.2.0 with previous version 0.1.0 dated 2018-08-31

Title: Casting Values into Shape
Description: Helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking.
Author: Kevin Kuo [aut, cre] (<https://orcid.org/0000-0001-7803-7901>)
Maintainer: Kevin Kuo <kevin.kuo@rstudio.com>

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New package motoRneuron with initial version 1.0.0
Package: motoRneuron
Type: Package
Title: Analyzing Paired Neuron Discharge Times for Time-Domain Synchronization
Version: 1.0.0
Authors@R: c(person("Andrew", "Tweedell", email = "atweedell315@gmail.com", role = c("aut", "cre")), person("Matthew", "Tenan", role = "aut"))
Description: The temporal relationship between motor neurons can offer explanations for neural strategies. We combined functions to reduce neuron action potential discharge data and analyze it for short-term, time-domain synchronization. Even more so, motoRneuron combines most available methods for the determining cross correlation histogram peaks and most available indices for calculating synchronization into simple functions. See Nordstrom, Fuglevand, and Enoka (1992) <doi:10.1113/jphysiol.1992.sp019244> for a more thorough introduction.
URL: http://github.com/tweedell/motoRneuron
BugReports: http://github.com/tweedell/motoRneuron/issues
Depends: R (>= 3.3), ggplot2, dygraphs
License: GPL-2
Encoding: UTF-8
LazyData: TRUE
Imports: stats (>= 3.4.1), methods
RoxygenNote: 6.1.0
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-15 15:08:51 UTC; andrew.tweedell.ctr
Author: Andrew Tweedell [aut, cre], Matthew Tenan [aut]
Maintainer: Andrew Tweedell <atweedell315@gmail.com>
Repository: CRAN
Date/Publication: 2019-02-26 14:40:03 UTC

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New package gama with initial version 1.0.3
Package: gama
Type: Package
Title: Genetic Approach to Maximize Clustering Criterion
Version: 1.0.3
Date: 2019-02-15
Authors@R: c(person(given = "Jairson", family ="Rodrigues", role = c("aut", "cre"), email = "jairson.rodrigues@univasf.edu.br", comment = c(ORCID = "0000-0003-1176-3903")), person(given = "Germano", family ="Vasconcelos", role = c("aut", "ths"), email = "gcv@cin.ufpe.br", comment = c(ORCID = "0000-0002-1899-1506")), person(given = "Renato", family = "Tin\'{o}s", role = c("aut", "rev"), email = "rtinos@ffclrp.usp.br", comment = c(ORCID = "0000-0003-4027-8851")))
Maintainer: Jairson Rodrigues <jairson.rodrigues@univasf.edu.br>
Description: An evolutionary approach to performing hard partitional clustering. The algorithm uses genetic operators guided by information about the quality of individual partitions. The method looks for the best barycenters/centroids configuration (encoded as real-value) to maximize or minimize one of the given clustering validation criteria: Silhouette, Dunn Index, C-Index or Calinski-Harabasz Index. As many other clustering algorithms, 'gama' asks for k: a fixed a priori established number of partitions. If the user does not know the best value for k, the algorithm estimates it by using one of two user-specified options: minimum or broad. The first method uses an approximation of the second derivative of a set of points to automatically detect the maximum curvature (the 'elbow') in the within-cluster sum of squares error (WCSSE) graph. The second method estimates the best k value through majority voting of 24 indices. One of the major advantages of 'gama' is to introduce a bias to detect partitions which attend a particular criterion. References: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04>; CHARRAD, Malika et al. (2014) <doi:10.18637/jss.v061.i06>; Tsagris M, Papadakis M. (2018) <doi:10.7287/peerj.preprints.26605v1>; Kaufman, L., & Rousseeuw, P. (1990, ISBN:0-47 1-73578-7).
Imports: ArgumentCheck, cluster, clusterCrit, NbClust, GA, ggplot2, methods, Rfast
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
License: GPL (>= 2)
LazyData: yes
URL: https://github.com/jairsonrodrigues/gama
NeedsCompilation: no
Packaged: 2019-02-15 14:55:12 UTC; jairson
Author: Jairson Rodrigues [aut, cre] (<https://orcid.org/0000-0003-1176-3903>), Germano Vasconcelos [aut, ths] (<https://orcid.org/0000-0002-1899-1506>), Renato Tin'{o}s [aut, rev] (<https://orcid.org/0000-0003-4027-8851>)
Repository: CRAN
Date/Publication: 2019-02-26 14:40:07 UTC

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New package plsr with initial version 0.0.1
Package: plsr
Type: Package
Title: Pleasure - Partial Least Squares Analysis with Permutation Testing
Version: 0.0.1
Authors@R: c( person(c("Jan","Niklas"), "Schneider", email = "jan_schneider@live.de",role = c("aut", "cre")), person(c("Timothy","Raymond"), "Brick", email = "tbrick@psu.edu",role = c("aut")))
Description: Provides partial least squares analysis for the analysis of the relation between two high-dimensional data sets. Includes permutation testing and bootstrapping for resulting latent variables (following McIntosh & Lobaugh (2004) <doi:10.1016/j.neuroimage.2004.07.020>) and several visualization functions.
Depends: R (>= 2.10)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: boot, ggplot2, reshape2, shiny
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-15 10:02:06 UTC; schneijn
Author: Jan Niklas Schneider [aut, cre], Timothy Raymond Brick [aut]
Maintainer: Jan Niklas Schneider <jan_schneider@live.de>
Repository: CRAN
Date/Publication: 2019-02-26 13:30:03 UTC

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Package sdm updated to version 1.0-67 with previous version 1.0-46 dated 2018-02-21

Title: Species Distribution Modelling
Description: An extensible framework for developing species distribution models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time. For more information, please check the following paper: Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.
Author: Babak Naimi, Miguel B. Araujo
Maintainer: Babak Naimi <naimi.b@gmail.com>

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Package RWeka updated to version 0.4-40 with previous version 0.4-39 dated 2018-09-10

Title: R/Weka Interface
Description: An R interface to Weka (Version 3.9.3). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Package 'RWeka' contains the interface code, the Weka jar is in a separate package 'RWekajars'. For more information on Weka see <http://www.cs.waikato.ac.nz/ml/weka/>.
Author: Kurt Hornik [aut, cre] (<https://orcid.org/0000-0003-4198-9911>), Christian Buchta [ctb], Torsten Hothorn [ctb], Alexandros Karatzoglou [ctb], David Meyer [ctb], Achim Zeileis [ctb] (<https://orcid.org/0000-0003-0918-3766>)
Maintainer: Kurt Hornik <Kurt.Hornik@R-project.org>

Diff between RWeka versions 0.4-39 dated 2018-09-10 and 0.4-40 dated 2019-02-26

 CHANGELOG                     |    4 ++++
 DESCRIPTION                   |    6 +++---
 MD5                           |   24 ++++++++++++------------
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 13 files changed, 54 insertions(+), 29 deletions(-)

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Package mmapcharr updated to version 0.3.0 with previous version 0.1.0 dated 2017-12-12

Title: Memory-Map Character Files
Description: Uses memory-mapping to enable the random access of elements of a text file of characters separated by characters as if it were a simple R(cpp) matrix.
Author: Florian Privé [aut, cre]
Maintainer: Florian Privé <florian.prive.21@gmail.com>

Diff between mmapcharr versions 0.1.0 dated 2017-12-12 and 0.3.0 dated 2019-02-26

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 mmapcharr-0.3.0/mmapcharr/inst/include/mmapcharr/charsep-acc.h |   38 ++++------
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Package float updated to version 0.2-2 with previous version 0.2-1 dated 2018-12-07

Title: 32-Bit Floats
Description: R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their "numeric"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off. The internal representation is an S4 class, which allows us to keep the syntax identical to that of base R's. Interaction between floats and base types for binary operators is generally possible; in these cases, type promotion always defaults to the higher precision. The package ships with copies of the single precision 'BLAS' and 'LAPACK', which are automatically built in the event they are not available on the system.
Author: Drew Schmidt [aut, cre, cph], Wei-Chen Chen [aut], Dmitriy Selivanov [ctb] (improvements in external package linking), ORNL [cph]
Maintainer: Drew Schmidt <wrathematics@gmail.com>

Diff between float versions 0.2-1 dated 2018-12-07 and 0.2-2 dated 2019-02-26

 ChangeLog          |    5 +++++
 DESCRIPTION        |    6 +++---
 MD5                |   18 +++++++++++-------
 NAMESPACE          |    2 ++
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 README.md          |    2 +-
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Package ThresholdROC updated to version 2.7 with previous version 2.6 dated 2017-11-24

Title: Optimum Threshold Estimation
Description: Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. The package also provides functions for sample size determination and estimation of diagnostic accuracy measures. It also includes graphical tools.
Author: Sara Perez-Jaume, Natalia Pallares, Konstantina Skaltsa
Maintainer: Sara Perez-Jaume <spjaume@gmail.com>

Diff between ThresholdROC versions 2.6 dated 2017-11-24 and 2.7 dated 2019-02-26

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Package slam updated to version 0.1-45 with previous version 0.1-44 dated 2018-12-21

Title: Sparse Lightweight Arrays and Matrices
Description: Data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively.
Author: Kurt Hornik [aut, cre] (<https://orcid.org/0000-0003-4198-9911>), David Meyer [aut], Christian Buchta [aut]
Maintainer: Kurt Hornik <Kurt.Hornik@R-project.org>

Diff between slam versions 0.1-44 dated 2018-12-21 and 0.1-45 dated 2019-02-26

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Package UCSCXenaTools updated to version 1.0.0 with previous version 0.2.7 dated 2019-01-22

Title: Download and Explore Datasets from UCSC Xena Data Hubs
Description: Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded. This package is based on 'XenaR' package.
Author: Shixiang Wang [aut, cre] (<https://orcid.org/0000-0001-9855-7357>), Martin Morgan [aut]
Maintainer: Shixiang Wang <w_shixiang@163.com>

Diff between UCSCXenaTools versions 0.2.7 dated 2019-01-22 and 1.0.0 dated 2019-02-26

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 32 files changed, 898 insertions(+), 1059 deletions(-)

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New package MetaUtility with initial version 1.0.0
Package: MetaUtility
Type: Package
Title: Utility Functions for Conducting and Reporting Meta-Analysis
Version: 1.0.0
Author: Maya B. Mathur, Tyler J. VanderWeele
Maintainer: Maya B. Mathur <mmathur@stanford.edu>
Description: Contains functions to estimate the proportion of effects stronger than a threshold of scientific importance (per Mathur & VanderWeele, 2018 [<https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8057>]), to make various effect size conversions, and to compute and format inference in a meta-analysis.
License: GPL-2
Encoding: UTF-8
Imports: metafor, stats
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-02-20 23:39:43 UTC; mmathur
Repository: CRAN
Date/Publication: 2019-02-26 09:50:03 UTC

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Package paleobioDB updated to version 0.6.0 with previous version 0.5.0 dated 2016-09-02

Title: Download and Process Data from the Paleobiology Database
Description: Includes 19 functions to wrap each endpoint of the PaleobioDB API, plus 8 functions to visualize and process the fossil data. The API documentation for the Paleobiology Database can be found in <http://paleobiodb.org/data1.1/>.
Author: Sara Varela [aut, cre], Javier González Hernández [aut], Luciano Fabris Sgarbi [aut]
Maintainer: Sara Varela <svarela@paleobiogeography.org>

Diff between paleobioDB versions 0.5.0 dated 2016-09-02 and 0.6.0 dated 2019-02-26

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 13 files changed, 104 insertions(+), 113 deletions(-)

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Package kscons (with last version 0.7.0) was removed from CRAN

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

2017-03-31 0.7.0

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Package stacomiR (with last version 0.5.4.1) was removed from CRAN

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

2019-01-27 0.5.4.1
2018-10-11 0.5.4.0
2018-01-04 0.5.3.1
2017-06-26 0.5.3

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