Mon, 30 Sep 2019

Package RcppEnsmallen updated to version 0.2.10.3.1 with previous version 0.2.10.0.1 dated 2019-09-07

Title: Header-Only C++ Mathematical Optimization Library for 'Armadillo'
Description: 'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++11' and 'Armadillo' 8.400 or later.
Author: James Joseph Balamuta [aut, cre, cph] (<https://orcid.org/0000-0003-2826-8458>), Dirk Eddelbuettel [aut, cph] (<https://orcid.org/0000-0001-6419-907X>)
Maintainer: James Joseph Balamuta <balamut2@illinois.edu>

Diff between RcppEnsmallen versions 0.2.10.0.1 dated 2019-09-07 and 0.2.10.3.1 dated 2019-09-30

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Package gets updated to version 0.21 with previous version 0.20 dated 2019-09-18

Title: General-to-Specific (GETS) Modelling and Indicator Saturation Methods
Description: Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean.
Author: Genaro Sucarrat [aut, cre], Felix Pretis [aut], James Reade [aut]
Maintainer: Genaro Sucarrat <genaro.sucarrat@bi.no>

Diff between gets versions 0.20 dated 2019-09-18 and 0.21 dated 2019-09-30

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Package GlobalOptions updated to version 0.1.1 with previous version 0.1.0 dated 2018-06-09

Title: Generate Functions to Get or Set Global Options
Description: It provides more configurations on the option values such as validation and filtering on the values, making options invisible or private.
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>

Diff between GlobalOptions versions 0.1.0 dated 2018-06-09 and 0.1.1 dated 2019-09-30

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Package kergp updated to version 0.5.0 with previous version 0.4.0 dated 2018-09-17

Title: Gaussian Process Laboratory
Description: Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation.
Author: Yves Deville, David Ginsbourger, Olivier Roustant. Contributors: Nicolas Durrande.
Maintainer: Olivier Roustant <roustant@insa-toulouse.fr>

Diff between kergp versions 0.4.0 dated 2018-09-17 and 0.5.0 dated 2019-09-30

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New package ModStatR with initial version 1.3.0
Package: ModStatR
Type: Package
Title: Statistical Modelling in Action with R
Version: 1.3.0
Date: 2019-09-23
Depends: R (>= 3.5.0)
Imports: stats, boot, ggplot2, BioStatR, jmuOutlier, ellipse, hypergeo, gsl
Authors@R: c( person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-0837-8281")), person(given = "Emmanuelle", family= "Claeys", role = c("aut"), email = "emmanuelle.claeys@unistra.fr"), person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy-bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-4615-1512")))
Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Emmanuelle Claeys [aut], Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>)
Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr>
Description: Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses.
LazyLoad: yes
LazyData: yes
License: GPL-3
Encoding: UTF-8
Classification/MSC:
URL: http://www-irma.u-strasbg.fr/~fbertran/, https://github.com/fbertran/ModStatR
BugReports: https://github.com/fbertran/ModStatR/issues
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-09-24 00:29:41 UTC; fbertran
Repository: CRAN
Date/Publication: 2019-09-30 16:20:02 UTC

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New package Mapinguari with initial version 1.0.0
Package: Mapinguari
Type: Package
Title: Process-Based Biogeographical Analysis
Version: 1.0.0
Authors@R: c(person("Gabriel", "Caetano", email = "gabrielhoc@gmail.com", role = c("aut", "cre")), person("Juan", "Santos", email = "infraguttatus@gmail.com", role = c("aut")), person("Barry", "Sinervo", email = "lizardrps@gmail.com", role = c("aut")))
Description: Facilitates the incorporation of biological processes in biogeographical analyses. It offers conveniences in fitting, comparing and extrapolating models of biological processes such as physiology and phenology. These spatial extrapolations can be informative by themselves, but also complement traditional correlative species distribution models, by mixing environmental and process-based predictors.
Depends: R (>= 3.5)
License: GPL-2
Encoding: UTF-8
LazyData: true
URL: http://github.com/gabrielhoc/Mapinguari
BugReports: http://github.com/gabrielhoc/Mapinguari/issues
Suggests: EcoHydRology, geosphere, mgcv
Imports: dplyr, magrittr, parallel, raster, rgdal, rlang, stringr, testthat
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-09-23 19:43:39 UTC; gabrielhenriquedeoliveiracaetano
Author: Gabriel Caetano [aut, cre], Juan Santos [aut], Barry Sinervo [aut]
Maintainer: Gabriel Caetano <gabrielhoc@gmail.com>
Repository: CRAN
Date/Publication: 2019-09-30 16:40:02 UTC

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New package gg.gap with initial version 1.3
Package: gg.gap
Type: Package
Title: Define Segments in y-Axis for 'ggplot2'
Version: 1.3
Authors@R: c( person("Jiacheng", "Lou", email = "loujiacheng1986@foxmail.com", role = c("aut", "cre")), person("Jing", "Zhang", role = "aut"), person("Yizhu", "Lvy", role = "aut"), person("Zhi", "Jin", role = "aut") )
Description: It is not very easy to define segments for y-axis in a 'ggplot2' plot. gg.gap() function in this package can carry it out.
Imports: ggplot2, cowplot, grid
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
URL: https://github.com/ChrisLou-bioinfo/gg.gap
BugReports: https://github.com/ChrisLou-bioinfo/gg.gap/issues
NeedsCompilation: no
Packaged: 2019-09-23 15:12:20 UTC; asus
Author: Jiacheng Lou [aut, cre], Jing Zhang [aut], Yizhu Lvy [aut], Zhi Jin [aut]
Maintainer: Jiacheng Lou <loujiacheng1986@foxmail.com>
Repository: CRAN
Date/Publication: 2019-09-30 16:10:02 UTC

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Package MLCIRTwithin updated to version 2.1.1 with previous version 2.1 dated 2016-08-06

Title: Latent Class Item Response Theory (LC-IRT) Models under Within-Item Multidimensionality
Description: Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.
Author: Francesco Bartolucci, Silvia Bacci - University of Perugia (IT)
Maintainer: Francesco Bartolucci <bart@stat.unipg.it>

Diff between MLCIRTwithin versions 2.1 dated 2016-08-06 and 2.1.1 dated 2019-09-30

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 4 files changed, 14 insertions(+), 13 deletions(-)

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New package mlr3tuning with initial version 0.1.0
Package: mlr3tuning
Title: Tuning for 'mlr3'
Version: 0.1.0
Authors@R: c(person(given = "Michel", family = "Lang", role = c("cre", "aut"), email = "michellang@gmail.com", comment = c(ORCID = "0000-0001-9754-0393")), person(given = "Jakob", family = "Richter", role = "aut", email = "jakob1richter@gmail.com", comment = c(ORCID = "0000-0003-4481-5554")), person(given = "Bernd", family = "Bischl", role = "aut", email = "bernd_bischl@gmx.net", comment = c(ORCID = "0000-0001-6002-6980")), person(given = "Daniel", family = "Schalk", role = "aut", email = "daniel.schalk@stat.uni-muenchen.de", comment = c(ORCID = "0000-0003-0950-1947")))
Description: Implements methods for hyperparameter tuning with 'mlr3', e.g. Grid Search, Random Search, or Simulated Annealing. Various termination criteria can be set and combined. The class 'AutoTuner' provides a convenient way to perform nested resampling in combination with 'mlr3'.
License: LGPL-3
URL: https://mlr3tuning.mlr-org.com, https://github.com/mlr-org/mlr3tuning
BugReports: https://github.com/mlr-org/mlr3tuning/issues
Depends: R (>= 3.1.0)
Imports: checkmate (>= 1.9.4), data.table, lgr, mlr3, mlr3misc, paradox, R6
Suggests: GenSA, rpart, testthat
Encoding: UTF-8
NeedsCompilation: no
RoxygenNote: 6.1.1
Collate: 'AutoTuner.R' 'mlr_terminators.R' 'Terminator.R' 'TerminatorClockTime.R' 'TerminatorCombo.R' 'TerminatorEvals.R' 'TerminatorModelTime.R' 'TerminatorNone.R' 'TerminatorPerfReached.R' 'TerminatorStagnation.R' 'mlr_tuners.R' 'Tuner.R' 'TunerDesignPoints.R' 'TunerGenSA.R' 'TunerGridSearch.R' 'TunerRandomSearch.R' 'TuningInstance.R' 'assertions.R' 'helper.R' 'sugar.R' 'zzz.R'
Packaged: 2019-09-22 19:00:14 UTC; michel
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Daniel Schalk [aut] (<https://orcid.org/0000-0003-0950-1947>)
Maintainer: Michel Lang <michellang@gmail.com>
Repository: CRAN
Date/Publication: 2019-09-30 15:20:02 UTC

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New package rasterpdf with initial version 0.1.0
Package: rasterpdf
Title: Plot Raster Graphics in PDF Files
Version: 0.1.0
Authors@R: person(given = "Ilari", family = "Scheinin", role = c("aut", "cre"), email = "ilari.scheinin+rasterpdf@gmail.com", comment = c(ORCID = "0000-0002-4696-9066"))
Description: The ability to plot raster graphics in PDF files can be useful when one needs multi-page documents, but the plots contain so many individual elements that (the usual) use of vector graphics results in inconveniently large file sizes. Internally, the package plots each individual page as a PNG, and then combines them in one PDF file.
License: MIT + file LICENSE
Imports: methods, png
Suggests: covr, lintr, pkgdown, ragg, testthat
URL: https://ilarischeinin.github.io/rasterpdf, https://github.com/ilarischeinin/rasterpdf
BugReports: https://github.com/ilarischeinin/rasterpdf/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-09-22 13:53:57 UTC; ischeini
Author: Ilari Scheinin [aut, cre] (<https://orcid.org/0000-0002-4696-9066>)
Maintainer: Ilari Scheinin <ilari.scheinin+rasterpdf@gmail.com>
Repository: CRAN
Date/Publication: 2019-09-30 15:00:02 UTC

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New package support.BWS3 with initial version 0.1-1
Package: support.BWS3
Type: Package
Title: Basic Functions for Supporting Case 3 Best-Worst Scaling
Version: 0.1-1
Date: 2019-09-21
Author: Hideo Aizaki
Maintainer: Hideo Aizaki <azk-r@spa.nifty.com>
Description: Provides basic functions that support an implementation of Case 3 (multi-profile case) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for Case 3 BWS analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, see Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
License: GPL (>= 2)
Suggests: support.CEs, survival
NeedsCompilation: no
Packaged: 2019-09-21 07:17:58 UTC; user
Repository: CRAN
Date/Publication: 2019-09-30 12:10:02 UTC

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Package NonProbEst updated to version 0.2.1 with previous version 0.2.0 dated 2019-09-07

Title: Estimation in Nonprobability Sampling
Description: Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.
Author: Luis Castro Martín <luiscastro193@gmail.com>, Ramón Ferri García <rferri@ugr.es> and María del Mar Rueda <mrueda@ugr.es>
Maintainer: Luis Castro Martín <luiscastro193@gmail.com>

Diff between NonProbEst versions 0.2.0 dated 2019-09-07 and 0.2.1 dated 2019-09-30

 DESCRIPTION               |    8 +++---
 MD5                       |   14 +++++------
 R/NonProbEst.R            |   55 +++++++++++++++++++++++-----------------------
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 8 files changed, 51 insertions(+), 44 deletions(-)

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New package gnn with initial version 0.0-1
Package: gnn
Version: 0.0-1
Title: Generative Neural Networks
Description: Tools to set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided. See Hofert, Prasad, Zhu (2018) <arXiv:abs/1811.00683> for more details.
Authors@R: c(person(given = "Marius", family = "Hofert", role = c("aut", "cre"), email = "marius.hofert@uwaterloo.ca", comment = c(ORCID = "0000-0001-8009-4665")), person(given = "Avinash", family = "Prasad", role = "aut", email = "a2prasad@uwaterloo.ca"))
Maintainer: Marius Hofert <marius.hofert@uwaterloo.ca>
Depends: R (>= 3.5.0)
Imports: keras, tensorflow, qrng, methods, tools
Suggests: copula, qrmtools, qrmdata, latticeExtra, RnavGraphImageData
Enhances:
License: GPL (>= 3)
SystemRequirements: TensorFlow (https://www.tensorflow.org/)
NeedsCompilation: no
Repository: CRAN
Encoding: UTF-8
Date/Publication: 2019-09-30 11:30:02 UTC
Packaged: 2019-09-20 07:45:40 UTC; mhofert
Author: Marius Hofert [aut, cre] (<https://orcid.org/0000-0001-8009-4665>), Avinash Prasad [aut]

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Package geosapi updated to version 0.4-1 with previous version 0.4-0 dated 2019-05-02

Title: GeoServer REST API R Interface
Description: Provides an R interface to the GeoServer REST API, allowing to upload and publish data in a GeoServer web-application and expose data to OGC Web-Services. The package currently supports all CRUD (Create,Read,Update,Delete) operations on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes, layers, styles, as well as vector data upload operations. For more information about the GeoServer REST API, see <http://docs.geoserver.org/stable/en/user/rest/>.
Author: Emmanuel Blondel [aut, cre] (<https://orcid.org/0000-0002-5870-5762>)
Maintainer: Emmanuel Blondel <emmanuel.blondel1@gmail.com>

Diff between geosapi versions 0.4-0 dated 2019-05-02 and 0.4-1 dated 2019-09-30

 DESCRIPTION                               |    8 ++++----
 MD5                                       |   18 +++++++++---------
 R/GSUtils.R                               |    2 +-
 R/GSWorkspaceManager.R                    |    6 +++---
 R/GSWorkspaceSettings.R                   |    2 +-
 R/geosapi.R                               |    4 ++--
 README.md                                 |    4 ++--
 man/GSUtils.Rd                            |    2 +-
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 tests/testthat/test_GSWorkspaceSettings.R |    5 +++++
 10 files changed, 30 insertions(+), 25 deletions(-)

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New package ggmr with initial version 0.1.1
Package: ggmr
Type: Package
Title: Generalized Gauss Markov Regression
Version: 0.1.1
Date: 2019-09-20
Author: Hugo Gasca-Aragon
Maintainer: Hugo Gasca-Aragon <hugo_gasca_aragon@hotmail.com>
Description: Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.
Depends: stats (>= 3.4.0), MASS (>= 7.3), R (>= 3.4.0)
License: GPL (>= 2)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-09-21 02:30:13 UTC; hugo_
Repository: CRAN
Date/Publication: 2019-09-30 11:50:02 UTC

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New package biclustermd with initial version 0.1.0
Package: biclustermd
Type: Package
Title: Biclustering with Missing Data
Version: 0.1.0
Authors@R: c( person("John", "Reisner", email = "johntreisner@gmail.com", role = c("cre", "aut", "cph")), person("Hieu", "Pham", email = "phieu93@gmail.com", role = c("ctb", "cph")), person("Jing", "Li", email = "jingli2014cymail@gmail.com", role = c("ctb", "cph")))
Maintainer: John Reisner <johntreisner@gmail.com>
Description: Biclustering is a statistical learning technique that simultaneously partitions and clusters rows and columns of a data matrix. Since the solution space of biclustering is in infeasible to completely search with current computational mechanisms, this package uses a greedy heuristic. The algorithm featured in this package is, to the best our knowledge, the first biclustering algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., Olafsson, S., and Vardeman, S. (2019) Biclustering for Missing Data. Information Sciences, Submitted.
URL: http://github.com/jreisner/biclustermd
BugReports: http://github.com/jreisner/biclustermd/issues
Depends: ggplot2 (>= 3.0.0), R (>= 3.5.0), tidyr (>= 0.8.1)
Imports: biclust (>= 2.0.1), clues (>= 0.5.9), doParallel (>= 1.0.14), dplyr (>= 0.7.6), foreach (>= 1.4.4), magrittr (>= 1.5), nycflights13 (>= 1.0.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-09-21 01:22:48 UTC; johnreisner
Author: John Reisner [cre, aut, cph], Hieu Pham [ctb, cph], Jing Li [ctb, cph]
Repository: CRAN
Date/Publication: 2019-09-30 11:40:06 UTC

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New package bama with initial version 0.9.1
Package: bama
Title: High Dimensional Bayesian Mediation Analysis
Version: 0.9.1
URL: https://github.com/umich-cphds/bama
BugReports: https://github.com/umich-cphds/bama/issues
Authors@R: c(person(given = "Alexander", family = "Rix", role = c("aut", "cre"), email = "alexrix@umich.edu"), person(given = "Yanyi", family = "Song", role = c("aut"), email = "yanys@umich.edu"))
Description: Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2018) <doi:10.1101/467399>, relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-09-19 13:31:02 UTC; alexrix
Author: Alexander Rix [aut, cre], Yanyi Song [aut]
Maintainer: Alexander Rix <alexrix@umich.edu>
Repository: CRAN
Date/Publication: 2019-09-30 11:30:05 UTC

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Package osrm updated to version 3.3.2 with previous version 3.3.1 dated 2019-07-12

Title: Interface Between R and the OpenStreetMap-Based Routing Service OSRM
Description: An interface between R and the OSRM API. OSRM is a routing service based on OpenStreetMap data. See <http://project-osrm.org/> for more information. This package allows to compute distances (travel time and kilometric distance) between points and travel time matrices.
Author: Timothée Giraud [cre, aut] (<https://orcid.org/0000-0002-1932-3323>), Robin Cura [ctb], Matthieu Viry [ctb]
Maintainer: Timothée Giraud <timothee.giraud@cnrs.fr>

Diff between osrm versions 3.3.1 dated 2019-07-12 and 3.3.2 dated 2019-09-30

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 osrm-3.3.2/osrm/R/osrmIsochrone.R          |   20 +--
 osrm-3.3.2/osrm/R/osrmRoute.R              |  163 +++++++++++++++-----------
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 osrm-3.3.2/osrm/R/package.R                |   19 ++-
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 osrm-3.3.2/osrm/inst/test.r                |  181 ++++++++++-------------------
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 19 files changed, 361 insertions(+), 288 deletions(-)

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Package acebayes updated to version 1.8 with previous version 1.7 dated 2019-07-18

Title: Optimal Bayesian Experimental Design using the ACE Algorithm
Description: Optimal Bayesian experimental design using the approximate coordinate exchange (ACE) algorithm.
Author: Antony M. Overstall, David C. Woods, Maria Adamou & Damianos Michaelides
Maintainer: Antony M. Overstall <A.M.Overstall@soton.ac.uk>

Diff between acebayes versions 1.7 dated 2019-07-18 and 1.8 dated 2019-09-30

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Package wyz.code.offensiveProgramming updated to version 1.1.12 with previous version 1.1.11 dated 2019-09-21

Title: Wizardry Code Offensive Programming
Description: Allows to change R coding from defensive programming (i.e. many input parameter checks implementation required) to offensive programming (none/reduced number of parameter checks required). Provides code instrumentation to ease this change. Should reduce the code size as many controls and type checks have no more reason to exist. Should also speed up processing as many checks will be reduced to single check.
Author: Fabien Gelineau <neonira@gmail.com>
Maintainer: Fabien Gelineau <neonira@gmail.com>

Diff between wyz.code.offensiveProgramming versions 1.1.11 dated 2019-09-21 and 1.1.12 dated 2019-09-30

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 wyz.code.offensiveProgramming-1.1.12/wyz.code.offensiveProgramming/NAMESPACE                                                           |    2 
 wyz.code.offensiveProgramming-1.1.12/wyz.code.offensiveProgramming/R/FunctionParameterTypeFactory.R                                    |    4 
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 wyz.code.offensiveProgramming-1.1.12/wyz.code.offensiveProgramming/R/getObjectFunctionArgumentNames.R                                  |    2 
 wyz.code.offensiveProgramming-1.1.12/wyz.code.offensiveProgramming/R/retrieveFactory.R                                                 |    2 
 wyz.code.offensiveProgramming-1.1.12/wyz.code.offensiveProgramming/R/retrieveFunctionArguments.R                                       |   12 +-
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 wyz.code.offensiveProgramming-1.1.12/wyz.code.offensiveProgramming/R/verifyClassName.R                                                 |    2 
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Package dti updated to version 1.4.2 with previous version 1.4.1 dated 2019-07-10

Title: Analysis of Diffusion Weighted Imaging (DWI) Data
Description: Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.
Author: Karsten Tabelow [aut, cre], Joerg Polzehl [aut], Felix Anker [ctb]
Maintainer: Karsten Tabelow <karsten.tabelow@wias-berlin.de>

Diff between dti versions 1.4.1 dated 2019-07-10 and 1.4.2 dated 2019-09-30

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Package cppRouting updated to version 1.2 with previous version 1.1 dated 2019-06-21

Title: Fast Implementation of Dijkstra Algorithm
Description: Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>), bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://pdfs.semanticscholar.org/0761/18dfbe1d5a220f6ac59b4de4ad07b50283ac.pdf>), A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>), new bidirectional A* (Pijls & Post (2009) <http://repub.eur.nl/pub/16100/ei2009-10.pdf>).
Author: Vincent Larmet
Maintainer: Vincent Larmet <larmet.vincent@gmail.com>

Diff between cppRouting versions 1.1 dated 2019-06-21 and 1.2 dated 2019-09-30

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Package FlowCAr updated to version 1.1.1 with previous version 1.1.0 dated 2019-09-25

Title: Flow Network Construction and Analysis
Description: Creates and assesses a list of possible networks solved using 'LIM' (Linear Inverse Modelling) (Soetaert, Karline, and Dick Van Oevelen (2009) <doi:10.1007/s10021-009-9297-6>) and restructuring this list, enabling ENA (Ecological Network Analysis) to be performed on the flow network in R package 'enaR' (Borrett, Stuart R., and Matthew K. Lau (2014) <doi:10.1111/2041-210X.12282>).
Author: Christopher Waspe, Ruchit Mahabir, Ursula Scharler
Maintainer: Christopher Waspe <waspe60@gmail.com>

Diff between FlowCAr versions 1.1.0 dated 2019-09-25 and 1.1.1 dated 2019-09-30

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Package RobinHood updated to version 1.2 with previous version 1.0.7 dated 2019-05-24

Title: Interface for the RobinHood.com No Commission Investing Platform
Description: Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.
Author: Joseph Blubaugh
Maintainer: Joseph Blubaugh <jestonblu@gmail.com>

Diff between RobinHood versions 1.0.7 dated 2019-05-24 and 1.2 dated 2019-09-30

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 man/api_quote_crypto.Rd         |only
 man/cancel_order_crypto.Rd      |only
 man/check_rh.Rd                 |only
 man/get_accounts.Rd             |    4 
 man/get_order_history_crypto.Rd |only
 man/get_order_status_crypto.Rd  |only
 man/get_positions_crypto.Rd     |only
 man/get_quote_crypto.Rd         |only
 man/mod_json.Rd                 |only
 man/place_order.Rd              |    2 
 man/place_order_crypto.Rd       |only
 70 files changed, 1019 insertions(+), 872 deletions(-)

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