Fri, 13 Sep 2019

Package rqdatatable updated to version 1.2.2 with previous version 1.2.0 dated 2019-08-19

Title: 'rquery' for 'data.table'
Description: Implements the 'rquery' piped Codd-style query algebra using 'data.table'. This allows for a high-speed in memory implementation of Codd-style data manipulation tools.
Author: John Mount [aut, cre], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

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Package HTSSIP updated to version 1.4.1 with previous version 1.4.0 dated 2018-05-15

Title: High Throughput Sequencing of Stable Isotope Probing Data Analysis
Description: Functions for analyzing high throughput sequencing stable isotope probing (HTS-SIP) data. Analyses include high resolution stable isotope probing (HR-SIP), multi-window high resolution stable isotope probing (MW-HR-SIP), and quantitative stable isotope probing (q-SIP).
Author: Nicholas Youngblut [aut], Samuel Barnett [cre, ctb]
Maintainer: Samuel Barnett <seb369@cornell.edu>

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Package NeuralSens updated to version 0.0.7 with previous version 0.0.5 dated 2019-07-11

Title: Sensitivity Analysis of Neural Networks
Description: Analysis functions to quantify inputs importance in neural network models. Functions are available for calculating and plotting the inputs importance and obtaining the activation function of each neuron layer and its derivatives. The importance of a given input is defined as the distribution of the derivatives of the output with respect to that input in each training data point.
Author: José Portela González [aut], Antonio Muñoz San Roque [aut], Jaime Pizarroso Gonzalo [ctb, cre]
Maintainer: Jaime Pizarroso Gonzalo <jpizarroso@alu.comillas.edu>

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Package robmed updated to version 0.4.0 with previous version 0.3.0 dated 2018-11-05

Title: (Robust) Mediation Analysis
Description: Perform mediation analysis via a (fast and robust) bootstrap test.
Author: Andreas Alfons [aut, cre]
Maintainer: Andreas Alfons <alfons@ese.eur.nl>

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Package PACVr updated to version 0.8.2 with previous version 0.8.1 dated 2019-07-26

Title: Plastome Assembly Coverage Visualization
Description: Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2019) <doi:10.1101/697821>.
Author: Michael Gruenstaeudl [aut, cre], Nils Jenke [ctb]
Maintainer: Michael Gruenstaeudl <m.gruenstaeudl@fu-berlin.de>

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Package loa updated to version 0.2.45.4 with previous version 0.2.44.2 dated 2018-10-26

Title: Lattice Options and Add-Ins
Description: Various plots and functions that make use of the lattice/trellis plotting framework. The plots, which include loaPlot(), RgoogleMapsPlot() and trianglePlot(), use panelPal(), a function that extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel and panel-to-key synchronization/management.
Author: Karl Ropkins
Maintainer: Karl Ropkins <karl.ropkins@gmail.com>

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Package valueEQ5D updated to version 0.4.4 with previous version 0.4.3 dated 2019-08-05

Title: Scoring the EQ-5D Descriptive System
Description: EQ-5D is a standard instrument (<https://euroqol.org/eq-5d-instruments/>) that measures the quality of life often used in clinical and economic evaluations of health care technologies. Both adult versions of EQ-5D (EQ-5D-3L and EQ-5D-5L) contain a descriptive system and visual analog scale. The descriptive system measures the patient's health in 5 dimensions: the 5L versions has 5 levels and 3L version has 3 levels. The descriptive system scores are usually converted to index values using country specific values sets (that incorporates the country preferences). This package allows the calculation of both descriptive system scores to the index value scores. The value sets for EQ-5D-3L are from the references mentioned in the website <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/valuation/> The value sets for EQ-5D-3L for a total of 31 countries are used for the valuation (see the user guide for a complete list of references). The value sets for EQ-5D-5L are obtained from references mentioned in the <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/valuation-standard-value-sets/> and other sources. The value sets for EQ-5D-5L for a total of 17 countries are used for the valuation (see the user guide for a complete list of references). The package can also be used to map 5L scores to 3L index values for 10 countries: Denmark, France, Germany, Japan, Netherlands, Spain, Thailand, UK, USA, and Zimbabwe. The value set and method for mapping are obtained from Van Hout et al (2012) <doi: 10.1016/j.jval.2012.02.008>.
Author: Sheeja Manchira Krishnan [aut, cre]
Maintainer: Sheeja Manchira Krishnan <sheejamk@gmail.com>

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Package surveillance updated to version 1.17.1 with previous version 1.17.0 dated 2019-02-22

Title: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena
Description: Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Höhle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Höhle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
Author: Michael Höhle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Sabanés Bové [ctb], Maëlle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)
Maintainer: Sebastian Meyer <seb.meyer@fau.de>

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Package SpaDES.core updated to version 0.2.6 with previous version 0.2.5 dated 2019-03-19

Title: Core Utilities for Developing and Running Spatially Explicit Discrete Event Simulation Models
Description: Provide the core discrete event simulation (DES) framework for implementing spatially explicit simulation models. The core DES components facilitate modularity, and easily enable the user to include additional functionality by running user-built simulation modules. Now includes (experimental) conditional scheduling.
Author: Alex M Chubaty [aut, cre] (<https://orcid.org/0000-0001-7146-8135>), Eliot J B McIntire [aut] (<https://orcid.org/0000-0002-6914-8316>), Yong Luo [ctb], Steve Cumming [ctb], Ceres Barros [ctb] (<https://orcid.org/0000-0003-4036-977X>), Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada [cph]
Maintainer: Alex M Chubaty <alex.chubaty@gmail.com>

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Package desplot updated to version 1.6 with previous version 1.5 dated 2019-04-03

Title: Plotting Field Plans for Agricultural Experiments
Description: A function for plotting maps of agricultural field experiments that are laid out in grids.
Author: Kevin Wright [aut, cre] (<https://orcid.org/0000-0002-0617-8673>)
Maintainer: Kevin Wright <kw.stat@gmail.com>

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Package easyreg updated to version 4.0 with previous version 3.0 dated 2019-08-02

Title: Easy Regression
Description: Performs analysis of regression in simple designs with quantitative treatments, including mixed models and non linear models.
Author: Emmanuel Arnhold
Maintainer: Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>

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Package dragon updated to version 0.2.0 with previous version 0.1.0 dated 2019-05-08

Title: Deep Time Redox Analysis of the Geobiology Ontology Network
Description: Visualization and manipulation of the mineral-chemistry network across deep time on earth.
Author: Stephanie J. Spielman [aut, cre]
Maintainer: Stephanie J. Spielman <spielman@rowan.edu>

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Package sjlabelled updated to version 1.1.1 with previous version 1.1.0 dated 2019-06-06

Title: Labelled Data Utility Functions
Description: Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.
Author: Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>), David Ranzolin [ctb]
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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Package freesurferformats updated to version 0.1.1 with previous version 0.1.0 dated 2019-09-04

Title: Read and Write 'FreeSurfer' Neuroimaging File Formats
Description: Provides functions to read and write data from neuroimaging files in 'FreeSurfer' <http://freesurfer.net/> binary formats. This includes the following file formats: 1) MGH/MGZ format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) 'FreeSurfer' morphometry data files in binary 'curv' format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas.
Author: Tim Schäfer
Maintainer: Tim Schäfer <ts+code@rcmd.org>

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New package confoundr with initial version 1.1
Package: confoundr
Type: Package
Title: Diagnostics for Confounding of Time-Varying and Other Joint Exposures
Version: 1.1
Date: 2019-08-23
Authors@R: c(person(given = "John W.", family = "Jackson",email = "john.jackson@jhu.edu", role = c("aut", "cre")),person(given = "Erin", family = "Schnellinger",role = c("ctb")),person(given = "Linda", family = "Valeri",role = c("ctb")))
Maintainer: John W. Jackson <john.jackson@jhu.edu>
Description: Implements three covariate-balance diagnostics for time-varying confounding and selection-bias in complex longitudinal data, as described in Jackson (2016) <doi:10.1097/EDE.0000000000000547> and Jackson (2019) <doi:10.1093/aje/kwz136>. Diagnostic 1 assesses measured confounding/selection-bias, diagnostic 2 assesses exposure-covariate feedback, and diagnostic 3 assesses residual confounding/selection-bias after inverse probability weighting or propensity score stratification. All diagnostics appropriately account for exposure history, can be adapted to assess a particular depth of covariate history, and can be implemented in right-censored data. Balance assessments can be obtained for all times, selected-times, or averaged across person-time. The balance measures are reported as tables or plots. These diagnostics can be applied to the study of multivariate exposures including time-varying exposures, direct effects, interaction, and censoring.
Depends: R (>= 3.1.0),
License: GPL-3
Imports: grid,gridExtra (>= 2.3),scales (>= 0.5.0),Rmpfr (>= 0.7-0),dplyr (>= 0.7.5),tidyr (>= 0.8.1),ggplot2 (>= 2.2.1),magrittr (>= 1.5),stringr (>= 1.3.1),purrr (>= 0.2.5),rlang (>= 0.2.1),stats
LazyData: TRUE
VignetteBuilder: knitr
Suggests: covr,knitr,rmarkdown,testthat,broom (>= 0.4.4)
BugReports: https://github.com/jwjackson/confoundr/issues
RoxygenNote: 6.1.1
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-09-09 21:40:32 UTC; jojackso
Author: John W. Jackson [aut, cre], Erin Schnellinger [ctb], Linda Valeri [ctb]
Repository: CRAN
Date/Publication: 2019-09-13 11:20:03 UTC

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New package TEAM with initial version 0.1.0
Package: TEAM
Type: Package
Title: Multiple Hypothesis Testing on an Aggregation Tree Method
Version: 0.1.0
Author: John Pura, Cliburn Chan, and Jichun Xie
Maintainer: John Pura <john.pura@duke.edu>
Description: An implementation of the TEAM algorithm to identify local differences between two (e.g. case and control) independent, univariate distributions, as described in J Pura, C Chan, and J Xie (2019) <arXiv:1906.07757>. The algorithm is based on embedding a multiple-testing procedure on a hierarchical structure to identify high-resolution differences between two distributions. The hierarchical structure is designed to identify strong, short-range differences at lower layers and weaker, but long-range differences at increasing layers. TEAM yields consistent layer-specific and overall false discovery rate control.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: plyr,ggplot2,ks
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-09-09 14:53:02 UTC; John_Pura
Repository: CRAN
Date/Publication: 2019-09-13 10:10:02 UTC

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Package rmgarch updated to version 1.3-7 with previous version 1.3-6 dated 2019-01-15

Title: Multivariate GARCH Models
Description: Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH. See Boudt, Galanos, Payseur and Zivot (2019) for a review of multivariate GARCH models <doi:10.1016/bs.host.2019.01.001>.
Author: Alexios Ghalanos <alexios@4dscape.com>
Maintainer: Alexios Ghalanos <alexios@4dscape.com>

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Package MVar.pt updated to version 2.1.1 with previous version 2.1.0 dated 2019-09-12

Title: Analise multivariada (brazilian portuguese)
Description: Pacote para analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.
Author: Paulo Cesar Ossani <ossanipc@hotmail.com> Marcelo Angelo Cirillo <macufla@des.ufla.br>
Maintainer: Paulo Cesar Ossani <ossanipc@hotmail.com>

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Package MVar updated to version 2.1.1 with previous version 2.1.0 dated 2019-09-12

Title: Multivariate Analysis
Description: Package for multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.
Author: Paulo Cesar Ossani <ossanipc@hotmail.com> Marcelo Angelo Cirillo <macufla@des.ufla.br>
Maintainer: Paulo Cesar Ossani <ossanipc@hotmail.com>

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Package gmat updated to version 0.2.1 with previous version 0.2.0 dated 2018-12-03

Title: Simulation of Graphical Correlation Matrices
Description: Simulation of correlation matrices possibly constrained by a given undirected or acyclic directed graph. In particular, the package provides functions that implement the simulation methods described in Córdoba et al. (2018a) <doi:10.1007/978-3-030-03493-1_13>, Córdoba et al. (2018b) <doi:10.1145/2695664.2695717> and Córdoba et al. (2019) <arXiv:1909.01062>.
Author: Irene Córdoba [aut, cre] (<https://orcid.org/0000-0002-3252-4234>), Gherardo Varando [aut] (<https://orcid.org/0000-0002-6708-1103>), Concha Bielza [ths] (<https://orcid.org/0000-0001-7109-2668>), Pedro Larrañaga [ths] (<https://orcid.org/0000-0003-0652-9872>)
Maintainer: Irene Córdoba <irene.cordoba@upm.es>

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Package FluMoDL updated to version 0.0.3 with previous version 0.0.2 dated 2019-07-28

Title: Influenza-Attributable Mortality with Distributed-Lag Models
Description: Functions to estimate the mortality attributable to influenza and temperature, using distributed-lag nonlinear models (DLNMs), as first implemented in Lytras et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.14.1800118>. Full descriptions of underlying DLNM methodology in Gasparrini et al. <doi:10.1002/sim.3940> (DLNMs), <doi:10.1186/1471-2288-14-55> (attributable risk from DLNMs) and <doi:10.1002/sim.5471> (multivariate meta-analysis).
Author: Theodore Lytras [aut, cre] (<https://orcid.org/0000-0002-4146-4122>), Antonio Gasparrini [ctb] (<https://orcid.org/0000-0002-2271-3568>), Shuangcai Wang [ctb]
Maintainer: Theodore Lytras <thlytras@gmail.com>

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Package eq5d updated to version 0.3.0 with previous version 0.2.0 dated 2019-06-27

Title: Methods for Calculating 'EQ-5D' Utility Index Scores
Description: EQ-5D is a popular health related quality of life instrument used in the clinical and economic evaluation of health care. Developed by the EuroQol group <https://www.euroqol.org>, the instrument consists of two components: health state description and evaluation. For the description component a subject self-rates their health in terms of five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression using either a three-level (EQ-5D-3L, <https://www.euroqol.org/eq-5d-instruments/eq-5d-3l-about>) or a five-level (EQ-5D-5L, <https://www.euroqol.org/eq-5d-instruments/eq-5d-5l-about>) scale. Frequently the scores on these five dimensions are converted to a single utility index using country specific value sets, which can be used in the clinical and economic evaluation of health care as well as in population health surveys. The eq5d package provides methods to calculate index scores from a subject's dimension scores. 20 TTO and 11 VAS EQ-5D-3L value sets including those for countries in Szende et al (2007) <doi:10.1007/1-4020-5511-0> and Szende et al (2014) <doi:10.1007/978-94-007-7596-1>, 16 EQ-5D-5L EQ-VT value sets from the EuroQol website, and the EQ-5D-5L crosswalk value sets developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> are included. Additionally, a shiny web tool is included to enable the calculation and visualisation of EQ-5D index values via a web browser using EQ-5D dimension scores stored in CSV or Excel files.
Author: Fraser Morton [aut, cre], Jagtar Singh Nijjar [aut]
Maintainer: Fraser Morton <fraser.morton@glasgow.ac.uk>

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Package creditmodel updated to version 1.1.3 with previous version 1.1.2 dated 2019-09-02

Title: Toolkit for Credit Modeling
Description: Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization. Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. 1.Anderson, R. (2007). The credit scoring toolkit: Theory and practice for retail credit risk management and decision automation. 2.Find, S. (2012, ISBN13: 9780230347762). Credit scoring, response modelling and insurance rating:A practical guide to forecasting consumer behaviour.
Author: Dongping Fan [aut, cre]
Maintainer: Dongping Fan <fdp@pku.edu.cn>

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Package CAvariants updated to version 5.0 with previous version 4.0 dated 2019-01-14

Title: Correspondence Analysis Variants
Description: Provides six variants of two-way correspondence analysis (ca): simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca, singly ordered non symmetrical ca, and doubly ordered non symmetrical ca.
Author: Rosaria Lombardo and Eric J Beh
Maintainer: Rosaria Lombardo <rosaria.lombardo@unicampania.it>

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Package zoib updated to version 1.5.3 with previous version 1.5.2 dated 2019-08-22

Title: Bayesian Inference for Beta Regression and Zero-or-One Inflated Beta Regression
Description: Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
Author: Fang Liu <fang.liu.131@nd.edu> with contributions from Yunchuan Kong
Maintainer: Fang Liu <fang.liu.131@nd.edu>

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Package qrnn updated to version 2.0.5 with previous version 2.0.4 dated 2019-06-13

Title: Quantile Regression Neural Network
Description: Fit quantile regression neural network models with optional left censoring, partial monotonicity constraints, generalized additive model constraints, and the ability to fit multiple non-crossing quantile functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Author: Alex J. Cannon [aut, cre] (<https://orcid.org/0000-0002-8025-3790>)
Maintainer: Alex J. Cannon <alex.cannon@canada.ca>

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Package pmml updated to version 2.1.0 with previous version 2.0.0 dated 2019-06-03

Title: Generate PMML for Various Models
Description: The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms. The package isofor (used for anomaly detection) can be installed with devtools::install_github("Zelazny7/isofor").
Author: Dmitriy Bolotov [aut, cre], Tridivesh Jena [aut], Graham Williams [aut], Wen-Ching Lin [aut], Michael Hahsler [aut], Hemant Ishwaran [aut], Udaya B. Kogalur [aut], Rajarshi Guha [aut], Software AG [cph]
Maintainer: Dmitriy Bolotov <rpmmlsupport@softwareag.com>

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Package strex updated to version 1.2.0 with previous version 1.1.1 dated 2019-06-23

Title: Extra String Manipulation Functions
Description: There are some things that I wish were easier with the 'stringr' or 'stringi' packages. The foremost of these is the extraction of numbers from strings. 'stringr' and 'stringi' make you figure out the regular expression for yourself; 'strex' takes care of this for you. There are many other handy functionalities in 'strex'. Contributions to this package are encouraged: it is intended as a miscellany of string manipulation functions that cannot be found in 'stringi' or 'stringr'.
Author: Rory Nolan [aut, cre] (<https://orcid.org/0000-0002-5239-4043>)
Maintainer: Rory Nolan <rorynoolan@gmail.com>

Diff between strex versions 1.1.1 dated 2019-06-23 and 1.2.0 dated 2019-09-13

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Package imagefx updated to version 0.2.0 with previous version 0.1.0 dated 2019-08-05

Title: Extract Features from Images
Description: Synthesize images into characteristic features for time-series analysis or machine learning applications. The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity. However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting.
Author: Alex J.C. Witsil
Maintainer: Alex J.C. Witsil <alexjcwitsil@gmail.com>

Diff between imagefx versions 0.1.0 dated 2019-08-05 and 0.2.0 dated 2019-09-13

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Package iai updated to version 1.1.0 with previous version 1.0.0 dated 2019-07-18

Title: Interface to 'Interpretable AI' Modules
Description: An interface to the algorithms of 'Interpretable AI' <https://www.interpretable.ai> from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available.
Author: Jack Dunn [aut, cre], Ying Zhuo [aut], Interpretable AI LLC [cph]
Maintainer: Jack Dunn <jack@interpretable.ai>

Diff between iai versions 1.0.0 dated 2019-07-18 and 1.1.0 dated 2019-09-13

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Package gimme updated to version 0.6-1 with previous version 0.6-0 dated 2019-05-23

Title: Group Iterative Multiple Model Estimation
Description: Automated identification and estimation of group- and individual-level relations in time series data from within a structural equation modeling framework.
Author: Stephanie Lane [aut, trl], Kathleen Gates [aut, cre], Zachary Fisher [aut], Cara Arizmendi [aut], Peter Molenaar [aut], Michael Hallquist [ctb], Hallie Pike [ctb], Teague Henry [ctb], Kelly Duffy [ctb], Lan Luo [ctb], Adriene Beltz [csp]
Maintainer: KM Gates <gateskm@email.unc.edu>

Diff between gimme versions 0.6-0 dated 2019-05-23 and 0.6-1 dated 2019-09-13

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Package AzureAuth updated to version 1.2.2 with previous version 1.2.1 dated 2019-09-08

Title: Authentication Services for Azure Active Directory
Description: Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud <https://azure.microsoft.com/>. Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages.
Author: Hong Ooi [aut, cre], httr development team [ctb] (Original OAuth listener code), Scott Holden [ctb] (Advice on AAD authentication), Chris Stone [ctb] (Advice on AAD authentication), Microsoft [cph]
Maintainer: Hong Ooi <hongooi@microsoft.com>

Diff between AzureAuth versions 1.2.1 dated 2019-09-08 and 1.2.2 dated 2019-09-13

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More information about AzureAuth at CRAN
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