Thu, 21 Feb 2019

New package stlcsb with initial version 0.1.2
Package: stlcsb
Type: Package
Title: Tidy Manipulation of CSB Data for St. Louis
Version: 0.1.2
Authors@R: c( person("Christopher", "Prener", ,"chris.prener@slu.edu", c("aut", "cre"), comment = c(ORCID = "0000-0002-4310-9888")), person("Branson", "Fox", ,"branson.fox@slu.edu", c("aut"), comment = c(ORCID = "0000-0002-4361-2811")) )
Description: The Citizens' Service Bureau of the City of St. Louis is a clearing house for non-emergency service requests. This package provides functions for downloading, categorizing problem requests, cleaning and subsetting CSB data, and projecting the data using the x and y coordinates included with CSB data releases.
Depends: R (>= 3.3)
License: GPL-3
URL: https://github.com/slu-openGIS/stlcsb
BugReports: https://github.com/slu-openGIS/stlcsb/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: dplyr, lubridate, purrr, readr, readxl, rlang, rvest, sf, stringr, tibble, tools, xml2
Suggests: ggplot2, knitr, mapview, rmarkdown, testthat, covr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-08 01:04:25 UTC; chris
Author: Christopher Prener [aut, cre] (<https://orcid.org/0000-0002-4310-9888>), Branson Fox [aut] (<https://orcid.org/0000-0002-4361-2811>)
Maintainer: Christopher Prener <chris.prener@slu.edu>
Repository: CRAN
Date/Publication: 2019-02-22 00:20:02 UTC

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Package urbin updated to version 0.1-8 with previous version 0.1-6 dated 2018-11-03

Title: Unifying Estimation Results with Binary Dependent Variables
Description: Calculate unified measures that quantify the effect of a covariate on a binary dependent variable (e.g., for meta-analyses). This can be particularly important if the estimation results are obtained with different models/estimators (e.g., linear probability model, logit, probit, ...) and/or with different transformations of the explanatory variable of interest (e.g., linear, quadratic, interval-coded, ...). The calculated unified measures are: (a) semi-elasticities of linear, quadratic, or interval-coded covariates and (b) effects of linear, quadratic, interval-coded, or categorical covariates when a linear or quadratic covariate changes between distinct intervals, the reference category of a categorical variable or the reference interval of an interval-coded variable needs to be changed, or some categories of a categorical covariate or some intervals of an interval-coded covariate need to be grouped together. Approximate standard errors of the unified measures are also calculated. All methods that are implemented in this package are described in the 'vignette' "Extracting and Unifying Semi-Elasticities and Effect Sizes from Studies with Binary Dependent Variables" that is included in this package.
Author: Arne Henningsen, Geraldine Henningsen
Maintainer: Arne Henningsen <arne.henningsen@gmail.com>

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Package morse updated to version 3.2.2 with previous version 3.2.0 dated 2018-11-12

Title: Modelling Tools for Reproduction and Survival Data in Ecotoxicology
Description: Tools for ecotoxicologists and regulators dedicated to the mathematical and statistical modelling of toxicity test data. They use advanced and innovative methods for a valuable quantitative environmental risk assessment.
Author: Virgile Baudrot [aut], Sandrine Charles [aut], Marie Laure Delignette-Muller [aut], Wandrille Duchemin [ctb], Benoit Goussen [ctb], Guillaume Kon-Kam-King [ctb], Christelle Lopes [ctb], Philippe Ruiz [aut], Philippe Veber [aut]
Maintainer: Philippe Veber <philippe.veber@univ-lyon1.fr>

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New package lin.eval with initial version 0.1.2
Package: lin.eval
Type: Package
Title: Perform Polynomial Evaluation of Linearity
Version: 0.1.2
Author: Vishesh Shrivastav
Maintainer: Vishesh Shrivastav <vishesh2k6@gmail.com>
Description: Evaluates whether the relationship between two vectors is linear or nonlinear. Performs a test to determine how well a linear model fits the data compared to higher order polynomial models. Jhang et al. (2004) <doi:10.1043/1543-2165(2004)128%3C44:EOLITC%3E2.0.CO;2>.
Imports: broom
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-02-21 21:28:33 UTC; Shrivatav
Repository: CRAN
Date/Publication: 2019-02-22 00:00:03 UTC

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Package broom.mixed updated to version 0.2.4 with previous version 0.2.3 dated 2018-10-21

Title: Tidying Methods for Mixed Models
Description: Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.
Author: Ben Bolker [aut, cre] (<https://orcid.org/0000-0002-2127-0443>), David Robinson [aut], Dieter Menne [ctb], Jonah Gabry [ctb], Paul Buerkner [ctb], Christopher Hua [ctb], William Petry [ctb] (<https://orcid.org/0000-0002-5230-5987>), Joshua Wiley [ctb] (<https://orcid.org/0000-0002-0271-6702>), Patrick Kennedy [ctb]
Maintainer: Ben Bolker <bolker@mcmaster.ca>

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Package wru updated to version 0.1-9 with previous version 0.1-8 dated 2019-02-13

Title: Who are You? Bayesian Prediction of Racial Category Using Surname and Geolocation
Description: Predicts individual race/ethnicity using surname, geolocation, and other attributes, such as gender and age. The method utilizes the Bayes' Rule to compute the posterior probability of each racial category for any given individual. The package implements methods described in Imai and Khanna (2015) "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records" <DOI:10.1093/pan/mpw001>.
Author: Kabir Khanna [aut, cre], Kosuke Imai [aut]
Maintainer: Kabir Khanna <kabirkhanna@gmail.com>

Diff between wru versions 0.1-8 dated 2019-02-13 and 0.1-9 dated 2019-02-21

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Package picasso updated to version 1.3.1 with previous version 1.3.0 dated 2018-10-03

Title: Pathwise Calibrated Sparse Shooting Algorithm
Description: Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output.
Author: Jason Ge, Xingguo Li, Haoming Jiang, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao
Maintainer: Jason Ge <jiange@princeton.edu>

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Package bmlm updated to version 1.3.11 with previous version 1.3.9 dated 2018-11-09

Title: Bayesian Multilevel Mediation
Description: Easy estimation of Bayesian multilevel mediation models with Stan.
Author: Matti Vuorre [aut, cre] (<https://orcid.org/0000-0001-5052-066X>)
Maintainer: Matti Vuorre <mv2521@columbia.edu>

Diff between bmlm versions 1.3.9 dated 2018-11-09 and 1.3.11 dated 2019-02-21

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Package FossilSim updated to version 2.1.1 with previous version 2.1.0 dated 2018-11-16

Title: Simulation of Fossil and Taxonomy Data
Description: Simulating taxonomy and fossil data on phylogenetic trees under mechanistic models of speciation, preservation and sampling.
Author: Rachel Warnock [aut, cph], Joelle Barido-Sottani [aut, cre, cph], Walker Pett [aut, cph], O'Reilly Joseph [aut, cph]
Maintainer: Joelle Barido-Sottani <joelle.barido-sottani@m4x.org>

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Package DatabaseConnector updated to version 2.3.0 with previous version 2.2.1 dated 2018-11-25

Title: Connecting to Various Database Platforms
Description: An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server', 'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', and 'SQLite'). Also includes support for fetching data as 'ffdf' objects. Uses 'Java Database Connectivity' ('JDBC') to connect to databases (except SQLite).
Author: Martijn Schuemie [aut, cre], Marc Suchard [aut], Observational Health Data Science and Informatics [cph], Microsoft Inc. [cph] (SQL Server JDBC driver), PostgreSQL Global Development Group [cph] (PostgreSQL JDBC driver), Oracle Inc. [cph] (Oracle JDBC driver), Amazon Inc. [cph] (RedShift JDBC driver)
Maintainer: Martijn Schuemie <schuemie@ohdsi.org>

Diff between DatabaseConnector versions 2.2.1 dated 2018-11-25 and 2.3.0 dated 2019-02-21

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New package dang with initial version 0.0.10
Package: dang
Type: Package
Title: 'Dang' Associated New Goodies
Version: 0.0.10
Date: 2019-02-11
Author: Dirk Eddelbuettel with contributions by Brodie Gaslam, Kevin Denny, Kabira Namit, and others
Maintainer: Dirk Eddelbuettel <edd@debian.org>
Description: A collection of utility functions.
Suggests: anytime, data.table, zoo, xts, TTR, quantmod
License: GPL (>= 2)
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-02-11 14:06:31.042782 UTC; edd
Repository: CRAN
Date/Publication: 2019-02-21 15:20:03 UTC

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Package codebook updated to version 0.8.0 with previous version 0.7.6 dated 2019-01-08

Title: Automatic Codebooks from Metadata Encoded in Dataset Attributes
Description: Easily automate the following tasks to describe data frames: Summarise the distributions, and labelled missings of variables graphically and using descriptive statistics. For surveys, compute and summarise reliabilities (internal consistencies, retest, multilevel) for psychological scales. Combine this information with metadata (such as item labels and labelled values) that is derived from R attributes. To do so, the package relies on 'rmarkdown' partials, so you can generate HTML, PDF, and Word documents. Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so that search engines can find your data and index the metadata. The metadata are also available at your fingertips via RStudio Addins.
Author: Ruben Arslan [aut, cre]
Maintainer: Ruben Arslan <ruben.arslan@gmail.com>

Diff between codebook versions 0.7.6 dated 2019-01-08 and 0.8.0 dated 2019-02-21

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Package cbsodataR updated to version 0.3.2 with previous version 0.3 dated 2018-04-30

Title: Statistics Netherlands (CBS) Open Data API Client
Description: The data and meta data from Statistics Netherlands (www.cbs.nl) can be browsed and downloaded. The client uses the open data API of Statistics Netherlands.
Author: Edwin de Jonge [aut, cre]
Maintainer: Edwin de Jonge <edwindjonge@gmail.com>

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Package rsimsum updated to version 0.5.0 with previous version 0.3.3 dated 2018-06-20

Title: Analysis of Simulation Studies Including Monte Carlo Error
Description: Summarise results from simulation studies and compute Monte Carlo standard errors of commonly used summary statistics. This package is modelled on the 'simsum' user-written command in 'Stata' (See White I.R., 2010 <http://www.stata-journal.com/article.html?article=st0200>).
Author: Alessandro Gasparini [aut, cre] (<https://orcid.org/0000-0002-8319-7624>), Ian R. White [aut]
Maintainer: Alessandro Gasparini <ag475@leicester.ac.uk>

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Package rSEA updated to version 1.0.1 with previous version 1.0 dated 2018-11-30

Title: Simultaneous Enrichment Analysis
Description: SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to a threshold." Family-wise error rate control is provided through use of closed testing with Simes test. There are some practical functions to make use of three databases for genomics, GO, Reactome and Wikipathways.
Author: Mitra Ebrahimpoor
Maintainer: Mitra Ebrahimpoor<m.ebrahimpoor@lumc.nl>

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Package sf updated to version 0.7-3 with previous version 0.7-2 dated 2018-12-20

Title: Simple Features for R
Description: Support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations.
Author: Edzer Pebesma [aut, cre] (<https://orcid.org/0000-0001-8049-7069>), Roger Bivand [ctb] (<https://orcid.org/0000-0003-2392-6140>), Etienne Racine [ctb], Michael Sumner [ctb], Ian Cook [ctb], Tim Keitt [ctb], Robin Lovelace [ctb], Hadley Wickham [ctb], Jeroen Ooms [ctb] (<https://orcid.org/0000-0002-4035-0289>), Kirill Müller [ctb], Thomas Lin Pedersen [ctb]
Maintainer: Edzer Pebesma <edzer.pebesma@uni-muenster.de>

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Package RcppCWB updated to version 0.2.8 with previous version 0.2.7 dated 2019-01-09

Title: 'Rcpp' Bindings for the 'Corpus Workbench' ('CWB')
Description: 'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (<http://cwb.sourceforge.net>). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (<https://www.r-project.org/ Licenses/GPL-3>). The 'CWB' relies on 'pcre' (BSD license, see <https://www.pcre.org/ licence.txt>) and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en. html>). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'.
Author: Andreas Blaette [aut, cre], Bernard Desgraupes [aut], Sylvain Loiseau [aut], Oliver Christ [ctb], Bruno Maximilian Schulze [ctb], Stefan Evert [ctb], Arne Fitschen [ctb]
Maintainer: Andreas Blaette <andreas.blaette@uni-due.de>

Diff between RcppCWB versions 0.2.7 dated 2019-01-09 and 0.2.8 dated 2019-02-21

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Package Rblpapi updated to version 0.3.9 with previous version 0.3.8 dated 2018-01-21

Title: R Interface to 'Bloomberg'
Description: An R Interface to 'Bloomberg' is provided via the 'Blp API'.
Author: Whit Armstrong, Dirk Eddelbuettel and John Laing
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between Rblpapi versions 0.3.8 dated 2018-01-21 and 0.3.9 dated 2019-02-21

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Package GB2group updated to version 0.2.0 with previous version 0.1.0 dated 2018-11-14

Title: Estimation of the Generalised Beta Distribution of the Second Kind from Grouped Data
Description: Estimation of the generalized beta distribution of the second kind (GB2) and related models using grouped data in form of income shares. The GB2 family is a general class of distributions that provides an accurate fit to income data. 'GB2group' includes functions to estimate the GB2, the Singh-Maddala, the Dagum, the Beta 2, the Lognormal and the Fisk distributions. 'GB2group' deploys two different econometric strategies to estimate these parametric distributions, non-linear least squares (NLS) and the generalised method of moments (GMM). Asymptotic standard errors are reported for the GMM estimates. Standard errors of the NLS estimates are obtained by Monte Carlo simulation. See Jorda et al. (2018) <arXiv:1808.09831> for a detailed description of the estimation procedure.
Author: Vanesa Jorda <jordav@unican.es>, Jose Maria Sarabia <jose.sarabia@unican.es>, Markus Jäntti <markus.jantti@sofi.su.se>.
Maintainer: Vanesa Jorda <jordav@unican.es>

Diff between GB2group versions 0.1.0 dated 2018-11-14 and 0.2.0 dated 2019-02-21

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Package distreg.vis updated to version 1.1.0 with previous version 1.0.1 dated 2018-10-30

Title: Framework for the Visualization of Distributional Regression Models
Description: Functions for visualizing distributional regression models fitted using the 'gamlss' or 'bamlss' R package. The core of the package consists of a 'shiny' application, where the model results can be interactively explored and visualized.
Author: Stanislaus Stadlmann [cre, aut] (<https://orcid.org/0000-0001-6542-6342>)
Maintainer: Stanislaus Stadlmann <stadlmann@uni-goettingen.de>

Diff between distreg.vis versions 1.0.1 dated 2018-10-30 and 1.1.0 dated 2019-02-21

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Package rgdal updated to version 1.3-9 with previous version 1.3-6 dated 2018-10-16

Title: Bindings for the 'Geospatial' Data Abstraction Library
Description: Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4 and <= 2.5.0) and access to projection/transformation operations from the 'PROJ.4' library. The 'GDAL' and 'PROJ.4' libraries are external to the package, and, when installing the package from source, must be correctly installed first. Both 'GDAL' raster and 'OGR' vector map data can be imported into R, and 'GDAL' raster data and 'OGR' vector data exported. Use is made of classes defined in the 'sp' package. Windows and Mac Intel OS X binaries (including 'GDAL', 'PROJ.4' and 'Expat') are provided on 'CRAN'.
Author: Roger Bivand [cre, aut] (<https://orcid.org/0000-0003-2392-6140>), Tim Keitt [aut], Barry Rowlingson [aut, ctb], Edzer Pebesma [ctb], Michael Sumner [ctb], Robert Hijmans [ctb], Even Rouault [cph, ctb], Frank Warmerdam [cph, ctb], Jeroen Ooms [ctb], Colin Rundel [ctb]
Maintainer: Roger Bivand <Roger.Bivand@nhh.no>

Diff between rgdal versions 1.3-6 dated 2018-10-16 and 1.3-9 dated 2019-02-21

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Package nonlinearTseries updated to version 0.2.6 with previous version 0.2.5 dated 2018-09-23

Title: Nonlinear Time Series Analysis
Description: Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Author: Constantino A. Garcia [aut, cre], Gunther Sawitzki [ctb]
Maintainer: Constantino A. Garcia <constantino.garciama@ceu.es>

Diff between nonlinearTseries versions 0.2.5 dated 2018-09-23 and 0.2.6 dated 2019-02-21

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Package MixGHD updated to version 2.3 with previous version 2.2 dated 2018-01-31

Title: Model Based Clustering, Classification and Discriminant Analysis Using the Mixture of Generalized Hyperbolic Distributions
Description: Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015)<doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016)<doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD'(Tortora et al. (2016)<arXiv:1403.2332v7>), mixture of multiple scaled generalized hyperbolic distributions. The 'cMSGHD' (Tortora et al. (2016)<arXiv:1403.2332v7>) is a 'MSGHD' with convex contour plots. The 'MCGHD' (Tortora et al. (2016)<arXiv:1403.2332v7>), mixture of coalesced generalized hyperbolic distributions is a new more flexible model.
Author: Cristina Tortora [aut, cre, cph], Aisha ElSherbiny [com], Ryan P. Browne [aut, cph], Brian C. Franczak [aut, cph], and Paul D. McNicholas [aut, cph], and Amos Donald D. [ctb].
Maintainer: Cristina Tortora <grikris1@gmail.com>

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Package GPCMlasso updated to version 0.1-3 with previous version 0.1-2 dated 2018-08-02

Title: Differential Item Functioning in Generalized Partial Credit Models
Description: Provides a function to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
Author: Gunther Schauberger
Maintainer: Gunther Schauberger <gunther.schauberger@tum.de>

Diff between GPCMlasso versions 0.1-2 dated 2018-08-02 and 0.1-3 dated 2019-02-21

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Package BTLLasso updated to version 0.1-8 with previous version 0.1-7 dated 2017-10-26

Title: Modelling Heterogeneity in Paired Comparison Data
Description: Performs 'BTLLasso' (Schauberger and Tutz, 2017: Subject-Specific Modelling of Paired Comparison Data - a Lasso-Type Penalty Approach), a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.
Author: Gunther Schauberger
Maintainer: Gunther Schauberger <gunther.schauberger@tum.de>

Diff between BTLLasso versions 0.1-7 dated 2017-10-26 and 0.1-8 dated 2019-02-21

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Package librarian updated to version 1.6.0 with previous version 1.3.0 dated 2018-07-07

Title: Install, Update, Load Packages from CRAN, 'GitHub', and 'Bioconductor' in One Step
Description: Automatically install, update, and load 'CRAN', 'GitHub', and 'Bioconductor' packages in a single function call. By accepting bare unquoted names for packages, it's easy to add or remove packages from the list.
Author: Desi Quintans [aut, cre]
Maintainer: Desi Quintans <science@desiquintans.com>

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Package BeSS updated to version 1.0.6 with previous version 1.0.5 dated 2018-01-30

Title: Best Subset Selection in Linear, Logistic and CoxPH Models
Description: An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2017) <arXiv:1709.06254>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
Author: Canhong Wen, Aijun Zhang, Shijie Quan, Xueqin Wang
Maintainer: Canhong Wen <wencanhong@gmail.com>

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Package vtreat updated to version 1.3.7 with previous version 1.3.6 dated 2019-02-09

Title: A Statistically Sound 'data.frame' Processor/Conditioner
Description: A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. 'vtreat' prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems 'vtreat' defends against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: "'vtreat': a data.frame Processor for Predictive Modeling", 'Zumel', 'Mount', 2016, DOI:10.5281/zenodo.1173314.
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

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Package sugrrants updated to version 0.2.3 with previous version 0.2.2 dated 2019-02-02

Title: Supporting Graphs for Analysing Time Series
Description: Provides 'ggplot2' graphics for analysing time series data. It aims to fit into the 'tidyverse' and grammar of graphics framework for handling temporal data.
Author: Earo Wang [aut, cre] (<https://orcid.org/0000-0001-6448-5260>), Di Cook [aut, ths] (<https://orcid.org/0000-0002-3813-7155>), Rob Hyndman [aut, ths] (<https://orcid.org/0000-0002-2140-5352>)
Maintainer: Earo Wang <earo.wang@gmail.com>

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Package ordinalNet updated to version 2.6 with previous version 2.5 dated 2018-09-10

Title: Penalized Ordinal Regression
Description: Fits ordinal regression models with elastic net penalty. Supported model families include cumulative probability, stopping ratio, continuation ratio, and adjacent category. These families are a subset of vector glm's which belong to a model class we call the elementwise link multinomial-ordinal (ELMO) class. Each family in this class links a vector of covariates to a vector of class probabilities. Each of these families has a parallel form, which is appropriate for ordinal response data, as well as a nonparallel form that is appropriate for an unordered categorical response, or as a more flexible model for ordinal data. The parallel model has a single set of coefficients, whereas the nonparallel model has a set of coefficients for each response category except the baseline category. It is also possible to fit a model with both parallel and nonparallel terms, which we call the semi-parallel model. The semi-parallel model has the flexibility of the nonparallel model, but the elastic net penalty shrinks it toward the parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2017) <arXiv:1706.05003>.
Author: Michael Wurm [aut, cre], Paul Rathouz [aut], Bret Hanlon [aut]
Maintainer: Michael Wurm <wurm@uwalumni.com>

Diff between ordinalNet versions 2.5 dated 2018-09-10 and 2.6 dated 2019-02-21

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Package gamRR updated to version 0.5.0 with previous version 0.4.0 dated 2018-03-19

Title: Calculate the RR for the GAM
Description: To calculate the relative risk (RR) for the generalized additive model.
Author: Zhicheng Du,Wangjian Zhang, Yuantao Hao
Maintainer: Zhicheng Du<dgdzc@hotmail.com>

Diff between gamRR versions 0.4.0 dated 2018-03-19 and 0.5.0 dated 2019-02-21

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