Sun, 17 Jun 2018

Package refinr updated to version 0.3.1 with previous version 0.3.0 dated 2018-05-06

Title: Cluster and Merge Similar Values Within a Character Vector
Description: These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The functions are an implementation of the key collision and ngram fingerprint algorithms from the open source tool Open Refine <http://openrefine.org/>. More info on key collision and ngram fingerprint can be found here <https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth>.
Author: Chris Muir [aut, cre]
Maintainer: Chris Muir <chrismuirRVA@gmail.com>

Diff between refinr versions 0.3.0 dated 2018-05-06 and 0.3.1 dated 2018-06-17

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Package MDR (with last version 1.2) was removed from CRAN

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Package deal (with last version 1.2-37) was removed from CRAN

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Package curvetest (with last version 2.2) was removed from CRAN

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2014-01-27 0.9.1

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2014-01-23 1.1
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Package MLCM (with last version 0.4.1) was removed from CRAN

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Package boolean3 (with last version 3.1.6) was removed from CRAN

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Package rgdax updated to version 0.6.0 with previous version 0.5.0 dated 2018-04-08

Title: Wrapper for 'GDAX' Cryptocurrency Exchange
Description: Allow access to both public and private end points to 'GDAX' cryptocurrency exchange. For authenticated flow, users must have valid api, secret and passphrase to be able to connect. Read more details on getting access to 'GDAX' API at: <https://support.gdax.com/customer/en/portal/articles/2425383-how-can-i-create-an-api-key-for-gdax->.
Author: person("Dheeraj", "Agarwal", email = "dheeeraj.agarwal@gmail.com", role = c("aut", "cre"))
Maintainer: Dheeraj Agarwal <dheeeraj.agarwal@gmail.com>

Diff between rgdax versions 0.5.0 dated 2018-04-08 and 0.6.0 dated 2018-06-17

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Package optparse updated to version 1.6.0 with previous version 1.4.4 dated 2017-07-18

Title: Command Line Option Parser
Description: A command line parser inspired by Python's 'optparse' library to be used with Rscript to write "#!" shebang scripts that accept short and long flag/options.
Author: Trevor L Davis [aut, cre], Allen Day [ctb] (Some documentation and examples ported from the getopt package.), Python Software Foundation [ctb] (Some documentation from the optparse Python module.), Steve Lianoglou [ctb], Jim Nikelski [ctb], Kirill Müller [ctb], Peter Humburg [ctb], Rich FitzJohn [ctb], Gyu Jin Choi [ctb]
Maintainer: Trevor L Davis <trevor.l.davis@gmail.com>

Diff between optparse versions 1.4.4 dated 2017-07-18 and 1.6.0 dated 2018-06-17

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Package iplots updated to version 1.1-7.1 with previous version 1.1-7 dated 2013-12-03

Title: iPlots - interactive graphics for R
Description: Interactive plots for R.
Author: Simon Urbanek <simon.urbanek@r-project.org>, Tobias Wichtrey <tobias@tarphos.de>
Maintainer: Simon Urbanek <simon.urbanek@r-project.org>

Diff between iplots versions 1.1-7 dated 2013-12-03 and 1.1-7.1 dated 2018-06-17

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Package hypervolume updated to version 2.0.10 with previous version 2.0.9 dated 2018-05-28

Title: High Dimensional Geometry and Set Operations Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls
Description: Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
Author: Benjamin Blonder, with contributions from David J. Harris
Maintainer: Benjamin Blonder <bblonder@gmail.com>

Diff between hypervolume versions 2.0.9 dated 2018-05-28 and 2.0.10 dated 2018-06-17

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New package fuser with initial version 1.0.1
Package: fuser
Title: Fused Lasso for High-Dimensional Regression over Groups
Version: 1.0.1
Authors@R: c( person("Frank", "Dondelinger", email = "fdondelinger.work@gmail.com", role = c("aut", "cre")), person("Olivier", "Wilkinson", role = c("aut")) )
Description: Enables high-dimensional penalized regression across heterogeneous subgroups. Fusion penalties are used to share information about the linear parameters across subgroups. The underlying model is described in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.
Depends: R (>= 3.2.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: testthat, ggplot2, knitr, rmarkdown
Imports: Matrix, irlba, Rcpp, glmnet, RSpectra
LinkingTo: Rcpp, RcppEigen
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2018-06-17 19:15:57 UTC; levendis
Author: Frank Dondelinger [aut, cre], Olivier Wilkinson [aut]
Maintainer: Frank Dondelinger <fdondelinger.work@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-17 20:22:54 UTC

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Package LogicForest (with last version 2.1.0) was removed from CRAN

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2014-09-18 2.1.0

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2014-04-14 1.0

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Package CellularAutomaton (with last version 1.1-1) was removed from CRAN

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Package HEAT (with last version 1.2) was removed from CRAN

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Package Agreement (with last version 0.8-1) was removed from CRAN

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Package spData updated to version 0.2.9.0 with previous version 0.2.8.3 dated 2018-03-25

Title: Datasets for Spatial Analysis
Description: Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps', it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.
Author: Roger Bivand [aut] (<https://orcid.org/0000-0003-2392-6140>), Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>), Robin Lovelace [aut] (<https://orcid.org/0000-0001-5679-6536>), Mark Monmonier [ctb] (author of the state.vbm dataset), Greg Snow [ctb] (author of the state.vbm dataset)
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>

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Package reproducible updated to version 0.2.0 with previous version 0.1.4 dated 2018-01-25

Title: A Set of Tools that Enhance Reproducibility Beyond Package Management
Description: Collection of high-level, robust, machine- and OS-independent tools for making deeply reproducible and reusable content in R. This includes light weight package management (similar to 'packrat' and 'checkpoint', but more flexible, lightweight and simpler than both), tools for caching, downloading and verifying or writing checksums, post-processing of common spatial datasets, and accessing GitHub repositories. Some features are still under active development.
Author: Eliot J B McIntire [aut, cre], Alex M Chubaty [aut], Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources Canada [cph]
Maintainer: Eliot J B McIntire <eliot.mcintire@canada.ca>

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Package MetaLandSim updated to version 1.0.4 with previous version 1.0.3 dated 2018-03-16

Title: Landscape and Range Expansion Simulation
Description: Tools to generate random landscape graphs, evaluate species occurrence in dynamic landscapes, simulate future landscape occupation and evaluate range expansion when new empty patches are available (e.g. as a result of climate change).
Author: Frederico Mestre, Fernando Canovas, Benjamin Risk, Ricardo Pita, Antonio Mira, Pedro Beja.
Maintainer: Frederico Mestre <mestre.frederico@gmail.com>

Diff between MetaLandSim versions 1.0.3 dated 2018-03-16 and 1.0.4 dated 2018-06-17

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Package belg updated to version 0.2.3 with previous version 0.2.1 dated 2018-03-24

Title: Boltzmann Entropy of a Landscape Gradient
Description: Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>). It also extend the original idea by allowing calculations on data with missing values.
Author: Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>), Space Informatics Lab [cph]
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>

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Package h2o updated to version 3.20.0.2 with previous version 3.18.0.11 dated 2018-05-24

Title: R Interface for 'H2O'
Description: R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as generalized linear models, gradient boosting machines (including xgboost), random forests, deep neural networks (deep learning), stacked ensembles, naive bayes, cox proportional hazards, k-means, PCA, word2vec, as well as a fully automatic machine learning algorithm (AutoML).
Author: Erin LeDell [aut, cre], Navdeep Gill [aut], Spencer Aiello [aut], Anqi Fu [aut], Arno Candel [aut], Cliff Click [aut], Tom Kraljevic [aut], Tomas Nykodym [aut], Patrick Aboyoun [aut], Michal Kurka [aut], Michal Malohlava [aut], Ludi Rehak [ctb], Eric Eckstrand [ctb], Brandon Hill [ctb], Sebastian Vidrio [ctb], Surekha Jadhawani [ctb], Amy Wang [ctb], Raymond Peck [ctb], Wendy Wong [ctb], Jan Gorecki [ctb], Matt Dowle [ctb], Yuan Tang [ctb], Lauren DiPerna [ctb], H2O.ai [cph, fnd]
Maintainer: Erin LeDell <erin@h2o.ai>

Diff between h2o versions 3.18.0.11 dated 2018-05-24 and 3.20.0.2 dated 2018-06-17

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Package PMCMRplus updated to version 1.1.0 with previous version 1.0.1 dated 2018-04-26

Title: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended
Description: For one-way layout experiments the one-way ANOVA can be performed as an omnibus test. All-pairs multiple comparisons tests (Tukey-Kramer test, Scheffe test, LSD-test) and many-to-one tests (Dunnett test) for normally distributed residuals and equal within variance are available. Furthermore, all-pairs tests (Games-Howell test, Tamhane's T2 test, Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one (Tamhane-Dunnett Test) for normally distributed residuals and heterogeneous variances are provided. Van der Waerden's normal scores test for omnibus, all-pairs and many-to-one tests is provided for non-normally distributed residuals and homogeneous variances. The Kruskal-Wallis, BWS and Anderson-Darling omnibus test and all-pairs tests (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow- Fligner test) as well as many-to-one (Nemenyi test, Dunn test, U-test) are given for the analysis of variance by ranks. Non-parametric trend tests (Jonckheere test, Cuzick test, Johnson-Mehrotra test, Spearman test) are included. In addition, a Friedman-test for one-way ANOVA with repeated measures on ranks (CRBD) and Skillings-Mack test for unbalanced CRBD is provided with consequent all-pairs tests (Nemenyi test, Siegel test, Miller test, Conover test, Exact test) and many-to-one tests (Nemenyi test, Demsar test, Exact test). A trend can be tested with Pages's test. Durbin's test for a two-way balanced incomplete block design (BIBD) is given in this package as well as Gore's test for CRBD with multiple observations per cell is given. Outlier tests, Mandel's k- and h statistic as well as functions for Type I error and Power analysis as well as generic summary, print and plot methods are provided.
Author: Thorsten Pohlert
Maintainer: Thorsten Pohlert <thorsten.pohlert@gmx.de>

Diff between PMCMRplus versions 1.0.1 dated 2018-04-26 and 1.1.0 dated 2018-06-17

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New package multistateutils with initial version 1.1.0
Package: multistateutils
Type: Package
Title: Utility Functions for Parametric Multi-State Models
Version: 1.1.0
Date: 2018-06-05
Author: Stuart Lacy
Maintainer: Stuart Lacy <stuart.lacy@gmail.com>
Description: Provides functions for working with multi-state modelling, such as efficient simulation routines for estimating transition probabilities and length of stay. It is designed as an extension to multi-state modelling capabilities provided with the 'flexsurv' package (see Jackson (2016) <doi:10.18637/jss.v070.i08>).
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.10), data.table, dplyr, magrittr, networkD3, survival, tidyr
Suggests: flexsurv, mstate, microbenchmark, rmarkdown, knitr
URL: https://github.com/stulacy/multistateutils
LinkingTo: Rcpp
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2018-06-15 23:44:15 UTC; stuart
Repository: CRAN
Date/Publication: 2018-06-17 14:12:54 UTC

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New package exuber with initial version 0.1.0
Package: exuber
Type: Package
Title: Econometric Analysis of Explosive Time Series
Version: 0.1.0
Authors@R: c( person("Kostas", "Vasilopoulos", , email = "k.vasilopoulo@gmail.com", c("cre", "aut")), person("Eftymios", "Pavlidis", email = "e.pavlidis@lancaster.ac.uk", role = "aut"), person("Simon", "Spavound", email = "simon.spavound@googlemail.com", role = "aut") )
Description: Testing for and dating periods of explosive dynamics (exuberance) in time series using recursive unit root tests as proposed by Phillips, P. C., Shi, S. and Yu, J. (2015a) <doi:10.1111/iere.12132>. Simulate a variety of periodically-collapsing bubble models. The estimation and simulation utilizes the matrix inversion lemma from the recursive least squares algorithm, which results in a significant speed improvement.
License: GPL-3
URL: https://github.com/kvasilopoulos/exuber
BugReports: https://github.com/kvasilopoulos/exuber/issues
Imports: doParallel, parallel, foreach, Rcpp, rlang, dplyr, ggplot2, purrr
Suggests: knitr, rmarkdown, covr, testthat, withr, gridExtra
LinkingTo: Rcpp
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-06-15 15:43:09 UTC; T460p
Author: Kostas Vasilopoulos [cre, aut], Eftymios Pavlidis [aut], Simon Spavound [aut]
Maintainer: Kostas Vasilopoulos <k.vasilopoulo@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-17 14:07:54 UTC

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New package dragulaR with initial version 0.3.1
Package: dragulaR
Type: Package
Title: Drag and Drop Elements in 'Shiny' using 'Dragula Javascript Library'
Version: 0.3.1
Authors@R: c(person("Nicolas", "Bevacqua", role = c("aut", "cph"), comment = "dragula library in htmlwidgets/lib, https://github.com/bevacqua/dragula"), person("Zygmunt", "Zawadzki", role = c("aut", "cre"), comment = "R interface", email = "zygmunt@zstat.pl"))
Maintainer: Zygmunt Zawadzki <zygmunt@zstat.pl>
Description: Move elements between containers in 'Shiny' without explicitly using 'JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables.
License: GPL-2
LazyData: TRUE
Depends: htmlwidgets, shiny
Imports: shinyjs
RoxygenNote: 6.0.1
Suggests: covr, testthat
BugReports: https://github.com/zzawadz/dragulaR/issues
URL: https://dragular.zstat.pl/
NeedsCompilation: no
Packaged: 2018-06-15 18:16:18 UTC; zzawadz
Author: Nicolas Bevacqua [aut, cph] (dragula library in htmlwidgets/lib, https://github.com/bevacqua/dragula), Zygmunt Zawadzki [aut, cre] (R interface)
Repository: CRAN
Date/Publication: 2018-06-17 14:08:01 UTC

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Package walrus updated to version 1.0.3 with previous version 1.0.1 dated 2017-08-07

Title: Robust Statistical Methods
Description: A toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for 'jamovi' (see <https://www.jamovi.org> for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book 'Introduction to Robust Estimation & Hypothesis Testing'.
Author: Jonathon Love, Patrick Mair
Maintainer: Jonathon Love <jon@thon.cc>

Diff between walrus versions 1.0.1 dated 2017-08-07 and 1.0.3 dated 2018-06-17

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Package PSPManalysis updated to version 0.2.0 with previous version 0.1.1 dated 2018-01-12

Title: Analysis of Physiologically Structured Population Models
Description: Performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level. A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) <doi:10.1142/S0218202501001264>. O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) <doi:10.1016/S0040-5809(02)00058-8>. A.M. de Roos (2008) <doi:10.1111/j.1461-0248.2007.01121.x>.
Author: Andre M. de Roos [aut, cre], Ernst Hairer [ctb], Gerhard Wanner [ctb]
Maintainer: Andre M. de Roos <A.M.deRoos@uva.nl>

Diff between PSPManalysis versions 0.1.1 dated 2018-01-12 and 0.2.0 dated 2018-06-17

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Package dplyr.teradata updated to version 0.3.0 with previous version 0.2.0 dated 2018-02-22

Title: A 'Teradata' Backend for 'dplyr'
Description: A 'Teradata' backend for 'dplyr'. It makes it possible to operate 'Teradata' database <https://www.teradata.com/products-and-services/teradata-database/> in the same way as manipulating data frames with 'dplyr'.
Author: Koji Makiyama [cre, aut]
Maintainer: Koji Makiyama <hoxo.smile@gmail.com>

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Package summarytools updated to version 0.8.5 with previous version 0.8.4 dated 2018-06-07

Title: Tools to Quickly and Neatly Summarize Data
Description: Data frame summaries, cross-tabulations, weight-enabled frequency tables and common univariate statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.
Author: Dominic Comtois
Maintainer: Dominic Comtois <dominic.comtois@gmail.com>

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Package jrvFinance updated to version 1.4.0 with previous version 1.03 dated 2015-10-06

Title: Basic Finance; NPV/IRR/Annuities/Bond-Pricing; Black Scholes
Description: Implements the basic financial analysis functions similar to (but not identical to) what is available in most spreadsheet software. This includes finding the IRR and NPV of regularly spaced cash flows and annuities. Bond pricing and YTM calculations are included. In addition, Black Scholes option pricing and Greeks are also provided.
Author: Jayanth Varma [aut, cre]
Maintainer: Jayanth Varma <jrvarma@iima.ac.in>

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Package egg updated to version 0.4.0 with previous version 0.2.0 dated 2017-09-12

Title: Extensions for 'ggplot2': Custom Geom, Plot Alignment, Symmetrised Scale, and Fixed Panel Size
Description: Miscellaneous functions to help customise 'ggplot2' objects. High-level functions are provided to post-process 'ggplot2' layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom 'geom', and a helper function to enforce symmetric scales in facetted plots.
Author: Baptiste Auguie [aut, cre]
Maintainer: Baptiste Auguie <baptiste.auguie@gmail.com>

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Package CBPS updated to version 0.19 with previous version 0.18 dated 2018-03-10

Title: Covariate Balancing Propensity Score
Description: Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements several extensions of the CBPS beyond the cross-sectional, binary treatment setting. The current version implements the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) <DOI:10.1080/01621459.2014.956872>, treatments with three- and four- valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2015) <DOI:10.1214/17-AOAS1101>, and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates. Recently we have added the optimal CBPS which chooses the optimal balancing function and results in doubly robust and efficient estimator for the treatment effect as well as high dimensional CBPS when a large number of covariates exist.
Author: Christian Fong [aut, cre], Marc Ratkovic [aut], Kosuke Imai [aut], Chad Hazlett [ctb], Xiaolin Yang [ctb], Sida Peng [ctb]
Maintainer: Christian Fong <christianfong@stanford.edu>

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Package spatialwarnings (with last version 1.1) was removed from CRAN

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

2018-06-04 1.1
2017-11-03 1.0

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Package statprograms updated to version 0.2.0 with previous version 0.1.0 dated 2016-08-15

Title: Graduate Statistics Program Datasets
Description: A small collection of data on graduate statistics programs from the United States.
Author: Brett Klamer [aut, cre]
Maintainer: Brett Klamer <code@brettklamer.com>

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Package Ball updated to version 1.2.0 with previous version 1.1.0 dated 2018-05-12

Title: Statistical Inference and Sure Independence Screening via Ball Statistics
Description: Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence <doi:10.1214/17-AOS1579>, ball covariance, and ball correlation <doi:10.1080/01621459.2018.1462709>, are developed to analyze complex data. The ball divergence and ball covariance based distribution-free tests are implemented to examine equality of multivariate distributions and independence between random vectors of arbitrary dimensions. Furthermore, a generic non-parametric SIS procedure based on ball correlation and all of its variants are implemented to tackle the challenge in the context of ultra high dimensional data.
Author: Xueqin Wang, Wenliang Pan, Heping Zhang, Hongtu Zhu, Yuan Tian, Weinan Xiao, Chengfeng Liu, Jin Zhu
Maintainer: Jin Zhu <zhuj37@mail2.sysu.edu.cn>

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Sat, 16 Jun 2018

Package ahnr updated to version 0.3.0 with previous version 0.2.0 dated 2017-08-18

Title: An Implementation of the Artificial Hydrocarbon Networks
Description: Implementation of the Artificial Hydrocarbon Networks for data modeling.
Author: Jose Roberto Ayala Solares [aut, cre]
Maintainer: Jose Roberto Ayala Solares <ichbinjras@gmail.com>

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Package sads updated to version 0.4.2 with previous version 0.4.1 dated 2017-10-24

Title: Maximum Likelihood Models for Species Abundance Distributions
Description: Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions.
Author: Paulo I. Prado, Murilo Dantas Miranda and Andre Chalom
Maintainer: Paulo I. Prado <prado@ib.usp.br>

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Package supernova updated to version 1.1 with previous version 1.0 dated 2018-02-23

Title: Judd & McClelland Formatting for ANOVA Output
Description: Produces ANOVA tables in the format used by Judd, McClelland, and Ryan (2017, ISBN:978-1138819832) in their introductory textbook, Data Analysis. This includes proportional reduction in error and formatting to improve ease the transition between the book and R.
Author: Jim Stigler [cre, aut], Jeff Chrabaszcz [aut]
Maintainer: Jim Stigler <jstigler@gmail.com>

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New package RxODE with initial version 0.7.2-4
Package: RxODE
Version: 0.7.2-4
Title: Facilities for Simulating from ODE-Based Models
Authors@R: c( person("Matthew L.","Fidler", role = "aut", email = "matthew.fidler@gmail.com"), person("Melissa", "Hallow", role = "aut", email = "hallowkm@uga.edu"), person("Wenping", "Wang", role = c("aut", "cre"), email = "wwang8198@gmail.com"), person("Zufar", "Mulyukov", role="ctb", email="zufar.mulyukov@novartis.com"), person("Justin", "Wilkins", role = "ctb", email = "justin.wilkins@occams.com"), person("Simon", "Frost", role="ctb"), person("Heng", "Li", role="ctb"), person("Yu", "Feng", role="ctb"), person("Alan", "Hindmarsh",role="ctb"), person("Linda", "Petzold", role="ctb"), person("Ernst", "Hairer", role="ctb"), person("Gerhard", "Wanner", role="ctb"), person("J", "Colinge", role="ctb"), person("Hadley", "Wickham", role="ctb"), person("G", "Grothendieck", role="ctb"), person("Robert", "Gentleman",role="ctb"), person("Ross", "Ihaka",role="ctb"), person("R core team", role="cph"), person("odepack authors", role="cph") )
Maintainer: Wenping Wang <wwang8198@gmail.com>
Depends: R (>= 3.3.0)
Suggests: knitr, nlme, shiny, tcltk, testthat, devtools, covr, rmarkdown, SnakeCharmR, rSymPy, dplyr, tidyr, tibble, curl, ggplot2, gridExtra, microbenchmark, scales, stringi, htmltools
Imports: utils, methods, digest, rex, dparser (>= 0.1.8), brew, memoise, magrittr, Rcpp (>= 0.12.3), inline, Matrix, R.utils, PreciseSums (>= 0.3), mvnfast, cli, crayon
Description: Facilities for running simulations from ordinary differential equation (ODE) models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The VODE and LSODA are in the public domain. The information is available in the inst/COPYRIGHTS.
BugReports: https://github.com/nlmixrdevelopment/RxODE/issues
NeedsCompilation: yes
VignetteBuilder: knitr
License: GPL (>= 2)
URL: https://www.r-project.org, https://github.com/nlmixrdevelopment/RxODE
RoxygenNote: 6.0.1
LinkingTo: dparser(>= 0.1.8), Rcpp (>= 0.12.3), RcppArmadillo(>= 0.5.600.2.0), PreciseSums (>= 0.3)
Packaged: 2018-06-16 17:01:38 UTC; WANGWEZ
Author: Matthew L. Fidler [aut], Melissa Hallow [aut], Wenping Wang [aut, cre], Zufar Mulyukov [ctb], Justin Wilkins [ctb], Simon Frost [ctb], Heng Li [ctb], Yu Feng [ctb], Alan Hindmarsh [ctb], Linda Petzold [ctb], Ernst Hairer [ctb], Gerhard Wanner [ctb], J Colinge [ctb], Hadley Wickham [ctb], G Grothendieck [ctb], Robert Gentleman [ctb], Ross Ihaka [ctb], R core team [cph], odepack authors [cph]
Repository: CRAN
Date/Publication: 2018-06-16 19:32:24 UTC

More information about RxODE at CRAN
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Package recipes updated to version 0.1.3 with previous version 0.1.2 dated 2018-01-11

Title: Preprocessing Tools to Create Design Matrices
Description: An extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.
Author: Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]
Maintainer: Max Kuhn <max@rstudio.com>

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New package graphicalVAR with initial version 0.2.2
Package: graphicalVAR
Type: Package
Title: Graphical VAR for Experience Sampling Data
Version: 0.2.2
Author: Sacha Epskamp
Maintainer: Sacha Epskamp <mail@sachaepskamp.com>
Description: Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
License: GPL (>= 2)
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp (>= 0.11.3), Matrix, glasso, glmnet, mvtnorm, qgraph (>= 1.3.1), dplyr, methods, igraph
Depends: R (>= 3.1.0)
NeedsCompilation: yes
Packaged: 2018-06-16 13:50:55 UTC; sachaepskamp
Repository: CRAN
Date/Publication: 2018-06-16 19:28:45 UTC

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Package languageserver updated to version 0.2.3 with previous version 0.2.2 dated 2018-05-09

Title: Language Server Protocol
Description: An implementation of the Language Server Protocol for R. The Language Server protocol is used by an editor client to integrate features like auto completion. See <https://microsoft.github.io/language-server-protocol> for details.
Author: Randy Lai
Maintainer: Randy Lai <randy.cs.lai@gmail.com>

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Package cdata updated to version 0.7.1 with previous version 0.7.0 dated 2018-04-09

Title: Fluid Data Transformations
Description: Supplies higher-order fluid data transform operators that include pivot and anti-pivot as special cases. The methodology is describe in 'Zumel', 2018, "Fluid data reshaping with 'cdata'", <http://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , doi:10.5281/zenodo.1173299 . Based on the 'DBI' database interface.
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

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Package isni updated to version 0.3 with previous version 0.2 dated 2018-04-09

Title: Index of Local Sensitivity to Nonignorability
Description: The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the linear mixed model for longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random is with respect to the assumption of ignorability. For a tutorial, download at <http://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>. For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and Xie (2008) <doi:10.1002/sim.3117> and Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Author: Hui Xie <huixie@uic.edu>, Weihua Gao, Baodong Xing, Daniel Heitjan, Donald Hedeker, Chengbo Yuan
Maintainer: Hui Xie <huixie@uic.edu>

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Package data.tree updated to version 0.7.6 with previous version 0.7.5 dated 2018-03-06

Title: General Purpose Hierarchical Data Structure
Description: Create tree structures from hierarchical data, and traverse the tree in various orders. Aggregate, cumulate, print, plot, convert to and from data.frame and more. Useful for decision trees, machine learning, finance, conversion from and to JSON, and many other applications.
Author: Facundo Munoz [ctb] (improve list conversion), Markus Wamser [ctb] (fixed some typos), Pierre Formont [ctb] (additional features), Kent Russel [ctb] (documentation), Noam Ross [ctb] (fixes), Duncan Garmonsway [ctb] (fixes), Christoph Glur [aut, cre] (R interface)
Maintainer: Christoph Glur <christoph.glur@ipub.com>

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Package DisImpact updated to version 0.0.2 with previous version 0.0.1 dated 2018-05-27

Title: Calculates Disproportionate Impact When Binary Success Data are Disaggregated by Subgroups
Description: Implements methods for calculating disproportionate impact: the percentage point gap, proportionality index, and the 80% index. California Community Colleges Chancellor's Office (2017). Percentage Point Gap Method. <http://extranet.cccco.edu/Portals/1/TRIS/Research/Analysis/PercentagePointGapMethod2017.pdf>. California Community Colleges Chancellor's Office (2014). Guidelines for Measuring Disproportionate Impact in Equity Plans. <http://extranet.cccco.edu/Portals/1/TRIS/Research/Accountability/GUIDELINES%20FOR%20MEASURING%20DISPROPORTIONATE%20IMPACT%20IN%20EQUITY%20PLANS.pdf>.
Author: Vinh Nguyen [aut, cre]
Maintainer: Vinh Nguyen <nguyenvq714@gmail.com>

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Package GSODR updated to version 1.2.1 with previous version 1.2.0 dated 2018-03-29

Title: Global Surface Summary of the Day (GSOD) Weather Data from R
Description: Provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day (GSOD) weather data from the from the USA National Centers for Environmental Information (NCEI) for use in R. Units are converted from from United States Customary System (USCS) units to International System of Units (SI). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure (es), actual vapour pressure (ea) and relative humidity are calculated from the original data and included in the final data set. The resulting data include station identification information, state, country, latitude, longitude, elevation, weather observations and associated flags. Data may be automatically saved to disk. File output may be returned as a comma-separated values (CSV) or GeoPackage (GPKG) file. Additional data are included with this R package: a list of elevation values for stations between -60 and 60 degrees latitude derived from the Shuttle Radar Topography Measuring Mission (SRTM). For information on the GSOD data from NCEI, please see the GSOD readme.txt file available from, <http://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.
Author: Adam Sparks [aut, cre] (<https://orcid.org/0000-0002-0061-8359>), Tomislav Hengl [aut] (<https://orcid.org/0000-0002-9921-5129>), Andrew Nelson [aut] (<https://orcid.org/0000-0002-7249-3778>), Hugh Parsonage [cph, ctb] (<https://orcid.org/0000-0003-4055-0835>), Bob Rudis [cph, ctb] (<https://orcid.org/0000-0001-5670-2640>), Gwenael Giboire [ctb] (Several bug reports in early versions and testing feedback), Łukasz Pawlik [ctb] (Reported bug in windspeed conversion calculation), Ross Darnell [ctb] (Reported bug in Windows OS versions causing GSOD data untarring to fail)
Maintainer: Adam Sparks <adamhsparks@gmail.com>

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Package dmm updated to version 2.1-4 with previous version 2.1-3 dated 2018-01-19

Title: Dyadic Mixed Model for Pedigree Data
Description: Dyadic mixed model analysis with multi-trait responses and pedigree-based partitioning of individual variation into a range of environmental and genetic variance components for individual and maternal effects.
Author: Neville Jackson
Maintainer: Neville Jackson <nanddjackson@bigpond.com>

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Package QTL.gCIMapping updated to version 3.0 with previous version 2.0 dated 2018-04-30

Title: QTL Genome-Wide Composite Interval Mapping
Description: Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect mixed linear model. First, each position on the genome is detected in order to construct a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid or by adaptive lasso in F2, and true QTL are identified by likelihood radio test. Wang S-B, Wen Y-J, Ren W-L, Ni Y-L, Zhang J, Feng J-Y, Zhang Y-M (2016) <doi:10.1038/srep29951>.
Author: Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, and Zhang Yuan-Ming
Maintainer: Yuanming Zhang<soyzhang@mail.hzau.edu.cn>

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Package gggenes updated to version 0.3.1 with previous version 0.3.0 dated 2018-05-26

Title: Draw Gene Arrow Maps in 'ggplot2'
Description: Provides a 'ggplot2' geom and helper functions for drawing gene arrow maps.
Author: David Wilkins [aut, cre]
Maintainer: David Wilkins <david@wilkox.org>

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Package FunChisq updated to version 2.4.5-1 with previous version 2.4.5 dated 2018-02-20

Title: Chi-Square and Exact Tests for Model-Free Functional Dependency
Description: Statistical hypothesis testing methods for model-free functional dependency using asymptotic chi-square or exact distributions. Functional chi-squares are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-square or Fisher's exact tests.
Author: Yang Zhang [aut], Hua Zhong [aut], Ruby Sharma [aut], Sajal Kumar [aut], Joe Song [aut, cre]
Maintainer: Joe Song <joemsong@cs.nmsu.edu>

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Package bomrang updated to version 0.2.1 with previous version 0.2.0 dated 2018-05-19

Title: Australian Government Bureau of Meteorology Data from R
Description: Provides functions to interface with Australian Government Bureau of Meteorology (BOM) data, fetching data and returning a tidy data frame of précis forecasts, historical and current weather data from stations, agriculture bulletin data, BOM 0900 or 1500 weather bulletins or a raster stack object of satellite imagery from GeoTIFF files. Data (c) Australian Government Bureau of Meteorology Creative Commons (CC) Attribution 3.0 licence or Public Access Licence (PAL) as appropriate. See <http://www.bom.gov.au/other/copyright.shtml> for further details.
Author: Adam Sparks [aut, cre] (<https://orcid.org/0000-0002-0061-8359>), Jonathan Carroll [aut] (<https://orcid.org/0000-0002-1404-5264>), Mark Padgham [aut, rev] (<https://orcid.org/0000-0003-2172-5265>), Hugh Parsonage [aut] (<https://orcid.org/0000-0003-4055-0835>), Keith Pembleton [aut] (<https://orcid.org/0000-0002-1896-4516>), James Balamuta [ctb] (<https://orcid.org/0000-0003-2826-8458>), Brooke Anderson [rev] (<https://orcid.org/0000-0002-5012-9035>)
Maintainer: Adam Sparks <adamhsparks@gmail.com>

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Package tranSurv (with last version 1.1-5) was removed from CRAN

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

2018-04-18 1.1-5
2017-02-08 1.1-4
2016-07-18 1.0-0

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Package xfun updated to version 0.2 with previous version 0.1 dated 2018-01-22

Title: Miscellaneous Functions by 'Yihui Xie'
Description: Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.
Author: Yihui Xie [aut, cre, cph] (<https://orcid.org/0000-0003-0645-5666>)
Maintainer: Yihui Xie <xie@yihui.name>

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Package getCRUCLdata updated to version 0.2.4 with previous version 0.2.3 dated 2018-05-17

Title: Use and Explore CRU CL v. 2.0 Climatology Elements in R
Description: Provides functions that automate downloading and importing University of East Anglia Climate Research Unit (CRU) CL v. 2.0 climatology data into R, facilitates the calculation of minimum temperature and maximum temperature and formats the data into a tidy data frame as a tibble or a list of raster stack objects for use in an R session. CRU CL v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arcminutes (0.1666667 degree) resolution. For more information see the description of the data provided by the University of East Anglia Climate Research Unit, <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.
Author: Adam Sparks [aut, cre] (<https://orcid.org/0000-0002-0061-8359>)
Maintainer: Adam Sparks <adamhsparks@gmail.com>

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Fri, 15 Jun 2018

Package Bchron updated to version 4.3.0 with previous version 4.2.7 dated 2018-02-27

Title: Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate Estimation, and Non-Parametric Phase Modelling
Description: Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the Oxcal function SUM; currently unpublished), and reverse calibration of dates from calibrated into un-calibrated years (also unpublished).
Author: Andrew Parnell
Maintainer: Andrew Parnell <Andrew.Parnell@mu.ie>

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Package grpreg updated to version 3.1-4 with previous version 3.1-3 dated 2018-04-08

Title: Regularization Paths for Regression Models with Grouped Covariates
Description: Efficient algorithms for fitting the regularization path of linear or logistic 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], Yaohui Zeng [ctb]
Maintainer: Patrick Breheny <patrick-breheny@uiowa.edu>

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Package hddplot updated to version 0.59 with previous version 0.57-5 dated 2017-09-03

Title: Use Known Groups in High-Dimensional Data to Derive Scores for Plots
Description: Cross-validated linear discriminant calculations determine the optimum number of features. Test and training scores from successive cross-validation steps determine, via a principal components calculation, a low-dimensional global space onto which test scores are projected, in order to plot them. Further functions are included that are intended for didactic use. The package implements, and extends, methods described in J.H. Maindonald and C.J. Burden (2005) <https://journal.austms.org.au/V46/CTAC2004/Main/home.html>.
Author: John Maindonald
Maintainer: John Maindonald <jhmaindonald@gmail.com>

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Package gamboostLSS updated to version 2.0-1 with previous version 2.0-0 dated 2017-05-05

Title: Boosting Methods for 'GAMLSS'
Description: Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.
Author: Benjamin Hofner [aut, cre] (<https://orcid.org/0000-0003-2810-3186>), Andreas Mayr [aut], Nora Fenske [aut], Janek Thomas [aut], Matthias Schmid [aut]
Maintainer: Benjamin Hofner <benjamin.hofner@pei.de>

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Package rgeopat2 updated to version 0.2.5 with previous version 0.2.4 dated 2018-03-24

Title: Additional Functions for 'geoPAT' 2
Description: Supports analysis of spatial data processed with the 'geoPAT' 2 software <http://sil.uc.edu/cms/index.php?id=geopat2>. Available features include creation of a grid based on the 'geoPAT' 2 grid header file.
Author: Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>), Space Informatics Lab [cph]
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>

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Package hybridModels updated to version 0.3.5 with previous version 0.2.15 dated 2017-09-27

Title: Stochastic Hybrid Models in Dynamic Networks
Description: Simulates stochastic hybrid models for transmission of infectious diseases in dynamic networks. It is a metapopulation model in which each node in the network is a sub-population and disease spreads within nodes and among them, combining two approaches: stochastic simulation algorithm or its approximations (Gillespie DT (2007) <doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based approach, respectively. Movement among nodes are data based and can be irregular. Equations that models spread within nodes are customizable and there are two link types among nodes: migration and influence (commuting).
Author: Fernando S. Marques [aut, cre], Jose H. H. Grisi-Filho [aut], Marcos Amaku [aut]
Maintainer: Fernando S. Marques <fernandosix@gmail.com>

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Package radiant updated to version 0.9.5 with previous version 0.8.0 dated 2017-04-29

Title: Business Analytics using R and Shiny
Description: A platform-independent browser-based interface for business analytics in R, based on the shiny package. The application combines the functionality of radiant.data, radiant.design, radiant.basics, radiant.model, and radiant.multivariate.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

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Package segregation updated to version 0.1.0 with previous version 0.0.1 dated 2018-04-17

Title: Entropy-Based Segregation Indices
Description: Computes entropy-based segregation indices, as developed by Theil (1971) <isbn:978-0471858454>, with a focus on the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>, is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. Includes standard error estimation by bootstrapping.
Author: Benjamin Elbers [aut, cre] (<https://orcid.org/0000-0001-5392-3448>)
Maintainer: Benjamin Elbers <be2239@columbia.edu>

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Package randomizeR updated to version 1.4.2 with previous version 1.4.1 dated 2018-06-05

Title: Randomization for Clinical Trials
Description: This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, 'randomizeR' provides a function for the comparison of randomization procedures.
Author: David Schindler [aut], Diane Uschner [aut, cre], Martin Manolov [ctb], Thi Mui Pham [ctb], Ralf-Dieter Hilgers [aut, ths], Nicole Heussen [aut, ths]
Maintainer: Diane Uschner <duschner@ukaachen.de>

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 man/cash-randPar-method.Rd         |   34 
 man/cash-randSeq-method.Rd         |   34 
 man/chenPar.Rd                     |   90 -
 man/chenRand.Rd                    |   62 -
 man/chronBias.Rd                   |  186 +--
 man/coin.Rd                        |   28 
 man/combineBias.Rd                 |   62 -
 man/compare.Rd                     |  138 +-
 man/corGuess.Rd                    |   86 -
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 man/desirability.Rd                |  136 +-
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 man/randomizeR-package.Rd          |   90 -
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 man/saveAssess.Rd                  |   46 
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 man/setPower.Rd                    |   94 -
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 vignettes/article.Rnw              | 2056 +++++++++++++++++++------------------
 vignettes/references.bib           | 1530 +++++++++++++--------------
 115 files changed, 7203 insertions(+), 6969 deletions(-)

More information about randomizeR at CRAN
Permanent link

Package radiant.multivariate updated to version 0.9.5 with previous version 0.8.0 dated 2017-04-29

Title: Multivariate Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in radiant.data.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant.multivariate versions 0.8.0 dated 2017-04-29 and 0.9.5 dated 2018-06-15

 radiant.multivariate-0.8.0/radiant.multivariate/R/pmap.R                                                     |only
 radiant.multivariate-0.8.0/radiant.multivariate/inst/app/tools/analysis/pmap_ui.R                            |only
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 radiant.multivariate-0.9.5/radiant.multivariate/DESCRIPTION                                                  |   25 
 radiant.multivariate-0.9.5/radiant.multivariate/MD5                                                          |  187 +-
 radiant.multivariate-0.9.5/radiant.multivariate/NAMESPACE                                                    |   24 
 radiant.multivariate-0.9.5/radiant.multivariate/NEWS.md                                                      |   85 -
 radiant.multivariate-0.9.5/radiant.multivariate/R/aaa.R                                                      |   28 
 radiant.multivariate-0.9.5/radiant.multivariate/R/conjoint.R                                                 |  776 ++++------
 radiant.multivariate-0.9.5/radiant.multivariate/R/deprecated.R                                               |   34 
 radiant.multivariate-0.9.5/radiant.multivariate/R/full_factor.R                                              |  324 ++--
 radiant.multivariate-0.9.5/radiant.multivariate/R/hclus.R                                                    |  228 +-
 radiant.multivariate-0.9.5/radiant.multivariate/R/kclus.R                                                    |  408 ++---
 radiant.multivariate-0.9.5/radiant.multivariate/R/mds.R                                                      |  267 +--
 radiant.multivariate-0.9.5/radiant.multivariate/R/pre_factor.R                                               |  244 +--
 radiant.multivariate-0.9.5/radiant.multivariate/R/prmap.R                                                    |only
 radiant.multivariate-0.9.5/radiant.multivariate/R/radiant.R                                                  |   41 
 radiant.multivariate-0.9.5/radiant.multivariate/README.md                                                    |   70 
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/global.R                                            |    4 
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/help.R                                              |   26 
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/init.R                                              |   51 
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/server.R                                            |   25 
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/analysis/conjoint_ui.R                        |  665 +++++---
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/analysis/full_factor_ui.R                     |  306 ++-
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/analysis/hclus_ui.R                           |  230 +-
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/analysis/kclus_ui.R                           |  321 ++--
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/analysis/mds_ui.R                             |  251 ++-
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 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/help/hclus.md                                 |   14 
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/help/kclus.md                                 |   20 
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/help/mds.md                                   |   33 
 radiant.multivariate-0.9.5/radiant.multivariate/inst/app/tools/help/pre_factor.md                            |   22 
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 radiant.multivariate-0.9.5/radiant.multivariate/man/carpet.Rd                                                |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/city.Rd                                                  |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/city2.Rd                                                 |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/clean_loadings.Rd                                        |    8 
 radiant.multivariate-0.9.5/radiant.multivariate/man/computer.Rd                                              |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/conjoint.Rd                                              |    5 
 radiant.multivariate-0.9.5/radiant.multivariate/man/full_factor.Rd                                           |    6 
 radiant.multivariate-0.9.5/radiant.multivariate/man/hclus.Rd                                                 |    6 
 radiant.multivariate-0.9.5/radiant.multivariate/man/kclus.Rd                                                 |    4 
 radiant.multivariate-0.9.5/radiant.multivariate/man/mds.Rd                                                   |    8 
 radiant.multivariate-0.9.5/radiant.multivariate/man/movie.Rd                                                 |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/mp3.Rd                                                   |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/plot.conjoint.Rd                                         |    6 
 radiant.multivariate-0.9.5/radiant.multivariate/man/plot.full_factor.Rd                                      |    6 
 radiant.multivariate-0.9.5/radiant.multivariate/man/plot.hclus.Rd                                            |    7 
 radiant.multivariate-0.9.5/radiant.multivariate/man/plot.kclus.Rd                                            |    5 
 radiant.multivariate-0.9.5/radiant.multivariate/man/plot.mds.Rd                                              |   12 
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 radiant.multivariate-0.9.5/radiant.multivariate/man/pre_factor.Rd                                            |    4 
 radiant.multivariate-0.9.5/radiant.multivariate/man/predict.conjoint.Rd                                      |   12 
 radiant.multivariate-0.9.5/radiant.multivariate/man/predict_conjoint_by.Rd                                   |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/print.conjoint.predict.Rd                                |    2 
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 radiant.multivariate-0.9.5/radiant.multivariate/man/radiant.multivariate-deprecated.Rd                       |   14 
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 radiant.multivariate-0.9.5/radiant.multivariate/man/radiant.multivariate_window.Rd                           |only
 radiant.multivariate-0.9.5/radiant.multivariate/man/retailers.Rd                                             |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/shopping.Rd                                              |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/store.conjoint.Rd                                        |   11 
 radiant.multivariate-0.9.5/radiant.multivariate/man/store.conjoint.predict.Rd                                |   17 
 radiant.multivariate-0.9.5/radiant.multivariate/man/store.full_factor.Rd                                     |   14 
 radiant.multivariate-0.9.5/radiant.multivariate/man/store.kclus.Rd                                           |   12 
 radiant.multivariate-0.9.5/radiant.multivariate/man/summary.conjoint.Rd                                      |    5 
 radiant.multivariate-0.9.5/radiant.multivariate/man/summary.full_factor.Rd                                   |    6 
 radiant.multivariate-0.9.5/radiant.multivariate/man/summary.hclus.Rd                                         |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/summary.kclus.Rd                                         |    3 
 radiant.multivariate-0.9.5/radiant.multivariate/man/summary.mds.Rd                                           |    6 
 radiant.multivariate-0.9.5/radiant.multivariate/man/summary.pre_factor.Rd                                    |    6 
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 radiant.multivariate-0.9.5/radiant.multivariate/man/the_table.Rd                                             |    8 
 radiant.multivariate-0.9.5/radiant.multivariate/man/toothpaste.Rd                                            |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/man/tpbrands.Rd                                              |    2 
 radiant.multivariate-0.9.5/radiant.multivariate/tests/testthat/test_stats.R                                  |  168 +-
 118 files changed, 3098 insertions(+), 2237 deletions(-)

More information about radiant.multivariate at CRAN
Permanent link

Package frailtySurv updated to version 1.3.4 with previous version 1.3.3 dated 2018-06-05

Title: General Semiparametric Shared Frailty Model
Description: Simulates and fits semiparametric shared frailty models under a wide range of frailty distributions using a consistent and asymptotically-normal estimator. Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models.
Author: Vinnie Monaco [aut, cre], Malka Gorfine [aut], Li Hsu [aut]
Maintainer: Vinnie Monaco <contact@vmonaco.com>

Diff between frailtySurv versions 1.3.3 dated 2018-06-05 and 1.3.4 dated 2018-06-15

 DESCRIPTION                |    8 +++----
 MD5                        |   16 +++++++-------
 NEWS.md                    |    6 +++++
 R/frailtySurv.R            |    2 -
 R/genfrail.control.R       |    4 +--
 R/print.summary.simfrail.R |    3 --
 R/simfrail.R               |   50 ++++++++++++++++++++++++++++++++-------------
 man/fitfrail.control.Rd    |    2 -
 man/genfrail.control.Rd    |    8 +++----
 9 files changed, 63 insertions(+), 36 deletions(-)

More information about frailtySurv at CRAN
Permanent link

Package SpaDES updated to version 2.0.2 with previous version 2.0.1 dated 2018-02-01

Title: Develop and Run Spatially Explicit Discrete Event Simulation Models
Description: Metapackage for implementing a variety of event-based models, with a focus on spatially explicit models. These include raster-based, event-based, and agent-based models. The core simulation components (provided by 'SpaDES.core') are built upon a discrete event simulation (DES; see Matloff (2011) ch 7.8.3 <https://nostarch.com/artofr.htm>) framework that facilitates modularity, and easily enables the user to include additional functionality by running user-built simulation modules (see also 'SpaDES.tools'). Included are numerous tools to visualize rasters and other maps (via 'quickPlot'), and caching methods for reproducible simulations (via 'reproducible'). Additional functionality is provided by the 'SpaDES.addins' and 'SpaDES.shiny' packages.
Author: Alex M Chubaty [aut, cre], Eliot J B McIntire [aut], Yong Luo [ctb], Steve Cumming [ctb], 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>

Diff between SpaDES versions 2.0.1 dated 2018-02-01 and 2.0.2 dated 2018-06-15

 DESCRIPTION             |   16 -
 MD5                     |   18 -
 NEWS.md                 |   10 
 README.md               |   20 -
 build/vignette.rds      |binary
 inst/doc/iii-cache.R    |   14 +
 inst/doc/iii-cache.Rmd  |   78 +++++
 inst/doc/iii-cache.html |  629 +++++++++++++++++++-----------------------------
 man/SpaDES-package.Rd   |    2 
 vignettes/iii-cache.Rmd |   78 +++++
 10 files changed, 468 insertions(+), 397 deletions(-)

More information about SpaDES at CRAN
Permanent link

New package SLIDE with initial version 1.0.0
Package: SLIDE
Title: Single Cell Linkage by Distance Estimation is SLIDE
Version: 1.0.0
Authors@R: c(person("Gourab", "Mukherjee", email = "gourab@usc.edu", role = c("aut")), person("Arjun", "Panda", email = "arjunpanda@gwu.edu", role = c("aut","cre")), person("Ann", "Arvin", email = "aarvin@stanford.edu", role = c("aut")), person("Adrish", "Sen", email = "adrishs@stanford.edu", role = c("aut")), person("Nandini", "Sen", email = "nandinis@stanford.edu", role = c("aut")))
Description: This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.
Depends: R (>= 3.4.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-15 14:57:27 UTC; arjun
Author: Gourab Mukherjee [aut], Arjun Panda [aut, cre], Ann Arvin [aut], Adrish Sen [aut], Nandini Sen [aut]
Maintainer: Arjun Panda <arjunpanda@gwu.edu>
Repository: CRAN
Date/Publication: 2018-06-15 15:50:12 UTC

More information about SLIDE at CRAN
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New package simIReff with initial version 1.0
Package: simIReff
Type: Package
Title: Stochastic Simulation for Information Retrieval Evaluation: Effectiveness Scores
Version: 1.0
Authors@R: c(person("Julián", "Urbano", email = "urbano.julian@gmail.com", role = c("aut", "cre")), person("Thomas", "Nagler", email = "thomas.nagler@tum.de", role = c("ctb")))
Description: Provides tools for the stochastic simulation of effectiveness scores to mitigate data-related limitations of Information Retrieval evaluation research, as described in Urbano and Nagler (2018) <doi:10.1145/3209978.3210043>. These tools include: fitting, selection and plotting distributions to model system effectiveness, transformation towards a prespecified expected value, proxy to fitting of copula models based on these distributions, and simulation of new evaluation data from these distributions and copula models.
BugReports: https://github.com/julian-urbano/simIReff/issues
URL: https://github.com/julian-urbano/simIReff/
Depends: R (>= 3.4)
Imports: stats, graphics, MASS, rvinecopulib (>= 0.2.8.1.0), truncnorm, bde, ks, np, extraDistr
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-15 14:53:35 UTC; jurbano
Author: Julián Urbano [aut, cre], Thomas Nagler [ctb]
Maintainer: Julián Urbano <urbano.julian@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-15 15:26:32 UTC

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New package QTL.gCIMapping.GUI with initial version 1.0
Package: QTL.gCIMapping.GUI
Type: Package
Title: QTL Genome-Wide Composite Interval Mapping with Graphical User Interface
Version: 1.0
Date: 2018-6-12
Author: Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, and Zhang Yuan-Ming
Maintainer: Yuanming Zhang<soyzhang@mail.hzau.edu.cn>
Description: Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect mixed linear model. First, each position on the genome is detected in order to construct a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid or by adaptive lasso in F2, and true QTL are identified by likelihood radio test. Wang S-B, Wen Y-J, Ren W-L, Ni Y-L, Zhang J, Feng J-Y, Zhang Y-M (2016) <doi:10.1038/srep29951>.
Encoding: UTF-8
Depends: shiny,MASS,qtl,doParallel,foreach
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.17),methods,stringr,openxlsx,data.table,parcor
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2018-06-13 03:46:03 UTC; 亚雯
Repository: CRAN
Date/Publication: 2018-06-15 15:26:36 UTC

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New package PPQplan with initial version 0.1.0
Package: PPQplan
Type: Package
Title: Process Performance Qualification (PPQ) Plans in Chemistry, Manufacturing and Controls (CMC) Statistical Analysis
Version: 0.1.0
Author: Yalin Zhu
Maintainer: Yalin Zhu <yalin.zhu@merck.com>
Imports: tolerance, ggplot2, plotly
Description: Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook "Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham."
License: GPL (>= 2)
Copyright: Copyright 2018, Center for Mathematical Sciences, Merck & Co., Inc.
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-15 13:50:03 UTC; zhuyal
Repository: CRAN
Date/Publication: 2018-06-15 15:26:39 UTC

More information about PPQplan at CRAN
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New package Orcs with initial version 1.0.0
Package: Orcs
Type: Package
Title: Omnidirectional R Code Snippets
Version: 1.0.0
Date: 2018-06-15
Authors@R: c( person("Florian", "Detsch", role = c("cre", "aut"), email = "fdetsch@web.de"), person("Tim", "Appelhans", role = "ctb"), person("Baptiste", "Auguie", role = "ctb"), person("OpenStreetMap contributors", role = "cph"))
Maintainer: Florian Detsch <fdetsch@web.de>
Description: I tend to repeat the same code chunks over and over again. At first, this was fine for me and I paid little attention to such redundancies. A little later, when I got tired of manually replacing Linux filepaths with the referring Windows versions, and vice versa, I started to stuff some very frequently used work-steps into functions and, even later, into a proper R package. And that's what this package is - a hodgepodge of various R functions meant to simplify (my) everyday-life coding work without, at the same time, being devoted to a particular scope of application.
License: MIT + file LICENSE
URL: https://github.com/fdetsch/Orcs
BugReports: https://github.com/fdetsch/Orcs/issues
LazyData: TRUE
Depends: R (>= 2.10), methods, raster
Imports: bookdown, devtools, grDevices, grid, knitr, lattice, latticeExtra, plotrix, Rcpp (>= 0.11.3), rgdal, sf, sp, stats
LinkingTo: Rcpp
RoxygenNote: 6.0.1
SystemRequirements: GNU make, 7zip, unix2dos
Suggests: testthat, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2018-06-15 12:38:19 UTC; FlorianD
Author: Florian Detsch [cre, aut], Tim Appelhans [ctb], Baptiste Auguie [ctb], OpenStreetMap contributors [cph]
Repository: CRAN
Date/Publication: 2018-06-15 15:14:35 UTC

More information about Orcs at CRAN
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New package MVar with initial version 2.0.2
Package: MVar
Type: Package
Title: Multivariate Analysis
Version: 2.0.2
Date: 2018-06-15
Author: Paulo Cesar Ossani <ossanipc@hotmail.com> Marcelo Angelo Cirillo <macufla@des.ufla.br>
Maintainer: Paulo Cesar Ossani <ossanipc@hotmail.com>
Suggests: MASS
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), hierarchical and non-hierarchical cluster analysis, 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.
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2018-06-15 11:50:16 UTC; Pc
Repository: CRAN
Date/Publication: 2018-06-15 15:14:39 UTC

More information about MVar at CRAN
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Package loder updated to version 0.2.0 with previous version 0.1.2 dated 2017-05-30

Title: Dependency-Free Access to PNG Image Files
Description: Read and write access to PNG image files using the LodePNG library. The package has no external dependencies.
Author: Jon Clayden [aut, cre], Lode Vandevenne [aut]
Maintainer: Jon Clayden <code@clayden.org>

Diff between loder versions 0.1.2 dated 2017-05-30 and 0.2.0 dated 2018-06-15

 DESCRIPTION                        |   10 -
 LICENCE                            |    2 
 MD5                                |   48 +++++--
 NAMESPACE                          |    3 
 NEWS.md                            |    8 +
 R/png.R                            |  119 ++++++++++++++++--
 README.md                          |   14 ++
 inst/extdata/pngsuite/ct1n0g04.png |only
 inst/extdata/pngsuite/cten0g04.png |only
 inst/extdata/pngsuite/ctgn0g04.png |only
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 man/inspectPng.Rd                  |only
 man/readPng.Rd                     |   19 ++
 man/writePng.Rd                    |   40 ++++--
 src/lodepng.c                      |  129 +++++++++++---------
 src/lodepng.h                      |   14 +-
 src/main.c                         |  236 ++++++++++++++++++++++++++-----------
 tests/testthat/test-05-read.R      |   23 +++
 tests/testthat/test-10-metadata.R  |   17 ++
 tests/testthat/test-15-write.R     |   28 ++++
 35 files changed, 537 insertions(+), 173 deletions(-)

More information about loder at CRAN
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Package Cyclops updated to version 1.3.4 with previous version 1.3.2 dated 2018-05-06

Title: Cyclic Coordinate Descent for Logistic, Poisson and Survival Analysis
Description: This model fitting tool incorporates cyclic coordinate descent and majorization-minimization approaches to fit a variety of regression models found in large-scale observational healthcare data. Implementations focus on computational optimization and fine-scale parallelization to yield efficient inference in massive datasets. Please see: Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.
Author: Marc A. Suchard [aut, cre], Martijn J. Schuemie [aut], Trevor R. Shaddox [aut], Yuxi Tian [aut], Sushil Mittal [ctb], Observational Health Data Sciences and Informatics [cph], Marcus Geelnard [cph, ctb] (provided the TinyThread library), Rutgers University [cph, ctb] (provided the HParSearch routine), R Development Core Team [cph, ctb] (provided the ZeroIn routine)
Maintainer: Marc A. Suchard <msuchard@ucla.edu>

Diff between Cyclops versions 1.3.2 dated 2018-05-06 and 1.3.4 dated 2018-06-15

 Cyclops-1.3.2/Cyclops/tests/testthat/test-KKTSwindle.R                 |only
 Cyclops-1.3.4/Cyclops/DESCRIPTION                                      |   41 ++-
 Cyclops-1.3.4/Cyclops/MD5                                              |   84 +++----
 Cyclops-1.3.4/Cyclops/NAMESPACE                                        |    3 
 Cyclops-1.3.4/Cyclops/NEWS                                             |   14 +
 Cyclops-1.3.4/Cyclops/R/DataManagement.R                               |   34 ++
 Cyclops-1.3.4/Cyclops/R/ModelFit.R                                     |   15 -
 Cyclops-1.3.4/Cyclops/R/RcppExports.R                                  |    4 
 Cyclops-1.3.4/Cyclops/R/SpecialPriors.R                                |only
 Cyclops-1.3.4/Cyclops/R/cyclops.R                                      |    2 
 Cyclops-1.3.4/Cyclops/man/aconfint.Rd                                  |    2 
 Cyclops-1.3.4/Cyclops/man/coef.cyclopsFit.Rd                           |    5 
 Cyclops-1.3.4/Cyclops/man/createNonSeparablePrior.Rd                   |only
 Cyclops-1.3.4/Cyclops/man/fitCyclopsModel.Rd                           |    2 
 Cyclops-1.3.4/Cyclops/man/getUnivariableSeparability.Rd                |only
 Cyclops-1.3.4/Cyclops/man/vcov.cyclopsFit.Rd                           |    2 
 Cyclops-1.3.4/Cyclops/src/RcppExports.cpp                              |   13 +
 Cyclops-1.3.4/Cyclops/src/RcppModelData.cpp                            |   53 ++++
 Cyclops-1.3.4/Cyclops/src/RcppProgressLogger.h                         |   56 ++--
 Cyclops-1.3.4/Cyclops/src/cyclops/CcdInterface.cpp                     |    1 
 Cyclops-1.3.4/Cyclops/src/cyclops/Timer.cpp                            |   68 -----
 Cyclops-1.3.4/Cyclops/src/cyclops/engine/AbstractModelSpecifics.cpp    |   14 -
 Cyclops-1.3.4/Cyclops/src/cyclops/engine/AbstractModelSpecifics.h      |   51 ++--
 Cyclops-1.3.4/Cyclops/tests/testthat/test-believedBroken.R             |   32 +-
 Cyclops-1.3.4/Cyclops/tests/testthat/test-conditionalPoisson.R         |    2 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-correlation.R                |    2 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-covariateRegularization.R    |    8 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-cprViaSql.R                  |   20 -
 Cyclops-1.3.4/Cyclops/tests/testthat/test-cv.R                         |    2 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-dataConversionStratified.R   |   84 +++----
 Cyclops-1.3.4/Cyclops/tests/testthat/test-dataConversionUnstratified.R |   64 ++---
 Cyclops-1.3.4/Cyclops/tests/testthat/test-dataManagement.R             |   16 -
 Cyclops-1.3.4/Cyclops/tests/testthat/test-isSorted.R                   |    2 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-largeBernoulli.R             |   78 ++++++
 Cyclops-1.3.4/Cyclops/tests/testthat/test-multitypePoisson.R           |  114 +++++-----
 Cyclops-1.3.4/Cyclops/tests/testthat/test-normalization.R              |    2 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-offsetPoisson.R              |   42 +--
 Cyclops-1.3.4/Cyclops/tests/testthat/test-parameterizedPriors.R        |    2 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-predict.R                    |    2 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-reductions.R                 |   22 +
 Cyclops-1.3.4/Cyclops/tests/testthat/test-smallBernoulli.R             |   32 +-
 Cyclops-1.3.4/Cyclops/tests/testthat/test-smallCLR.R                   |   24 +-
 Cyclops-1.3.4/Cyclops/tests/testthat/test-sqlConstructor.R             |   66 ++---
 Cyclops-1.3.4/Cyclops/tests/testthat/test-survfit.R                    |    2 
 Cyclops-1.3.4/Cyclops/tests/testthat/test-xyConstructor.R              |    4 
 45 files changed, 646 insertions(+), 440 deletions(-)

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New package CSESA with initial version 1.0.4
Package: CSESA
Type: Package
Title: CRISPR-Based Salmonella Enterica Serotype Analyzer
Version: 1.0.4
Authors@R: c( person("Xia", "Zhang", role = c("aut", "cre"), email = "zhangxia9403@gmail.com"), person("Lang", "Yang", role = "aut", email = "ylang1992@126.com"))
Description: Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) <doi:10.1128/JCM.00696-14> that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package 'CSESA' to predict the serotype based on this finding. Considering it’s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using 'CSESA' before doing the traditional serotyping.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: testthat
Imports: Biostrings
NeedsCompilation: no
Packaged: 2018-06-15 02:59:07 UTC; XiaXia
Author: Xia Zhang [aut, cre], Lang Yang [aut]
Maintainer: Xia Zhang <zhangxia9403@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-15 15:26:43 UTC

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New package autoshiny with initial version 0.0.1
Package: autoshiny
Title: Automatic Transformation of an 'R' Function into a 'shiny' App
Version: 0.0.1
Authors@R: person("Aleksander", "Rutkowski", email = "alek.rutkowski@gmail.com", role = c("aut", "cre"))
Description: Static code compilation of a 'shiny' app given an R function (into 'ui.R' and 'server.R' files or into a 'shiny' app object). See examples at <https://github.com/alekrutkowski/autoshiny>.
URL: https://github.com/alekrutkowski/autoshiny
Depends: R (>= 3.4.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Imports: shiny, utils
Suggests: roxygen2, magrittr, webshot
NeedsCompilation: no
Packaged: 2018-06-12 15:08:52 UTC; AsiaiAlek
Author: Aleksander Rutkowski [aut, cre]
Maintainer: Aleksander Rutkowski <alek.rutkowski@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-15 15:02:10 UTC

More information about autoshiny at CRAN
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New package alphastable with initial version 0.1.0
Package: alphastable
Title: Inference for Stable Distribution
Version: 0.1.0
Author: Mahdi Teimouri
Maintainer: Mahdi Teimouri <teimouri@aut.ac.ir>
Description: Developed to perform the tasks given by the following. 1-computing the probability density function and distribution function of a univariate stable distribution; 2- generating realization from univariate stable, truncated stable, multivariate elliptically contoured stable, and bivariate strictly stable distributions; 3- estimating the parameters of univariate symmetric stable, skew stable, Cauchy, multivariate elliptically contoured stable, and multivariate strictly stable distributions; 4- estimating the parameters of the mixture of symmetric stable and mixture of Cauchy distributions.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R(>= 3.4.0)
Imports: base, methods, mvtnorm, nlme, nnls, stabledist, stats
Suggests: Matrix, fBasics, FMStable, RUnit, Rmpfr, sfsmisc
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-15 12:41:53 UTC; NikPardaz
Repository: CRAN
Date/Publication: 2018-06-15 15:14:31 UTC

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Package RcppCWB updated to version 0.2.4 with previous version 0.2.3 dated 2018-05-13

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 0.2.3 dated 2018-05-13 and 0.2.4 dated 2018-06-15

 RcppCWB-0.2.3/RcppCWB/src/cwb/config/platform/darwin-core2           |only
 RcppCWB-0.2.3/RcppCWB/src/cwb/config/platform/darwin-g4              |only
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 RcppCWB-0.2.3/RcppCWB/src/cwb/config/platform/darwin-i386            |only
 RcppCWB-0.2.3/RcppCWB/src/cwb/cqp/paths.c                            |only
 RcppCWB-0.2.3/RcppCWB/src/cwb/cqp/paths.h                            |only
 RcppCWB-0.2.4/RcppCWB/DESCRIPTION                                    |   13 
 RcppCWB-0.2.4/RcppCWB/MD5                                            |  365 -
 RcppCWB-0.2.4/RcppCWB/NAMESPACE                                      |    6 
 RcppCWB-0.2.4/RcppCWB/NEWS.md                                        |   18 
 RcppCWB-0.2.4/RcppCWB/R/RcppCWB_package.R                            |    8 
 RcppCWB-0.2.4/RcppCWB/R/RcppExports.R                                |   12 
 RcppCWB-0.2.4/RcppCWB/R/cbow.R                                       |    3 
 RcppCWB-0.2.4/RcppCWB/R/checks.R                                     |   79 
 RcppCWB-0.2.4/RcppCWB/R/cl.R                                         |   10 
 RcppCWB-0.2.4/RcppCWB/R/count.R                                      |    2 
 RcppCWB-0.2.4/RcppCWB/R/cqp.R                                        |    7 
 RcppCWB-0.2.4/RcppCWB/R/cwb.R                                        |only
 RcppCWB-0.2.4/RcppCWB/R/decode.R                                     |    2 
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 RcppCWB-0.2.4/RcppCWB/R/region_matrix.R                              |    4 
 RcppCWB-0.2.4/RcppCWB/R/zzz.R                                        |   41 
 RcppCWB-0.2.4/RcppCWB/README.md                                      |  121 
 RcppCWB-0.2.4/RcppCWB/cleanup                                        |    5 
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 RcppCWB-0.2.4/RcppCWB/man/cl_lexicon_size.Rd                         |    2 
 RcppCWB-0.2.4/RcppCWB/man/cqp_initialize.Rd                          |    5 
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 RcppCWB-0.2.4/RcppCWB/man/s_attribute_decode.Rd                      |    2 
 RcppCWB-0.2.4/RcppCWB/man/s_attributes.Rd                            |    3 
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 RcppCWB-0.2.4/RcppCWB/src/Makevars.win                               |    2 
 RcppCWB-0.2.4/RcppCWB/src/RcppExports.cpp                            |   42 
 RcppCWB-0.2.4/RcppCWB/src/cl.cpp                                     |    2 
 RcppCWB-0.2.4/RcppCWB/src/cl_min.h                                   |only
 RcppCWB-0.2.4/RcppCWB/src/cqp.cpp                                    |    4 
 RcppCWB-0.2.4/RcppCWB/src/cwb/AUTHORS                                |   12 
 RcppCWB-0.2.4/RcppCWB/src/cwb/CHANGES                                |  251 
 RcppCWB-0.2.4/RcppCWB/src/cwb/COPYING                                | 1366 ++++
 RcppCWB-0.2.4/RcppCWB/src/cwb/CQi/auth.c                             |   74 
 RcppCWB-0.2.4/RcppCWB/src/cwb/CQi/auth.h                             |   16 
 RcppCWB-0.2.4/RcppCWB/src/cwb/CQi/cqi.h                              |   84 
 RcppCWB-0.2.4/RcppCWB/src/cwb/CQi/cqpserver.c                        |  471 +
 RcppCWB-0.2.4/RcppCWB/src/cwb/CQi/server.c                           |  607 +-
 RcppCWB-0.2.4/RcppCWB/src/cwb/CQi/server.h                           |    6 
 RcppCWB-0.2.4/RcppCWB/src/cwb/INSTALL                                |  210 
 RcppCWB-0.2.4/RcppCWB/src/cwb/Makefile                               |   83 
 RcppCWB-0.2.4/RcppCWB/src/cwb/README                                 |   14 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/Makefile                            |  100 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/attributes.c                        |  348 -
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/attributes.h                        |   46 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/binsert.c                           |    5 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/bitfields.c                         |    9 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/bitio.c                             |   69 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/cdaccess.c                          |  679 +-
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/cdaccess.h                          |    3 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/cl.h                                | 1413 +++-
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/class-mapping.c                     |   21 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/compression.c                       |    9 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/corpus.c                            |  226 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/corpus.h                            |    9 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/endian.c                            |   11 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/endian2.h                           |   32 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/fileutils.c                         |  278 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/fileutils.h                         |   11 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/globals.c                           |   13 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/globals.h                           |  102 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/lex.creg.c                          |  147 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/lexhash.c                           |  305 -
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/list.c                              |    4 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/macros.c                            |  166 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/macros.h                            |   37 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/makecomps.c                         |  145 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/ngram-hash.c                        |   31 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/registry.l                          |    6 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/registry.tab.c                      |   43 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/registry.y                          |    5 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/regopt.c                            | 1355 +++-
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/regopt.h                            |   26 
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/special-chars.c                     | 2896 +++++++++-
 RcppCWB-0.2.4/RcppCWB/src/cwb/cl/special-chars.h                     |    7 
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New package populationPDXdesign with initial version 1.0.2
Package: populationPDXdesign
Type: Package
Title: Designing Population PDX Studies
Version: 1.0.2
Date: 2018-06-12
Authors@R: c(person("Maria Luisa", "Guerriero", email = "maria.guerriero@astrazeneca.com", role = c("aut", "cre")), person("Natasha", "Karp", email = "natasha.karp@astrazeneca.com", role = c("aut")))
Description: Run simulations to assess the impact of various designs features and the underlying biological behaviour on the outcome of a Patient Derived Xenograft (PDX) population study. This project can either be deployed to a server as a 'shiny' app or installed locally as a package and run the app using the command 'populationPDXdesignApp()'.
License: GPL (>= 3)
Depends: R (>= 3.0.0)
Imports: devtools, ggplot2, plyr, roxygen2, shiny, shinycssloaders
Suggests: testthat
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-12 16:50:50 UTC; kpkr710
Author: Maria Luisa Guerriero [aut, cre], Natasha Karp [aut]
Maintainer: Maria Luisa Guerriero <maria.guerriero@astrazeneca.com>
Repository: CRAN
Date/Publication: 2018-06-15 14:52:30 UTC

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New package opensensmapr with initial version 0.4.1
Package: opensensmapr
Type: Package
Title: Client for the Data API of openSenseMap.org
Version: 0.4.1
URL: http://github.com/noerw/opensensmapR
BugReports: http://github.com/noerw/opensensmapR/issues
Imports: dplyr, httr, digest, magrittr
Suggests: maps, maptools, readr, tibble, rgeos, sf, knitr, rmarkdown, lubridate, units, jsonlite, ggplot2, zoo, lintr, testthat, covr
Authors@R: c(person("Norwin", "Roosen", role = c("aut", "cre"), email = "hello@nroo.de"), person("Daniel", "Nuest", role = c("ctb"), email = "daniel.nuest@uni-muenster.de", comment = c(ORCID = "0000-0003-2392-6140")))
Description: Download environmental measurements and sensor station metadata from the API of open data sensor web platform <https://opensensemap.org> for analysis in R. This platform provides real time data of more than 1500 low-cost sensor stations for PM10, PM2.5, temperature, humidity, UV-A intensity and more phenomena. The package aims to be compatible with 'sf' and the 'Tidyverse', and provides several helper functions for data exploration and transformation.
License: GPL (>= 2) | file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-11 21:07:07 UTC; kreis
Author: Norwin Roosen [aut, cre], Daniel Nuest [ctb] (<https://orcid.org/0000-0003-2392-6140>)
Maintainer: Norwin Roosen <hello@nroo.de>
Repository: CRAN
Date/Publication: 2018-06-15 14:38:22 UTC

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Package distfree.cr updated to version 1.5.1 with previous version 1.0 dated 2012-11-26

Title: Distribution-Free Confidence Region
Description: Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>.
Author: Zhiqiu Hu, Rong-cai Yang
Maintainer: Zhiqiu Hu <zhiqiu.hu@gmail.com>

Diff between distfree.cr versions 1.0 dated 2012-11-26 and 1.5.1 dated 2018-06-15

 DESCRIPTION        |   16 +++---
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 NAMESPACE          |    4 +
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 5 files changed, 119 insertions(+), 66 deletions(-)

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New package RcppEigenAD with initial version 1.0.0
Package: RcppEigenAD
Type: Package
Title: Compiles 'C++' Code using 'Rcpp', 'Eigen' and 'CppAD' to Produce First and Second Order Partial Derivatives
Version: 1.0.0
Date: 2018-06-14
Author: Damon Berridge <d.m.berridge@swansea.ac.uk> [ctb] Robert Crouchley <r.crouchley@lancaster.ac.uk> [ctb] Daniel Grose <dan.grose@lancaster.ac.uk> [aut,cre,ctb]
Maintainer: Daniel Grose <dan.grose@lancaster.ac.uk>
Description: Compiles 'C++' code using 'Rcpp', 'Eigen' and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions, (see Hardy, M (2006) <arXiv:math/0601149v1>).
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.12), methods, RcppEigen, functional, memoise, readr, Rdpack
LinkingTo: Rcpp, RcppEigen, BH
SystemRequirements: C++11
RdMacros: Rdpack
NeedsCompilation: yes
Packaged: 2018-06-15 11:36:31 UTC; grosed
Repository: CRAN
Date/Publication: 2018-06-15 13:10:04 UTC

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Package PReMiuM updated to version 3.2.0 with previous version 3.1.8 dated 2018-06-14

Title: Dirichlet Process Bayesian Clustering, Profile Regression
Description: Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
Author: David I. Hastie, Silvia Liverani <liveranis@gmail.com> and Sylvia Richardson with contributions from Aurore J. Lavigne, Lucy Leigh, Lamiae Azizi, Xi Liu, Ruizhu Huang, Austin Gratton, Wei Jing
Maintainer: Silvia Liverani <liveranis@gmail.com>

Diff between PReMiuM versions 3.1.8 dated 2018-06-14 and 3.2.0 dated 2018-06-15

 DESCRIPTION                |    6 +++---
 MD5                        |    6 +++---
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Package kvh updated to version 1.3 with previous version 1.2 dated 2018-05-14

Title: Read/Write Files in Key-Value-Hierarchy Format
Description: The format KVH is a lightweight format that can be read/written both by humans and machines. It can be useful in situations where XML or alike formats seem to be an overkill. We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use.
Author: Serguei Sokol
Maintainer: Serguei Sokol <sokol@insa-toulouse.fr>

Diff between kvh versions 1.2 dated 2018-05-14 and 1.3 dated 2018-06-15

 DESCRIPTION               |    8 +--
 MD5                       |   16 +++---
 NEWS                      |    6 ++
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 man/kvh_read.Rd           |    9 ++-
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 tests/testthat/test_kvh.R |   50 ++++++++++++++++++-
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Package drugCombo updated to version 1.0.2 with previous version 1.0.0 dated 2018-05-11

Title: Drug Interaction Modeling Based on Loewe Additivity Following Harbron's Approach
Description: Perform assessment of synergy/antagonism for drug combinations based on the Loewe additivity model, following Harbron's approach (Statistics in Medicine, 2010, <doi:10.1002/sim.3916>). The package allows flexible modeling of the drug interaction index and supports "2-stage" estimation in addition to "1-stage" estimation, including bootstrap-based confidence intervals. The method requires data on the monotherapy responses and the package accommodates both checkerboard and ray designs. Functions are available for graphical exploration of model goodness-of-fit and diagnostics, as well as for synergy/antagonism assessment in 2D and 3D.
Author: Maxim Nazarov, Nele Goeyvaerts, Chris Harbron (original article and code)
Maintainer: Maxim Nazarov <maxim.nazarov@openanalytics.eu>

Diff between drugCombo versions 1.0.0 dated 2018-05-11 and 1.0.2 dated 2018-06-15

 DESCRIPTION                      |    8 ++--
 MD5                              |   18 ++++-----
 R/plot.tauSurface.R              |   16 +++++++-
 inst/NEWS                        |    9 +++-
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 inst/doc/userGuide.Rmd           |   10 ++---
 inst/doc/userGuide.html          |   72 ++++++++++++++++++++-------------------
 tests/testthat/test-fitModel.R   |   24 ++++---------
 tests/testthat/test-tauSurface.R |   58 +++++++++----------------------
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 10 files changed, 112 insertions(+), 125 deletions(-)

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Package bsamGP updated to version 1.2.1 with previous version 1.2.0 dated 2018-06-10

Title: Bayesian Spectral Analysis Models using Gaussian Process Priors
Description: Contains functions to perform Bayesian inference using a spectral analysis of Gaussian process priors. Gaussian processes are represented with a Fourier series based on cosine basis functions. Currently the package includes parametric linear models, partial linear additive models with/without shape restrictions, generalized linear additive models with/without shape restrictions, and density estimation model. To maximize computational efficiency, the actual Markov chain Monte Carlo sampling for each model is done using codes written in FORTRAN 90. This software has been developed using funding supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).
Author: Seongil Jo [aut, cre], Taeryon Choi [aut], Beomjo Park [aut, cre], Peter J. Lenk [ctb]
Maintainer: Beomjo Park <beomjo@korea.ac.kr>

Diff between bsamGP versions 1.2.0 dated 2018-06-10 and 1.2.1 dated 2018-06-15

 DESCRIPTION                   |    8 ++++----
 MD5                           |   28 ++++++++++++++--------------
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 15 files changed, 43 insertions(+), 24 deletions(-)

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New package wevid with initial version 0.4.2
Package: wevid
Type: Package
Title: Quantifying Performance of a Binary Classifier Through Weight of Evidence
Version: 0.4.2
Date: 2018-06-15
Authors@R: c(person("Paul", "McKeigue", email="paul.mckeigue@ed.ac.uk", role=c("aut")), person("Marco", "Colombo", email="m.colombo@ed.ac.uk", role=c("ctb", "cre")))
Description: The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue P., Quantifying performance of a diagnostic test as the expected information for discrimination: relation to the C-statistic. Statistical Methods for Medical Research 2018, in press). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.
Depends: R (>= 2.10)
License: GPL-3
URL: http://www.homepages.ed.ac.uk/pmckeigu/preprints/classify/wevidtutorial.html
LazyLoad: yes
Imports: ggplot2, pROC, reshape2, zoo
ByteCompile: TRUE
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-15 10:37:58 UTC; marco
Author: Paul McKeigue [aut], Marco Colombo [ctb, cre]
Maintainer: Marco Colombo <m.colombo@ed.ac.uk>
Repository: CRAN
Date/Publication: 2018-06-15 11:17:55 UTC

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New package txtq with initial version 0.0.4
Package: txtq
Title: A Small Message Queue for Parallel Processes
Description: This queue is a data structure that lets parallel processes send and receive messages, and it can help coordinate the work of complicated parallel tasks. Processes can push new messages to the queue, pop old messages, and obtain a log of all the messages ever pushed. File locking preserves the integrity of the data even when multiple processes access the queue simultaneously.
Version: 0.0.4
License: MIT + file LICENSE
URL: https://github.com/wlandau/txtq
BugReports: https://github.com/wlandau/txtq/issues
Authors@R: c( person( family = "Landau", given = c("William", "Michael"), email = "will.landau@gmail.com", role = c("aut", "cre") ), person( family = "Eli Lilly and Company", role = "cph" ))
Imports: base64url, filelock, fs, R6
Suggests: parallel, testthat
Encoding: UTF-8
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-14 21:13:45 UTC; landau
Author: William Michael Landau [aut, cre], Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-15 11:12:55 UTC

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New package sssc with initial version 1.0.0
Package: sssc
Title: Same Species Sample Contamination Detection
Version: 1.0.0
Authors@R: c(person("Tao", "Jiang", role = c("aut", "cre"), email = "tjiang8@ncsu.edu"))
Description: Imports Variant Calling Format file into R. It can detect whether a sample contains contaminant from the same species. In the first stage of the approach, a change-point detection method is used to identify copy number variations for filtering. Next, features are extracted from the data for a support vector machine model. For log-likelihood calculation, the deviation parameter is estimated by maximum likelihood method. Using a radial basis function kernel support vector machine, the contamination of a sample can be detected.
Depends: R (>= 3.4.0)
Imports: changepoint, e1071, ggplot2, stats, VGAM
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-15 04:21:27 UTC; tjiang8
Author: Tao Jiang [aut, cre]
Maintainer: Tao Jiang <tjiang8@ncsu.edu>
Repository: CRAN
Date/Publication: 2018-06-15 11:22:54 UTC

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Package QGglmm updated to version 0.7.1 with previous version 0.6.0 dated 2017-10-08

Title: Estimate Quantitative Genetics Parameters from Generalised Linear Mixed Models
Description: Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Author: Pierre de Villemereuil <bonamy@horus.ens.fr>
Maintainer: Pierre de Villemereuil <bonamy@horus.ens.fr>

Diff between QGglmm versions 0.6.0 dated 2017-10-08 and 0.7.1 dated 2018-06-15

 DESCRIPTION               |   10 
 MD5                       |   55 +-
 NAMESPACE                 |    6 
 NEWS.md                   |   33 +
 R/source.R                |  973 +++++++++++++++++++++++++++++++++++-----------
 R/source.icc.R            |  781 ++++++++++++++++++++++++++----------
 R/source.mv.R             |  665 +++++++++++++++++++++----------
 R/source.ordinal.R        |   96 ++--
 README.md                 |    4 
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 inst/doc/QGglmmHowTo.pdf  |binary
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 man/QGlink.funcs.Rd       |   34 -
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 man/QGvar.dist.Rd         |   48 +-
 man/QGvar.exp.Rd          |   52 +-
 man/QGvcov.Rd             |   65 +--
 vignettes/QGglmmHowTo.Rnw |  488 ++++++++++++-----------
 29 files changed, 2847 insertions(+), 1585 deletions(-)

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New package MDFS with initial version 1.0.0
Package: MDFS
Title: MultiDimensional Feature Selection
Version: 1.0.0
Date: 2018-06-15
Authors@R: c( person("Radosław", "Piliszek", email = "r.piliszek@uwb.edu.pl", role = c("aut", "cre")), person("Krzysztof", "Mnich", email = "k.mnich@uwb.edu.pl", role = "aut"), person("Paweł", "Tabaszewski", email = "tabaszewski.pawel@gmail.com", role = "aut"), person("Szymon", "Migacz", email = "szmigacz@gmail.com", role = "aut"), person("Andrzej", "Sułecki", email = "asulecki@gmail.com", role = "aut"), person(c("Witold", "Remigiusz"), "Rudnicki", email = "w.rudnicki@icm.edu.pl", role = "aut"))
URL: https://featureselector.uco.uwb.edu.pl/software/mdfs/
Description: Functions for MultiDimensional Feature Selection (MDFS): calculating multidimensional information gains, scoring variables, finding important variables, plotting selection results. This package includes an optional CUDA implementation that speeds up information gain calculation using NVIDIA GPGPUs.
Depends: R (>= 3.4.0)
License: GPL-3
SystemRequirements: C++11
NeedsCompilation: yes
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Packaged: 2018-06-15 06:53:04 UTC; r.piliszek
Author: Radosław Piliszek [aut, cre], Krzysztof Mnich [aut], Paweł Tabaszewski [aut], Szymon Migacz [aut], Andrzej Sułecki [aut], Witold Remigiusz Rudnicki [aut]
Maintainer: Radosław Piliszek <r.piliszek@uwb.edu.pl>
Repository: CRAN
Date/Publication: 2018-06-15 11:18:04 UTC

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New package mcen with initial version 1.0
Package: mcen
Type: Package
Title: Multivariate Cluster Elastic Net
Version: 1.0
Date: 2018-06-14
Author: Ben Sherwood [aut, cre], Brad Price [aut]
Depends: R (>= 3.0.0), glmnet, flexclust, Matrix, parallel, faraway, methods, stats
Maintainer: Ben Sherwood <ben.sherwood@ku.edu>
Description: Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arXiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.
ByteCompile: TRUE
License: MIT + file LICENSE
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-06-14 19:12:03 UTC; bsherwoo
Repository: CRAN
Date/Publication: 2018-06-15 11:12:59 UTC

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New package IntervalSurgeon with initial version 1.0
Package: IntervalSurgeon
Type: Package
Title: Operating on Integer-Bounded Intervals
Version: 1.0
Date: 2018-06-04
Author: Daniel Greene
Maintainer: Daniel Greene <dg333@cam.ac.uk>
Description: Functions for manipulating integer-bounded intervals including finding overlaps, piling and merging.
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.4)
LinkingTo: Rcpp
Suggests: knitr
VignetteBuilder: knitr
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-06-07 06:08:28 UTC; dg
Repository: CRAN
Date/Publication: 2018-06-15 11:02:55 UTC

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Package clusterlab updated to version 0.0.2.0 with previous version 0.0.1.9 dated 2018-06-15

Title: Flexible Gaussian Cluster Simulator
Description: Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created 'clusterlab'. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. The algorithm is highly customizable and well suited to testing class discovery tools across a range of fields.
Author: Christopher R John
Maintainer: Christopher R John <chris.r.john86@gmail.com>

Diff between clusterlab versions 0.0.1.9 dated 2018-06-15 and 0.0.2.0 dated 2018-06-15

 DESCRIPTION                |    6 ++---
 MD5                        |   10 ++++-----
 inst/doc/introduction.R    |   11 ---------
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 6 files changed, 68 insertions(+), 81 deletions(-)

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New package bfw with initial version 0.0.1
Package: bfw
Version: 0.0.1
Date: 2018-06-15
Title: Bayesian Framework for Computational Modelling
Authors@R: person( "Øystein Olav","Skaar", email="bayesianfw@gmail.com", role=c("aut","cre"))
Maintainer: Øystein Olav Skaar <bayesianfw@gmail.com>
URL: https://github.com/oeysan/bfw/
BugReports: https://github.com/oeysan/bfw/issues/
Description: Derived from the work of Kruschke (2015, <ISBN:9780124058880>), the present package aim to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <http://mcmc-jags.sourceforge.net/>). The initial version include several modules for conducting Bayesian equivalents of chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical) regression, softmax regression, and for fitting data (e.g., structural equation modeling).
SystemRequirements: JAGS >=4.3.0 <http://mcmc-jags.sourceforge.net/>, Java JDK >=1.4 <https://www.java.com/en/download/manual.jsp>
Depends: R (>= 3.4.0),
Imports: plyr (>= 1.8.4), methods (>= 3.5.0), MASS (>= 7.3-47), pbapply (>= 1.3-4), ReporteRs (>= 0.8.9), coda (>= 0.19-1), psych (>= 1.7.8), rJava (>= 0.9-9), runjags (>= 2.0.4-2), ggplot2 (>= 2.2.1), scales (>= 0.5.0), truncnorm (>= 1.0-8), robust (>= 0.4-18), lavaan (>= 0.6-1)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-15 10:05:57 UTC; Melkor
Author: Øystein Olav Skaar [aut, cre]
Repository: CRAN
Date/Publication: 2018-06-15 11:17:58 UTC

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New package vip with initial version 0.1.0
Package: vip
Type: Package
Title: Variable Importance Plots
Version: 0.1.0
Authors@R: c( person("Brandon", "Greenwell", email = "greenwell.brandon@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8120-0084")), person("Brad", "Boehmke", email = "bradleyboehmke@gmail.com", role = c("aut")) )
Description: A general framework for constructing variable importance plots from various types machine learning models in R. Aside from some standard model- based variable importance measures, this package also provides a novel approach based on partial dependence plots (PDPs) and individual conditional expectation (ICE) curves as described in Greenwell et al. (2018) <arXiv:1805.04755>.
License: GPL (>= 2)
URL: https://github.com/koalaverse/vip
BugReports: https://github.com/koalaverse/vip/issues
Encoding: UTF-8
LazyData: true
Imports: dplyr, ggplot2 (>= 0.9.0), gridExtra, magrittr, pdp, plyr, stats, tibble, tidyr, utils
Suggests: C50, caret, earth, gbm, h2o, knitr, party, partykit, ranger, rpart, randomForest, rmarkdown, xgboost, glmnet, testthat
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-14 01:39:04 UTC; bgreenwell
Author: Brandon Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>), Brad Boehmke [aut]
Maintainer: Brandon Greenwell <greenwell.brandon@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-15 09:10:57 UTC

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Package model4you updated to version 0.9-2 with previous version 0.9-1 dated 2018-02-08

Title: Stratified and Personalised Models Based on Model-Based Trees and Forests
Description: Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.
Author: Heidi Seibold [aut, cre], Achim Zeileis [aut], Torsten Hothorn [aut]
Maintainer: Heidi Seibold <heidi@seibold.co>

Diff between model4you versions 0.9-1 dated 2018-02-08 and 0.9-2 dated 2018-06-15

 DESCRIPTION                 |    8 -
 MD5                         |   35 +++--
 R/helpers.R                 |   97 ++++++++++------
 R/plot-pmtree.R             |  259 +++++++++++++++++++++++---------------------
 R/pmodel.R                  |    2 
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 inst/JORS/man_model4you.Rnw |   52 ++++----
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 tests/test-pmodel.R         |   30 ++---
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 tests/test-pmtree.R         |   94 +++++++++++++++
 tests/test-pmtree.Rout.save |  182 +++++++++++++++++++++++++++---
 19 files changed, 608 insertions(+), 339 deletions(-)

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Package AdhereR updated to version 0.2.0 with previous version 0.1.0 dated 2017-04-27

Title: Adherence to Medications
Description: Computation of adherence to medications from Electronic Health care Data and visualization of individual medication histories and adherence patterns. The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings.
Author: Dan Dediu [aut, cre], Alexandra Dima [aut]
Maintainer: Dan Dediu <ddediu@gmail.com>

Diff between AdhereR versions 0.1.0 dated 2017-04-27 and 0.2.0 dated 2018-06-15

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 29 files changed, 1200 insertions(+), 314 deletions(-)

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New package multicastR with initial version 1.0.0
Package: multicastR
Type: Package
Title: A Companion to the Multi-CAST Collection
Version: 1.0.0
Authors@R: person("Nils Norman", "Schiborr", email = "nils-norman.schiborr@uni-bamberg.de", role=c("aut", "cre"))
URL: https://lac.uni-koeln.de/en/multicast/
Description: Provides a basic interface for accessing annotation data from the Multi-CAST collection, a database of spoken natural language texts edited by Geoffrey Haig and Stefan Schnell. The collection draws from a diverse set of languages and has been annotated across multiple levels. Annotation data is downloaded on request from the servers of the Language Archive Cologne. See the Multi-CAST website <https://lac.uni-koeln.de/multicast/> for more information and a list of related publications.
License: CC BY 4.0
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0.0), data.table (>= 1.10.0)
RoxygenNote: 6.0.1
Suggests: testthat
NeedsCompilation: no
Packaged: 2018-06-13 06:18:01 UTC; Nils
Author: Nils Norman Schiborr [aut, cre]
Maintainer: Nils Norman Schiborr <nils-norman.schiborr@uni-bamberg.de>
Repository: CRAN
Date/Publication: 2018-06-15 08:37:55 UTC

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New package BiocManager with initial version 1.30.1
Package: BiocManager
Title: Access the Bioconductor Project Package Repository
Description: A convenient tool to install and update Bioconductor packages.
Version: 1.30.1
Authors@R: c( person("Martin", "Morgan", email = "martin.morgan@roswellpark.org", role = "aut", comment = c(ORCID = "0000-0002-5874-8148")), person("Marcel", "Ramos", email = "marcel.ramos@roswellpark.org", role = c("ctb", "cre"), comment = c(ORCID = "0000-0002-3242-0582")))
Depends: R (>= 3.5.0)
Imports: utils
Suggests: BiocVersion, remotes, testthat, knitr
BugReports: https://github.com/Bioconductor/BiocManager/issues
VignetteBuilder: knitr
License: Artistic-2.0
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-13 15:50:53 UTC; mr148
Author: Martin Morgan [aut] (<https://orcid.org/0000-0002-5874-8148>), Marcel Ramos [ctb, cre] (<https://orcid.org/0000-0002-3242-0582>)
Maintainer: Marcel Ramos <marcel.ramos@roswellpark.org>
Repository: CRAN
Date/Publication: 2018-06-15 08:57:55 UTC

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Package spatstat updated to version 1.56-0 with previous version 1.55-1 dated 2018-04-05

Title: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Description: Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Author: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>, Rolf Turner <r.turner@auckland.ac.nz> and Ege Rubak <rubak@math.aau.dk>, with substantial contributions of code by Kasper Klitgaard Berthelsen; Ottmar Cronie; Yongtao Guan; Ute Hahn; Abdollah Jalilian; Marie-Colette van Lieshout; Greg McSwiggan; Tuomas Rajala; Suman Rakshit; Dominic Schuhmacher; Rasmus Waagepetersen; and Hangsheng Wang. Additional contributions by M. Adepeju; C. Anderson; Q.W. Ang; J. Astrom; M. Austenfeld; S. Azaele; M. Baddeley; C. Beale; M. Bell; R. Bernhardt; T. Bendtsen; A. Bevan; B. Biggerstaff; A. Bilgrau; L. Bischof; C. Biscio; R. Bivand; J.M. Blanco Moreno; F. Bonneu; J. Burgos; S. Byers; Y.M. Chang; J.B. Chen; I. Chernayavsky; Y.C. Chin; B. Christensen; J.-F. Coeurjolly; K. Colyvas; R. Constantine; R. Corria Ainslie; R. Cotton; M. de la Cruz; P. Dalgaard; M. D'Antuono; S. Das; T. Davies; P.J. Diggle; P. Donnelly; I. Dryden; S. Eglen; A. El-Gabbas; B. Fandohan; O. Flores; E.D. Ford; P. Forbes; S. Frank; J. Franklin; N. Funwi-Gabga; O. Garcia; A. Gault; J. Geldmann; M. Genton; S. Ghalandarayeshi; J. Gilbey; J. Goldstick; P. Grabarnik; C. Graf; U. Hahn; A. Hardegen; M.B. Hansen; M. Hazelton; J. Heikkinen; M. Hering; M. Herrmann; P. Hewson; K. Hingee; K. Hornik; P. Hunziker; J. Hywood; R. Ihaka; C. Icos; A. Jammalamadaka; R. John-Chandran; D. Johnson; M. Khanmohammadi; R. Klaver; P. Kovesi; L. Kozmian-Ledward; M. Kuhn; J. Laake; F. Lavancier; T. Lawrence; R.A. Lamb; J. Lee; G.P. Leser; A. Li; H.T. Li; G. Limitsios; A. Lister; B. Madin; M. Maechler; J. Marcus; K. Marchikanti; R. Mark; J. Mateu; P. McCullagh; U. Mehlig; F. Mestre; S. Meyer; X.C. Mi; L. De Middeleer; R.K. Milne; E. Miranda; J. Moller; I. Moncada; M. Moradi; V. Morera Pujol; E. Mudrak; G.M. Nair; N. Najari; N. Nava; L.S. Nielsen; F. Nunes; J.R. Nyengaard; J. Oehlschlaegel; T. Onkelinx; S. O'Riordan; E. Parilov; J. Picka; N. Picard; M. Porter; S. Protsiv; A. Raftery; S. Rakshit; B. Ramage; P. Ramon; X. Raynaud; N. Read; M. Reiter; I. Renner; T.O. Richardson; B.D. Ripley; E. Rosenbaum; B. Rowlingson; J. Rudokas; J. Rudge; C. Ryan; F. Safavimanesh; A. Sarkka; C. Schank; K. Schladitz; S. Schutte; B.T. Scott; O. Semboli; F. Semecurbe; V. Shcherbakov; G.C. Shen; P. Shi; H.-J. Ship; T.L. Silva; I.-M. Sintorn; Y. Song; M. Spiess; M. Stevenson; K. Stucki; M. Sumner; P. Surovy; B. Taylor; T. Thorarinsdottir; L. Torres; B. Turlach; T. Tvedebrink; K. Ummer; M. Uppala; A. van Burgel; T. Verbeke; M. Vihtakari; A. Villers; F. Vinatier; S. Voss; S. Wagner; H. Wang; H. Wendrock; J. Wild; C. Witthoft; S. Wong; M. Woringer; M.E. Zamboni and A. Zeileis.
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat versions 1.55-1 dated 2018-04-05 and 1.56-0 dated 2018-06-15

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Package radiant.model updated to version 0.9.5 with previous version 0.8.0 dated 2017-04-28

Title: Model Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in radiant.data.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant.model versions 0.8.0 dated 2017-04-28 and 0.9.5 dated 2018-06-15

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Permanent link

Package prophet updated to version 0.3.0.1 with previous version 0.3 dated 2018-06-02

Title: Automatic Forecasting Procedure
Description: Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
Author: Sean Taylor [cre, aut], Ben Letham [aut]
Maintainer: Sean Taylor <sjt@fb.com>

Diff between prophet versions 0.3 dated 2018-06-02 and 0.3.0.1 dated 2018-06-15

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Permanent link

Package MultisiteMediation updated to version 0.0.2 with previous version 0.0.1 dated 2017-02-26

Title: Causal Mediation Analysis in Multisite Trials
Description: We implement multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879> and Qin, Hong, Deutsch, and Bein (under review). It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. Because the identification assumptions are not always warranted, the package also provides a weighting-based balance checking function for assessing the remaining overt bias, as well as a weighting-based sensitivity analysis function for further evaluating the potential bias related to omitted confounding or to propensity score model misspecification.
Author: Xu Qin, Guanglei Hong, Jonah Deutsch, and Edward Bein
Maintainer: Xu Qin <xuqin@uchicago.edu>

Diff between MultisiteMediation versions 0.0.1 dated 2017-02-26 and 0.0.2 dated 2018-06-15

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Package mmtfa updated to version 0.3 with previous version 0.1 dated 2015-06-13

Title: Model-Based Clustering and Classification with Mixtures of Modified t Factor Analyzers
Description: Fits a family of mixtures of multivariate t-distributions under a continuous t-distributed latent variable structure for the purpose of clustering or classification. The alternating expectation-conditional maximization algorithm is used for parameter estimation.
Author: Jeffrey L. Andrews, Paul D. McNicholas, and Mathieu Chalifour
Maintainer: Jeffrey L. Andrews <jeff.andrews@ubc.ca>

Diff between mmtfa versions 0.1 dated 2015-06-13 and 0.3 dated 2018-06-15

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Package dng updated to version 0.2.0 with previous version 0.1.1 dated 2017-11-22

Title: Distributions and Gradients
Description: Provides density, distribution function, quantile function and random generation for the split normal and split-t distributions, and computes their mean, variance, skewness and kurtosis for the two distributions (Li, F, Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>).
Author: Jiayue Zeng [aut, cre], Feng Li [aut]
Maintainer: Jiayue Zeng <zengjiayue@126.com>

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Package WRS2 updated to version 0.10-0 with previous version 0.9-2 dated 2017-05-01

Title: A Collection of Robust Statistical Methods
Description: A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Author: Patrick Mair [cre, aut], Rand Wilcox [aut]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

Diff between WRS2 versions 0.9-2 dated 2017-05-01 and 0.10-0 dated 2018-06-15

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Package qrng updated to version 0.0-4 with previous version 0.0-3 dated 2016-06-13

Title: (Randomized) Quasi-Random Number Generators
Description: Functionality for generating (randomized) quasi-random numbers in high dimensions.
Author: Marius Hofert [aut, cre], Christiane Lemieux [aut]
Maintainer: Marius Hofert <marius.hofert@uwaterloo.ca>

Diff between qrng versions 0.0-3 dated 2016-06-13 and 0.0-4 dated 2018-06-15

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Package glinternet updated to version 1.0.8 with previous version 1.0.7 dated 2018-02-15

Title: Learning Interactions via Hierarchical Group-Lasso Regularization
Description: Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
Author: Michael Lim, Trevor Hastie
Maintainer: Michael Lim <michael626@gmail.com>

Diff between glinternet versions 1.0.7 dated 2018-02-15 and 1.0.8 dated 2018-06-15

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Package clusterlab updated to version 0.0.1.9 with previous version 0.0.0.9 dated 2018-05-31

Title: Flexible Gaussian Cluster Simulator
Description: Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created 'clusterlab'. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. The algorithm is highly customizable and well suited to testing class discovery tools across a range of fields.
Author: Christopher R John
Maintainer: Christopher R John <chris.r.john86@gmail.com>

Diff between clusterlab versions 0.0.0.9 dated 2018-05-31 and 0.0.1.9 dated 2018-06-15

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Thu, 14 Jun 2018

Package TrendInTrend updated to version 1.1.2 with previous version 1.0.2 dated 2017-08-22

Title: Odds Ratio Estimation and Power Calculation for the Trend in Trend Model
Description: Estimation of causal odds ratio and power calculation given trends in exposure prevalence and outcome frequencies of stratified data.
Author: Xinyao Ji and Ashkan Ertefaie
Maintainer: Ashkan Ertefaie <ashkan_ertefaie@urmc.rochester.edu>

Diff between TrendInTrend versions 1.0.2 dated 2017-08-22 and 1.1.2 dated 2018-06-14

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New package lidR with initial version 1.5.1
Package: lidR
Type: Package
Title: Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
Version: 1.5.1
Date: 2018-06-14
Authors@R: c( person("Jean-Romain", "Roussel", email = "jean-romain.roussel.1@ulaval.ca", role = c("aut", "cre", "cph")), person("David", "Auty", email = "", role = c("aut", "ctb"), comment = "Reviews the documentation"), person("Florian", "De Boissieu", email = "", role = ("ctb"), comment = "Fixed bugs and improved catalog features"), person("Andrew", "Sánchez Meador", email = "", role = ("ctb"), comment = "Implemented lassnags"))
Description: Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.
URL: https://github.com/Jean-Romain/lidR
BugReports: https://github.com/Jean-Romain/lidR/issues
License: GPL-3
Depends: R (>= 3.1.0),methods
Imports: data.table, future, gdalUtils, geometry, grDevices, gstat, lazyeval, mapview, mapedit, memoise, RANN, raster, Rcpp, rgeos, rgl, rlas (>= 1.1.10), settings, sp, stats, tools, utils
Suggests: rgdal, testthat, EBImage, hexbin
LazyData: true
RoxygenNote: 6.0.1
LinkingTo: Rcpp
Encoding: UTF-8
ByteCompile: true
biocViews:
Collate: 'RcppExports.R' 'catalog_apply.r' 'catalog_clip.r' 'catalog_index.r' 'catalog_laxindex.r' 'catalog_makecluster.r' 'catalog_query.r' 'catalog_reshape.r' 'catalog_select.r' 'class-lasheader.r' 'class-las.r' 'class-lascatalog.r' 'class-lascluster.r' 'constant.R' 'deprecated.r' 'grid_canopy.r' 'grid_catalog.r' 'grid_density.r' 'grid_hexametrics.r' 'grid_metrics.r' 'grid_metrics3d.r' 'grid_terrain.r' 'grid_tincanopy.r' 'lasaggreagte.r' 'lascheck.r' 'lasclassify.r' 'lasclip.r' 'lascolor.r' 'lasfilter.r' 'lasfilterdecimate.r' 'lasfiltersurfacepoints.r' 'lasground.r' 'lasindentify.r' 'lasmetrics.r' 'lasnormalize.r' 'lasroi.r' 'lassmooth.r' 'lassnags.r' 'lastrees.r' 'lastrees_dalponte.r' 'lastrees_li.r' 'lastrees_li2.r' 'lastrees_silva.r' 'lastrees_watershed.r' 'lasupdateheader.r' 'lidRError.r' 'metrics.r' 'metrics_canopy_roughness.r' 'mutatebyref.r' 'options.r' 'plot.catalog.r' 'plot.las.r' 'plot.lashexametrics.r' 'plot.lasmetrics.r' 'plot.lasmetrics3d.r' 'plot3d.r' 'readLAS.r' 'subcircled.r' 'tree_detection.r' 'tree_metrics.r' 'utils_colors.r' 'utils_geometry.r' 'utils_interpolations.r' 'utils_misc.r' 'utils_projection.r' 'utils_spatial.r' 'utils_typecast.r' 'writeLAS.r' 'zzz.r'
NeedsCompilation: yes
Packaged: 2018-06-14 21:40:31 UTC; jr
Author: Jean-Romain Roussel [aut, cre, cph], David Auty [aut, ctb] (Reviews the documentation), Florian De Boissieu [ctb] (Fixed bugs and improved catalog features), Andrew Sánchez Meador [ctb] (Implemented lassnags)
Maintainer: Jean-Romain Roussel <jean-romain.roussel.1@ulaval.ca>
Repository: CRAN
Date/Publication: 2018-06-14 22:24:14 UTC

More information about lidR at CRAN
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Package radiant.basics updated to version 0.9.5 with previous version 0.8.0 dated 2017-04-27

Title: Basics Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Basics menu includes interfaces for probability calculation, central limit theorem simulation, comparing means and proportions, goodness-of-fit testing, cross-tabs, and correlation. The application extends the functionality in radiant.data.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

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 radiant.basics-0.9.5/radiant.basics/man/prob_tdist.Rd                                                |   15 
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 radiant.basics-0.9.5/radiant.basics/man/summary.single_mean.Rd                                       |    4 
 radiant.basics-0.9.5/radiant.basics/man/summary.single_prop.Rd                                       |    4 
 radiant.basics-0.9.5/radiant.basics/tests/testthat/test_stats.R                                      |   80 
 123 files changed, 5193 insertions(+), 3874 deletions(-)

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Package PReMiuM updated to version 3.1.8 with previous version 3.1.7 dated 2018-04-01

Title: Dirichlet Process Bayesian Clustering, Profile Regression
Description: Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
Author: David I. Hastie, Silvia Liverani <liveranis@gmail.com> and Sylvia Richardson with contributions from Aurore J. Lavigne, Lucy Leigh, Lamiae Azizi, Xi Liu, Ruizhu Huang, Austin Gratton, Wei Jing
Maintainer: Silvia Liverani <liveranis@gmail.com>

Diff between PReMiuM versions 3.1.7 dated 2018-04-01 and 3.1.8 dated 2018-06-14

 ChangeLog                           |    9 
 DESCRIPTION                         |   12 
 MD5                                 |   44 
 NAMESPACE                           |    1 
 R/postProcess.R                     | 6192 ++++++++++++++++++------------------
 man/PReMiuM-package.Rd              |    4 
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 31 files changed, 3481 insertions(+), 3128 deletions(-)

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Package polspline updated to version 1.1.13 with previous version 1.1.12 dated 2015-07-14

Title: Polynomial Spline Routines
Description: Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
Author: Charles Kooperberg <clk@fredhutch.org>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between polspline versions 1.1.12 dated 2015-07-14 and 1.1.13 dated 2018-06-14

 DESCRIPTION                 |    8 +--
 MD5                         |   23 ++++----
 NAMESPACE                   |    2 
 R/polspline.R               |    4 +
 src/allpack.f               |   19 ++++---
 src/hareall.c               |    8 +--
 src/heftall.c               |    9 +--
 src/lsdall.c                |  113 ++++++++++++++++++++++++++------------------
 src/lspecall.c              |   19 ++++---
 src/nlsd.c                  |   49 +++++++++++--------
 src/polyall.c               |    4 +
 src/polymars.c              |   61 +++--------------------
 src/registerDynamicSymbol.c |only
 13 files changed, 158 insertions(+), 161 deletions(-)

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New package MODISTools with initial version 1.0.0
Package: MODISTools
Title: Interface to the 'MODIS Land Products Subsets' Web Services
Version: 1.0.0
Authors@R: person("Hufkens","Koen", email="koen.hufkens@gmail.com", role=c("aut", "cre"), comment = c(ORCID = "0000-0002-5070-8109"))
Description: Programmatic interface to the 'MODIS Land Products Subsets' web services (<https://modis.ornl.gov/data/modis_webservice.html>). Allows for easy downloads of 'MODIS' time series directly to your R workspace or your computer.
URL: https://github.com/khufkens/MODISTools
BugReports: https://github.com/khufkens/MODISTools/issues
Depends: R (>= 3.4)
Imports: httr, utils, tidyr, jsonlite
License: AGPL-3
LazyData: true
ByteCompile: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown, covr, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-14 17:41:59 UTC; khufkens
Author: Hufkens Koen [aut, cre] (<https://orcid.org/0000-0002-5070-8109>)
Maintainer: Hufkens Koen <koen.hufkens@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-14 21:58:16 UTC

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New package mixor with initial version 1.0.4
Package: mixor
Type: Package
Title: Mixed-Effects Ordinal Regression Analysis
Version: 1.0.4
Date: 2018-06-13
Author: Kellie J. Archer, Donald Hedeker, Rachel Nordgren, Robert D. Gibbons
Maintainer: Kellie J. Archer <archer.43@osu.edu>
Description: Provides the function 'mixor' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
License: GPL (>= 2)
Depends: R (>= 2.10), survival
BuildResaveData: best
Biarch: yes
NeedsCompilation: yes
LazyLoad: true
Packaged: 2018-06-14 16:09:02 UTC; kjarcher
Repository: CRAN
Date/Publication: 2018-06-14 21:17:56 UTC

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Package LogicReg updated to version 1.5.10 with previous version 1.5.9 dated 2016-08-30

Title: Logic Regression
Description: Routines for fitting Logic Regression models.
Author: Charles Kooperberg <clk@fredhutch.org> and Ingo Ruczinski <ingo@jhu.edu>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between LogicReg versions 1.5.9 dated 2016-08-30 and 1.5.10 dated 2018-06-14

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 MD5                         |   10 ++++++----
 NAMESPACE                   |    2 +-
 man/LogicReg-internal.Rd    |only
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New package eseis with initial version 0.4.0
Package: eseis
Type: Package
Title: Environmental Seismology Toolbox
Version: 0.4.0
Date: 2018-06-14
Authors@R: c( person("Michael", "Dietze", role = c("cre", "aut", "trl"), email = "mdietze@gfz-potsdam.de"), person("Christoph", "Burow", role = c("ctb")), person("Sophie", "Lagarde", role = c("ctb", "trl")))
Maintainer: Michael Dietze <mdietze@gfz-potsdam.de>
Description: Environmental seismology is a scientific field that studies the seismic signals, emitted by Earth surface processes. This package provides all relevant functions to read/write seismic data files, prepare, analyse and visualise seismic data, and generate reports of the processing history.
License: GPL-3
Depends: R (>= 3.4.0)
LinkingTo: Rcpp (>= 0.12.5)
Imports: sp, multitaper, raster, rgdal, caTools, signal, fftw, matrixStats, methods, IRISSeismic, XML, rmarkdown, Rcpp (>= 0.12.5)
Suggests: plot3D, rgl
SystemRequirements: gipptools dataselect
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-06-14 21:26:51 UTC; mdietze
Author: Michael Dietze [cre, aut, trl], Christoph Burow [ctb], Sophie Lagarde [ctb, trl]
Repository: CRAN
Date/Publication: 2018-06-14 23:39:08

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Package docxtools updated to version 0.1.2 with previous version 0.1.1 dated 2017-03-12

Title: Tools for R Markdown to Docx Documents
Description: A set of helper functions for using R Markdown to create documents in docx format, especially documents for use in a classroom or workshop setting.
Author: Richard Layton [aut, cre]
Maintainer: Richard Layton <graphdoctor@gmail.com>

Diff between docxtools versions 0.1.1 dated 2017-03-12 and 0.1.2 dated 2018-06-14

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New package coxed with initial version 0.1.1
Package: coxed
Type: Package
Title: Duration-Based Quantities of Interest for the Cox Proportional Hazards Model
Version: 0.1.1
Depends: R (>= 2.13.0), rms, survival, mgcv
Authors@R: c( person("Kropko,", "Jonathan", email = "jkropko@virginia.edu", role = c("aut", "cre")), person("Harden,", "Jeffrey J.", email = "jeff.harden@nd.edu", role = c("aut")))
Description: Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model.
License: GPL-2
Encoding: UTF-8
URL: https://github.com/jkropko/coxed
LazyData: true
Imports: PermAlgo, dplyr, tidyr, ggplot2, gridExtra, utils
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-14 18:38:31 UTC; jk8sd
Author: Kropko, Jonathan [aut, cre], Harden, Jeffrey J. [aut]
Maintainer: "Kropko, Jonathan" <jkropko@virginia.edu>
Repository: CRAN
Date/Publication: 2018-06-14 21:39:48 UTC

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Package BHTSpack updated to version 0.3 with previous version 0.2 dated 2018-06-12

Title: Bayesian Multi-Plate High-Throughput Screening of Compounds
Description: Can be used for joint identification of candidate compound hits from multiple assays, in drug discovery. This package implements the framework of I. D. Shterev, D. B. Dunson, C. Chan and G. D. Sempowski. "Bayesian Multi-Plate High-Throughput Screening of Compounds", Scientific Reports (to appear). This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
Author: Ivo D. Shterev [aut, cre], David B. Dunson [aut], Cliburn Chan [aut], Gregory D. Sempowski [aut]
Maintainer: Ivo D. Shterev <i.shterev@duke.edu>

Diff between BHTSpack versions 0.2 dated 2018-06-12 and 0.3 dated 2018-06-14

 DESCRIPTION                 |    6 +++---
 MD5                         |   10 +++++-----
 build/partial.rdb           |binary
 inst/doc/BHTSpackManual.pdf |binary
 man/h.pr.u.Rd               |    2 +-
 src/BHTSpack.cpp            |    2 +-
 6 files changed, 10 insertions(+), 10 deletions(-)

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Package logspline updated to version 2.1.11 with previous version 2.1.10 dated 2018-06-02

Title: Routines for Logspline Density Estimation
Description: Contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e. the Kooperberg and Stone (1992) <DOI:10.2307/1390786> algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997) <DOI:10.1214/aos/1031594728>.
Author: Charles Kooperberg <clk@fredhutch.org>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between logspline versions 2.1.10 dated 2018-06-02 and 2.1.11 dated 2018-06-14

 DESCRIPTION               |    8 +++---
 MD5                       |   10 +++----
 R/logspline.R             |    2 -
 man/logspline-internal.Rd |    4 +--
 src/lsdall.c              |   60 +++++++++++++++++++++++++++++++---------------
 src/nlsd.c                |    5 ++-
 6 files changed, 56 insertions(+), 33 deletions(-)

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New package jeek with initial version 1.0.0
Package: jeek
Type: Package
Date: 2018-06-15
Title: A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Version: 1.0.0
Authors@R: c(person("Beilun", "Wang", role = c("aut", "cre"), email = "bw4mw@virginia.edu"), person("Yanjun", "Qi", role = "aut", email = "yanjun@virginia.edu"))
Author: Beilun Wang [aut, cre], Yanjun Qi [aut]
Maintainer: Beilun Wang <bw4mw@virginia.edu>
Description: Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeekDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) <arXiv:1806.00548>.
Depends: R (>= 3.0.0), lpSolve, pcaPP, igraph
Suggests: parallel
License: GPL-2
Encoding: UTF-8
URL: https://github.com/QData/jeek
BugReports: https://github.com/QData/jeek
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-13 14:53:50 UTC; beilunwang
Repository: CRAN
Date/Publication: 2018-06-14 19:24:50 UTC

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New package genesysr with initial version 0.9.1
Package: genesysr
Version: 0.9.1
Title: Genesys PGR Client
Description: Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>). Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.
Authors@R: c(person(family = "Global Crop Diversity Trust", role = c("cph")), person("Matija", "Obreza", email = "matija.obreza@croptrust.org", role = c("aut", "cre")), person("Nora", "Castaneda", email = "nora.castaneda@croptrust.org", role = c("ctb")))
Maintainer: Matija Obreza <matija.obreza@croptrust.org>
Depends: R (>= 3.1.0)
Imports: httr, jsonlite
License: Apache License 2.0
RoxygenNote: 6.0.1
URL: https://gitlab.croptrust.org/genesys-pgr/genesysr
BugReports: https://gitlab.croptrust.org/genesys-pgr/genesysr/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-06-12 19:12:29 UTC; mobreza
Author: Global Crop Diversity Trust [cph], Matija Obreza [aut, cre], Nora Castaneda [ctb]
Repository: CRAN
Date/Publication: 2018-06-14 19:24:54 UTC

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New package varjmcm with initial version 0.1.0
Package: varjmcm
Type: Package
Title: Estimations for the Covariance of Estimated Parameters in Joint Mean-Covariance Models
Version: 0.1.0
Authors@R: c(person("Naimin", "Jing", email = "naimin.jing@temple.edu", role = c("aut", "cre")), person("Hexin", "Bai", role = "aut"), person("Tong", "Wang", role = "aut"), person("Cheng Yong", "Tang", role = "aut"))
Description: The goal of the package is to equip the 'jmcm' package (current version 0.1.8.0) with estimations of the covariance of estimated parameters. Two methods are provided. The first method is to use the inverse of estimated Fisher's information matrix, see M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) <doi:10.1214/aos/1176351062> for reference.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: jmcm
Imports: expm, MASS, stats, Matrix
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-12 16:19:38 UTC; jnm
Author: Naimin Jing [aut, cre], Hexin Bai [aut], Tong Wang [aut], Cheng Yong Tang [aut]
Maintainer: Naimin Jing <naimin.jing@temple.edu>
Repository: CRAN
Date/Publication: 2018-06-14 19:00:00 UTC

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New package stockR with initial version 1.0.68
Package: stockR
Title: Identifying Stocks in Genetic Data
Version: 1.0.68
Authors@R: c(person("Scott D.", "Foster", role = c("aut", "cre"), email = "scott.foster@csiro.au"))
Author: Scott D. Foster [aut, cre]
Description: Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after find good starting values via a K-means clustering of the genetic data. Details can be found in Foster, Feutry, Grewe, Berry, Hui, Davies (in press) Reliably Discriminating Stock Structure with Genetic Markers: Mixture Models with Robust and Fast Computation. Molecular Ecology Resources.
Maintainer: Scott D. Foster <scott.foster@csiro.au>
License: GPL (>= 2)
Imports: stats, gtools, parallel
Suggests: knitr
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2018-06-12 01:02:44 UTC; fos085
Depends: R (>= 2.10)
Repository: CRAN
Date/Publication: 2018-06-14 18:32:57 UTC

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Package splines2 updated to version 0.2.8 with previous version 0.2.7 dated 2017-12-01

Title: Regression Spline Functions and Classes
Description: Constructs B-splines and its integral, monotone splines (M-splines) and its integral (I-splines), convex splines (C-splines), and their derivatives of given order. Piecewise constant basis is allowed for B-splines and M-splines. See De Boor (1978) <doi:10.1002/zamm.19800600129>, Ramsay (1988) <doi:10.1214/ss/1177012761>, and Meyer (2008) <doi:10.1214/08-AOAS167> for more information.
Author: Wenjie Wang [aut, cre], Jun Yan [aut]
Maintainer: Wenjie Wang <wenjie.2.wang@uconn.edu>

Diff between splines2 versions 0.2.7 dated 2017-12-01 and 0.2.8 dated 2018-06-14

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Package smacof updated to version 1.10-8 with previous version 1.10-5 dated 2018-03-20

Title: Multidimensional Scaling
Description: Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, inverse MDS, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Author: Patrick Mair [aut, cre], Jan De Leeuw [aut], Ingwer Borg [ctb], Patrick J. F. Groenen [ctb]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

Diff between smacof versions 1.10-5 dated 2018-03-20 and 1.10-8 dated 2018-06-14

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Package semds updated to version 0.9-4 with previous version 0.9-3 dated 2018-05-26

Title: Structural Equation Multidimensional Scaling
Description: Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within an SEM framework while the objects are represented in a low-dimensional space as in MDS.
Author: Patrick Mair [aut, cre], Jose Fernando Vera [aut]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

Diff between semds versions 0.9-3 dated 2018-05-26 and 0.9-4 dated 2018-06-14

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New package nparMD with initial version 0.1.0
Package: nparMD
Type: Package
Title: Nonparametric Analysis of Multivariate Data in Factorial Designs
Version: 0.1.0
Depends: R (>= 3.1.0)
Imports: matrixStats, matrixcalc, MASS, gtools, Formula, methods, stats
Author: Maximilian Kiefel and Arne C. Bathke
Maintainer: Maximilian Kiefel <physik210@hotmail.com>
Description: Analysis of multivariate data with two-way completely randomized factorial design. The analysis is based on fully nonparametric, rank-based methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda, Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The package offers two functions performing the analysis, one for small and the other for large sample sizes. The underlying methodology is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8>.
License: GPL-2 | GPL-3
Encoding: UTF-8
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-11 13:57:59 UTC; Maxi
Repository: CRAN
Date/Publication: 2018-06-14 18:33:00 UTC

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New package morpheus with initial version 0.2-0
Package: morpheus
Title: Estimate Parameters of Mixtures of Logistic Regressions
Description: Mixture of logistic regressions parameters (H)estimation with (U)spectral methods. The main methods take d-dimensional inputs and a vector of binary outputs, and return parameters according to the GLMs mixture model (General Linear Model). For more details see chapter 3 in the PhD thesis of Mor-Absa Loum: <http://www.theses.fr/s156435>, available here <https://www.math.u-psud.fr/~loum/IMG/pdf/these.compressed-2.pdf>.
Version: 0.2-0
Author: Benjamin Auder <Benjamin.Auder@u-psud.fr> [aut,cre], Mor-Absa Loum <Mor-Absa.Loum@u-psud.fr> [aut]
Maintainer: Benjamin Auder <Benjamin.Auder@u-psud.fr>
Depends: R (>= 3.0.0),
Imports: MASS, jointDiag, methods, pracma
Suggests: devtools, flexmix, parallel, testthat, roxygen2, tensor, nloptr
License: MIT + file LICENSE
RoxygenNote: 5.0.1
Collate: 'utils.R' 'A_NAMESPACE.R' 'computeMu.R' 'multiRun.R' 'optimParams.R' 'plot.R' 'sampleIO.R'
NeedsCompilation: yes
Packaged: 2018-06-12 12:24:17 UTC; auder
Repository: CRAN
Date/Publication: 2018-06-14 18:48:14 UTC

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New package mase with initial version 0.1.1
Package: mase
Type: Package
Title: Model-Assisted Survey Estimators
Version: 0.1.1
Date: 2018-06-06
Authors@R: c(person("Kelly", "McConville", role = c("aut", "cre", "cph"), email = "mcconville@reed.edu"), person("Becky", "Tang", role = "aut"), person("George", "Zhu", role = "aut"), person("Sida", "Li", role = "ctb"), person("Shirley", "Chueng", role = "ctb"), person("Daniell", "Toth", role = c("ctb", "cph"), comment = "Author and copyright holder of treeDesignMatrix helper function") )
Maintainer: Kelly McConville <mcconville@reed.edu>
Description: A set of model-assisted survey estimators and corresponding variance estimators for single stage, unequal probability, without replacement sampling designs. All of the estimators can be written as a generalized regression estimator with the Horvitz-Thompson, ratio, post-stratified, and regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6). Two of the estimators employ a statistical learning model as the assisting model: the elastic net regression estimator, which is an extension of the lasso regression estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>. The variance estimators which approximate the joint inclusion probabilities can be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the bootstrap variance estimator is presented in Mashreghi et al. (2016) <doi:10.1214/16-SS113>.
License: GPL-2
LazyData: TRUE
Imports: MASS, glmnet, Matrix, foreach, survey, dplyr, magrittr, rpms, boot, stats, Rdpack
Suggests: roxygen2, testthat
Depends: R (>= 3.1)
Collate: 'gregt.R' 'varMase.R' 'GREG.R' 'gregElasticNett.R' 'gregElasticNet.R' 'gregTree.R' 'gregtreet.R' 'htt.R' 'horvitzThompson.R' 'logisticGregElasticNett.R' 'logisticGregt.R' 'postStratt.R' 'postStrat.R' 'ratioEstimatort.R' 'ratioEstimator.R' 'treeDesignMatrix.R'
RoxygenNote: 6.0.1
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2018-06-11 19:21:21 UTC; kswatmac
Author: Kelly McConville [aut, cre, cph], Becky Tang [aut], George Zhu [aut], Sida Li [ctb], Shirley Chueng [ctb], Daniell Toth [ctb, cph] (Author and copyright holder of treeDesignMatrix helper function)
Repository: CRAN
Date/Publication: 2018-06-14 18:33:03 UTC

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New package liger with initial version 0.1
Package: liger
Type: Package
Title: Lightweight Iterative Geneset Enrichment
Version: 0.1
Authors@R: c(person("Jean", "Fan", role = c("aut", "cre"), email = "jeanfan@fas.harvard.edu", comment = c(ORCID = "0000-0002-0212-5451")), person("Peter", "Kharchenko", role = "aut", email = "Peter_Kharchenko@hms.harvard.edu"))
Description: Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. The original algorithm is detailed in Subramanian et al. with 'Java' implementations available through the Broad Institute (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>). The 'liger' package provides a lightweight R implementation of this enrichment test on a list of values (Fan et al., 2017 <doi:10.5281/zenodo.887386>). Given a list of values, such as p-values or log-fold changes derived from differential expression analysis or other analyses comparing biological states, this package enables you to test a priori defined set of genes for enrichment to enable interpretability of highly significant or high fold-change genes.
License: GPL-3 | file LICENSE
LazyData: TRUE
Depends: R (>= 2.10)
Imports: graphics, stats, Rcpp, matrixStats, parallel
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/JEFworks/liger
BugReports: https://github.com/JEFworks/liger/issues
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-06-12 13:38:26 UTC; Jean
Author: Jean Fan [aut, cre] (<https://orcid.org/0000-0002-0212-5451>), Peter Kharchenko [aut]
Maintainer: Jean Fan <jeanfan@fas.harvard.edu>
Repository: CRAN
Date/Publication: 2018-06-14 18:33:07 UTC

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New package IPCAPS with initial version 1.1.5
Package: IPCAPS
Type: Package
Title: Iterative Pruning to Capture Population Structure
Version: 1.1.5
Authors@R: c(person(given = "Kridsadakorn", family = "Chaichoompu", email = "kridsadakorn@biostatgen.org", role = c("aut", "cre")), person(given = "Kristel", family = 'Van Steen', role = "aut"), person(given = "Fentaw", family = "Abegaz", role = "aut"), person(given = "Sissades", family = "Tongsima", role = "aut"), person(given = "Philip", family = "Shaw", role = "aut"), person(given = "Anavaj", family = "Sakuntabhai", role = "aut"), person(given = "Luisa", family = "Pereira", role = "aut"))
Description: An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
Depends: R (>= 3.2.4.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Imports: stats,utils,graphics,grDevices,MASS,Matrix,expm,KRIS,fpc,LPCM,apcluster,Rmixmod
Suggests: testthat
BugReports: https://gitlab.com/kris.ccp/ipcaps/issues
URL: https://gitlab.com/kris.ccp/ipcaps
Collate: 'parallelization.R' 'check.stopping.R' 'clustering.mode.R' 'clustering.R' 'data.R' 'export.groups.R' 'get.node.info.R' 'ipcaps-package.R' 'process.each.node.R' 'output.template.R' 'save.html.R' 'save.eigenplots.html.R' 'save.plots.label.html.R' 'save.plots.cluster.html.R' 'save.plots.R' 'postprocess.R' 'preprocess.R' 'ipcaps.R' 'top.discriminator.R'
NeedsCompilation: no
Packaged: 2018-06-11 13:05:39 UTC; kridsadakorn
Author: Kridsadakorn Chaichoompu [aut, cre], Kristel Van Steen [aut], Fentaw Abegaz [aut], Sissades Tongsima [aut], Philip Shaw [aut], Anavaj Sakuntabhai [aut], Luisa Pereira [aut]
Maintainer: Kridsadakorn Chaichoompu <kridsadakorn@biostatgen.org>
Repository: CRAN
Date/Publication: 2018-06-14 18:01:51 UTC

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New package INDperform with initial version 0.1.0
Type: Package
Package: INDperform
Version: 0.1.0
Title: Evaluation of Indicator Performances for Assessing Ecosystem States
Authors@R: c( person("Saskia A.", "Otto", email = "saskia.a.otto@gmail.com", role = c("aut", "cre")), person("Rene", "Plonus", role = "aut"), person("Steffen", "Funk", role = "aut"), person("Alexander", "Keth", role = "aut") )
Description: An implementation of the 7-step approach suggested by Otto et al. (2018) <doi:10.1016/j.ecolind.2017.05.045> to validate ecological state indicators and to select a suite of complimentary and well performing indicators. This suite can be then used to assess the current state of the system in comparison to a reference period. However, the tools in this package are very generic and can be used to test any type of indicator (e.g. social or economic indicators).
URL: https://github.com/saskiaotto/INDperform
BugReports: https://github.com/SaskiaAOtto/INDperform/issues
Encoding: UTF-8
Depends: R(>= 3.3)
Imports: cowplot (>= 0.7.0), dplyr (>= 0.5.0), grDevices (>= 1.8-17), ggplot2 (>= 2.2.1), htmlwidgets (>= 0.8), jsonlite (>= 1.4), lazyeval (>= 0.2.0), magrittr (>= 1.5), mgcv (>= 1.8-17), nlme (>= 3.1-131), purrr (>= 0.2.2), RColorBrewer (>= 1.1-2), rhandsontable (>= 0.3.4), shiny (>= 1.0.1), stringr (>= 1.2.0), tibble (>= 1.3.0), tidyr (>= 0.6.1), vdiffr (>= 0.1.1), vegan (>= 2.4-3)
Suggests: doParallel (>= 1.0.11), ggdendro (>= 0.1-20), gridExtra (>= 2.2.1), knitr (>= 1.16), parallel (>= 3.3.1), pbapply (>= 1.3-3), testthat (>= 1.0.2), tripack (>= 1.3-8)
License: GPL
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-12 18:19:08 UTC; saskiaotto
Author: Saskia A. Otto [aut, cre], Rene Plonus [aut], Steffen Funk [aut], Alexander Keth [aut]
Maintainer: Saskia A. Otto <saskia.a.otto@gmail.com>
Repository: CRAN
Date/Publication: 2018-06-14 19:00:07 UTC

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New package GFORCE with initial version 0.1.2
Package: GFORCE
Type: Package
Title: Clustering and Inference Procedures for High-Dimensional Latent Variable Models
Version: 0.1.2
Authors@R: person("Carson", "Eisenach", email = "eisenach@princeton.edu", role = c("aut", "cre"))
Description: A complete suite of computationally efficient methods for high dimensional clustering and inference problems in G-Latent Models (a type of Latent Variable Gaussian graphical model). The main feature is the FORCE (First-Order, Certifiable, Efficient) clustering algorithm which is a fast solver for a semi-definite programming (SDP) relaxation of the K-means problem. For certain types of graphical models (G-Latent Models), with high probability the algorithm not only finds the optimal clustering, but produces a certificate of having done so. This certificate, however, is model independent and so can also be used to certify data clustering problems. The 'GFORCE' package also contains implementations of inferential procedures for G-Latent graphical models using n-fold cross validation. Also included are native code implementations of other popular clustering methods such as Lloyd's algorithm with kmeans++ initialization and complete linkage hierarchical clustering. The FORCE method is due to Eisenach and Liu (2017) <arxiv:1806.00530>.
License: GPL-2
LazyData: TRUE
RoxygenNote: 6.0.1
Imports: MASS,lpSolve,stats
Suggests: testthat
NeedsCompilation: yes
Packaged: 2018-06-12 16:17:02 UTC; carson
Author: Carson Eisenach [aut, cre]
Maintainer: Carson Eisenach <eisenach@princeton.edu>
Repository: CRAN
Date/Publication: 2018-06-14 19:00:12 UTC

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New package DiPhiSeq with initial version 0.1.0
Package: DiPhiSeq
Type: Package
Title: Robust Tests for Differential Dispersion and Differential Expression in RNA-Sequencing Data
Version: 0.1.0
Authors@R: c(person("Jun", "Li", email = "jun.li@nd.edu", role = c("aut", "cre")), person("Alicia T.", "Lamere", role = c("aut")))
Description: Implements the algorithm described in Jun Li and Alicia T. Lamere, "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes" (Unpublished). Detects not only genes that show different average expressions ("differential expression", DE), but also genes that show different diversities of expressions in different groups ("differentially dispersed", DD). DD genes can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise.
Depends: R (>= 3.1.0)
Imports: stats (>= 3.1.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-06-12 15:15:40 UTC; jun
Author: Jun Li [aut, cre], Alicia T. Lamere [aut]
Maintainer: Jun Li <jun.li@nd.edu>
Repository: CRAN
Date/Publication: 2018-06-14 19:00:17 UTC

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New package corTest with initial version 0.9.6
Package: corTest
Type: Package
Title: Robust Tests for Equal Correlation
Version: 0.9.6
Author: Danyang Yu, Weiliang Qiu
Maintainer: Danyang Yu <danyangyu@hnu.edu.cn>
Description: There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The difference between 6 methods is the difference of U which are proportion to different types of correlation. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the passage New Statistical Methods for Constructing Robust Differential Correlation Networks (expected to be public).
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
Imports: MASS, graphics, stats
NeedsCompilation: no
Packaged: 2018-06-12 12:38:37 UTC; yudan
Repository: CRAN
Date/Publication: 2018-06-14 18:37:57 UTC

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Package breakDown updated to version 0.1.6 with previous version 0.1.5 dated 2018-04-06

Title: Model Agnostic Explainers for Individual Predictions
Description: Model agnostic tool for decomposition of predictions from black boxes. Break Down Table shows contributions of every variable to a final prediction. Break Down Plot presents variable contributions in a concise graphical way. This package work for binary classifiers and general regression models.
Author: Przemyslaw Biecek [aut, cre], Aleksandra Grudziaz [ctb]
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>

Diff between breakDown versions 0.1.5 dated 2018-04-06 and 0.1.6 dated 2018-06-14

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Package Arothron updated to version 1.0.1 with previous version 1.0.0 dated 2018-04-03

Title: Geometric Morphometrics Analyses
Description: Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2018 <doi:10.1002/ajpa.23493>), and functions to import and export the coordinates of landmarks and 3D paths into 'landmarkAscii' and 'am' format files.
Author: Antonio Profico, Alessio Veneziano, Marina Melchionna, Pasquale Raia
Maintainer: Antonio Profico <antonio.profico@uniroma1.it>

Diff between Arothron versions 1.0.0 dated 2018-04-03 and 1.0.1 dated 2018-06-14

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New package jaccard with initial version 0.1.0
Package: jaccard
Type: Package
Title: Test Similarity Between Binary Data using Jaccard/Tanimoto Coefficients
Version: 0.1.0
Date: 2018-06-06
Author: Neo Christopher Chung <nchchung@gmail.com>, Błażej Miasojedow <bmiasojedow@gmail.com>, Michał Startek <M.Startek@mimuw.edu.pl>, Anna Gambin <aniag@mimuw.edu.pl>
Maintainer: Neo Christopher Chung <nchchung@gmail.com>
Description: Calculate statistical significance of Jaccard/Tanimoto similarity coefficients for binary data.
biocViews:
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 0.12.6), qvalue, dplyr, magrittr
LinkingTo: Rcpp
NeedsCompilation: yes
SystemRequirements: C++11
RoxygenNote: 6.0.1
Packaged: 2018-06-10 01:52:22 UTC; nc
Repository: CRAN
Date/Publication: 2018-06-14 17:53:00 UTC

More information about jaccard at CRAN
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Package tuckerR.mmgg updated to version 1.5.1 with previous version 1.5.0 dated 2017-02-23

Title: Three-Mode Principal Components Analysis
Description: Performs Three-Mode Principal Components Analysis, which carries out Tucker Models.
Author: Marta Marticorena [aut], Gustavo Gimenez [cre], Cecilia Gonzalez [ctb], Sergio Bramardi [aut]
Maintainer: Gustavo Gimenez <gustavo.gimenez@faea.uncoma.edu.ar>

Diff between tuckerR.mmgg versions 1.5.0 dated 2017-02-23 and 1.5.1 dated 2018-06-14

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Package radiant.design updated to version 0.9.5 with previous version 0.8.0 dated 2017-04-27

Title: Design Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Design menu includes interfaces for design of experiments, sampling, and sample size calculation. The application extends the functionality in radiant.data.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant.design versions 0.8.0 dated 2017-04-27 and 0.9.5 dated 2018-06-14

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Package tensorflow updated to version 1.8 with previous version 1.5 dated 2018-01-17

Title: R Interface to 'TensorFlow'
Description: Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
Author: JJ Allaire [aut, cre], RStudio [cph, fnd], Yuan Tang [aut, cph] (<https://orcid.org/0000-0001-5243-233X>), Dirk Eddelbuettel [ctb, cph], Nick Golding [ctb, cph], Tomasz Kalinowski [ctb, cph], Google Inc. [ctb, cph] (Examples and Tutorials)
Maintainer: JJ Allaire <jj@rstudio.com>

Diff between tensorflow versions 1.5 dated 2018-01-17 and 1.8 dated 2018-06-14

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Package RcppAlgos updated to version 2.0.2 with previous version 2.0.1 dated 2018-06-10

Title: Tools for Combinatorics and Computational Mathematics
Description: Provides optimized functions implemented in C++ with 'Rcpp' for solving problems in combinatorics and computational mathematics. There are combination/permutations function that are both flexible as well as efficient with respect to speed and memory. There are optional constraint parameters that when utilized, generate all combinations/ permutations of a vector meeting specific criteria (e.g. finding all combinations such that the sum is less than a bound). These functions are capable of generating specific combinations/permutations (e.g. retrieve only the 50th lexicographical permutation of 5 elements choose 3) by utilizing the bounds arguments. This sets up nicely for parallelization and allows for fast generation of combinations/ permutations beyond 2^31 - 1 results as chunks are generated independently. All combinatorial functions are capable of handling multisets as well. Additionally, there are several highly efficient number theoretic functions that are useful for problems common in computational mathematics. These include various sieving functions that can quickly generate the following: prime numbers, number of coprime elements, number of divisors, prime factorizations, and complete factorizations. Some of these functions make use of the fast integer division library 'libdivide' by <http://ridiculousfish.com>. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is capable of generating all primes less than a billion in just over 1 second. It can also quickly generate prime numbers over a range (e.g. primeSieve(10^13, 10^13+10^9)). There are stand-alone vectorized functions for general factoring (e.g. all divisors of number), primality testing, as well as prime factoring via Pollard's rho algorithm. Finally, there is a prime counting function that implements a simple variation of Legendre's formula based on the algorithm by Kim Walisch. It is capable of returning the number of primes below a trillion in under 0.5 seconds.
Author: Joseph Wood
Maintainer: Joseph Wood <jwood000@gmail.com>

Diff between RcppAlgos versions 2.0.1 dated 2018-06-10 and 2.0.2 dated 2018-06-14

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Package jpndistrict updated to version 0.3.2 with previous version 0.3.1 dated 2018-05-01

Title: Create Japanese Administration Area and Office Maps
Description: Utilizing the data that Japanese administration area provided by the National Land Numerical Information download service (<http://nlftp.mlit.go.jp/ksj/index.html>). This package provide map data is based on the Digital Map 25000 (Map Image) published by Geospatial Information Authority of Japan (Approval No.603FY2017 information usage <http://www.gsi.go.jp>).
Author: Shinya Uryu [aut, cre] (<https://orcid.org/0000-0002-0493-6186>), Geospatial Information Authority of Japan [dtc] (This package data sets, National Land numerical information by the Geographical Survey Institute with the approval of Geographical Survey Institute Head (Approval No.603FY2017 information usage))
Maintainer: Shinya Uryu <suika1127@gmail.com>

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Package CGPfunctions updated to version 0.4 with previous version 0.3 dated 2018-04-19

Title: Powell Miscellaneous Functions for Teaching and Learning Statistics
Description: Miscellaneous functions useful for teaching statistics as well as actually practicing the art. They typically are not “new” methods but rather wrappers around either base R or other packages. Currently contains: 'Plot2WayANOVA' which as the name implies conducts a 2 way ANOVA and plots the results using 'ggplot2'. 'neweta' which is a helper function that appends the results of a Type II eta squared calculation onto a classic ANOVA table. Mode which finds the modal value in a vector of data. 'SeeDist' which wraps around 'ggplot2' to provide visualizations of univariate data.
Author: Chuck Powell [aut, cre]
Maintainer: Chuck Powell <ibecav@gmail.com>

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Package progress updated to version 1.2.0 with previous version 1.1.2 dated 2016-12-14

Title: Terminal Progress Bars
Description: Configurable Progress bars, they may include percentage, elapsed time, and/or the estimated completion time. They work in terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the 'macOS' 'R.app'. The package also provides a 'C++' 'API', that works with or without 'Rcpp'.
Author: Gábor Csárdi [aut, cre], Rich FitzJohn [aut]
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

Diff between progress versions 1.1.2 dated 2016-12-14 and 1.2.0 dated 2018-06-14

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Package lue updated to version 0.2.1 with previous version 0.2.0 dated 2018-05-14

Title: Light Use Efficiency Model to Estimate Biomass and YIELD with and Without Vapour Pressure Deficit
Description: Contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.
Author: Maninder Singh Dhillon [aut,cre], Thorsten Dahms [ctb], Leon Nill [ctb]
Maintainer: Maninder Singh Dhillon <manidhillon1989@gmail.com>

Diff between lue versions 0.2.0 dated 2018-05-14 and 0.2.1 dated 2018-06-14

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Package compositions updated to version 1.40-2 with previous version 1.40-1 dated 2014-06-07

Title: Compositional Data Analysis
Description: Provides functions for the consistent analysis of compositional data (e.g. portions of substances) and positive numbers (e.g. concentrations) in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
Author: K. Gerald van den Boogaart <boogaart@hzdr.de>, Raimon Tolosana-Delgado, Matevz Bren
Maintainer: K. Gerald van den Boogaart <support@boogaart.de>

Diff between compositions versions 1.40-1 dated 2014-06-07 and 1.40-2 dated 2018-06-14

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Package wnl updated to version 0.4.1 with previous version 0.4.0 dated 2018-04-02

Title: Minimization Tool for Pharmacokinetic-Pharmacodynamic Data Analysis
Description: This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.
Author: Kyun-Seop Bae [aut]
Maintainer: Kyun-Seop Bae <k@acr.kr>

Diff between wnl versions 0.4.0 dated 2018-04-02 and 0.4.1 dated 2018-06-14

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Package lingtypology updated to version 1.0.13 with previous version 1.0.12 dated 2018-03-10

Title: Linguistic Typology and Mapping
Description: Provides R with the Glottolog database <http://glottolog.org> and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project <http://clld.org/>, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages <https://ropensci.github.io/lingtypology/> and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website.
Author: George Moroz
Maintainer: George Moroz <agricolamz@gmail.com>

Diff between lingtypology versions 1.0.12 dated 2018-03-10 and 1.0.13 dated 2018-06-14

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Package qpcR updated to version 1.4-1 with previous version 1.4-0 dated 2014-09-15

Title: Modelling and Analysis of Real-Time PCR Data
Description: Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
Author: Andrej-Nikolai Spiess <a.spiess@uke.uni-hamburg.de>
Maintainer: Andrej-Nikolai Spiess <a.spiess@uke.uni-hamburg.de>

Diff between qpcR versions 1.4-0 dated 2014-09-15 and 1.4-1 dated 2018-06-14

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Package visNetwork updated to version 2.0.4 with previous version 2.0.3 dated 2018-01-09

Title: Network Visualization using 'vis.js' Library
Description: Provides an R interface to the 'vis.js' JavaScript charting library. It allows an interactive visualization of networks.
Author: Almende B.V. [aut, cph] (vis.js library in htmlwidgets/lib, http://visjs.org, http://www.almende.com/home), Benoit Thieurmel [aut, cre] (R interface), Titouan Robert [aut, ctb]
Maintainer: Benoit Thieurmel <benoit.thieurmel@datastorm.fr>

Diff between visNetwork versions 2.0.3 dated 2018-01-09 and 2.0.4 dated 2018-06-14

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Package MVN updated to version 5.5 with previous version 5.4 dated 2018-06-09

Title: Multivariate Normality Tests
Description: Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests (Korkmaz et al, (2014), <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).
Author: Selcuk Korkmaz [aut, cre], Dincer Goksuluk [aut], Gokmen Zararsiz [aut]
Maintainer: Selcuk Korkmaz <selcukorkmaz@gmail.com>

Diff between MVN versions 5.4 dated 2018-06-09 and 5.5 dated 2018-06-14

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Package TPD updated to version 1.0.0 with previous version 0.1.2 dated 2017-11-09

Title: Methods for Measuring Functional Diversity Based on Trait Probability Density
Description: Tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.
Author: Carlos P. Carmona <perezcarmonacarlos@gmail.com>
Maintainer: Carlos P. Carmona <perezcarmonacarlos@gmail.com>

Diff between TPD versions 0.1.2 dated 2017-11-09 and 1.0.0 dated 2018-06-14

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Package nimble updated to version 0.6-11 with previous version 0.6-10 dated 2018-03-25

Title: MCMC, Particle Filtering, and Programmable Hierarchical Modeling
Description: A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <http://r-nimble.org>.
Author: Perry de Valpine, Christopher Paciorek, Daniel Turek, Cliff Anderson-Bergman, Nick Michaud, Fritz Obermeyer, Duncan Temple Lang, and see AUTHORS file for additional contributors.
Maintainer: Christopher Paciorek <paciorek@stat.berkeley.edu>

Diff between nimble versions 0.6-10 dated 2018-03-25 and 0.6-11 dated 2018-06-14

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Package ggfan updated to version 0.1.2 with previous version 0.1.1 dated 2017-11-22

Title: Summarise a Distribution Through Coloured Intervals
Description: Implements the functionality of the 'fanplot' package as 'geoms' for 'ggplot2'. Designed for summarising MCMC samples from a posterior distribution, where a visualisation is desired for several values of a continuous covariate. Increasing posterior intervals of the sampled quantity are mapped to a continuous colour scale.
Author: Jason Hilton [aut, cre]
Maintainer: Jason Hilton <jason_hilton@yahoo.com>

Diff between ggfan versions 0.1.1 dated 2017-11-22 and 0.1.2 dated 2018-06-14

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Permanent link

Package ref (with last version 0.99) was removed from CRAN

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

2013-12-05 0.99
2012-08-09 0.98
2009-10-11 0.97
2009-04-15 0.96
2007-10-22 0.95
2007-10-21 0.94-1
2007-08-25 0.94
2006-04-27 0.92

Permanent link
Package tree.bins updated to version 0.1.1 with previous version 0.1.0 dated 2018-02-26

Title: Recategorization of Factor Variables by Decision Tree Leaves
Description: Provides users the ability to categorize categorical variables dependent on a response variable. It creates a decision tree by using one of the categorical variables (class factor) and the selected response variable. The decision tree is created from the rpart() function from the 'rpart' package. The rules from the leaves of the decision tree are extracted, and used to recategorize the appropriate categorical variable (predictor). This step is performed for each of the categorical variables that is fed into the data component of the function. Only variables containing more than 2 factor levels will be considered in the function. The final output generates a data set containing the recategorized variables or a list containing a mapping table for each of the candidate variables. For more details see T. Hastie et al (2009, ISBN: 978-0-387-84857-0).
Author: Piro Polo [aut, cre]
Maintainer: Piro Polo <piropolo98@gmail.com>

Diff between tree.bins versions 0.1.0 dated 2018-02-26 and 0.1.1 dated 2018-06-14

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Package testit updated to version 0.8 with previous version 0.7 dated 2017-05-22

Title: A Simple Package for Testing R Packages
Description: Provides two convenience functions assert() and test_pkg() to facilitate testing R packages.
Author: Yihui Xie [aut, cre] (<https://orcid.org/0000-0003-0645-5666>)
Maintainer: Yihui Xie <xie@yihui.name>

Diff between testit versions 0.7 dated 2017-05-22 and 0.8 dated 2018-06-14

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Package spatstat.utils updated to version 1.8-2 with previous version 1.8-0 dated 2017-11-20

Title: Utility Functions for 'spatstat'
Description: Contains utility functions for the 'spatstat' package which may also be useful for other purposes.
Author: Adrian Baddeley [aut, cre], Rolf Turner [aut], Ege Rubak [aut]
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat.utils versions 1.8-0 dated 2017-11-20 and 1.8-2 dated 2018-06-14

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Package polyclip updated to version 1.9-0 with previous version 1.8-7 dated 2018-05-16

Title: Polygon Clipping
Description: R port of Angus Johnson's open source library Clipper. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Author: Angus Johnson [aut] (C++ original, http://www.angusj.com/delphi/clipper.php), Adrian Baddeley [aut, trl, cre], Kurt Hornik [ctb], Brian D. Ripley [ctb], Elliott Sales de Andrade [ctb]
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

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Package expss updated to version 0.8.7 with previous version 0.8.6 dated 2018-01-24

Title: Tables with Labels and Some Useful Functions from Spreadsheets and 'SPSS' Statistics
Description: Package provides tabulation functions with support for 'SPSS'-style labels, multiple / nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in 'knitr', R notebooks, 'Shiny' and 'Jupyter' notebooks. Proper methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package offers useful functions for data processing in marketing research / social surveys - popular data transformation functions from 'SPSS' Statistics ('RECODE', 'COUNT', 'COMPUTE', 'DO IF', etc.) and 'Excel' ('COUNTIF', 'VLOOKUP', etc.). Package is intended to help people to move data processing from 'Excel'/'SPSS' to R.
Author: Gregory Demin [aut, cre]
Maintainer: Gregory Demin <gdemin@gmail.com>

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Wed, 13 Jun 2018

Package spsurvey updated to version 3.4 with previous version 3.3 dated 2016-08-19

Title: Spatial Survey Design and Analysis
Description: This group of functions implements algorithms for design and analysis of probability surveys. The functions are tailored for Generalized Random Tessellation Stratified survey designs.
Author: Tom Kincaid [aut, cre], Tony Olsen [aut], Don Stevens [ctb], Christian Platt [ctb], Denis White [ctb], Richard Remington [ctb]
Maintainer: Tom Kincaid <Kincaid.Tom@epa.gov>

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Package mboost updated to version 2.9-0 with previous version 2.8-1 dated 2017-07-23

Title: Model-Based Boosting
Description: Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
Author: Torsten Hothorn [aut] (<https://orcid.org/0000-0001-8301-0471>), Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut, cre] (<https://orcid.org/0000-0003-2810-3186>), Fabian Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [ctb]
Maintainer: Benjamin Hofner <benjamin.hofner@pei.de>

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Package driftR updated to version 1.1.0 with previous version 1.0.0 dated 2017-12-18

Title: Drift Correcting Water Quality Data
Description: A tidy implementation of equations that correct for instrumental drift in continuous water quality monitoring data. There are many sources of water quality data including private (ex: YSI instruments) and open source (ex: USGS and NDBC), each of which are susceptible to errors/inaccuracies due to drift. This package allows the user to correct their data using one or two standard reference values in a uniform, reproducible way. The equations implemented are from Hasenmueller (2011) <doi:10.7936/K7N014KS>.
Author: Andrew Shaughnessy [aut, cre], Christopher Prener [aut], Elizabeth Hasenmueller [aut]
Maintainer: Andrew Shaughnessy <andrew.shaughnessy@slu.edu>

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Built and running on Debian GNU/Linux using R, littler and blosxom. Styled with Bootstrap.