Mon, 30 Apr 2018

Package sparklyr updated to version 0.8.0 with previous version 0.7.0 dated 2018-01-23

Title: R Interface to Apache Spark
Description: R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.
Author: Javier Luraschi [aut, cre], Kevin Kuo [aut] (<https://orcid.org/0000-0001-7803-7901>), Kevin Ushey [aut], JJ Allaire [aut], RStudio [cph], The Apache Software Foundation [aut, cph]
Maintainer: Javier Luraschi <javier@rstudio.com>

Diff between sparklyr versions 0.7.0 dated 2018-01-23 and 0.8.0 dated 2018-04-30

 sparklyr-0.7.0/sparklyr/inst/livy/spark-1.5.2/livyutils.scala                             |only
 sparklyr-0.7.0/sparklyr/java/spark-1.6.0/rdd.scala                                        |only
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 sparklyr-0.8.0/sparklyr/MD5                                                               |  395 -
 sparklyr-0.8.0/sparklyr/NAMESPACE                                                         |   26 
 sparklyr-0.8.0/sparklyr/NEWS.md                                                           |  109 
 sparklyr-0.8.0/sparklyr/R/connection_shinyapp.R                                           |    2 
 sparklyr-0.8.0/sparklyr/R/connection_spark.R                                              |   22 
 sparklyr-0.8.0/sparklyr/R/core_jobj.R                                                     |    1 
 sparklyr-0.8.0/sparklyr/R/core_serialize.R                                                |   20 
 sparklyr-0.8.0/sparklyr/R/core_worker_config.R                                            |    4 
 sparklyr-0.8.0/sparklyr/R/data_interface.R                                                |    2 
 sparklyr-0.8.0/sparklyr/R/dplyr_sql.R                                                     |   28 
 sparklyr-0.8.0/sparklyr/R/install_spark_versions.R                                        |   22 
 sparklyr-0.8.0/sparklyr/R/install_tools.R                                                 |    1 
 sparklyr-0.8.0/sparklyr/R/livy_connection.R                                               |  130 
 sparklyr-0.8.0/sparklyr/R/livy_install.R                                                  |   24 
 sparklyr-0.8.0/sparklyr/R/livy_sources.R                                                  |   23 
 sparklyr-0.8.0/sparklyr/R/ml_classification_decision_tree_classifier.R                    |    7 
 sparklyr-0.8.0/sparklyr/R/ml_classification_gbt_classifier.R                              |   17 
 sparklyr-0.8.0/sparklyr/R/ml_classification_linear_svc.R                                  |    5 
 sparklyr-0.8.0/sparklyr/R/ml_classification_logistic_regression.R                         |   43 
 sparklyr-0.8.0/sparklyr/R/ml_classification_multilayer_perceptron_classifier.R            |    3 
 sparklyr-0.8.0/sparklyr/R/ml_classification_naive_bayes.R                                 |    7 
 sparklyr-0.8.0/sparklyr/R/ml_classification_one_vs_rest.R                                 |    3 
 sparklyr-0.8.0/sparklyr/R/ml_classification_random_forest_classifier.R                    |    8 
 sparklyr-0.8.0/sparklyr/R/ml_clustering_bisecting_kmeans.R                                |    3 
 sparklyr-0.8.0/sparklyr/R/ml_clustering_gaussian_mixture.R                                |    3 
 sparklyr-0.8.0/sparklyr/R/ml_clustering_kmeans.R                                          |   16 
 sparklyr-0.8.0/sparklyr/R/ml_clustering_lda.R                                             |   28 
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 sparklyr-0.8.0/sparklyr/R/ml_evaluator.R                                                  |  232 
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 sparklyr-0.8.0/sparklyr/R/ml_feature_pca.R                                                |   12 
 sparklyr-0.8.0/sparklyr/R/ml_feature_quantile_discretizer_bucketizer.R                    |  124 
 sparklyr-0.8.0/sparklyr/R/ml_feature_stop_words_remover.R                                 |    4 
 sparklyr-0.8.0/sparklyr/R/ml_feature_string_indexer.R                                     |   34 
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 sparklyr-0.8.0/sparklyr/R/ml_mapping_tables.R                                             |   15 
 sparklyr-0.8.0/sparklyr/R/ml_model_constructors.R                                         |    2 
 sparklyr-0.8.0/sparklyr/R/ml_model_decision_tree.R                                        |    2 
 sparklyr-0.8.0/sparklyr/R/ml_model_gradient_boosted_trees.R                               |    4 
 sparklyr-0.8.0/sparklyr/R/ml_model_random_forest.R                                        |   13 
 sparklyr-0.8.0/sparklyr/R/ml_model_utils.R                                                |   30 
 sparklyr-0.8.0/sparklyr/R/ml_persistence.R                                                |    6 
 sparklyr-0.8.0/sparklyr/R/ml_print_utils.R                                                |   41 
 sparklyr-0.8.0/sparklyr/R/ml_regression_aft_survival_regression.R                         |    5 
 sparklyr-0.8.0/sparklyr/R/ml_regression_decision_tree_regressor.R                         |    8 
 sparklyr-0.8.0/sparklyr/R/ml_regression_gbt_regressor.R                                   |   17 
 sparklyr-0.8.0/sparklyr/R/ml_regression_generalized_linear_regression.R                   |   17 
 sparklyr-0.8.0/sparklyr/R/ml_regression_isotonic_regression.R                             |    3 
 sparklyr-0.8.0/sparklyr/R/ml_regression_linear_regression.R                               |   19 
 sparklyr-0.8.0/sparklyr/R/ml_regression_random_forest_regressor.R                         |    9 
 sparklyr-0.8.0/sparklyr/R/ml_stat.R                                                       |only
 sparklyr-0.8.0/sparklyr/R/ml_transformation_methods.R                                     |   41 
 sparklyr-0.8.0/sparklyr/R/ml_tuning.R                                                     |   37 
 sparklyr-0.8.0/sparklyr/R/ml_tuning_cross_validator.R                                     |   18 
 sparklyr-0.8.0/sparklyr/R/ml_tuning_train_validation_split.R                              |   18 
 sparklyr-0.8.0/sparklyr/R/ml_validator_feature.R                                          |   10 
 sparklyr-0.8.0/sparklyr/R/mutation.R                                                      |   30 
 sparklyr-0.8.0/sparklyr/R/shell_connection.R                                              |    8 
 sparklyr-0.8.0/sparklyr/R/spark_apply.R                                                   |   44 
 sparklyr-0.8.0/sparklyr/R/spark_compile.R                                                 |    7 
 sparklyr-0.8.0/sparklyr/R/spark_compile_embedded.R                                        |   10 
 sparklyr-0.8.0/sparklyr/R/spark_connection.R                                              |   17 
 sparklyr-0.8.0/sparklyr/R/spark_debug_backend.R                                           |    2 
 sparklyr-0.8.0/sparklyr/R/spark_utils.R                                                   |    4 
 sparklyr-0.8.0/sparklyr/R/sysdata.rda                                                     |binary
 sparklyr-0.8.0/sparklyr/R/worker_apply.R                                                  |   17 
 sparklyr-0.8.0/sparklyr/R/worker_main.R                                                   |   14 
 sparklyr-0.8.0/sparklyr/R/yarn_cluster.R                                                  |    4 
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 sparklyr-0.8.0/sparklyr/inst/extdata/versions.json                                        |   12 
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 sparklyr-0.8.0/sparklyr/inst/java/sparklyr-1.6-2.10.jar                                   |binary
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 sparklyr-0.8.0/sparklyr/inst/livy/spark-1.5.2/backend.scala                               |only
 sparklyr-0.8.0/sparklyr/inst/livy/spark-1.5.2/channel.scala                               |only
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 sparklyr-0.8.0/sparklyr/inst/livy/spark-1.5.2/invoke.scala                                |   48 
 sparklyr-0.8.0/sparklyr/inst/livy/spark-1.5.2/logger.scala                                |    3 
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 sparklyr-0.8.0/sparklyr/inst/livy/spark-1.5.2/serializer.scala                            |   18 
 sparklyr-0.8.0/sparklyr/inst/livy/spark-1.5.2/stream.scala                                |   92 
 sparklyr-0.8.0/sparklyr/inst/livy/spark-1.5.2/tracker.scala                               |   12 
 sparklyr-0.8.0/sparklyr/inst/livy/spark-1.5.2/utils.scala                                 |   15 
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 sparklyr-0.8.0/sparklyr/inst/livy/spark-2.0.0/livyutils.scala                             |only
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 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/backend.scala                                    |   47 
 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/channel.scala                                    |   38 
 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/handler.scala                                    |   47 
 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/invoke.scala                                     |   48 
 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/serializer.scala                                 |   18 
 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/stream.scala                                     |   92 
 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/test.scala                                       |    4 
 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/tracker.scala                                    |   12 
 sparklyr-0.8.0/sparklyr/java/spark-1.5.2/utils.scala                                      |   15 
 sparklyr-0.8.0/sparklyr/java/spark-1.6.0/classutils.scala                                 |    2 
 sparklyr-0.8.0/sparklyr/java/spark-1.6.0/fileutils.scala                                  |    6 
 sparklyr-0.8.0/sparklyr/java/spark-1.6.0/rscript.scala                                    |   94 
 sparklyr-0.8.0/sparklyr/java/spark-1.6.0/sources.scala                                    | 2432 +++++-----
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 sparklyr-0.8.0/sparklyr/man/ft_binarizer.Rd                                               |    1 
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 sparklyr-0.8.0/sparklyr/man/ft_count_vectorizer.Rd                                        |    8 
 sparklyr-0.8.0/sparklyr/man/ft_dct.Rd                                                     |    1 
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 sparklyr-0.8.0/sparklyr/man/ft_hashing_tf.Rd                                              |    3 
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 sparklyr-0.8.0/sparklyr/man/ft_imputer.Rd                                                 |    1 
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 sparklyr-0.8.0/sparklyr/man/ft_quantile_discretizer.Rd                                    |   20 
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 sparklyr-0.8.0/sparklyr/man/ml_generalized_linear_regression.Rd                           |   10 
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 sparklyr-0.8.0/sparklyr/man/ml_kmeans.Rd                                                  |    8 
 sparklyr-0.8.0/sparklyr/man/ml_lda.Rd                                                     |    3 
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Package sodavis updated to version 1.1 with previous version 1.0 dated 2017-05-29

Title: SODA: Main and Interaction Effects Selection for Logistic Regression, Quadratic Discriminant and General Index Models
Description: Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.
Author: Yang Li, Jun S. Liu
Maintainer: Yang Li <yangli.stat@gmail.com>

Diff between sodavis versions 1.0 dated 2017-05-29 and 1.1 dated 2018-04-30

 DESCRIPTION   |    8 ++++----
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 R/pure_soda.R |    8 ++++----
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More information about sodavis at CRAN
Permanent link

Package shinystan updated to version 2.5.0 with previous version 2.4.0 dated 2017-08-02

Title: Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models
Description: A graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the 'Shiny' web application framework from 'RStudio' and works with the output of MCMC programs written in any programming language (and has extended functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' packages).
Author: Jonah Gabry [aut, cre], Stan Development Team [ctb], Michael Andreae [ctb], Michael Betancourt [ctb], Bob Carpenter [ctb], Yuanjun Gao [ctb], Andrew Gelman [ctb], Ben Goodrich [ctb], Daniel Lee [ctb], Dongying Song [ctb], Rob Trangucci [ctb]
Maintainer: Jonah Gabry <jsg2201@columbia.edu>

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Package mizer updated to version 1.0 with previous version 0.4 dated 2017-12-14

Title: Multi-Species sIZE Spectrum Modelling in R
Description: A set of classes and methods to set up and run multi-species, trait based and community size spectrum ecological models, focused on the marine environment.
Author: Finlay Scott [aut, cph], Julia Blanchard [aut, cph], Ken Andersen [aut, cph], Gustav Delius [ctb, cre], Richard Southwell [ctb]
Maintainer: Gustav Delius <gustav.delius@york.ac.uk>

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Package RSDA updated to version 2.0.4 with previous version 2.0.3 dated 2018-02-14

Title: R to Symbolic Data Analysis
Description: Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Author: Oldemar Rodriguez R. with contributions from Carlos Aguero, Olger Calderon, Roberto Zuniga and Jorge Arce
Maintainer: Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr>

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Package recmap updated to version 0.5.33 with previous version 0.5.31 dated 2018-03-24

Title: Compute the Rectangular Statistical Cartogram
Description: Provides an interface and a C++ implementation of the RecMap MP2 construction heuristic (see 'citation("recmap")' for details). This algorithm draws maps according to a given statistical value (e.g., election results, population or epidemiological data). The basic idea of the RecMap algorithm is that each map region (e.g., different countries) is represented by a rectangle. The area of each rectangle represents the statistical value given as input (maintain zero cartographic error). Documentation about RecMap is provided by a vignette included in this package.
Author: Christian Panse [aut, cre] (<https://orcid.org/0000-0003-1975-3064>)
Maintainer: Christian Panse <Christian.Panse@gmail.com>

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Package originr updated to version 0.3.0 with previous version 0.2.0 dated 2016-12-07

Title: Fetch Species Origin Data from the Web
Description: Get species origin data (whether species is native/invasive) from the following sources on the web: Encyclopedia of Life (<http://eol.org>), Flora 'Europaea' (<http://rbg-web2.rbge.org.uk/FE/fe.html>), Global Invasive Species Database (<http://www.iucngisd.org/gisd>), the Native Species Resolver (<http://bien.nceas.ucsb.edu/bien/tools/nsr/nsr-ws/>), Integrated Taxonomic Information Service (<http://www.itis.gov/>), and Global Register of Introduced and Invasive Species (<http://www.griis.org/>).
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>), Ignasi Bartomeus [aut]
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package getlandsat updated to version 0.2.0 with previous version 0.1.0 dated 2016-08-16

Title: Get Landsat 8 Data from Amazon Public Data Sets
Description: Get Landsat 8 Data from Amazon Web Services ('AWS') public data sets (<https://registry.opendata.aws/landsat-8/>). Includes functions for listing images and fetching them, and handles caching to prevent unnecessary additional requests.
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>)
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package mets updated to version 1.2.3.1 with previous version 1.2.3 dated 2018-02-09

Title: Analysis of Multivariate Event Times
Description: Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Also contains two-stage binomial modelling that can do pairwise odds-ratio dependence modelling based marginal logistic regression models. This is an alternative to the alternating logistic regression approach (ALR).
Author: Klaus K. Holst and Thomas Scheike
Maintainer: Klaus K. Holst <klaus@holst.it>

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Package gamlss.data updated to version 5.0-1 with previous version 5.0-0 dated 2016-10-18

Title: GAMLSS Data
Description: Data used as examples to demonstrate GAMLSS models.
Author: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>, Bob Rigby, Fernanda De Bastiani
Maintainer: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>

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Package jsr223 updated to version 0.3.2 with previous version 0.3.1 dated 2018-04-05

Title: A 'Java' Platform Integration for 'R' with Programming Languages 'Groovy', 'JavaScript', 'JRuby' ('Ruby'), 'Jython' ('Python'), and 'Kotlin'
Description: Provides a high-level integration for the 'Java' platform that makes 'Java' objects easy to use from within 'R'; provides a unified interface to integrate 'R' with several programming languages; and features extensive data exchange between 'R' and 'Java'. The 'jsr223'-supported programming languages include 'Groovy', 'JavaScript', 'JRuby' ('Ruby'), 'Jython' ('Python'), and 'Kotlin'. Any of these languages can use and extend 'Java' classes in natural syntax. Furthermore, solutions developed in any of the 'jsr223'-supported languages are also accessible to 'R' developers. The 'jsr223' package also features callbacks, script compiling, and string interpolation. In all, 'jsr223' significantly extends the computing capabilities of the 'R' software environment.
Author: Floid R. Gilbert [aut, cre], David B. Dahl [aut]
Maintainer: Floid R. Gilbert <floid.r.gilbert@gmail.com>

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Package gamlss updated to version 5.0-8 with previous version 5.0-6 dated 2017-12-11

Title: Generalised Additive Models for Location Scale and Shape
Description: Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Author: Mikis Stasinopoulos [aut, cre, cph], Bob Rigby [aut], Vlasios Voudouris [ctb], Calliope Akantziliotou [ctb], Marco Enea [ctb], Daniil Kiose [ctb]
Maintainer: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>

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Package ddalpha updated to version 1.3.3 with previous version 1.3.2 dated 2018-04-08

Title: Depth-Based Classification and Calculation of Data Depth
Description: Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.
Author: Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut], Stanislav Nagy [aut]
Maintainer: Oleksii Pokotylo <alexey.pokotylo@gmail.com>

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Package mlbgameday updated to version 0.1.2 with previous version 0.1.1 dated 2018-03-15

Title: Tools to Gather Data from Major League Baseball Advanced Media (MLBAM)
Description: Multi-core processing of Gameday data from Major League Baseball Advanced Media <http://gd2.mlb.com/components/game/mlb/>. Additional tools to parallelize large data sets and write them to a database.
Author: Kris Eberwein [aut, cre]
Maintainer: Kris Eberwein <eberwein@knights.ucf.edu>

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Permanent link

Package umx updated to version 2.6.5 with previous version 2.4.0 dated 2018-03-19

Title: Structural Equation and Twin Modeling in R
Description: Quickly create, run, and report structural equation and twin models. See '?umx' for help, and file.show(system.file("NEWS", package="umx")) for NEWS.
Author: Timothy C Bates [aut, cre] (<https://orcid.org/0000-0002-1153-9007>)
Maintainer: Timothy C Bates <timothy.c.bates@gmail.com>

Diff between umx versions 2.4.0 dated 2018-03-19 and 2.6.5 dated 2018-04-30

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More information about umx at CRAN
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Package LatticeKrig updated to version 7.0 with previous version 6.4 dated 2017-05-22

Title: Multiresolution Kriging Based on Markov Random Fields
Description: Methods for the interpolation of large spatial datasets. This package follows a "fixed rank Kriging" approach using a large number of basis functions and provides spatial estimates that are comparable to standard families of covariance functions. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for this. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation.
Author: Douglas Nychka [aut, cre], Dorit Hammerling [aut], Stephan Sain [aut], Nathan Lenssen [aut], Colette Smirniotis [aut]
Maintainer: Douglas Nychka <nychka@ucar.edu>

Diff between LatticeKrig versions 6.4 dated 2017-05-22 and 7.0 dated 2018-04-30

 DESCRIPTION                                |   14 +
 MD5                                        |  132 +++++++-------
 NAMESPACE                                  |    1 
 R/LKinfoCheck.R                            |    6 
 R/LKrig.MLE.R                              |    7 
 R/LKrig.R                                  |    7 
 R/LKrig.basis.R                            |   50 ++++-
 R/LKrig.coef.R                             |   16 +
 R/LKrig.cov.R                              |    5 
 R/LKrig.lnPlike.R                          |    2 
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 R/LKrig.sim.R                              |   11 -
 R/LKrig.sim.conditional.R                  |   15 +
 R/LKrig.traceA.R                           |    3 
 R/LKrigFindLambdaAwght.R                   |   18 +-
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 R/predictSE.LKrig.R                        |   12 +
 R/predictSurface.LKrig.R                   |   24 ++
 R/print.LKinfo.R                           |   27 ++-
 R/print.LKrig.R                            |   21 +-
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 tests/LKrig.LKSphere.test.R                |    2 
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 tests/LKrig.lnPLike.test.R                 |   59 +++++-
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 tests/LKrig.precision.test.Rout.save       |   37 ----
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 tests/LKrig.testFindAwght.R                |   30 ++-
 tests/LKrig.testFindAwght.Rout.save        |   34 +--
 tests/LKrigMarginalVariance.test.R         |    2 
 tests/LKrigMarginalVariance.test.Rout.save |   26 --
 69 files changed, 938 insertions(+), 791 deletions(-)

More information about LatticeKrig at CRAN
Permanent link

New package ITRSelect with initial version 1.0
Package: ITRSelect
Type: Package
Title: Variable Selection for Optimal Individualized Dynamic Treatment Regime
Version: 1.0
Date: 2018-04-19
Author: Chengchun Shi, Ailin Fan, Rui Song and Wenbin Lu
Maintainer: Chengchun Shi <cshi4@ncsu.edu>
Description: Sequential advantage selection (SAS, Fan, Lu and Song, 2016) <arXiv:1405.5239> and penalized A-learning (PAL, Shi, et al., 2018) methods are implement for selecting important variables involved in optimal individualized (dynamic) treatment regime in both single-stage or multi-stage studies.
Imports: Formula, Rglpk, ncvreg, Matrix, svMisc
License: GPL-2
NeedsCompilation: yes
Repository: CRAN
Packaged: 2018-04-30 10:44:44 UTC; spring
Date/Publication: 2018-04-30 18:08:24 UTC

More information about ITRSelect at CRAN
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Package gamlss.dist updated to version 5.0-5 with previous version 5.0-4 dated 2017-12-11

Title: Distributions for Generalized Additive Models for Location Scale and Shape
Description: A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a "log" or a "logit" transformation respectively.
Author: c(person("Mikis", "Stasinopoulos", role = c("aut", "cre", "cph"), email = "d.stasinopoulos@londonmet.ac.uk"), person("Bob", "Rigby", role = c("aut")), person("Calliope", "Akantziliotou", role = "ctb"), person("Vlasios", "Voudouris", role = "ctb", email= "vlasios.voudouris@abm-analytics.com"), person("Gillian", "Heller", role = "ctb"), person("Raydonal", "Ospina", role = "ctb", email= "rospina@ime.usp.br"), person("Nicoletta", "Motpan", role = "ctb"), person("Fiona", "McElduff", role = "ctb"), person("Majid", "Djennad", role = "ctb"), person("Marco", "Enea", role = "ctb"), person("Alexios", "Ghalanos", role = "ctb"), person("Christos", "Argyropoulos", role = "ctb"), person("Almond", "Stocker", role = "ctb") )
Maintainer: Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>

Diff between gamlss.dist versions 5.0-4 dated 2017-12-11 and 5.0-5 dated 2018-04-30

 DESCRIPTION |   15 +++---
 MD5         |   10 ++--
 R/NET.R     |  141 ++++++++++++++++++++++++++++++++++--------------------------
 R/ZAGA.R    |   56 +++++++++--------------
 man/NET.Rd  |   21 ++++++--
 man/ZAGA.Rd |    7 --
 6 files changed, 133 insertions(+), 117 deletions(-)

More information about gamlss.dist at CRAN
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Package dkanr updated to version 0.1.1 with previous version 0.1.0 dated 2018-03-16

Title: Client for the 'DKAN' API
Description: Provides functions to facilitate access to the 'DKAN' API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the 'DKAN' REST API (metadata), and the 'DKAN' datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the 'DKAN' datastore.
Author: Tony Fujs [aut, cre], Meera Desai [aut], Karthik Ramanathan [aut]
Maintainer: Tony Fujs <tonyfujs@gmail.com>

Diff between dkanr versions 0.1.0 dated 2018-03-16 and 0.1.1 dated 2018-04-30

 DESCRIPTION                                               |    6 ++--
 MD5                                                       |   15 ++++++------
 NEWS.md                                                   |    5 ++++
 R/get_resource_url.R                                      |    5 +++-
 README.md                                                 |    1 
 build/vignette.rds                                        |binary
 inst/doc/getting_started_with_dkanr.html                  |    4 +--
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More information about dkanr at CRAN
Permanent link

New package bigIntegerAlgos with initial version 0.1.2
Package: bigIntegerAlgos
Type: Package
Title: R Tool for Factoring Big Integers
Version: 0.1.2
Author: Joseph Wood
Maintainer: Joseph Wood <jwood000@gmail.com>
Description: Features the multiple polynomial quadratic sieve algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. Utilizes the C library GMP (GNU Multiple Precision Arithmetic) and classes created by Antoine Lucas et al. found in the 'gmp' package.
License: GPL (>= 2)
BuildResaveData: no
Encoding: UTF-8
SystemRequirements: gmp (>= 4.2.3)
Suggests: testthat, numbers (>= 0.6-6), RcppAlgos (>= 1.0.1)
Depends: gmp
NeedsCompilation: yes
URL: https://github.com/jwood000/bigIntegerAlgos, http://mathworld.wolfram.com/QuadraticSieve.html
BugReports: https://github.com/jwood000/bigIntegerAlgos/issues
RoxygenNote: 6.0.1
Packaged: 2018-04-30 15:22:11 UTC; josephwood
Repository: CRAN
Date/Publication: 2018-04-30 17:09:30 UTC

More information about bigIntegerAlgos at CRAN
Permanent link

Package ASGS.foyer updated to version 0.2.0 with previous version 0.1.0 dated 2018-03-21

Title: Interface to the Australian Statistical Geography Standard
Description: The Australian Statistical Geography Standard ('ASGS') is a set of shapefiles by the Australian Bureau of Statistics. This package provides an interface to those shapefiles, as well as methods for converting coordinates to shapefiles.
Author: Hugh Parsonage [aut, cre]
Maintainer: Hugh Parsonage <hugh.parsonage@gmail.com>

Diff between ASGS.foyer versions 0.1.0 dated 2018-03-21 and 0.2.0 dated 2018-04-30

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More information about ASGS.foyer at CRAN
Permanent link

Package TSP updated to version 1.1-6 with previous version 1.1-5 dated 2017-02-22

Title: Traveling Salesperson Problem (TSP)
Description: Basic infrastructure and some algorithms for the traveling salesperson problem (also traveling salesman problem; TSP). The package provides some simple algorithms and an interface to the Concorde TSP solver and its implementation of the Chained-Lin-Kernighan heuristic. The code for Concorde itself is not included in the package and has to be obtained separately.
Author: Michael Hahsler [aut, cre, cph], Kurt Hornik [aut, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

Diff between TSP versions 1.1-5 dated 2017-02-22 and 1.1-6 dated 2018-04-30

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More information about TSP at CRAN
Permanent link

Package RcppArmadillo updated to version 0.8.500.0 with previous version 0.8.400.0.0 dated 2018-03-01

Title: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library
Description: 'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that Armadillo requires a fairly recent compiler; for the g++ family at least version 4.6.* is required.
Author: Dirk Eddelbuettel, Romain Francois, Doug Bates and Binxiang Ni
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RcppArmadillo versions 0.8.400.0.0 dated 2018-03-01 and 0.8.500.0 dated 2018-04-30

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More information about RcppArmadillo at CRAN
Permanent link

Package rbamtools updated to version 2.16.10 with previous version 2.16.6 dated 2017-03-24

Title: Read and Write BAM (Binary Alignment) Files
Description: Provides an R interface to functions of the 'SAMtools' C-Library by Heng Li <http://www.htslib.org/>.
Author: Wolfgang Kaisers [aut, cre], Heng Li [ctb, cph], Jue Ruan [ctb, cph]
Maintainer: Wolfgang Kaisers <wolfgang.kaisers@gmail.com>

Diff between rbamtools versions 2.16.6 dated 2017-03-24 and 2.16.10 dated 2018-04-30

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More information about rbamtools at CRAN
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Package pkgnet updated to version 0.2.0 with previous version 0.1.0 dated 2018-04-13

Title: Get Network Representation of an R Package
Description: Tools from the domain of graph theory can be used to quantify the complexity and vulnerability to failure of a software package. That is the guiding philosophy of this package. 'pkgnet' provides tools to analyze the dependencies between functions in an R package and between its imported packages.
Author: Brian Burns [aut, cre], James Lamb [aut], Patrick Boueri [ctb], Jay Qi [ctb]
Maintainer: Brian Burns <brian.burns@uptake.com>

Diff between pkgnet versions 0.1.0 dated 2018-04-13 and 0.2.0 dated 2018-04-30

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Package cbsodataR updated to version 0.3 with previous version 0.2.5 dated 2018-02-01

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

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Package cartography updated to version 2.1.0 with previous version 2.0.2 dated 2017-11-13

Title: Thematic Cartography
Description: Create and integrate maps in your R workflow. This package allows various cartographic representations such as proportional symbols, choropleth, typology, flows or discontinuities maps. It also offers several features enhancing the graphic presentation of maps like cartographic palettes, layout elements (scale, north arrow, title...), labels, legends or access to some cartographic APIs. See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.
Author: Timothée Giraud [cre, aut], Nicolas Lambert [aut], Ian Fellows [cph] (no overlap algorithm for labels, from wordcloud package)
Maintainer: Timothée Giraud <timothee.giraud@cnrs.fr>

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Package qte updated to version 1.2.2 with previous version 1.2.1 dated 2018-02-26

Title: Quantile Treatment Effects
Description: Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2017) <https://ssrn.com/abstract=3013341>, Callaway, Li, and Oka (2017) <arXiv:1702.03618>).
Author: Brantly Callaway [aut, cre]
Maintainer: Brantly Callaway <brantly.callaway@temple.edu>

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Package kernlab updated to version 0.9-26 with previous version 0.9-25 dated 2016-10-03

Title: Kernel-Based Machine Learning Lab
Description: Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.
Author: Alexandros Karatzoglou [aut, cre], Alex Smola [aut], Kurt Hornik [aut]
Maintainer: Alexandros Karatzoglou <alexandros.karatzoglou@gmail.com>

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Package experiment updated to version 1.1-4 with previous version 1.1-3 dated 2018-04-19

Title: R Package for Designing and Analyzing Randomized Experiments
Description: Provides various statistical methods for designing and analyzing randomized experiments. One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data.
Author: Kosuke Imai [aut, cre], Zhichao Jiang [aut]
Maintainer: Kosuke Imai <kimai@princeton.edu>

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Package FRegSigCom (with last version 0.2.1) was removed from CRAN

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Package npmlreg updated to version 0.46-3 with previous version 0.46-1 dated 2014-10-20

Title: Nonparametric Maximum Likelihood Estimation for Random Effect Models
Description: Nonparametric maximum likelihood estimation or Gaussian quadrature for overdispersed generalized linear models and variance component models.
Author: Jochen Einbeck, Ross Darnell and John Hinde
Maintainer: Jochen Einbeck <jochen.einbeck@durham.ac.uk>

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Package mrbsizeR updated to version 1.1.0 with previous version 1.0.1 dated 2017-04-03

Title: Scale Space Multiresolution Analysis of Random Signals
Description: A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features. mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales. To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011>.
Author: Thimo Schuster [aut], Roman Flury [cre, ctb], Leena Pasanen [ctb], Reinhard Furrer [ctb]
Maintainer: Roman Flury <roman.flury@math.uzh.ch>

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Package devRate updated to version 0.1.7 with previous version 0.1.6 dated 2018-01-25

Title: Quantify the Relationship Between Development Rate and Temperature in Ectotherms
Description: A set of functions to quantify the relationship between development rate and temperature and to built phenological models. The package comprises a set of models and estimated parameters borrowed from a literature review in ectotherms (mostly arthropods).
Author: Francois Rebaudo [aut, cre] (2016-2018), Tanusson Selvarajah [ctb] (L2 student; 2018), Nicolas Bonnal [ctb] (M1 student; 2018), Badre Rabhi [ctb] (L2 student; 2017), Quentin Struelens [ctb] (VIA; 2016)
Maintainer: Francois Rebaudo <francois.rebaudo@ird.fr>

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Package selection (with last version 1.0) was removed from CRAN

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

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Package quadrupen updated to version 0.2-6 with previous version 0.2-5 dated 2017-03-06

Title: Sparsity by Worst-Case Quadratic Penalties
Description: Fits classical sparse regression models with efficient active set algorithms by solving quadratic problems. Also provides a few methods for model selection purpose (cross-validation, stability selection).
Author: Julien Chiquet [aut, cre]
Maintainer: Julien Chiquet <julien.chiquet@inra.fr>

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Package gcmr updated to version 1.0.1 with previous version 1.0.0 dated 2017-04-28

Title: Gaussian Copula Marginal Regression
Description: Likelihood inference in Gaussian copula marginal regression models.
Author: Guido Masarotto and Cristiano Varin
Maintainer: Cristiano Varin <sammy@unive.it>

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Package rcdk updated to version 3.4.7 with previous version 3.4.5 dated 2017-10-31

Title: Interface to the 'CDK' Libraries
Description: Allows the user to access functionality in the 'CDK', a Java framework for chemoinformatics. This allows the user to load molecules, evaluate fingerprints, calculate molecular descriptors and so on. In addition, the 'CDK' API allows the user to view structures in 2D.
Author: Rajarshi Guha [aut, cph], Zachary Charlop-Powers [cre], Emma Schymanski [ctb]
Maintainer: Zachary Charlop-Powers <zach.charlop.powers@gmail.com>

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Package pcIRT updated to version 0.2.3 with previous version 0.2.2 dated 2016-04-29

Title: IRT Models for Polytomous and Continuous Item Responses
Description: Estimates the multidimensional polytomous Rasch model (Rasch, 1961) and the Continuous Rating Scale model (Mueller, 1987).
Author: Christine Hohensinn [cre,aut]
Maintainer: Christine Hohensinn <research@christinehohensinn.at>

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Package BoomSpikeSlab updated to version 1.0.0 with previous version 0.9.0 dated 2017-05-28

Title: MCMC for Spike and Slab Regression
Description: Spike and slab regression a la McCulloch and George (1997).
Author: Steven L. Scott <steve.the.bayesian@gmail.com>
Maintainer: Steven L. Scott <steve.the.bayesian@gmail.com>

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Package RZigZag updated to version 0.1.6 with previous version 0.1.5 dated 2018-04-12

Title: Zig-Zag Sampler
Description: Implements the Zig-Zag algorithm with subsampling and control variates (ZZ-CV) of (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> as applied to Bayesian logistic regression, as well as basic Zig-Zag for a Gaussian target distribution, and Bouncy Particle Sampler for a Gaussian target.
Author: Joris Bierkens
Maintainer: Joris Bierkens <joris.bierkens@tudelft.nl>

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Package PPRL (with last version 0.3.5) was removed from CRAN

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

2018-04-10 0.3.5
2018-03-20 0.3.4

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Package bigIntegerAlgos (with last version 0.1.1) was removed from CRAN

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

2018-04-25 0.1.1
2018-04-11 0.1.0

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Package treemapify updated to version 2.5.0 with previous version 2.4.0 dated 2017-11-18

Title: Draw Treemaps in 'ggplot2'
Description: Provides 'ggplot2' geoms for drawing treemaps.
Author: David Wilkins <david@wilkox.org>
Maintainer: David Wilkins <david@wilkox.org>

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Package simr updated to version 1.0.4 with previous version 1.0.3 dated 2017-09-29

Title: Power Analysis for Generalised Linear Mixed Models by Simulation
Description: Calculate power for generalised linear mixed models, using simulation. Designed to work with models fit using the 'lme4' package. Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.
Author: Green Peter [aut, cre] (<https://orcid.org/0000-0002-0238-9852>), MacLeod Catriona [aut], Alday Phillip [ctb]
Maintainer: Green Peter <simr.peter@gmail.com>

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Package QTL.gCIMapping updated to version 2.0 with previous version 1.0 dated 2018-04-24

Title: QTL Genome-Wide Composite Interval Mapping
Description: Conduct multi-Quantitative trait locus (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, Zhang Yuan-Ming
Maintainer: Yuanming Zhang<soyzhang@mail.hzau.edu.cn>

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Package GMDH2 updated to version 1.2 with previous version 1.1 dated 2018-04-14

Title: Binary Classification via GMDH-Type Neural Network Algorithm
Description: Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithm. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix and related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response. See Dag O. and Yozgatligil C. (2016, ISSN:2073-4859) and Kondo T. and Ueno J. (2016, ISSN:1349-4198) for the details of GMDH algorithms.
Author: Osman Dag [aut, cre], Erdem Karabulut [aut], Reha Alpar [aut]
Maintainer: Osman Dag <osman.dag@hacettepe.edu.tr>

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New package vMask with initial version 1.0
Package: vMask
Type: Package
Title: Detect Small Changes in Process Mean using CUSUM Control Chart by v-Mask
Version: 1.0
Date: 2018-04-22
Author: Abbas Parchami (Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran)
Maintainer: Abbas Parchami <parchami@uk.ac.ir>
Description: The cumulative sum (CUSUM) control chart is considered to be an alternative or complementary to Shewhart control charts in statistical process control (SPC) applications, owing to its higher sensitivity to small shifts in the process mean. It utilizes all the available data rather than the last few ones used in Shewhart control charts for quick decision making. V-mask is a traditional technique for separating meaningful data from unusual circumstances in a Cumulative Sum (CUSUM) control chart; for see details about v-mask see Montgomery (1985, ISBN:978-0471656319). The mask is a V-shaped overlay placed on the CUSUM chart so that one arm of the V lines up with the slope of data points, making it easy to see data points that lie outside the slope and to determine whether these points should be discarded as random events, or treated as a performance trend that should be addressed. But, complex computations is one disadvantage V-mask method for detect small changes in mean using CUSUM control chart. Package 'vMask' can help to the applied users to overcome this challenge by considering six different methods which each of them are based on different information.
License: LGPL (>= 3)
NeedsCompilation: no
Packaged: 2018-04-27 13:12:28 UTC; Parchami
Repository: CRAN
Date/Publication: 2018-04-30 08:07:11 UTC

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Package MASS updated to version 7.3-50 with previous version 7.3-49 dated 2018-02-23

Title: Support Functions and Datasets for Venables and Ripley's MASS
Description: Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).
Author: Brian Ripley [aut, cre, cph], Bill Venables [ctb], Douglas M. Bates [ctb], Kurt Hornik [trl] (partial port ca 1998), Albrecht Gebhardt [trl] (partial port ca 1998), David Firth [ctb]
Maintainer: Brian Ripley <ripley@stats.ox.ac.uk>

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Package BBMV updated to version 2.1 with previous version 2.0 dated 2017-10-23

Title: Models for Continuous Traits Evolving in Macroevolutionary Landscapes of any Shape
Description: Provides a set of functions to fit general macroevolutionary models for continuous traits evolving in adaptive landscapes of any shape. This package implements the Fokker-Planck-Kolmogorov model (FPK), in which the trait evolves under random diffusion but is also subject to a force that pulls it towards specific values - this force can be of any shape. FPK has a version in which hard reflective bounds exist at the extremes of the trait interval: this second model is called BBMV.
Author: Florian C. Boucher
Maintainer: Florian C. Boucher <flofloboucher@gmail.com>

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