Mon, 21 May 2018

Package momentuHMM updated to version 1.4.1 with previous version 1.4.0 dated 2018-02-27

Title: Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models
Description: Extended tools for analyzing telemetry data using generalized hidden Markov models. These include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2017) <doi:10.1111/2041-210X.12995>.
Author: Brett McClintock, Theo Michelot
Maintainer: Brett McClintock <brett.mcclintock@noaa.gov>

Diff between momentuHMM versions 1.4.0 dated 2018-02-27 and 1.4.1 dated 2018-05-21

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Package MCMCvis updated to version 0.11.0 with previous version 0.10.2 dated 2018-05-02

Title: Tools to Visualize, Manipulate, and Summarize MCMC Output
Description: Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with JAGS, Stan, or other MCMC samplers.
Author: Casey Youngflesh [aut, cre] (<https://orcid.org/0000-0001-6343-3311>>), Christian Che-Castaldo [ctb], Tyler Hardy [ctb]
Maintainer: Casey Youngflesh <caseyyoungflesh@gmail.com>

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Package tabularaster updated to version 0.5.0 with previous version 0.4.0 dated 2017-12-10

Title: Tidy Tools for 'Raster' Data
Description: Facilities to work with vector and raster data in efficient repeatable and systematic work flow. Missing functionality in existing packages is included here to allow extraction from raster data with 'simple features' and 'Spatial' types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells, values and weights as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use.
Author: Michael D. Sumner [aut, cre]
Maintainer: Michael D. Sumner <mdsumner@gmail.com>

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Package nlmixr updated to version 0.9.1-2 with previous version 0.9.1-0 dated 2018-05-19

Title: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics
Description: Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'RxODE' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Author: Matthew Fidler [aut], Yuan Xiong [aut], Rik Schoemaker [aut], Justin Wilkins [aut], Mirjam Trame [aut], Teun Post [aut], Robert Leary [ctb], Wenping Wang [aut, cre], Hadley Wickham [ctb], Dirk Eddelbuettel [cph], David Ardia [cph], Katharine Mullen [cph]
Maintainer: Wenping Wang <wwang8198@gmail.com>

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Package imager updated to version 0.41.0 with previous version 0.40.2 dated 2017-04-24

Title: Image Processing Library Based on 'CImg'
Description: Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.
Author: Simon Barthelme [aut, cre], David Tschumperle [ctb], Jan Wijffels [ctb], Haz Edine Assemlal [ctb]
Maintainer: Simon Barthelme <simon.barthelme@gipsa-lab.fr>

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Package HEMDAG updated to version 2.1.3 with previous version 2.1.2 dated 2018-05-07

Title: Hierarchical Ensemble Methods for Directed Acyclic Graphs
Description: An implementation of Hierarchical Ensemble Methods for Directed Acyclic Graphs (DAGs). The 'HEMDAG' package can be used to enhance the predictions of virtually any flat learning methods, by taking into account the hierarchical nature of the classes of a bio-ontology. 'HEMDAG' is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but it can be also safely applied to tree-structured taxonomies (as FunCat), since trees are DAGs. 'HEMDAG' scale nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).
Author: Marco Notaro [aut, cre] and Giorgio Valentini [aut] (AnacletoLab, Dipartimento di Informatica, Universita' degli Studi di Milano)
Maintainer: Marco Notaro <marco.notaro@unimi.it>

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

Title: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections
Description: Provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) <http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf> and developed further by Pustejovsky and Tipton (2017) <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple- contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple- contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), ivreg (from package 'AER'), plm() (from package 'plm'), gls() and lme() (from 'nlme'), robu() (from 'robumeta'), and rma.uni() and rma.mv() (from 'metafor').
Author: James Pustejovsky [aut, cre]
Maintainer: James Pustejovsky <jepusto@gmail.com>

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Package SARP.compo updated to version 0.0.7 with previous version 0.0.3 dated 2018-01-12

Title: Network-based Interpretation of Changes in Compositional Data
Description: Provides a set of functions to interpret changes in compositional data based on a network representation of all pairwise ratio comparisons: computation of all pairwise ratio, construction of a p-value matrix of all pairwise tests of these ratios between conditions, conversion of this matrix to a network.
Author: Emmanuel Curis [aut, cre]
Maintainer: Emmanuel Curis <emmanuel.curis@parisdescartes.fr>

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Package openEBGM updated to version 0.6.0 with previous version 0.5.0 dated 2018-04-02

Title: EBGM Scores for Mining Large Contingency Tables
Description: An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and quantile scores from the posterior distribution using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the 'PhViD' package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Now includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the 'mederrRank' package.
Author: John Ihrie [cre, aut], Travis Canida [aut], Ismaïl Ahmed [ctb] (author of 'PhViD' package (derived code)), Antoine Poncet [ctb] (author of 'PhViD'), Sergio Venturini [ctb] (author of 'mederrRank' package (derived code)), Jessica Myers [ctb] (author of 'mederrRank')
Maintainer: John Ihrie <John.Ihrie@fda.hhs.gov>

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Package EFAutilities updated to version 1.2.2 with previous version 1.2.1 dated 2017-12-09

Title: Utility Functions for Exploratory Factor Analysis
Description: A number of utility function for exploratory factor analysis are included in this package. In particular, it computes standard errors for parameter estimates and factor correlations under a variety of conditions.
Author: Guangjian Zhang, Ge Jiang, Minami Hattori, Lauren Trichtinger
Maintainer: Guangjian Zhang <gzhang3@nd.edu>

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Package easycsv updated to version 1.0.8 with previous version 1.0.7 dated 2018-01-23

Title: Load Multiple 'csv' and 'txt' Tables
Description: Allows users to easily read multiple comma separated tables and create a data frame under the same name. Is able to read multiple comma separated tables from a local directory, a zip file or a zip file on a remote directory.
Author: Dror Bogin [aut, cre]
Maintainer: Dror Bogin <dror.bogin@gmail.com>

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Package colorednoise updated to version 1.0.1 with previous version 0.0.2 dated 2018-01-30

Title: Simulate Temporally Autocorrelated Populations
Description: Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.
Author: Julia Pilowsky
Maintainer: Julia Pilowsky <jap2178@caa.columbia.edu>

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Package ssgraph updated to version 1.3 with previous version 1.2 dated 2018-05-14

Title: Bayesian Graphical Estimation using Spike-and-Slab Priors
Description: For Bayesian inference in undirected graphical models using spike-and-slab priors, continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models' literature, including Wang (2015) <doi:10.1214/14-BA916>. To speed up the computations, the computationally intensive tasks of the package are implemented in C++ in parallel using OpenMP.
Author: Reza Mohammadi <https://orcid.org/0000-0001-9538-0648>
Maintainer: Reza Mohammadi <a.mohammadi@uva.nl>

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Package SIRItoGTFS updated to version 0.2.4 with previous version 0.2.2 dated 2018-02-06

Title: Compare SIRI Datasets to GTFS Tables
Description: Allows the user to compare SIRI (Service Interface for Real Time Information) data sets to their GTFS (General Transit Feed Specification) counterparts, a "Request_id" column us needed for the SIRI data frame in order to subset parts of it for use.
Author: Dror Bogin [aut, cre]
Maintainer: Dror Bogin <dror.bogin@gmail.com>

Diff between SIRItoGTFS versions 0.2.2 dated 2018-02-06 and 0.2.4 dated 2018-05-21

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Package AIG updated to version 0.1.9 with previous version 0.1.8 dated 2017-12-18

Title: Automatic Item Generator
Description: A collection of Automatic Item Generators used mainly for psychological research. This package can generate linear syllogistic reasoning, arithmetic and 2D/3D/Double 3D spatial reasoning items. It is recommended for research purpose only.
Author: Bao Sheng Loe (Aiden) [aut, cre, cph], David Condon [ctb, cph], Francis Smart [ctb, cph]
Maintainer: Bao Sheng Loe (Aiden) <bsl28@cam.ac.uk>

Diff between AIG versions 0.1.8 dated 2017-12-18 and 0.1.9 dated 2018-05-21

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Package FedData updated to version 2.5.3 with previous version 2.5.2 dated 2018-03-12

Title: Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources
Description: Functions to automate downloading geospatial data available from several federated data sources (mainly sources maintained by the US Federal government). Currently, the package enables extraction from seven datasets: The National Elevation Dataset digital elevation models (1 and 1/3 arc-second; USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 3, available from the Oak Ridge National Laboratory's Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; and the National Land Cover Database (NLCD).
Author: R. Kyle Bocinsky [aut, cre], Dylan Beaudette [ctb], Scott Chamberlain [ctb]
Maintainer: R. Kyle Bocinsky <bocinsky@gmail.com>

Diff between FedData versions 2.5.2 dated 2018-03-12 and 2.5.3 dated 2018-05-21

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Package Cubist updated to version 0.2.2 with previous version 0.2.1 dated 2017-12-01

Title: Rule- And Instance-Based Regression Modeling
Description: Regression modeling using rules with added instance-based corrections.
Author: Max Kuhn [aut, cre], Steve Weston [ctb], Chris Keefer [ctb], Nathan Coulter [ctb], Ross Quinlan [aut] (Author of imported C code), Rulequest Research Pty Ltd. [cph] (Copyright holder of imported C code)
Maintainer: Max Kuhn <mxkuhn@gmail.com>

Diff between Cubist versions 0.2.1 dated 2017-12-01 and 0.2.2 dated 2018-05-21

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Package roadoi updated to version 0.5.1 with previous version 0.5 dated 2018-05-02

Title: Find Free Versions of Scholarly Publications via Unpaywall
Description: This web client interfaces Unpaywall <https://unpaywall.org/products/api>, formerly oaDOI, a service finding free full-texts of academic papers by linking DOIs with open access journals and repositories. It provides unified access to various data sources for open access full-text links including Crossref and the Directory of Open Access Journals (DOAJ). API usage is free and no registration is required.
Author: Najko Jahn [aut, cre], Tuija Sonkkila [rev] (Tuija Sonkkila reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/115), Ross Mounce [rev] (Ross Mounce reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/115)
Maintainer: Najko Jahn <najko.jahn@gmail.com>

Diff between roadoi versions 0.5 dated 2018-05-02 and 0.5.1 dated 2018-05-21

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Package LS2Wstat updated to version 2.1 with previous version 2.0-3 dated 2014-06-24

Title: A Multiscale Test of Spatial Stationarity for LS2W Processes
Description: Wavelet-based methods for testing stationarity and quadtree segmenting of images.
Author: Sarah Taylor [aut], Matt Nunes [aut, cre], Idris Eckley [ctb, ths]
Maintainer: Matt Nunes <m.nunes@lancaster.ac.uk>

Diff between LS2Wstat versions 2.0-3 dated 2014-06-24 and 2.1 dated 2018-05-21

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Package emmeans updated to version 1.2.1 with previous version 1.2 dated 2018-05-08

Title: Estimated Marginal Means, aka Least-Squares Means
Description: Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and compact letter displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Author: Russell Lenth [aut, cre, cph], Henrik Singmann [ctb], Jonathon Love [ctb], Paul Buerkner [ctb], Maxime Herve [ctb]
Maintainer: Russell Lenth <russell-lenth@uiowa.edu>

Diff between emmeans versions 1.2 dated 2018-05-08 and 1.2.1 dated 2018-05-21

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Package compareDF updated to version 1.5.0 with previous version 1.3.1 dated 2018-05-18

Title: Do a Git Style Diff of the Rows Between Two Dataframes with Similar Structure
Description: Compares two dataframes which have the same column structure to show the rows that have changed. Also gives a git style diff format to quickly see what has changes in addition to summary statistics.
Author: Alex Joseph [aut, cre]
Maintainer: Alex Joseph <alexsanjoseph@gmail.com>

Diff between compareDF versions 1.3.1 dated 2018-05-18 and 1.5.0 dated 2018-05-21

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Package tables updated to version 0.8.4 with previous version 0.8.3 dated 2017-11-13

Title: Formula-Driven Table Generation
Description: Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.
Author: Duncan Murdoch
Maintainer: Duncan Murdoch <murdoch@stats.uwo.ca>

Diff between tables versions 0.8.3 dated 2017-11-13 and 0.8.4 dated 2018-05-21

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Package quantregGrowth updated to version 0.4-2 with previous version 0.4-1 dated 2018-05-16

Title: Growth Charts via Regression Quantiles
Description: Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms via B-splines with L1-norm difference penalties. Monotonicity constraints on the fitted curves are allowed. See Muggeo, Sciandra, Tomasello and Calvo (2013) <doi:10.1007/s10651-012-0232-1> and <doi:10.13140/RG.2.2.12924.85122> for some code example.
Author: Vito M. R. Muggeo [aut, cre] (<https://orcid.org/0000-0002-3386-4054>>)
Maintainer: Vito M. R. Muggeo <vito.muggeo@unipa.it>

Diff between quantregGrowth versions 0.4-1 dated 2018-05-16 and 0.4-2 dated 2018-05-21

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Package nima updated to version 0.5.0 with previous version 0.4.5 dated 2017-09-05

Title: Nima Hejazi's R Toolbox
Description: Miscellaneous R functions developed over the course of statistical research and scientific computing. These include, for example, utilities that supplement existing idiosyncrasies of the R language, extend existing plotting functionality and aesthetics, provide alternative presentations of matrix decompositions, and extend access to command line tools and systems-level information.
Author: Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>>)
Maintainer: Nima Hejazi <nh@nimahejazi.org>

Diff between nima versions 0.4.5 dated 2017-09-05 and 0.5.0 dated 2018-05-21

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Package neutralitytestr updated to version 0.0.2 with previous version 0.0.1 dated 2018-04-11

Title: Test for a Neutral Evolutionary Model in Cancer Sequencing Data
Description: Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.
Author: Marc Williams [aut, cre]
Maintainer: Marc Williams <marcjwilliams1@gmail.com>

Diff between neutralitytestr versions 0.0.1 dated 2018-04-11 and 0.0.2 dated 2018-05-21

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Package miceadds updated to version 2.11-87 with previous version 2.10-14 dated 2018-03-29

Title: Some Additional Multiple Imputation Functions, Especially for 'mice'
Description: Contains some auxiliary functions for multiple imputation which complements existing functionality in R. In addition to some utility functions, main features include plausible value imputation, multilevel imputation functions (arbitrary number of levels, hierarchical and non-hierarchical datasets), imputation using partial least squares (PLS) for high dimensional predictors, and nested multiple imputation.
Author: Alexander Robitzsch [aut, cre], Simon Grund [aut], Thorsten Henke [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

Diff between miceadds versions 2.10-14 dated 2018-03-29 and 2.11-87 dated 2018-05-21

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 miceadds-2.11-87/miceadds/R/create.designMatrices.waldtest.R                   |    8 
 miceadds-2.11-87/miceadds/R/create_interactions.R                              |   26 
 miceadds-2.11-87/miceadds/R/crlrem.R                                           |    6 
 miceadds-2.11-87/miceadds/R/cxxfunction.copy.R                                 |   16 
 miceadds-2.11-87/miceadds/R/datalist2mids.R                                    |  169 +-
 miceadds-2.11-87/miceadds/R/datlist_create.R                                   |   61 
 miceadds-2.11-87/miceadds/R/draw.pv.ctt.R                                      |  148 -
 miceadds-2.11-87/miceadds/R/fast.groupmean.R                                   |    2 
 miceadds-2.11-87/miceadds/R/fast.groupsum.R                                    |    2 
 miceadds-2.11-87/miceadds/R/file_path.R                                        |   16 
 miceadds-2.11-87/miceadds/R/filename_split.R                                   |   42 
 miceadds-2.11-87/miceadds/R/files_move.R                                       |   70 
 miceadds-2.11-87/miceadds/R/fisher2cor.R                                       |   14 
 miceadds-2.11-87/miceadds/R/glm.cluster.R                                      |   61 
 miceadds-2.11-87/miceadds/R/grep.vec.R                                         |   33 
 miceadds-2.11-87/miceadds/R/grep_leading.R                                     |only
 miceadds-2.11-87/miceadds/R/grepvec.R                                          |only
 miceadds-2.11-87/miceadds/R/grepvec_leading.R                                  |only
 miceadds-2.11-87/miceadds/R/in_CI.R                                            |    7 
 miceadds-2.11-87/miceadds/R/include.2l.predictors_v1.R                         |   50 
 miceadds-2.11-87/miceadds/R/index.dataframe.R                                  |   20 
 miceadds-2.11-87/miceadds/R/jomo2datlist.R                                     |    8 
 miceadds-2.11-87/miceadds/R/jomo2mids.R                                        |   12 
 miceadds-2.11-87/miceadds/R/kernelpls.fit2.R                                   |   94 -
 miceadds-2.11-87/miceadds/R/latent.regression.em.R                             |   66 
 miceadds-2.11-87/miceadds/R/library_install.R                                  |   26 
 miceadds-2.11-87/miceadds/R/lm.cluster.R                                       |   78 
 miceadds-2.11-87/miceadds/R/load.Rdata.R                                       |    6 
 miceadds-2.11-87/miceadds/R/load.Rdata2.R                                      |   10 
 miceadds-2.11-87/miceadds/R/load.data.R                                        |   99 -
 miceadds-2.11-87/miceadds/R/ma.scale2.R                                        |   23 
 miceadds-2.11-87/miceadds/R/ma.wtd.aux.data.R                                  |  182 +-
 miceadds-2.11-87/miceadds/R/ma.wtd.corNA.R                                     |   87 -
 miceadds-2.11-87/miceadds/R/ma.wtd.covNA.R                                     |   86 -
 miceadds-2.11-87/miceadds/R/ma.wtd.curtosisNA.R                                |   70 
 miceadds-2.11-87/miceadds/R/ma.wtd.meanNA.R                                    |   47 
 miceadds-2.11-87/miceadds/R/ma.wtd.quantileNA.R                                |   46 
 miceadds-2.11-87/miceadds/R/ma.wtd.sdNA.R                                      |   64 
 miceadds-2.11-87/miceadds/R/ma.wtd.skewnessNA.R                                |   70 
 miceadds-2.11-87/miceadds/R/ma_rmvnorm.R                                       |   22 
 miceadds-2.11-87/miceadds/R/max0.R                                             |    6 
 miceadds-2.11-87/miceadds/R/mean0.R                                            |    6 
 miceadds-2.11-87/miceadds/R/mi.anova.R                                         |  218 +-
 miceadds-2.11-87/miceadds/R/mi_dstat.R                                         |   44 
 miceadds-2.11-87/miceadds/R/mice.1chain.R                                      |  283 +--
 miceadds-2.11-87/miceadds/R/mice.impute.2l.binary.R                            |   22 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.contextual.norm.R                   |   74 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.contextual.pmm.R                    |   31 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.continuous.R                        |   22 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.eap.R                               |    2 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.groupmean.R                         |   22 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.groupmean.elim.R                    |   22 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.latentgroupmean.mcmc.R              |   64 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.latentgroupmean.ml.R                |only
 miceadds-2.11-87/miceadds/R/mice.impute.2l.plausible.values.R                  |    2 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.pls.R                               |    2 
 miceadds-2.11-87/miceadds/R/mice.impute.2l.pls2.R                              |  826 +++++-----
 miceadds-2.11-87/miceadds/R/mice.impute.2l.pmm.R                               |   28 
 miceadds-2.11-87/miceadds/R/mice.impute.2lonly.function.R                      |   52 
 miceadds-2.11-87/miceadds/R/mice.impute.2lonly.norm2.R                         |    2 
 miceadds-2.11-87/miceadds/R/mice.impute.2lonly.pmm2.R                          |    2 
 miceadds-2.11-87/miceadds/R/mice.impute.bygroup.R                              |  118 -
 miceadds-2.11-87/miceadds/R/mice.impute.eap.R                                  |   22 
 miceadds-2.11-87/miceadds/R/mice.impute.grouped.R                              |   40 
 miceadds-2.11-87/miceadds/R/mice.impute.hotDeck.R                              |  106 -
 miceadds-2.11-87/miceadds/R/mice.impute.ml.lmer.R                              |  296 +--
 miceadds-2.11-87/miceadds/R/mice.impute.norm3.R                                |   18 
 miceadds-2.11-87/miceadds/R/mice.impute.plausible.values.R                     |  468 ++---
 miceadds-2.11-87/miceadds/R/mice.impute.pls.R                                  |  226 +-
 miceadds-2.11-87/miceadds/R/mice.impute.pmm3.R                                 |   56 
 miceadds-2.11-87/miceadds/R/mice.impute.pmm4.R                                 |  140 -
 miceadds-2.11-87/miceadds/R/mice.impute.pmm5.R                                 |   64 
 miceadds-2.11-87/miceadds/R/mice.impute.pmm6.R                                 |   36 
 miceadds-2.11-87/miceadds/R/mice.impute.tricube.pmm.R                          |   78 
 miceadds-2.11-87/miceadds/R/mice.impute.tricube.pmm2.R                         |  160 -
 miceadds-2.11-87/miceadds/R/mice.impute.weighted.norm.R                        |   40 
 miceadds-2.11-87/miceadds/R/mice.impute.weighted.pmm.R                         |   32 
 miceadds-2.11-87/miceadds/R/mice.nmi.R                                         |   89 -
 miceadds-2.11-87/miceadds/R/mice_1chain_check_input.R                          |only
 miceadds-2.11-87/miceadds/R/mice_imputation_create_interactions.R              |   52 
 miceadds-2.11-87/miceadds/R/mice_imputation_extract_arguments_list.R           |   12 
 miceadds-2.11-87/miceadds/R/mice_imputation_extract_list_arguments.R           |   14 
 miceadds-2.11-87/miceadds/R/mice_imputation_get_states.R                       |   19 
 miceadds-2.11-87/miceadds/R/mice_imputation_include_cluster_effect.R           |   36 
 miceadds-2.11-87/miceadds/R/mice_imputation_multilevel_include_2l_predictors.R |   50 
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_correlation_criteria.R         |   26 
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_do_impute.R                    |  100 -
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_estimate_pls_regression.R      |   82 
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_helper.R                       |   90 -
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_include_interactions.R         |  166 +-
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_include_quadratics.R           |   36 
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_largest_correlations.R         |  120 -
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_pca_reduction.R                |   72 
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_print_progress1.R              |   36 
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_print_progress2.R              |   28 
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_print_progress3.R              |   14 
 miceadds-2.11-87/miceadds/R/mice_imputation_pls_scale_x.R                      |   14 
 miceadds-2.11-87/miceadds/R/mice_imputation_prepare_2l_functions.R             |   60 
 miceadds-2.11-87/miceadds/R/mice_imputation_weighted_norm_draw.R               |   18 
 miceadds-2.11-87/miceadds/R/mice_impute_2l_lmer.R                              |  290 +--
 miceadds-2.11-87/miceadds/R/mice_impute_bygroup_modify_arguments.R             |   18 
 miceadds-2.11-87/miceadds/R/mice_impute_create_type_vector.R                   |   12 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_aggregate_data_higher_level.R         |   40 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_arrange_cluster_identifiers.R         |   32 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_choice_aggregated_variables.R         |   16 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_collect_lme4_input.R                  |   32 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_construct_lme4_formula.R              |   40 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_define_lmer_function.R                |   44 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_draw_random_effects.R                 |   66 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_extend_imputed_values_lower_level.R   |   22 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_extract_input.R                       |  108 -
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_include_cluster_means.R               |   80 
 miceadds-2.11-87/miceadds/R/mice_ml_lmer_interactions_pls.R                    |   34 
 miceadds-2.11-87/miceadds/R/mice_multilevel_add_groupmeans.R                   |   66 
 miceadds-2.11-87/miceadds/R/mice_multilevel_create_formula.R                   |   16 
 miceadds-2.11-87/miceadds/R/mice_multilevel_doCall_suppressWarnings.R          |   22 
 miceadds-2.11-87/miceadds/R/mice_multilevel_draw_binomial.R                    |   12 
 miceadds-2.11-87/miceadds/R/mice_multilevel_draw_rnorm1.R                      |   14 
 miceadds-2.11-87/miceadds/R/mice_multilevel_imputation_blme_args.R             |   20 
 miceadds-2.11-87/miceadds/R/mice_multilevel_imputation_draw_random_effects.R   |   48 
 miceadds-2.11-87/miceadds/R/mice_multilevel_imputation_draw_residuals.R        |   10 
 miceadds-2.11-87/miceadds/R/mice_multilevel_imputation_pmm5.R                  |   58 
 miceadds-2.11-87/miceadds/R/mice_multilevel_impute_groupmean.R                 |   28 
 miceadds-2.11-87/miceadds/R/micombine.F.R                                      |   10 
 miceadds-2.11-87/miceadds/R/micombine.chisquare.R                              |  160 -
 miceadds-2.11-87/miceadds/R/micombine.cor.R                                    |  214 +-
 miceadds-2.11-87/miceadds/R/micombine.cov.R                                    |  132 -
 miceadds-2.11-87/miceadds/R/mids2datlist.R                                     |   84 -
 miceadds-2.11-87/miceadds/R/mids2mlwin.R                                       |   26 
 miceadds-2.11-87/miceadds/R/min0.R                                             |    6 
 miceadds-2.11-87/miceadds/R/nested.datlist2datlist.R                           |  118 -
 miceadds-2.11-87/miceadds/R/nested.datlist_create.R                            |   56 
 miceadds-2.11-87/miceadds/R/nestedList2List.R                                  |   38 
 miceadds-2.11-87/miceadds/R/nesteddatlist2datlist.R                            |    2 
 miceadds-2.11-87/miceadds/R/norm.draw2.R                                       |   12 
 miceadds-2.11-87/miceadds/R/norm.draw3.R                                       |   14 
 miceadds-2.11-87/miceadds/R/normalize_vector.R                                 |    4 
 miceadds-2.11-87/miceadds/R/output.format1.R                                   |   10 
 miceadds-2.11-87/miceadds/R/pca.covridge.R                                     |   14 
 miceadds-2.11-87/miceadds/R/plausible.value.draw.R                             |   36 
 miceadds-2.11-87/miceadds/R/plausible.value.imputation.R                       |   66 
 miceadds-2.11-87/miceadds/R/pool.mi.R                                          |  128 -
 miceadds-2.11-87/miceadds/R/pool.mids.nmi.R                                    |  114 -
 miceadds-2.11-87/miceadds/R/pool.nmi.scalar.helper.R                           |  208 +-
 miceadds-2.11-87/miceadds/R/predict.kernelfit.pls2.R                           |    6 
 miceadds-2.11-87/miceadds/R/prop_miss.R                                        |    8 
 miceadds-2.11-87/miceadds/R/quantile0.R                                        |    8 
 miceadds-2.11-87/miceadds/R/rcpp_create_header_file.R                          |only
 miceadds-2.11-87/miceadds/R/read.fwf2.R                                        |   18 
 miceadds-2.11-87/miceadds/R/remove.lindep_miceadds.R                           |   80 
 miceadds-2.11-87/miceadds/R/replace_values_vector.R                            |    8 
 miceadds-2.11-87/miceadds/R/round2.R                                           |   10 
 miceadds-2.11-87/miceadds/R/save.Rdata.R                                       |   51 
 miceadds-2.11-87/miceadds/R/save.data.R                                        |  110 -
 miceadds-2.11-87/miceadds/R/save_data_calc_filename.R                          |   31 
 miceadds-2.11-87/miceadds/R/scale_datlist.R                                    |  169 +-
 miceadds-2.11-87/miceadds/R/scan0.R                                            |   24 
 miceadds-2.11-87/miceadds/R/sd0.R                                              |    6 
 miceadds-2.11-87/miceadds/R/source.Rcpp.all.R                                  |only
 miceadds-2.11-87/miceadds/R/source.all.R                                       |   23 
 miceadds-2.11-87/miceadds/R/stats0.R                                           |   16 
 miceadds-2.11-87/miceadds/R/str_C.expand.grid.R                                |   18 
 miceadds-2.11-87/miceadds/R/string_extract_part.R                              |   12 
 miceadds-2.11-87/miceadds/R/string_find_first.R                                |only
 miceadds-2.11-87/miceadds/R/string_find_last.R                                 |only
 miceadds-2.11-87/miceadds/R/sub.micombine.cor.R                                |   84 -
 miceadds-2.11-87/miceadds/R/sub.micombine.cov.R                                |   78 
 miceadds-2.11-87/miceadds/R/subset_datlist.R                                   |  266 +--
 miceadds-2.11-87/miceadds/R/subset_nested.datlist.R                            |  340 ++--
 miceadds-2.11-87/miceadds/R/summary.mipo.nmi.R                                 |   50 
 miceadds-2.11-87/miceadds/R/summary.mira.nmi.R                                 |   24 
 miceadds-2.11-87/miceadds/R/sumpreserving.rounding.R                           |   54 
 miceadds-2.11-87/miceadds/R/systime.R                                          |   38 
 miceadds-2.11-87/miceadds/R/tw.imputation.R                                    |   30 
 miceadds-2.11-87/miceadds/R/tw.mcmc.imputation.R                               |   66 
 miceadds-2.11-87/miceadds/R/var0.R                                             |    6 
 miceadds-2.11-87/miceadds/R/vector2matrix.R                                    |   23 
 miceadds-2.11-87/miceadds/R/visitSequence.determine.R                          |  117 -
 miceadds-2.11-87/miceadds/R/visitSequence_determine_add_entry.R                |only
 miceadds-2.11-87/miceadds/R/visitSequence_determine_equal_vecs.R               |only
 miceadds-2.11-87/miceadds/R/visitSequence_determine_handle_variable.R          |only
 miceadds-2.11-87/miceadds/R/visitSequence_determine_remove_entry.R             |only
 miceadds-2.11-87/miceadds/R/with.NestedImputationList.R                        |   54 
 miceadds-2.11-87/miceadds/R/with.datlist.R                                     |   12 
 miceadds-2.11-87/miceadds/R/with.mids.1chain.R                                 |   26 
 miceadds-2.11-87/miceadds/R/with.mids.nmi.R                                    |   56 
 miceadds-2.11-87/miceadds/R/with.nested.datlist.R                              |   56 
 miceadds-2.11-87/miceadds/R/withPool.R                                         |   56 
 miceadds-2.11-87/miceadds/R/within.NestedImputationList.R                      |   59 
 miceadds-2.11-87/miceadds/R/within.datlist.R                                   |   49 
 miceadds-2.11-87/miceadds/R/within.imputationList.R                            |   47 
 miceadds-2.11-87/miceadds/R/within.nested.datlist.R                            |   58 
 miceadds-2.11-87/miceadds/R/write.datlist.R                                    |  178 +-
 miceadds-2.11-87/miceadds/R/write.fwf2.R                                       |   58 
 miceadds-2.11-87/miceadds/R/write.mice.imputation.R                            |  144 -
 miceadds-2.11-87/miceadds/R/write.pspp.R                                       |  242 +-
 miceadds-2.11-87/miceadds/R/write_fwf2_format2.R                               |   40 
 miceadds-2.11-87/miceadds/R/zzz.R                                              |   22 
 miceadds-2.11-87/miceadds/README.md                                            |    5 
 miceadds-2.11-87/miceadds/inst/NEWS                                            |   31 
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 miceadds-2.11-87/miceadds/man/GroupMean.Rd                                     |   30 
 miceadds-2.11-87/miceadds/man/NMIwaldtest.Rd                                   |   90 -
 miceadds-2.11-87/miceadds/man/NestedImputationList.Rd                          |   26 
 miceadds-2.11-87/miceadds/man/Reval.Rd                                         |   45 
 miceadds-2.11-87/miceadds/man/Rfunction_include_argument_values.Rd             |   20 
 miceadds-2.11-87/miceadds/man/Rhat.mice.Rd                                     |   16 
 miceadds-2.11-87/miceadds/man/Rsessinfo.Rd                                     |    8 
 miceadds-2.11-87/miceadds/man/VariableNames2String.Rd                          |   24 
 miceadds-2.11-87/miceadds/man/complete.miceadds.Rd                             |   40 
 miceadds-2.11-87/miceadds/man/crlrem.Rd                                        |    6 
 miceadds-2.11-87/miceadds/man/cxxfunction.copy.Rd                              |   34 
 miceadds-2.11-87/miceadds/man/data.allison.Rd                                  |   14 
 miceadds-2.11-87/miceadds/man/data.enders.Rd                                   |   10 
 miceadds-2.11-87/miceadds/man/data.graham.Rd                                   |   62 
 miceadds-2.11-87/miceadds/man/data.internet.Rd                                 |   20 
 miceadds-2.11-87/miceadds/man/data.largescale.Rd                               |    6 
 miceadds-2.11-87/miceadds/man/data.ma.Rd                                       |   10 
 miceadds-2.11-87/miceadds/man/data.smallscale.Rd                               |    8 
 miceadds-2.11-87/miceadds/man/datalist2mids.Rd                                 |   25 
 miceadds-2.11-87/miceadds/man/datlist_create.Rd                                |   36 
 miceadds-2.11-87/miceadds/man/draw.pv.ctt.Rd                                   |   63 
 miceadds-2.11-87/miceadds/man/filename_split.Rd                                |   22 
 miceadds-2.11-87/miceadds/man/files_move.Rd                                    |   12 
 miceadds-2.11-87/miceadds/man/grep.vec.Rd                                      |   41 
 miceadds-2.11-87/miceadds/man/in_CI.Rd                                         |   14 
 miceadds-2.11-87/miceadds/man/index.dataframe.Rd                               |   10 
 miceadds-2.11-87/miceadds/man/jomo2datlist.Rd                                  |   14 
 miceadds-2.11-87/miceadds/man/kernelpls.fit2.Rd                                |   36 
 miceadds-2.11-87/miceadds/man/library_install.Rd                               |   16 
 miceadds-2.11-87/miceadds/man/lm.cluster.Rd                                    |   48 
 miceadds-2.11-87/miceadds/man/load.Rdata.Rd                                    |   12 
 miceadds-2.11-87/miceadds/man/load.data.Rd                                     |   38 
 miceadds-2.11-87/miceadds/man/ma.scale2.Rd                                     |   23 
 miceadds-2.11-87/miceadds/man/ma.wtd.statNA.Rd                                 |  103 -
 miceadds-2.11-87/miceadds/man/ma_rmvnorm.Rd                                    |    8 
 miceadds-2.11-87/miceadds/man/mi.anova.Rd                                      |   25 
 miceadds-2.11-87/miceadds/man/mi_dstat.Rd                                      |    8 
 miceadds-2.11-87/miceadds/man/mice.1chain.Rd                                   |   90 -
 miceadds-2.11-87/miceadds/man/mice.impute.2l.contextual.pmm.Rd                 |   50 
 miceadds-2.11-87/miceadds/man/mice.impute.2l.latentgroupmean.ml.Rd             |   98 -
 miceadds-2.11-87/miceadds/man/mice.impute.2lonly.function.Rd                   |   51 
 miceadds-2.11-87/miceadds/man/mice.impute.bygroup.Rd                           |   30 
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Package logiBin updated to version 0.3 with previous version 0.2 dated 2018-05-08

Title: Binning Variables to Use in Logistic Regression
Description: Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.
Author: Sneha Tody
Maintainer: Sneha Tody <sn.tody1@gmail.com>

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Package KFAS updated to version 1.3.2 with previous version 1.3.1 dated 2018-01-04

Title: Kalman Filter and Smoother for Exponential Family State Space Models
Description: State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Author: Jouni Helske <jouni.helske@iki.fi>
Maintainer: Jouni Helske <jouni.helske@iki.fi>

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Package hnp updated to version 1.2-6 with previous version 1.2-5 dated 2018-05-06

Title: Half-Normal Plots with Simulation Envelopes
Description: Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.
Author: Rafael de Andrade Moral [aut, cre], John Hinde [aut], Clarice Garcia Borges Demetrio [aut]
Maintainer: Rafael de Andrade Moral <rafael_moral@yahoo.com.br>

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Package yuima updated to version 1.8.1 with previous version 1.7.10 dated 2018-04-12

Title: The YUIMA Project Package for SDEs
Description: Simulation and Inference for SDEs and Other Stochastic Processes.
Author: YUIMA Project Team
Maintainer: Stefano M. Iacus <stefano.iacus@unimi.it>

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Package worrms updated to version 0.2.8 with previous version 0.2.0 dated 2017-08-24

Title: World Register of Marine Species (WoRMS) Client
Description: Client for World Register of Marine Species (<http://www.marinespecies.org/>). Includes functions for each of the API methods, including searching for names by name, date and common names, searching using external identifiers, fetching synonyms, as well as fetching taxonomic children and taxonomic classification.
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>>)
Maintainer: Scott Chamberlain <myrmecocystus@gmail.com>

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Package smaa updated to version 0.3-0 with previous version 0.2-5 dated 2016-12-23

Title: Stochastic Multi-Criteria Acceptability Analysis
Description: Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
Author: Gert van Valkenhoef
Maintainer: Gert van Valkenhoef <gert@gertvv.nl>

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Package hitandrun updated to version 0.5-4 with previous version 0.5-3 dated 2016-12-23

Title: "Hit and Run" and "Shake and Bake" for Sampling Uniformly from Convex Shapes
Description: The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.
Author: Gert van Valkenhoef, Tommi Tervonen
Maintainer: Gert van Valkenhoef <gert@gertvv.nl>

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Package mixAK updated to version 5.1 with previous version 5.0 dated 2017-03-16

Title: Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering
Description: Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Author: Arnošt Komárek <arnost.komarek@mff.cuni.cz>
Maintainer: Arnošt Komárek <arnost.komarek@mff.cuni.cz>

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Package FuzzyAHP updated to version 0.9.1 with previous version 0.9.0 dated 2017-03-12

Title: (Fuzzy) AHP Calculation
Description: Calculation of AHP (Analytic Hierarchy Process - <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights.
Author: Jan Caha [aut, cre], Aneta Drážná [ctb, com] (up to version 0.6.5)
Maintainer: Jan Caha <cahik@atlas.cz>

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Package BINCOR updated to version 0.2.0 with previous version 0.1.0 dated 2018-02-20

Title: Estimate the Correlation Between Two Irregular Time Series
Description: Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series).
Author: Josué M. Polanco-Martínez [aut, cph, cre], Mikko Korpela [ctb, trl], Aalto University [cph], Manfred Mudelsee [ctb]
Maintainer: Josué M. Polanco-Martínez <josue.m.polanco@gmail.com>

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Package SmartEDA updated to version 0.2.0 with previous version 0.1.0 dated 2018-04-06

Title: Summarize and Explore the Data
Description: Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
Author: Dayanand Ubrangala; Kiran R; Ravi Prasad Kondapalli
Maintainer: Dayanand Ubrangala<daya6489@gmail.com>

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New package gammSlice with initial version 2.0-1
Package: gammSlice
Version: 2.0-1
Date: 2018-05-20
Title: Generalized Additive Mixed Model Analysis via Slice Sampling
Authors@R: c(person("Tung H.", "Pham", role = c("aut"), email = "tungstats@gmail.com"), person("Matt P.", "Wand", role = c("aut", "cre"),, email = "matt.wand@uts.edu.au"))
Maintainer: Matt P. Wand <matt.wand@uts.edu.au>
Depends: KernSmooth, lattice, mgcv
Description: Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics (in press).
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2018-05-20 01:26:34 UTC; mwand
Author: Tung H. Pham [aut], Matt P. Wand [aut, cre]
Repository: CRAN
Date/Publication: 2018-05-21 06:50:53 UTC

More information about gammSlice at CRAN
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Package kableExtra updated to version 0.9.0 with previous version 0.8.0 dated 2018-04-05

Title: Construct Complex Table with 'kable' and Pipe Syntax
Description: Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' and the piping syntax from 'magrittr'. Function 'kable()' is a light weight table generator coming from 'knitr'. This package simplifies the way to manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows users to construct complex tables and customize styles using a readable syntax.
Author: Hao Zhu [aut, cre] (<https://orcid.org/0000-0002-3386-6076>>), Thomas Travison [ctb], Timothy Tsai [ctb], Will Beasley [ctb], Yihui Xie [ctb], GuangChuang Yu [ctb], Stéphane Laurent [ctb], Rob Shepherd [ctb], Yoni Sidi [ctb], Brian Salzer [ctb], George Gui [ctb], Yeliang Fan [ctb]
Maintainer: Hao Zhu <haozhu233@gmail.com>

Diff between kableExtra versions 0.8.0 dated 2018-04-05 and 0.9.0 dated 2018-05-21

 DESCRIPTION                                           |   18 
 MD5                                                   |   82 
 NAMESPACE                                             |    5 
 R/add_footnote.R                                      |   24 
 R/add_indent.R                                        |   10 
 R/auto_set_format.R                                   |only
 R/cell_spec.R                                         |    7 
 R/collapse_rows.R                                     |   32 
 R/column_spec.R                                       |  189 +
 R/footnote.R                                          |   50 
 R/group_rows.R                                        |   19 
 R/kableExtra-package.R                                |    2 
 R/kable_styling.R                                     |   27 
 R/magic_mirror.R                                      |   44 
 R/print.R                                             |   21 
 R/row_spec.R                                          |   75 
 R/save_kable.R                                        |only
 R/scroll_box.R                                        |   14 
 R/util.R                                              |   13 
 R/zzz.R                                               |    8 
 README.md                                             |    5 
 build/vignette.rds                                    |binary
 inst/NEWS.md                                          |   39 
 inst/bootstrapTable-3.3.7                             |only
 inst/doc/awesome_table_in_html.R                      |   90 
 inst/doc/awesome_table_in_html.Rmd                    |  118 
 inst/doc/awesome_table_in_html.html                   | 2528 +++++++++++++++++-
 inst/doc/awesome_table_in_pdf.R                       |   65 
 inst/doc/awesome_table_in_pdf.Rmd                     |  118 
 inst/doc/awesome_table_in_pdf.pdf                     |binary
 inst/doc/best_practice_for_newline_in_latex_table.pdf |binary
 inst/doc/legacy_features.html                         |    5 
 inst/doc/use_kableExtra_with_formattable.html         |   25 
 man/add_footnote.Rd                                   |    2 
 man/auto_index.Rd                                     |only
 man/cell_spec.Rd                                      |    4 
 man/collapse_rows.Rd                                  |    9 
 man/column_spec.Rd                                    |   14 
 man/footnote.Rd                                       |    5 
 man/html_dependency_bsTable.Rd                        |only
 man/html_dependency_kePrint.Rd                        |only
 man/save_kable.Rd                                     |only
 man/scroll_box.Rd                                     |   13 
 vignettes/awesome_table_in_html.Rmd                   |  118 
 vignettes/awesome_table_in_pdf.Rmd                    |  118 
 vignettes/kableExtra_sm.png                           |only
 46 files changed, 3384 insertions(+), 532 deletions(-)

More information about kableExtra at CRAN
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Package spuRs updated to version 2.0.2 with previous version 2.0.1 dated 2018-03-18

Title: Functions and Datasets for "Introduction to Scientific Programming and Simulation Using R"
Description: Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.
Author: Owen Jones, Robert Maillardet, Andrew Robinson, Olga Borovkova, and Steven Carnie
Maintainer: Andrew Robinson <apro@unimelb.edu.au>

Diff between spuRs versions 2.0.1 dated 2018-03-18 and 2.0.2 dated 2018-05-21

 spuRs-2.0.1/spuRs/man/spuRs-package.Rd |only
 spuRs-2.0.2/spuRs/DESCRIPTION          |    8 ++++----
 spuRs-2.0.2/spuRs/MD5                  |    3 +--
 3 files changed, 5 insertions(+), 6 deletions(-)

More information about spuRs at CRAN
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