Wed, 12 Jun 2019

Package pwt9 updated to version 9.1-0 with previous version 9.0-0 dated 2017-01-04

Title: Penn World Table (Version 9.x)
Description: The Penn World Table 9.x (<http://www.ggdc.net/pwt/>) provides information on relative levels of income, output, inputs, and productivity for 182 countries between 1950 and 2017.
Author: Achim Zeileis [aut, cre] (<https://orcid.org/0000-0003-0918-3766>)
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

Diff between pwt9 versions 9.0-0 dated 2017-01-04 and 9.1-0 dated 2019-06-12

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 9 files changed, 42 insertions(+), 41 deletions(-)

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Permanent link

Package Quandl updated to version 2.10.0 with previous version 2.9.1 dated 2018-08-14

Title: API Wrapper for Quandl.com
Description: Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to <https://www.quandl.com/docs/api>. For more help on the package itself go to <https://www.quandl.com/help/r>.
Author: Dave Dotson [cre], Raymond McTaggart [aut], Gergely Daroczi [aut], Clement Leung [aut], Quandl Inc. [cph]
Maintainer: Dave Dotson <dave@quandl.com>

Diff between Quandl versions 2.9.1 dated 2018-08-14 and 2.10.0 dated 2019-06-12

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Package tigris updated to version 0.8.2 with previous version 0.7 dated 2018-04-14

Title: Load Census TIGER/Line Shapefiles
Description: Download TIGER/Line shapefiles from the United States Census Bureau (<https://www.census.gov/geo/maps-data/data/tiger-line.html>) and load into R as 'SpatialDataFrame' or 'sf' objects.
Author: Kyle Walker [aut, cre], Bob Rudis [ctb]
Maintainer: Kyle Walker <kyle.walker@tcu.edu>

Diff between tigris versions 0.7 dated 2018-04-14 and 0.8.2 dated 2019-06-12

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More information about tigris at CRAN
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Package tidycensus updated to version 0.9.2 with previous version 0.9 dated 2019-01-09

Title: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames
Description: An integrated R interface to the decennial US Census and American Community Survey APIs and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for many geographies.
Author: Kyle Walker [aut, cre], Kris Eberwein [ctb], Matt Herman [ctb]
Maintainer: Kyle Walker <kyle.walker@tcu.edu>

Diff between tidycensus versions 0.9 dated 2019-01-09 and 0.9.2 dated 2019-06-12

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Package MBESS updated to version 4.6.0 with previous version 4.5.1 dated 2019-05-17

Title: The MBESS R Package
Description: Implements methods that useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but at this point MBESS contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from others, see <http://nd.edu/~kkelley/site/MBESS.html> for a detailed list of those that have contributed and other details.
Author: Ken Kelley [aut, cre]
Maintainer: Ken Kelley <kkelley@nd.edu>

Diff between MBESS versions 4.5.1 dated 2019-05-17 and 4.6.0 dated 2019-06-12

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Package divDyn updated to version 0.8.0 with previous version 0.7.1 dated 2019-02-19

Title: Diversity Dynamics using Fossil Sampling Data
Description: Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>.
Author: Adam T. Kocsis, John Alroy, Carl J. Reddin, Wolfgang Kiessling
Maintainer: Adam T. Kocsis <adam.t.kocsis@gmail.com>

Diff between divDyn versions 0.7.1 dated 2019-02-19 and 0.8.0 dated 2019-06-12

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Package fitODBOD updated to version 1.4.0 with previous version 1.3.0 dated 2019-05-07

Title: Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD
Description: Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>).
Author: Amalan Mahendran [aut, cre] (<https://orcid.org/0000-0002-0643-9052>), Pushpakanthie Wijekoon [aut, ctb] (<https://orcid.org/0000-0003-4242-1017>)
Maintainer: Amalan Mahendran <amalan0595@gmail.com>

Diff between fitODBOD versions 1.3.0 dated 2019-05-07 and 1.4.0 dated 2019-06-12

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Package vein updated to version 0.7.8 with previous version 0.7.0 dated 2019-03-26

Title: Vehicular Emissions Inventories
Description: Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>. Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the function inventory to create a structure of directories and template scripts.
Author: Sergio Ibarra-Espinosa [aut, cre] (<https://orcid.org/0000-0002-3162-1905>), Daniel Schuch [ctb] (<https://orcid.org/0000-0001-5977-4519>)
Maintainer: Sergio Ibarra-Espinosa <sergio.ibarra@usp.br>

Diff between vein versions 0.7.0 dated 2019-03-26 and 0.7.8 dated 2019-06-12

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Package GDINA updated to version 2.6.0 with previous version 2.5.2 dated 2019-05-10

Title: The Generalized DINA Model Framework
Description: A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070> for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre <DOI:10.1177/0146621613479818> for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided.
Author: Wenchao Ma [aut, cre, cph], Jimmy de la Torre [aut, cph], Miguel Sorrel [ctb], Zhehan Jiang [ctb]
Maintainer: Wenchao Ma <wenchao.ma@ua.edu>

Diff between GDINA versions 2.5.2 dated 2019-05-10 and 2.6.0 dated 2019-06-12

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Package BIFIEsurvey updated to version 3.3-12 with previous version 3.2-25 dated 2019-04-16

Title: Tools for Survey Statistics in Educational Assessment
Description: Contains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>, <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, <doi:10.1080/02331889708802523>). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016; <doi:10.17888/fdb-demo:bistE813I-16a>). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria).
Author: BIFIE [aut], Alexander Robitzsch [aut, cre], Konrad Oberwimmer [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

Diff between BIFIEsurvey versions 3.2-25 dated 2019-04-16 and 3.3-12 dated 2019-06-12

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Package autothresholdr updated to version 1.3.3 with previous version 1.3.2 dated 2019-05-30

Title: An R Port of the 'ImageJ' Plugin 'Auto Threshold'
Description: Algorithms for automatically finding appropriate thresholds for numerical data, with special functions for thresholding images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. See <http://imagej.net/Auto_Threshold> and Landini et al. (2017) <DOI:10.1111/jmi.12474>.
Author: Rory Nolan [aut, cre, trl] (<https://orcid.org/0000-0002-5239-4043>), Luis Alvarez [ctb] (<https://orcid.org/0000-0003-1316-1906>), Sergi Padilla-Parra [ctb, ths] (<https://orcid.org/0000-0002-8010-9481>), Gabriel Landini [ctb, cph] (<https://orcid.org/0000-0002-9689-0989>)
Maintainer: Rory Nolan <rorynoolan@gmail.com>

Diff between autothresholdr versions 1.3.2 dated 2019-05-30 and 1.3.3 dated 2019-06-12

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New package pwr2ppl with initial version 0.1.1
Package: pwr2ppl
Type: Package
Title: Power Analyses for Common Designs (Power to the People)
Version: 0.1.1
Author: Chris Aberson
Maintainer: Chris Aberson <cla18@humboldt.edu>
Description: Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: car (>= 3.0-0), MASS (>= 7.3-51), dplyr (>= 0.8.0), tidyr (>= 0.8.0), ez (>= 0.4.3), nlme (>= 3.1-139), phia (>= 0.2-0), afex (>= 0.22-1), MBESS (>= 4.5.0), lavaan (>= 0.6-2), stats (>= 3.5.0)
NeedsCompilation: no
Packaged: 2019-06-11 03:40:53 UTC; Chris Aberson
Repository: CRAN
Date/Publication: 2019-06-12 13:30:02 UTC

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Package Numero updated to version 1.2.0 with previous version 1.1.1 dated 2018-11-24

Title: Statistical Framework to Define Subgroups in Complex Datasets
Description: High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2018) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, dyy113, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Author: Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], Ville-Petteri Makinen [aut, cre]
Maintainer: Ville-Petteri Makinen <vpmakine@gmail.com>

Diff between Numero versions 1.1.1 dated 2018-11-24 and 1.2.0 dated 2019-06-12

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New package cblasr with initial version 1.0.0
Package: cblasr
Type: Package
Title: The C Interface to 'BLAS' Routines
Version: 1.0.0
Authors@R: c(person("Yi", "Pan", email = "ypan1988@gmail.com", role = c("aut", "cre")), person("Keita", "Teranishi", role = c("aut")))
Maintainer: Yi Pan <ypan1988@gmail.com>
Description: Provides the 'cblas.h' header file as C interface to the underlying internal 'BLAS' library in R. 'CBLAS' <https://www.netlib.org/blas/cblas.h> is a collection of wrappers originally written by Keita Teranishi and provides a C interface to the FORTRAN 'BLAS' library <https://www.netlib.org/blas/>. Note that as internal 'BLAS' library provided by R <https://svn.r-project.org/R/trunk/src/include/R_ext/BLAS.h> is used and only the double precision / double complex 'BLAS' routines are supported.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 1.0.0)
LinkingTo: Rcpp
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-06-10 19:21:45 UTC; pany
Author: Yi Pan [aut, cre], Keita Teranishi [aut]
Repository: CRAN
Date/Publication: 2019-06-12 13:10:02 UTC

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New package textdata with initial version 0.1.0
Package: textdata
Title: Download and Load Various Text Datasets
Version: 0.1.0
Authors@R: c(person(given = "Emil", family = "Hvitfeldt", role = c("aut", "cre"), email = "emilhhvitfeldt@gmail.com", comment = c(ORCID = "0000-0002-0679-1945")), person(given = "Julia", family = "Silge", role = c("ctb"), email = "julia.silge@gmail.com", comment = c(ORCID = "0000-0002-3671-836X")))
Description: Provides a framework to download, parse, and store text datasets on the disk and load them when needed. Includes various sentiment lexicons and labeled text data sets for classification and analysis.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: fs, readr, tibble, rappdirs
RoxygenNote: 6.1.1
Collate: 'dataset_sentence_polarity.R' 'lexicon_bing.R' 'lexicon_loughran.R' 'lexicon_afinn.R' 'download_functions.R' 'info.R' 'load_dataset.R' 'printer.R' 'process_functions.R'
Suggests: knitr, rmarkdown, testthat (>= 2.1.0)
VignetteBuilder: knitr
URL: https://github.com/EmilHvitfeldt/textdata
BugReports: https://github.com/EmilHvitfeldt/textdata/issues
NeedsCompilation: no
Packaged: 2019-06-11 16:54:10 UTC; emilhvitfeldthansen
Author: Emil Hvitfeldt [aut, cre] (<https://orcid.org/0000-0002-0679-1945>), Julia Silge [ctb] (<https://orcid.org/0000-0002-3671-836X>)
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>
Repository: CRAN
Date/Publication: 2019-06-12 12:20:03 UTC

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Package folderfun updated to version 0.1.2 with previous version 0.1.1 dated 2019-03-05

Title: Creates and Manages Folder Functions for Portable Large-Scale R Analysis
Description: If you find yourself working on multiple different projects in R, you'll want a series of folders pointing to raw data, processed data, plot results, intermediate table outputs, etc. This package makes it easier to do that by providing a quick and easy way to create and use functions for project-level directories.
Author: Nathan C. Sheffield [aut, cre], Michal Stolarczyk [ctb], Vince Reuter [ctb]
Maintainer: Nathan C. Sheffield <nathan@code.databio.org>

Diff between folderfun versions 0.1.1 dated 2019-03-05 and 0.1.2 dated 2019-06-12

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Package tergm updated to version 3.6.1 with previous version 3.6.0 dated 2019-05-15

Title: Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models
Description: An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis.
Author: Pavel N. Krivitsky [aut, cre] (<https://orcid.org/0000-0002-9101-3362>), Mark S. Handcock [aut, ths], David R. Hunter [ctb], Steven M. Goodreau [ctb, ths], Martina Morris [ctb, ths], Nicole Bohme Carnegie [ctb], Carter T. Butts [ctb], Ayn Leslie-Cook [ctb], Skye Bender-deMoll [ctb], Li Wang [ctb], Kirk Li [ctb]
Maintainer: Pavel N. Krivitsky <pavel@uow.edu.au>

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Package stabm updated to version 1.1.0 with previous version 1.0.0 dated 2019-02-22

Title: Stability Measures for Feature Selection
Description: An implementation of many measures for the assessment of the stability of feature selection. Both simple measures and measures which take into account the similarities between features are available, see Bommert et al. (2017) <doi:10.1155/2017/7907163>.
Author: Andrea Bommert [aut, cre]
Maintainer: Andrea Bommert <bommert@statistik.tu-dortmund.de>

Diff between stabm versions 1.0.0 dated 2019-02-22 and 1.1.0 dated 2019-06-12

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Package RcppArmadillo updated to version 0.9.500.2.0 with previous version 0.9.400.3.0 dated 2019-05-11

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 0.9.400.3.0 dated 2019-05-11 and 0.9.500.2.0 dated 2019-06-12

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Package processanimateR updated to version 1.0.1 with previous version 1.0.0 dated 2018-11-27

Title: Process Map Token Replay Animation
Description: Provides animated process maps based on the 'procesmapR' package. Cases stored in event logs created with with 'bupaR' S3 class eventlog() are rendered as tokens (SVG shapes) and animated according to their occurrence times on top of the process map. For rendering SVG animations ('SMIL') and the 'htmlwidget' package are used.
Author: Felix Mannhardt [aut, cre], Gert Janssenswillen [ctb]
Maintainer: Felix Mannhardt <felix.mannhardt@sintef.no>

Diff between processanimateR versions 1.0.0 dated 2018-11-27 and 1.0.1 dated 2019-06-12

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Package joint.Cox updated to version 3.3 with previous version 3.2 dated 2019-05-16

Title: Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis
Description: Perform likelihood estimation and dynamic prediction under joint frailty-copula models for tumour progression and death in meta-analysis. A penalized likelihood method is employed for estimating model parameters, where the baseline hazard functions are modeled by smoothing splines. The methods are applicable for meta-analytic data combining several studies. The methods can analyze data having information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). See Emura et al. (2017) <doi:10.1177/0962280215604510> for likelihood estimation, and Emura et al. (2018) <doi:10.1177/0962280216688032> for dynamic prediction. Survival data from ovarian cancer patients are also available.
Author: Takeshi Emura
Maintainer: Takeshi Emura <takeshiemura@gmail.com>

Diff between joint.Cox versions 3.2 dated 2019-05-16 and 3.3 dated 2019-06-12

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Package jackalope updated to version 0.1.1 with previous version 0.1.0 dated 2019-06-04

Title: A Swift, Versatile Phylogenomic and High-Throughput Sequencing Simulator
Description: Simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina <https://www.illumina.com/> and Pacific Biosciences (PacBio) <https://www.pacb.com/> platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations. 'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, as well as PacBio reads. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/polymerase chain reaction (PCR) duplicates. Simulating Illumina sequencing is based on ART by Huang et al. (2012) <doi:10.1093/bioinformatics/btr708>. PacBio sequencing simulation is based on SimLoRD by Stöcker et al. (2016) <doi:10.1093/bioinformatics/btw286>. All outputs can be written to standard file formats.
Author: Lucas A. Nell [cph, aut, cre] (<https://orcid.org/0000-0003-3209-0517>)
Maintainer: Lucas A. Nell <lucas@lucasnell.com>

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Package radtools (with last version 1.0.6) was removed from CRAN

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

2019-04-28 1.0.6
2019-04-16 1.0.5
2019-03-13 1.0.4
2019-01-16 1.0.3
2019-01-15 1.0.2
2018-10-19 1.0.0

Permanent link
Package nowcasting updated to version 1.1.3 with previous version 1.1.2 dated 2019-06-04

Title: Predicting Economic Variables using Dynamic Factor Models
Description: It contains the tools to implement dynamic factor models to forecast economic variables. The user will be able to construct pseudo real time vintages, use information criteria for determining the number of factors and shocks, estimate the model, and visualize results among other things.
Author: Daiane Marcolino de Mattos [aut, cre], Pedro Costa Ferreira [aut], Serge de Valk [aut], Guilherme Branco Gomes [aut]
Maintainer: Daiane Marcolino de Mattos <daiane.mattos@fgv.br>

Diff between nowcasting versions 1.1.2 dated 2019-06-04 and 1.1.3 dated 2019-06-12

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Package timeR updated to version 1.1.0 with previous version 1.0.0 dated 2019-01-25

Title: Time Your Codes
Description: Provides a 'timeR' class that makes timing codes easier. One can create 'timeR' objects and use them to record all timings, and extract recordings as data frame for later use.
Author: Yifu Yan
Maintainer: Yifu Yan <yanyifu94@hotmail.com>

Diff between timeR versions 1.0.0 dated 2019-01-25 and 1.1.0 dated 2019-06-12

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Package glinternet updated to version 1.0.9 with previous version 1.0.8 dated 2018-06-15

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

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Package BVSNLP updated to version 1.1.8 with previous version 1.1.5 dated 2018-05-17

Title: Bayesian Variable Selection in High Dimensional Settings using Nonlocal Priors
Description: Variable/Feature selection in high or ultra-high dimensional settings has gained a lot of attention recently specially in cancer genomic studies. This package provides a Bayesian approach to tackle this problem, where it exploits mixture of point masses at zero and nonlocal priors to improve the performance of variable selection and coefficient estimation. product moment (pMOM) and product inverse moment (piMOM) nonlocal priors are implemented and can be used for the analyses. This package performs variable selection for binary response and survival time response datasets which are widely used in biostatistic and bioinformatics community. Benefiting from parallel computing ability, it reports necessary outcomes of Bayesian variable selection such as Highest Posterior Probability Model (HPPM), Median Probability Model (MPM) and posterior inclusion probability for each of the covariates in the model. The option to use Bayesian Model Averaging (BMA) is also part of this package that can be exploited for predictive power measurements in real datasets.
Author: Amir Nikooienejad [aut, cre], Valen E. Johnson [ths]
Maintainer: Amir Nikooienejad <amir@stat.tamu.edu>

Diff between BVSNLP versions 1.1.5 dated 2018-05-17 and 1.1.8 dated 2019-06-12

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Package shipunov updated to version 1.2 with previous version 1.1 dated 2019-05-06

Title: Miscellaneous Functions from Alexey Shipunov
Description: A collection of functions for data manipulation, plotting and statistical computing, to use separately or with the book "Visual Statistics. Use R!": Shipunov (2019) <http://ashipunov.info/shipunov/software/r/r-en.htm>. Most useful functions are probably Bclust(), Jclust() and BootA() which bootstrap hierarchical clustering; Recode...() which multiple recode in a fast, flexible and simple way; Misclass() which outputs confusion matrix even if classes are not concerted; Overlap() which calculates overlaps of convex hulls from any projection; and Pleiad() which is fast and flexible correlogram. In fact, there are much more useful functions, please see documentation.
Author: Alexey Shipunov [aut, cre], Paul Murrell [ctb], Marcello D'Orazio [ctb], Stephen Turner [ctb], Eugeny Altshuler [ctb], Roland Rau [ctb], Marcus W Beck [ctb], Sebastian Gibb [ctb], Weiliang Qiu [ctb], Emmanuel Paradis [ctb], Roger Koenker [ctb], R Core Team [ctb]
Maintainer: Alexey Shipunov <dactylorhiza@gmail.com>

Diff between shipunov versions 1.1 dated 2019-05-06 and 1.2 dated 2019-06-12

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 R/ppoints.r                 |only
 R/s_value.r                 |only
 TODO                        |   50 +++---------
 data                        |only
 man/Adj.Rand.Rd             |    2 
 man/Bclabels.Rd             |   12 +-
 man/Bclust.Rd               |   16 +--
 man/BestOverlap.Rd          |    2 
 man/Biokey.Rd               |only
 man/BootA.Rd                |    4 
 man/BootKNN.Rd              |    2 
 man/BootRF.Rd               |    2 
 man/Boxplots.Rd             |    7 -
 man/Cdate.Rd                |    2 
 man/Classproj.Rd            |   11 +-
 man/Co.test.Rd              |    2 
 man/Coml.Rd                 |    3 
 man/DNN.Rd                  |only
 man/Dev.Rd                  |    6 -
 man/Dotchart.Rd             |    4 
 man/Ellipses.Rd             |   41 ++++++++--
 man/Ex.pch.Rd               |    2 
 man/Files.Rd                |    6 -
 man/Fill.Rd                 |only
 man/Gap.code.Rd             |    5 -
 man/Gen.cl.data.Rd          |    2 
 man/Gower.dist.Rd           |    8 -
 man/Gradd.Rd                |    4 
 man/Hcl2mat.Rd              |   13 ++-
 man/Hclust.match.Rd         |    6 -
 man/Hcoords.Rd              |    2 
 man/Histr.Rd                |    4 
 man/Infill.Rd               |    4 
 man/Jclust.Rd               |   14 +--
 man/K.Rd                    |   21 ++---
 man/Linechart.Rd            |    2 
 man/Ls.Rd                   |    2 
 man/MRH.Rd                  |only
 man/Misclass.Rd             |    9 +-
 man/Missing.map.Rd          |    3 
 man/MrBayes.Rd              |   61 ++++++++++----
 man/Normality.Rd            |    2 
 man/Overlap.Rd              |    2 
 man/Pleiad.Rd               |   18 ++--
 man/Plot.phylocl.Rd         |    8 -
 man/PlotBest.dist.Rd        |    2 
 man/PlotBest.hclust.Rd      |    2 
 man/PlotBest.mdist.Rd       |    7 -
 man/Points.Rd               |   19 +++-
 man/Read.tri.nts.Rd         |   33 ++++++--
 man/Recode.Rd               |   30 +++++--
 man/Root1.Rd                |    8 -
 man/Rostova.tbl.Rd          |    4 
 man/Rresults.Rd             |    2 
 man/S.value.Rd              |only
 man/Saynodynamite.Rd        |    6 +
 man/Str.Rd                  |   15 ++-
 man/Tobin.Rd                |    2 
 man/Toclip.Rd               |   10 +-
 man/Topm.Rd                 |    2 
 man/Updist.Rd               |   10 +-
 man/VTcoeffs.Rd             |    2 
 man/Write.fasta.Rd          |    3 
 man/Xpager.Rd               |    5 -
 man/atmospheres.Rd          |only
 man/chaetocnema.Rd          |only
 man/classifs.Rd             |only
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 man/haltica.Rd              |only
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 man/moldino.Rd              |only
 man/pairwise.Eff.Rd         |    4 
 man/pairwise.Rro.test.Rd    |    1 
 man/pairwise.Table2.test.Rd |    2 
 man/plantago.Rd             |only
 man/salix.Rd                |only
 94 files changed, 444 insertions(+), 454 deletions(-)

More information about shipunov at CRAN
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Package augSIMEX updated to version 3.7.3 with previous version 3.7.2 dated 2019-04-24

Title: Analysis of Data with Mixed Measurement Error and Misclassification in Covariates
Description: Implementation of the augmented Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777> for analyzing the data with mixed measurement error and misclassification. The main function provides a similar summary output as that of glm() function. Both parametric and empirical SIMEX are considered in the package.
Author: Qihuang Zhang <qihuang.zhang@uwaterloo.ca>, Grace Y. Yi <yyi@uwaterloo.ca>
Maintainer: Qihuang Zhang <qihuang.zhang@uwaterloo.ca>

Diff between augSIMEX versions 3.7.2 dated 2019-04-24 and 3.7.3 dated 2019-06-12

 DESCRIPTION |    8 ++++----
 MD5         |    3 ++-
 inst        |only
 3 files changed, 6 insertions(+), 5 deletions(-)

More information about augSIMEX at CRAN
Permanent link


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