Title: Exploratory Graph Analysis - A Framework for Estimating the
Number of Dimensions in Multivariate Data Using Network
Psychometrics
Description: An implementation of the Exploratory Graph Analysis (EGA) framework for dimensionality assessment. EGA is part of a new area called network psychometrics that focuses on the estimation of undirected network models in psychological datasets. EGA estimates the number of dimensions or factors using graphical lasso or Triangulated Maximally Filtered Graph (TMFG) and a weighted network community analysis. A bootstrap method for verifying the stability of the estimation is also available. The fit of the structure suggested by EGA can be verified using confirmatory factor analysis and a direct way to convert the EGA structure to a confirmatory factor model is also implemented. Documentation and examples are available. Golino, H. F., & Epskamp, S. (2017) <doi:10.1371/journal.pone.0174035>. Golino, H. F., & Demetriou, A. (2017) <doi:10.1016/j.intell.2017.02.007> Golino, H., Shi, D., Garrido, L. E., Christensen, A. P., Nieto, M. D., Sadana, R., & Thiyagarajan, J. A. (2018) <doi:10.31234/osf.io/gzcre>. Christensen, A. P. & Golino, H.F. (2019) <doi:10.31234/osf.io/9deay>.
Author: Hudson Golino [aut, cre] (<https://orcid.org/0000-0002-1601-1447>),
Alexander Christensen [aut] (<https://orcid.org/0000-0002-9798-7037>),
Robert Moulder [ctb] (<https://orcid.org/0000-0001-7504-9560>)
Maintainer: Hudson Golino <hfg9s@virginia.edu>
Diff between EGAnet versions 0.8 dated 2019-09-25 and 0.9.0 dated 2020-01-18
DESCRIPTION | 29 +- MD5 | 70 +++--- NAMESPACE | 7 NEWS | 21 + R/EGA.R | 358 ++++++++++++++++----------------- R/EGA.fit.R | 300 ++++++++++++++-------------- R/bootEGA.R | 13 - R/cmi.R | 55 ++--- R/dimStability.R |only R/net.loads.R | 343 ++++++++++++++++---------------- R/net.scores.R | 445 +++++++++++++++++++++--------------------- R/node.redundant.R |only R/node.redundant.combine.R |only R/node.redundant.names.R |only R/optimism.R | 52 ++-- R/tefi.R | 164 +++++++-------- R/zzz.R | 36 +-- build/vignette.rds |binary inst/CITATION | 30 +- inst/doc/Network_Scores.R | 10 inst/doc/Network_Scores.html | 18 + inst/doc/Redundant_Nodes.R |only inst/doc/Redundant_Nodes.Rmd |only inst/doc/Redundant_Nodes.html |only man/EBICglasso.qgraph.Rd | 17 + man/EGA.Rd | 13 + man/EGA.fit.Rd | 28 +- man/bootEGA.Rd | 15 + man/cmi.Rd | 40 +++ man/depression.Rd | 2 man/dimStability.Rd |only man/ega.wmt.Rd | 2 man/itemStability.Rd | 3 man/net.loads.Rd | 6 man/net.scores.Rd | 11 - man/node.redundant.Rd |only man/node.redundant.combine.Rd |only man/node.redundant.names.Rd |only man/toy.example.Rd | 2 man/wmt2.Rd | 2 vignettes/Figure_SI1_Code.png |only vignettes/Figure_SI2_NR.png |only vignettes/Redundant_Nodes.Rmd |only 43 files changed, 1112 insertions(+), 980 deletions(-)
Title: Structural Bayesian Vector Autoregression Models
Description: Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.
Author: Paul Richardson
Maintainer: Paul Richardson <p.richardson.54391@gmail.com>
Diff between BHSBVAR versions 2.0.1 dated 2019-12-12 and 2.0.2 dated 2020-01-18
DESCRIPTION | 8 ++++---- MD5 | 20 ++++++++++---------- R/BHSBVAR.R | 25 ++++++++++++------------- build/vignette.rds |binary inst/doc/BHSBVAR.Rnw | 2 +- inst/doc/BHSBVAR.pdf |binary vignettes/BHSBVAR.Rnw | 2 +- vignettes/fig/Dist_plots-1.pdf |binary vignettes/fig/HD_plots-1.pdf |binary vignettes/fig/IRF_plots-1.pdf |binary vignettes/fig/Model-1.pdf |binary 11 files changed, 28 insertions(+), 29 deletions(-)
Title: Implementing Dispersal into Species Distribution Models
Description: Functions for implementing species dispersal into projections
of species distribution models (e.g. under climate change scenarios).
Author: Robin Engler <robin.engler@gmail.com> and
Wim Hordijk <wim@WorldWideWanderings.net> and
Loic Pellissier <loic.pellissier@unil.ch>
Maintainer: ORPHANED
Diff between MigClim versions 1.6 dated 2013-12-23 and 1.6.1 dated 2020-01-18
DESCRIPTION | 16 ++++++++++------ MD5 | 11 ++++++----- NAMESPACE | 6 ++++++ R/MigClim.migrate.R | 4 ++-- man/MigClim.migrate.Rd | 7 +++++-- src/init.c |only src/migrate.c | 3 ++- 7 files changed, 31 insertions(+), 16 deletions(-)
Title: Tools for Parsing and Generating XML Within R and S-Plus
Description: Many approaches for both reading and
creating XML (and HTML) documents (including DTDs), both local
and accessible via HTTP or FTP. Also offers access to an
'XPath' "interpreter".
Author: CRAN Team [ctb, cre] (de facto maintainer since 2013),
Duncan Temple Lang [aut] (<https://orcid.org/0000-0003-0159-1546>),
Tomas Kalibera [ctb]
Maintainer: CRAN Team <CRAN@r-project.org>
Diff between XML versions 3.99-0.1 dated 2020-01-17 and 3.99-0.2 dated 2020-01-18
DESCRIPTION | 22 +++++++++++----------- MD5 | 2 +- 2 files changed, 12 insertions(+), 12 deletions(-)
Title: Korean NLP Package
Description: POS Tagger and Morphological Analyzer for Korean text based research. It provides tools for corpus linguistics research such as Keystroke converter, Hangul automata, Concordance, and Mutual Information. It also provides a convenient interface for users to apply, edit and add morphological dictionary selectively.
Author: Heewon Jeon [aut, cre],
Taekyung Kim [ctb]
Maintainer: Heewon Jeon <madjakarta@gmail.com>
Diff between KoNLP versions 0.80.1 dated 2016-12-15 and 0.80.2 dated 2020-01-18
DESCRIPTION | 6 +++--- MD5 | 4 ++-- R/utils.R | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-)
Title: Understand and Describe Bayesian Models and Posterior
Distributions
Description: Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors).
Author: Dominique Makowski [aut, cre] (<https://orcid.org/0000-0001-5375-9967>),
Daniel Lüdecke [aut] (<https://orcid.org/0000-0002-8895-3206>),
Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>),
Michael D. Wilson [aut] (<https://orcid.org/0000-0003-4143-7308>),
Paul-Christian Bürkner [rev],
Tristan Mahr [rev] (<https://orcid.org/0000-0002-8890-5116>),
Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>),
Quentin F. Gronau [ctb] (<https://orcid.org/0000-0001-5510-6943>)
Maintainer: Dominique Makowski <dom.makowski@gmail.com>
Diff between bayestestR versions 0.4.0 dated 2019-10-20 and 0.5.0 dated 2020-01-18
bayestestR-0.4.0/bayestestR/man/figures/unnamed-chunk-13-1.png |only bayestestR-0.4.0/bayestestR/man/figures/unnamed-chunk-15-1.png |only bayestestR-0.4.0/bayestestR/man/figures/unnamed-chunk-17-1.png |only bayestestR-0.4.0/bayestestR/tests/testthat/test-p_rope.R |only bayestestR-0.5.0/bayestestR/DESCRIPTION | 16 bayestestR-0.5.0/bayestestR/MD5 | 290 ++-- bayestestR-0.5.0/bayestestR/NAMESPACE | 63 bayestestR-0.5.0/bayestestR/NEWS.md | 32 bayestestR-0.5.0/bayestestR/R/bayesfactor.R | 18 bayestestR-0.5.0/bayestestR/R/bayesfactor_inclusion.R | 10 bayestestR-0.5.0/bayestestR/R/bayesfactor_models.R | 16 bayestestR-0.5.0/bayestestR/R/bayesfactor_parameters.R | 218 +-- bayestestR-0.5.0/bayestestR/R/bayesfactor_restricted.R | 69 bayestestR-0.5.0/bayestestR/R/check_prior.R | 117 - bayestestR-0.5.0/bayestestR/R/ci.R | 34 bayestestR-0.5.0/bayestestR/R/describe_posterior.R | 171 +- bayestestR-0.5.0/bayestestR/R/describe_prior.R | 17 bayestestR-0.5.0/bayestestR/R/diagnostic_posterior.R | 4 bayestestR-0.5.0/bayestestR/R/distribution.R | 32 bayestestR-0.5.0/bayestestR/R/equivalence_test.R | 50 bayestestR-0.5.0/bayestestR/R/estimate_density.R | 30 bayestestR-0.5.0/bayestestR/R/eti.R | 24 bayestestR-0.5.0/bayestestR/R/hdi.R | 20 bayestestR-0.5.0/bayestestR/R/mhdior.R |only bayestestR-0.5.0/bayestestR/R/p_direction.R | 43 bayestestR-0.5.0/bayestestR/R/p_map.R | 67 bayestestR-0.5.0/bayestestR/R/p_rope.R | 216 --- bayestestR-0.5.0/bayestestR/R/p_significance.R | 70 bayestestR-0.5.0/bayestestR/R/plot.R | 16 bayestestR-0.5.0/bayestestR/R/point_estimate.R | 36 bayestestR-0.5.0/bayestestR/R/print.bayesfactor_models.R | 2 bayestestR-0.5.0/bayestestR/R/print.bayesfactor_parameters.R | 2 bayestestR-0.5.0/bayestestR/R/print.bayesfactor_restricted.R | 4 bayestestR-0.5.0/bayestestR/R/print.ci.R | 6 bayestestR-0.5.0/bayestestR/R/print.describe_posterior.R | 7 bayestestR-0.5.0/bayestestR/R/print.equivalence_test.R | 82 + bayestestR-0.5.0/bayestestR/R/print.mhdior.R |only bayestestR-0.5.0/bayestestR/R/print.p_rope.R | 10 bayestestR-0.5.0/bayestestR/R/rope.R | 49 bayestestR-0.5.0/bayestestR/R/rope_range.R | 61 bayestestR-0.5.0/bayestestR/R/si.R |only bayestestR-0.5.0/bayestestR/R/simulate_priors.R | 17 bayestestR-0.5.0/bayestestR/R/utils.R | 13 bayestestR-0.5.0/bayestestR/R/utils_bayesfactor.R | 159 ++ bayestestR-0.5.0/bayestestR/R/utils_flatten_list.R | 24 bayestestR-0.5.0/bayestestR/R/utils_print_data_frame.R | 14 bayestestR-0.5.0/bayestestR/R/weighted_posteriors.R |only bayestestR-0.5.0/bayestestR/README.md | 303 ++-- bayestestR-0.5.0/bayestestR/build/partial.rdb |binary bayestestR-0.5.0/bayestestR/build/vignette.rds |binary bayestestR-0.5.0/bayestestR/inst/doc/bayes_factors.R | 497 ++++--- bayestestR-0.5.0/bayestestR/inst/doc/bayes_factors.Rmd | 702 ++++++---- bayestestR-0.5.0/bayestestR/inst/doc/bayes_factors.html | 682 +++++---- bayestestR-0.5.0/bayestestR/inst/doc/bayestestR.R | 14 bayestestR-0.5.0/bayestestR/inst/doc/credible_interval.R | 28 bayestestR-0.5.0/bayestestR/inst/doc/credible_interval.Rmd | 31 bayestestR-0.5.0/bayestestR/inst/doc/credible_interval.html | 35 bayestestR-0.5.0/bayestestR/inst/doc/example1.R | 66 bayestestR-0.5.0/bayestestR/inst/doc/example1.Rmd | 4 bayestestR-0.5.0/bayestestR/inst/doc/example1.html | 46 bayestestR-0.5.0/bayestestR/inst/doc/example2.R | 32 bayestestR-0.5.0/bayestestR/inst/doc/example2.Rmd | 4 bayestestR-0.5.0/bayestestR/inst/doc/example2.html | 26 bayestestR-0.5.0/bayestestR/inst/doc/example3.R | 4 bayestestR-0.5.0/bayestestR/inst/doc/example3.Rmd | 6 bayestestR-0.5.0/bayestestR/inst/doc/example3.html | 4 bayestestR-0.5.0/bayestestR/inst/doc/guidelines.R | 5 bayestestR-0.5.0/bayestestR/inst/doc/guidelines.Rmd | 11 bayestestR-0.5.0/bayestestR/inst/doc/guidelines.html | 8 bayestestR-0.5.0/bayestestR/inst/doc/indicesEstimationComparison.R | 16 bayestestR-0.5.0/bayestestR/inst/doc/indicesEstimationComparison.html | 8 bayestestR-0.5.0/bayestestR/inst/doc/indicesExistenceComparison.R | 5 bayestestR-0.5.0/bayestestR/inst/doc/indicesExistenceComparison.Rmd | 7 bayestestR-0.5.0/bayestestR/inst/doc/indicesExistenceComparison.html | 4 bayestestR-0.5.0/bayestestR/inst/doc/probability_of_direction.R | 8 bayestestR-0.5.0/bayestestR/inst/doc/probability_of_direction.Rmd | 2 bayestestR-0.5.0/bayestestR/inst/doc/probability_of_direction.html | 8 bayestestR-0.5.0/bayestestR/inst/doc/region_of_practical_equivalence.R | 14 bayestestR-0.5.0/bayestestR/inst/doc/region_of_practical_equivalence.Rmd | 2 bayestestR-0.5.0/bayestestR/inst/doc/region_of_practical_equivalence.html | 38 bayestestR-0.5.0/bayestestR/man/area_under_curve.Rd | 78 - 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bayestestR-0.5.0/bayestestR/man/mhdior.Rd |only bayestestR-0.5.0/bayestestR/man/overlap.Rd | 84 - bayestestR-0.5.0/bayestestR/man/p_direction.Rd | 264 +-- bayestestR-0.5.0/bayestestR/man/p_map.Rd | 225 +-- bayestestR-0.5.0/bayestestR/man/p_rope.Rd | 160 +- bayestestR-0.5.0/bayestestR/man/p_significance.Rd | 167 +- bayestestR-0.5.0/bayestestR/man/pd_to_p.Rd | 66 bayestestR-0.5.0/bayestestR/man/point_estimate.Rd | 193 +- bayestestR-0.5.0/bayestestR/man/reshape_ci.Rd | 54 bayestestR-0.5.0/bayestestR/man/rope.Rd | 328 ++-- bayestestR-0.5.0/bayestestR/man/rope_range.Rd | 108 - bayestestR-0.5.0/bayestestR/man/sensitivity_to_prior.Rd | 88 - bayestestR-0.5.0/bayestestR/man/si.Rd |only bayestestR-0.5.0/bayestestR/man/simulate_correlation.Rd | 131 - bayestestR-0.5.0/bayestestR/man/simulate_prior.Rd | 50 bayestestR-0.5.0/bayestestR/man/update.bayesfactor_models.Rd | 69 bayestestR-0.5.0/bayestestR/man/weighted_posteriors.Rd |only bayestestR-0.5.0/bayestestR/tests/testthat/test-bayesfactor_parameters.R | 7 bayestestR-0.5.0/bayestestR/tests/testthat/test-brms.R | 2 bayestestR-0.5.0/bayestestR/tests/testthat/test-describe_posterior.R | 48 bayestestR-0.5.0/bayestestR/tests/testthat/test-emmGrid.R | 59 bayestestR-0.5.0/bayestestR/tests/testthat/test-hdi.R | 4 bayestestR-0.5.0/bayestestR/tests/testthat/test-mhdior.R |only bayestestR-0.5.0/bayestestR/tests/testthat/test-rope.R | 6 bayestestR-0.5.0/bayestestR/tests/testthat/test-rstanarm.R | 4 bayestestR-0.5.0/bayestestR/tests/testthat/test-si.R |only bayestestR-0.5.0/bayestestR/tests/testthat/test-weighted_posteriors.R |only bayestestR-0.5.0/bayestestR/vignettes/bayes_factors.Rmd | 702 ++++++---- bayestestR-0.5.0/bayestestR/vignettes/bibliography.bib | 29 bayestestR-0.5.0/bayestestR/vignettes/credible_interval.Rmd | 31 bayestestR-0.5.0/bayestestR/vignettes/example1.Rmd | 4 bayestestR-0.5.0/bayestestR/vignettes/example2.Rmd | 4 bayestestR-0.5.0/bayestestR/vignettes/example3.Rmd | 6 bayestestR-0.5.0/bayestestR/vignettes/guidelines.Rmd | 11 bayestestR-0.5.0/bayestestR/vignettes/indicesExistenceComparison.Rmd | 7 bayestestR-0.5.0/bayestestR/vignettes/probability_of_direction.Rmd | 2 bayestestR-0.5.0/bayestestR/vignettes/region_of_practical_equivalence.Rmd | 2 153 files changed, 7043 insertions(+), 5187 deletions(-)
Title: Implementation of the Efficient Large-Scale Online Display
Advertising Algorithm
Description: An implementation of the algorithm described in "Efficient Large-
Scale Internet Media Selection Optimization for Online Display Advertising"
by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below
for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf>
for a full-text version of the paper). The algorithm here is designed to
allocate budget across a set of online advertising opportunities using a
coordinate-descent approach, but it can be used in any resource-allocation
problem with a matrix of visitation (in the case of the paper, website page-
views) and channels (in the paper, websites). The package contains allocation
functions both in the presence of bidding, when allocation is dependent on
channel-specific cost curves, and when advertising costs are fixed at each channel.
Author: Courtney Paulson [aut, cre],
Lan Luo [ctb],
Gareth James [ctb]
Maintainer: Courtney Paulson <courtneypaulson@suu.edu>
Diff between ELMSO versions 1.0.0 dated 2018-09-03 and 1.0.1 dated 2020-01-18
DESCRIPTION | 18 +++++++++--------- MD5 | 2 +- 2 files changed, 10 insertions(+), 10 deletions(-)
Title: A Fast and Lightweight Logging System for R, Based on 'log4j'
Description: The log4r package is meant to provide a fast, lightweight,
object-oriented approach to logging in R based on the widely-emulated
'log4j' system and etymology.
Author: John Myles White [aut, cph],
Kenton White [ctb],
Kirill Müller [ctb],
Aaron Jacobs [aut, cre]
Maintainer: Aaron Jacobs <atheriel@gmail.com>
Diff between log4r versions 0.3.1 dated 2019-09-04 and 0.3.2 dated 2020-01-18
DESCRIPTION | 10 +++++----- MD5 | 18 +++++++++--------- NEWS.md | 5 +++++ R/appenders.R | 2 ++ build/vignette.rds |binary inst/doc/performance.Rmd | 1 + inst/doc/performance.html | 39 ++++++++++++++++++++------------------- man/create.logger.Rd | 3 +-- tests/testthat/test-appenders.R | 13 +++++++++++++ vignettes/performance.Rmd | 1 + 10 files changed, 57 insertions(+), 35 deletions(-)
Title: R Implementation of Leiden Clustering Algorithm
Description: Implements the 'Python leidenalg' module to be called in R.
Enables clustering using the leiden algorithm for partition a graph into communities.
See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg>
Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
Author: S. Thomas Kelly [aut, cre, trl],
Vincent A. Traag [com]
Maintainer: S. Thomas Kelly <tom.kelly@riken.jp>
Diff between leiden versions 0.3.1 dated 2019-07-23 and 0.3.2 dated 2020-01-18
DESCRIPTION | 10 MD5 | 29 - NAMESPACE | 3 NEWS.md | 8 R/leiden.R | 49 +- inst/CITATION | 4 inst/doc/benchmarking.R | 929 ++++++++++++++++++++--------------------- inst/doc/benchmarking.Rmd | 93 ++-- inst/doc/benchmarking.html | 160 ------- inst/doc/run_igraph.R | 120 ++--- inst/doc/run_igraph.html | 47 -- inst/doc/run_leiden.R | 98 ++-- inst/doc/run_leiden.html | 36 - man/leiden.Rd | 20 tests/testthat/test_weighted.R |only vignettes/benchmarking.Rmd | 93 ++-- 16 files changed, 781 insertions(+), 918 deletions(-)
Title: Visualization and Analysis of Statistical Measures of Confidence
Description: Enables: (1) plotting two-dimensional confidence regions, (2) coverage analysis
of confidence region simulations and (3) calculating confidence intervals and the associated
actual coverage for binomial proportions. Each is given in greater detail next.
(1) Plots the two-dimensional confidence region for probability distribution parameters
(supported distribution suffixes: cauchy, gamma, invgauss, logis, llogis, lnorm, norm, unif,
weibull) corresponding to a user-given complete or right-censored dataset and level of
significance. The crplot() algorithm plots more points in areas of greater curvature to
ensure a smooth appearance throughout the confidence region boundary. An alternative
heuristic plots a specified number of points at roughly uniform intervals along its boundary.
Both heuristics build upon the radial profile log-likelihood ratio technique for plotting
confidence regions given by Jaeger (2016) <doi:10.1080/00031305.2016.1182946>, and
are detailed in a publication by Weld (2019) <doi:10.1080/00031305.2018.1564696>.
(2) Performs confidence region coverage simulations for a random sample drawn from a user-
specified parametric population distribution, or for a user-specified dataset and point of
interest with coversim(). (3) Calculates confidence interval bounds for a binomial
proportion with binomTest(), calculates the actual coverage with binomTestCoverage(), and
plots the actual coverage with binomTestCoveragePlot(). Calculates confidence interval
bounds for the binomial proportion using an ensemble of constituent confidence intervals
with binomTestEnsemble().
Author: Christopher Weld [aut, cre] (<https://orcid.org/0000-0001-5902-9738>),
Hayeon Park [aut],
Lawrence Leemis [aut],
Andrew Loh [ctb],
Yuan Chang [ctb],
Brock Crook [ctb],
Xin Zhang [ctb]
Maintainer: Christopher Weld <ceweld@email.wm.edu>
Diff between conf versions 1.6.1 dated 2019-07-02 and 1.6.2 dated 2020-01-18
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Title: Finite Mixture of Multivariate Censored/Missing Data
Description: It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) <doi:10.1016/j.jmva.2017.05.005> for the multivariate skew-normal case.
Author: Francisco H. C. de Alencar [aut, cre],
Christian E. Galarza [aut],
Larissa A. Matos [ctb],
Victor H. Lachos [ctb]
Maintainer: Francisco H. C. de Alencar <hildemardealencar@gmail.com>
Diff between CensMFM versions 1.5 dated 2019-10-23 and 2.0 dated 2020-01-18
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