Title: Extended Structural Equation Modelling
Description: Create structural equation models that can be manipulated programmatically.
Models may be specified with matrices or paths (LISREL or RAM)
Example models include confirmatory factor, multiple group, mixture
distribution, categorical threshold, modern test theory, differential
Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares.
equations, state space, and many others.
Support and advanced package binaries available at <http://openmx.ssri.psu.edu>.
The software is described in Neale, Hunter, Pritikin, Zahery, Brick,
Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.
Author: Steven M. Boker [aut],
Michael C. Neale [aut],
Hermine H. Maes [aut],
Michael J. Wilde [ctb],
Michael Spiegel [aut],
Timothy R. Brick [aut],
Ryne Estabrook [aut],
Timothy C. Bates [aut],
Paras Mehta [ctb],
Timo von Oertzen [ctb],
Ross J. Gore [aut],
Michael D. Hunter [aut],
Daniel C. Hackett [ctb],
Julian Karch [ctb],
Andreas M. Brandmaier [ctb],
Joshua N. Pritikin [aut, cre],
Mahsa Zahery [aut],
Robert M. Kirkpatrick [aut],
Yang Wang [ctb],
Ben Goodrich [ctb],
Charles Driver [ctb],
Massachusetts Institute of Technology [cph],
S. G. Johnson [cph],
Association for Computing Machinery [cph],
Dieter Kraft [cph],
Stefan Wilhelm [cph],
Sarah Medland [cph],
Carl F. Falk [cph],
Matt Keller [cph],
Manjunath B G [cph],
The Regents of the University of California [cph],
Lester Ingber [cph],
Wong Shao Voon [cph],
Juan Palacios [cph],
Jiang Yang [cph],
Gael Guennebaud [cph],
Jitse Niesen [cph]
Maintainer: Joshua N. Pritikin <jpritikin@pobox.com>
Diff between OpenMx versions 2.19.8 dated 2021-09-06 and 2.20.0 dated 2022-01-15
OpenMx-2.19.8/OpenMx/tests/testthat/test-cor.R |only OpenMx-2.20.0/OpenMx/DESCRIPTION | 16 OpenMx-2.20.0/OpenMx/MD5 | 439 ++-- OpenMx-2.20.0/OpenMx/R/MxAlgebraFunctions.R | 2 OpenMx-2.20.0/OpenMx/R/MxApply.R | 2 OpenMx-2.20.0/OpenMx/R/MxAutoStart.R | 4 OpenMx-2.20.0/OpenMx/R/MxBaseNamed.R | 20 OpenMx-2.20.0/OpenMx/R/MxCompute.R | 211 +- OpenMx-2.20.0/OpenMx/R/MxData.R | 18 OpenMx-2.20.0/OpenMx/R/MxDataWLS.R | 8 OpenMx-2.20.0/OpenMx/R/MxEval.R | 66 OpenMx-2.20.0/OpenMx/R/MxExpectationLISREL.R | 2 OpenMx-2.20.0/OpenMx/R/MxExpectationNormal.R | 52 OpenMx-2.20.0/OpenMx/R/MxExpectationRAM.R | 4 OpenMx-2.20.0/OpenMx/R/MxExpectationStateSpace.R | 2 OpenMx-2.20.0/OpenMx/R/MxFactorScores.R | 3 OpenMx-2.20.0/OpenMx/R/MxFitFunction.R | 54 OpenMx-2.20.0/OpenMx/R/MxFitFunctionAlgebra.R | 30 OpenMx-2.20.0/OpenMx/R/MxFitFunctionGREML.R | 50 OpenMx-2.20.0/OpenMx/R/MxFitFunctionML.R | 33 OpenMx-2.20.0/OpenMx/R/MxFitFunctionMultigroup.R | 20 OpenMx-2.20.0/OpenMx/R/MxFitFunctionR.R | 42 OpenMx-2.20.0/OpenMx/R/MxFitFunctionRow.R | 104 - OpenMx-2.20.0/OpenMx/R/MxFitFunctionWLS.R | 16 OpenMx-2.20.0/OpenMx/R/MxFlatSearchReplace.R | 21 OpenMx-2.20.0/OpenMx/R/MxInterval.R | 2 OpenMx-2.20.0/OpenMx/R/MxModel.R | 70 OpenMx-2.20.0/OpenMx/R/MxModelDisplay.R | 21 OpenMx-2.20.0/OpenMx/R/MxModelFunctions.R | 10 OpenMx-2.20.0/OpenMx/R/MxModelParameters.R | 14 OpenMx-2.20.0/OpenMx/R/MxMultiModel.R | 27 OpenMx-2.20.0/OpenMx/R/MxNamespace.R | 111 - OpenMx-2.20.0/OpenMx/R/MxOptions.R | 2 OpenMx-2.20.0/OpenMx/R/MxPath.R | 2 OpenMx-2.20.0/OpenMx/R/MxPenalty.R |only OpenMx-2.20.0/OpenMx/R/MxRAMModel.R | 24 OpenMx-2.20.0/OpenMx/R/MxRename.R | 2 OpenMx-2.20.0/OpenMx/R/MxRestore.R | 2 OpenMx-2.20.0/OpenMx/R/MxRobustSE.R | 18 OpenMx-2.20.0/OpenMx/R/MxRun.R | 68 OpenMx-2.20.0/OpenMx/R/MxRunHelperFunctions.R | 3 OpenMx-2.20.0/OpenMx/R/MxSearchReplace.R | 26 OpenMx-2.20.0/OpenMx/R/MxSummary.R | 18 OpenMx-2.20.0/OpenMx/R/MxThreshold.R | 2 OpenMx-2.20.0/OpenMx/R/MxUnitTesting.R | 2 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OpenMx-2.20.0/OpenMx/man/mxFactorScores.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxFitFunctionAlgebra.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxFitFunctionML.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxFitFunctionR.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxFitFunctionRow.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxFitFunctionWLS.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxGenerateData.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxGetExpected.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxKalmanScores.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxLISRELObjective.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxMI.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxMLObjective.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxMatrix.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxModel.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxOption.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxPath.Rd | 2 OpenMx-2.20.0/OpenMx/man/mxPenalty.Rd |only OpenMx-2.20.0/OpenMx/man/mxPenaltyElasticNet.Rd |only OpenMx-2.20.0/OpenMx/man/mxPenaltyLASSO.Rd |only OpenMx-2.20.0/OpenMx/man/mxPenaltyRidge.Rd |only OpenMx-2.20.0/OpenMx/man/mxPenaltySearch.Rd |only 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OpenMx-2.20.0/OpenMx/src/omxDefines.h | 3 OpenMx-2.20.0/OpenMx/src/omxExpectation.cpp | 40 OpenMx-2.20.0/OpenMx/src/omxExpectation.h | 17 OpenMx-2.20.0/OpenMx/src/omxExportBackendState.cpp | 11 OpenMx-2.20.0/OpenMx/src/omxFIMLFitFunction.cpp | 4 OpenMx-2.20.0/OpenMx/src/omxFIMLFitFunction.h | 4 OpenMx-2.20.0/OpenMx/src/omxFitFunction.cpp | 159 - OpenMx-2.20.0/OpenMx/src/omxFitFunction.h | 24 OpenMx-2.20.0/OpenMx/src/omxFitFunctionBA81.cpp | 4 OpenMx-2.20.0/OpenMx/src/omxGREMLExpectation.cpp | 22 OpenMx-2.20.0/OpenMx/src/omxGREMLExpectation.h | 30 OpenMx-2.20.0/OpenMx/src/omxGREMLfitfunction.cpp | 920 ++++------ OpenMx-2.20.0/OpenMx/src/omxHessianCalculation.cpp | 156 - OpenMx-2.20.0/OpenMx/src/omxImportFrontendState.cpp | 21 OpenMx-2.20.0/OpenMx/src/omxLISRELExpectation.cpp | 14 OpenMx-2.20.0/OpenMx/src/omxLISRELExpectation.h | 2 OpenMx-2.20.0/OpenMx/src/omxMLFitFunction.cpp | 6 OpenMx-2.20.0/OpenMx/src/omxMatrix.cpp | 6 OpenMx-2.20.0/OpenMx/src/omxMatrix.h | 12 OpenMx-2.20.0/OpenMx/src/omxNPSOLSpecific.h | 2 OpenMx-2.20.0/OpenMx/src/omxNormalExpectation.cpp | 66 OpenMx-2.20.0/OpenMx/src/omxRAMExpectation.cpp | 30 OpenMx-2.20.0/OpenMx/src/omxRFitFunction.cpp | 4 OpenMx-2.20.0/OpenMx/src/omxRFitFunction.h | 4 OpenMx-2.20.0/OpenMx/src/omxRowFitFunction.cpp | 6 OpenMx-2.20.0/OpenMx/src/omxRowFitFunction.h | 2 OpenMx-2.20.0/OpenMx/src/omxSadmvnWrapper.h | 2 OpenMx-2.20.0/OpenMx/src/omxState.cpp | 6 OpenMx-2.20.0/OpenMx/src/omxState.h | 11 OpenMx-2.20.0/OpenMx/src/omxStateSpaceExpectation.cpp | 2 OpenMx-2.20.0/OpenMx/src/omxWLSFitFunction.cpp | 15 OpenMx-2.20.0/OpenMx/src/penalty.cpp |only OpenMx-2.20.0/OpenMx/src/penalty.h |only OpenMx-2.20.0/OpenMx/src/ssMLFit.cpp | 6 OpenMx-2.20.0/OpenMx/tests/testthat/test-ACELRTCI20160808.R | 4 OpenMx-2.20.0/OpenMx/tests/testthat/test-AlgebraComputePassing.R | 2 OpenMx-2.20.0/OpenMx/tests/testthat/test-LegacyMultipleGroupWLS.R | 2 OpenMx-2.20.0/OpenMx/tests/testthat/test-ModelIdentification.R | 2 OpenMx-2.20.0/OpenMx/tests/testthat/test-loadDataByRow.R | 10 OpenMx-2.20.0/OpenMx/tests/testthat/test-mxsave.R | 2 OpenMx-2.20.0/OpenMx/tools/wls-regression | 25 OpenMx-2.20.0/OpenMx/vignettes |only 222 files changed, 2682 insertions(+), 1878 deletions(-)
Title: Markov-Switching GARCH Models
Description: Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2019) <doi:10.18637/jss.v091.i04>.
Author: David Ardia [aut] (<https://orcid.org/0000-0003-2823-782X>),
Keven Bluteau [aut, cre] (<https://orcid.org/0000-0003-2990-4807>),
Kris Boudt [ctb] (<https://orcid.org/0000-0002-1000-5142>),
Leopoldo Catania [aut] (<https://orcid.org/0000-0002-0981-1921>),
Alexios Ghalanos [ctb],
Brian Peterson [ctb],
Denis-Alexandre Trottier [aut]
Maintainer: Keven Bluteau <Keven.Bluteau@usherbrooke.ca>
Diff between MSGARCH versions 2.42 dated 2020-04-20 and 2.50 dated 2022-01-15
DESCRIPTION | 15 ++++++++------- MD5 | 37 +++++++++++++++++++------------------ NEWS | 2 ++ R/CreateSpec.R | 14 +++++++------- R/DIC.R | 2 +- R/FitMCMC.R | 6 +++--- R/FitML.R | 2 +- R/MSGARCH.R | 20 ++++++++++---------- R/Transmat.R | 1 + R/predict.R | 1 - build |only man/CreateSpec.Rd | 14 +++++++------- man/DIC.Rd | 2 +- man/FitMCMC.Rd | 6 +++--- man/FitML.Rd | 2 +- man/MSGARCH-package.Rd | 16 ++++++++-------- man/SMI.Rd | 2 +- man/dem2gbp.Rd | 4 ++-- src/RcppExports.cpp | 5 +++++ src/pdf_c.cpp | 6 +++--- 20 files changed, 83 insertions(+), 74 deletions(-)
Title: Mechanical Loading Prediction Through Accelerometer Data
Description: Functions to read, process and analyse accelerometer
data related to mechanical loading variables. This package is
developed and tested for use with raw accelerometer data from
triaxial 'ActiGraph' <https://actigraphcorp.com> accelerometers.
Author: Lucas Veras [aut, cre] (<https://orcid.org/0000-0003-0562-5803>)
Maintainer: Lucas Veras <lucasdsveras@gmail.com>
Diff between impactr versions 0.3.0 dated 2021-11-18 and 0.4.0 dated 2022-01-15
impactr-0.3.0/impactr/src/compute_resultant.cpp |only impactr-0.4.0/impactr/DESCRIPTION | 13 +- impactr-0.4.0/impactr/MD5 | 56 ++++++---- impactr-0.4.0/impactr/NAMESPACE | 9 + impactr-0.4.0/impactr/NEWS.md | 21 ++- impactr-0.4.0/impactr/R/RcppExports.R | 5 impactr-0.4.0/impactr/R/data.R |only impactr-0.4.0/impactr/R/impactr_data.R | 20 ++- impactr-0.4.0/impactr/R/non_wear.R |only impactr-0.4.0/impactr/R/read_acc.R | 4 impactr-0.4.0/impactr/R/signal_processing.R | 2 impactr-0.4.0/impactr/R/summarise_loading.R |only impactr-0.4.0/impactr/R/utils.R | 4 impactr-0.4.0/impactr/README.md | 2 impactr-0.4.0/impactr/build/partial.rdb |binary impactr-0.4.0/impactr/build/vignette.rds |binary impactr-0.4.0/impactr/inst/WORDLIST |only impactr-0.4.0/impactr/inst/doc/impactr.Rmd | 4 impactr-0.4.0/impactr/inst/doc/impactr.html | 6 - impactr-0.4.0/impactr/man/delete_nonwear.Rd |only impactr-0.4.0/impactr/man/detect_nonwear.Rd |only impactr-0.4.0/impactr/man/import_dataset.Rd |only impactr-0.4.0/impactr/man/install_accdata.Rd |only impactr-0.4.0/impactr/man/mark_nonwear.Rd |only impactr-0.4.0/impactr/man/new_impactr_data.Rd | 1 impactr-0.4.0/impactr/man/new_impactr_peaks.Rd | 1 impactr-0.4.0/impactr/man/plot_nonwear.Rd |only impactr-0.4.0/impactr/man/remove_nonwear.Rd |only impactr-0.4.0/impactr/man/summarise_loading.Rd |only impactr-0.4.0/impactr/man/summarise_nonwear.Rd |only impactr-0.4.0/impactr/src/RcppExports.cpp | 14 -- impactr-0.4.0/impactr/tests/testthat/_snaps/non_wear.md |only impactr-0.4.0/impactr/tests/testthat/_snaps/summarise_loading.md |only impactr-0.4.0/impactr/tests/testthat/test-non_wear.R |only impactr-0.4.0/impactr/tests/testthat/test-read_acc.R | 6 - impactr-0.4.0/impactr/tests/testthat/test-signal_processing.R | 4 impactr-0.4.0/impactr/tests/testthat/test-summarise_loading.R |only impactr-0.4.0/impactr/vignettes/impactr.Rmd | 4 38 files changed, 104 insertions(+), 72 deletions(-)
Title: Efficiency Analysis Trees
Description: Functions are provided to determine production frontiers and technical
efficiency measures through non-parametric techniques based upon regression trees.
The package includes code for estimating radial input, output, directional and
additive measures, plotting graphical representations of the scores and the production
frontiers by means of trees, and determining rankings of importance of input variables
in the analysis. Additionally, an adaptation of Random Forest by a set of individual
Efficiency Analysis Trees for estimating technical efficiency is also included. More
details in: <doi:10.1016/j.eswa.2020.113783>.
Author: Miriam Esteve [cre, aut] (<https://orcid.org/0000-0002-5908-0581>),
Víctor España [aut] (<https://orcid.org/0000-0002-1807-6180>),
Juan Aparicio [aut] (<https://orcid.org/0000-0002-0867-0004>),
Xavier Barber [aut] (<https://orcid.org/0000-0003-3079-5855>)
Maintainer: Miriam Esteve <mestevecampello@gmail.com>
Diff between eat versions 0.1.1 dated 2022-01-14 and 0.1.2 dated 2022-01-15
eat-0.1.1/eat/man/descrEAT.Rd |only eat-0.1.1/eat/man/frontier.levels.Rd |only eat-0.1.1/eat/man/size.Rd |only eat-0.1.2/eat/DESCRIPTION | 8 eat-0.1.2/eat/MD5 | 78 - eat-0.1.2/eat/NAMESPACE | 6 eat-0.1.2/eat/NEWS.md | 16 eat-0.1.2/eat/R/EAT.R | 66 eat-0.1.2/eat/R/alpha.R | 12 eat-0.1.2/eat/R/bestModels.R | 23 eat-0.1.2/eat/R/checkEAT.R | 2 eat-0.1.2/eat/R/efficiencyCEAT.R | 46 eat-0.1.2/eat/R/efficiencyEAT.R | 49 eat-0.1.2/eat/R/efficiencyRFEAT.R | 46 eat-0.1.2/eat/R/efficiency_plots.R | 9 eat-0.1.2/eat/R/predictorRFEAT.R | 2 eat-0.1.2/eat/R/rfEAT.R | 10 eat-0.1.2/eat/R/simulations.R | 10 eat-0.1.2/eat/R/treesForRCV.R | 2 eat-0.1.2/eat/R/userPredictions.R | 4 eat-0.1.2/eat/build/partial.rdb |binary eat-0.1.2/eat/build/vignette.rds |binary eat-0.1.2/eat/inst/doc/EAT.R | 195 +- eat-0.1.2/eat/inst/doc/EAT.Rmd | 243 +-- eat-0.1.2/eat/inst/doc/EAT.html | 2320 ++++++++++++++----------------- eat-0.1.2/eat/man/EAT_frontier_levels.Rd |only eat-0.1.2/eat/man/EAT_leaf_stats.Rd |only eat-0.1.2/eat/man/EAT_size.Rd |only eat-0.1.2/eat/man/RBranch.Rd | 6 eat-0.1.2/eat/man/RF_predictor.Rd | 2 eat-0.1.2/eat/man/X2Y2.sim.Rd | 4 eat-0.1.2/eat/man/Y1.sim.Rd | 6 eat-0.1.2/eat/man/alpha.Rd | 6 eat-0.1.2/eat/man/bestEAT.Rd | 12 eat-0.1.2/eat/man/bestRFEAT.Rd | 10 eat-0.1.2/eat/man/checkEAT.Rd | 2 eat-0.1.2/eat/man/deepEAT.Rd | 12 eat-0.1.2/eat/man/efficiencyCEAT.Rd | 10 eat-0.1.2/eat/man/efficiencyDensity.Rd | 7 eat-0.1.2/eat/man/efficiencyEAT.Rd | 10 eat-0.1.2/eat/man/efficiencyRFEAT.Rd | 17 eat-0.1.2/eat/man/generateLv.Rd | 2 eat-0.1.2/eat/vignettes/EAT.Rmd | 243 +-- 43 files changed, 1685 insertions(+), 1811 deletions(-)
Title: Clustering Big Data using Expectation Maximization Star (EM*)
Algorithm
Description: Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering
big data (gaussian mixture models for both multivariate and univariate datasets). This version
implements the faster alternative-EM* that expedites convergence via structure based data segregation.
The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma,
Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban,
Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.
Author: Sharma Parichit [aut, cre, ctb],
Kurban Hasan [aut, ctb],
Dalkilic Mehmet [aut]
Maintainer: Sharma Parichit <parishar@iu.edu>
Diff between DCEM versions 2.0.4 dated 2020-08-02 and 2.0.5 dated 2022-01-15
DESCRIPTION | 18 +++---- MD5 | 104 ++++++++++++++++++++++---------------------- NAMESPACE | 3 + NEWS.md | 8 +++ R/RcppExports.R | 6 -- R/dcem.R | 51 +++++++++------------ R/dcem_cluster_mv.R | 10 +--- R/dcem_cluster_uv.R | 13 ++--- R/dcem_em_utils.R | 62 +++----------------------- R/dcem_heap_util.R | 24 +++------- R/dcem_init_mv.R | 15 ------ R/dcem_init_uv.R | 12 ----- R/dcem_predict.R |only R/dcem_star_cluster_mv.R | 17 ++----- R/dcem_star_cluster_uv.R | 9 +-- R/dcem_star_train.R | 20 +++----- R/dcem_test.R | 12 +---- R/dcem_train.R | 12 +---- R/sanitycheck.R | 19 -------- R/zzz.R |only README.md | 30 +++++++++++- build/vignette.rds |binary inst/CITATION |only inst/doc/DCEM.Rmd | 4 - inst/doc/DCEM.html | 14 +++++ man/DCEM.Rd | 54 +++++++++------------- man/build_heap.Rd | 4 - man/dcem_cluster_mv.Rd | 12 +---- man/dcem_cluster_uv.Rd | 10 +--- man/dcem_predict.Rd |only man/dcem_star_cluster_mv.Rd | 12 +---- man/dcem_star_cluster_uv.Rd | 10 +--- man/dcem_star_train.Rd | 22 +++------ man/dcem_test.Rd | 12 +---- man/dcem_train.Rd | 14 +---- man/expectation_mv.Rd | 11 ---- man/expectation_uv.Rd | 11 ---- man/get_priors.Rd | 5 -- man/insert_nodes.Rd | 12 +---- man/max_heapify.Rd | 4 - man/maximisation_mv.Rd | 11 ---- man/maximisation_uv.Rd | 11 ---- man/meu_mv.Rd | 5 -- man/meu_mv_impr.Rd | 5 -- man/meu_uv.Rd | 5 -- man/meu_uv_impr.Rd | 5 -- man/separate_data.Rd | 12 +---- man/sigma_mv.Rd | 5 -- man/sigma_uv.Rd | 5 -- man/trim_data.Rd | 10 ---- man/update_weights.Rd | 11 ---- man/validate_data.Rd | 10 ---- src/RcppExports.cpp | 5 ++ src/heap.cpp | 6 -- vignettes/DCEM.Rmd | 4 - 55 files changed, 261 insertions(+), 505 deletions(-)
Title: Graph/Network Visualization
Description: Build graph/network structures using functions for stepwise addition and
deletion of nodes and edges. Work with data available in tables for bulk
addition of nodes, edges, and associated metadata. Use graph selections
and traversals to apply changes to specific nodes or edges. A wide
selection of graph algorithms allow for the analysis of graphs. Visualize
the graphs and take advantage of any aesthetic properties assigned to
nodes and edges.
Author: Richard Iannone [aut, cre] (<https://orcid.org/0000-0003-3925-190X>)
Maintainer: Richard Iannone <riannone@me.com>
Diff between DiagrammeR versions 1.0.6.1 dated 2020-05-08 and 1.0.7 dated 2022-01-15
DiagrammeR-1.0.6.1/DiagrammeR/build |only DiagrammeR-1.0.6.1/DiagrammeR/inst/doc |only DiagrammeR-1.0.6.1/DiagrammeR/vignettes |only DiagrammeR-1.0.7/DiagrammeR/DESCRIPTION | 25 +- DiagrammeR-1.0.7/DiagrammeR/LICENSE | 2 DiagrammeR-1.0.7/DiagrammeR/MD5 | 57 +----- DiagrammeR-1.0.7/DiagrammeR/NEWS.md | 8 DiagrammeR-1.0.7/DiagrammeR/R/DiagrammeR.R | 2 DiagrammeR-1.0.7/DiagrammeR/R/colorize_edge_attrs.R | 41 ++-- DiagrammeR-1.0.7/DiagrammeR/R/colorize_node_attrs.R | 40 ++-- DiagrammeR-1.0.7/DiagrammeR/R/generate_dot.R | 13 - DiagrammeR-1.0.7/DiagrammeR/R/grViz.R | 6 DiagrammeR-1.0.7/DiagrammeR/R/mermaid.R | 4 DiagrammeR-1.0.7/DiagrammeR/R/spectools.R | 5 DiagrammeR-1.0.7/DiagrammeR/inst/htmlwidgets/grViz.js | 4 DiagrammeR-1.0.7/DiagrammeR/inst/htmlwidgets/lib/mermaid/dist/mermaid.css | 39 ++-- DiagrammeR-1.0.7/DiagrammeR/inst/htmlwidgets/lib/mermaid/dist/mermaid.slim.min.js | 29 +-- DiagrammeR-1.0.7/DiagrammeR/man/DiagrammeR.Rd | 4 DiagrammeR-1.0.7/DiagrammeR/man/grViz.Rd | 5 DiagrammeR-1.0.7/DiagrammeR/man/mermaid.Rd | 6 DiagrammeR-1.0.7/DiagrammeR/man/replace_in_spec.Rd | 4 DiagrammeR-1.0.7/DiagrammeR/tests/testthat/test-colorize_nodes_edges.R | 92 ++++++++++ 22 files changed, 247 insertions(+), 139 deletions(-)
Title: Visualize Data on Spirals
Description: It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart.
It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high
resolution. 2. It is efficient for time series data to reveal periodic patterns.
Author: Zuguang Gu [aut, cre] (<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
Diff between spiralize versions 1.0.3 dated 2021-10-12 and 1.0.4 dated 2022-01-15
DESCRIPTION | 10 ++-- MD5 | 17 +++---- NEWS | 6 ++ R/initialize.R | 104 ++++++++++++++++++++++------------------------ R/spiral.R | 10 ++-- R/zzz.R | 5 ++ build/vignette.rds |binary inst/CITATION |only inst/doc/spiralize.html | 4 - man/polar_to_cartesian.Rd | 1 10 files changed, 82 insertions(+), 75 deletions(-)
Title: Selective Bayesian Forest Classifier
Description: An MCMC algorithm for simultaneous feature selection and classification,
and visualization of the selected features and feature interactions.
An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.
Author: Viktoriya Krakovna
Maintainer: Viktoriya Krakovna <vkrakovna@gmail.com>
Diff between sbfc versions 1.0.2 dated 2020-06-23 and 1.0.3 dated 2022-01-15
DESCRIPTION | 12 ++++++------ MD5 | 6 +++--- build/partial.rdb |binary src/sbfc.cpp | 26 +++++++++++++------------- 4 files changed, 22 insertions(+), 22 deletions(-)
Title: Translate English Words into Chinese Words
Description: If translate English words into Chinese, you might consider
looking up a dictionary or online query, in fact, there is a faster
way for R user. Ke-Hao Wu (2014) <RYoudaoTranslate: R package provide
functions to translate English words into Chinese.> provides interface
to Youdao translation open API for R user. But this software is not
very friendly to use, I have made some improvements on the basis of
this software. You can pass in a words or a vector consisting of multiple
words, which will return the corresponding type of Chinese representation
and be easy to reuse.
Author: Xinyuan Chu [aut, cre]
Maintainer: Xinyuan Chu <chuxinyuan@outlook.com>
Diff between entcn versions 0.1.0 dated 2020-02-21 and 1.0.0 dated 2022-01-15
DESCRIPTION | 20 ++++++++--------- LICENSE |only MD5 | 11 +++++---- NAMESPACE | 6 ++--- R/entcn.R | 64 +++++++++++++++++++++++++++---------------------------- README.md | 26 ++++++++++++---------- man/translate.Rd | 56 ++++++++++++++++++++++++------------------------ 7 files changed, 94 insertions(+), 89 deletions(-)
Title: Density-Based Spatial Clustering of Applications with Noise
(DBSCAN) and Related Algorithms
Description: A fast reimplementation of several density-based algorithms of
the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based
spatial clustering of applications with noise) and HDBSCAN (hierarchical
DBSCAN), the ordering algorithm OPTICS (ordering points to identify the
clustering structure), shared nearest neighbor clustering, and the outlier
detection algorithms LOF (local outlier factor) and GLOSH (global-local
outlier score from hierarchies). The implementations use the kd-tree data
structure (from library ANN) for faster k-nearest neighbor search. An R
interface to fast kNN and fixed-radius NN search is also provided.
Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.
Author: Michael Hahsler [aut, cre, cph],
Matthew Piekenbrock [aut, cph],
Sunil Arya [ctb, cph],
David Mount [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>
Diff between dbscan versions 1.1-9 dated 2022-01-10 and 1.1-10 dated 2022-01-15
dbscan-1.1-10/dbscan/DESCRIPTION | 27 ++-- dbscan-1.1-10/dbscan/MD5 | 78 ++++++------- dbscan-1.1-10/dbscan/NAMESPACE | 25 +++- dbscan-1.1-10/dbscan/NEWS.md | 13 ++ dbscan-1.1-10/dbscan/R/NN.R | 21 ++- dbscan-1.1-10/dbscan/R/RcppExports.R | 20 ++- dbscan-1.1-10/dbscan/R/comps.R |only dbscan-1.1-10/dbscan/R/dbscan.R | 36 +++--- dbscan-1.1-10/dbscan/R/hdbscan.R | 124 ++++++++++++--------- dbscan-1.1-10/dbscan/R/kNN.R | 2 dbscan-1.1-10/dbscan/R/optics.R | 57 ++++++--- dbscan-1.1-10/dbscan/R/predict.R | 84 +++++++------- dbscan-1.1-10/dbscan/R/reachability.R | 120 ++++++++++---------- dbscan-1.1-10/dbscan/build/partial.rdb |binary dbscan-1.1-10/dbscan/build/vignette.rds |binary dbscan-1.1-10/dbscan/inst/doc/dbscan.R | 20 +-- dbscan-1.1-10/dbscan/inst/doc/dbscan.Rnw | 3 dbscan-1.1-10/dbscan/inst/doc/dbscan.pdf |binary dbscan-1.1-10/dbscan/inst/doc/hdbscan.Rmd | 2 dbscan-1.1-10/dbscan/inst/doc/hdbscan.html | 8 - dbscan-1.1-10/dbscan/man/NN.Rd | 11 + dbscan-1.1-10/dbscan/man/comps.Rd |only dbscan-1.1-10/dbscan/man/dbscan.Rd | 39 ++++-- dbscan-1.1-10/dbscan/man/extractFOSC.Rd | 1 dbscan-1.1-10/dbscan/man/frNN.Rd | 1 dbscan-1.1-10/dbscan/man/hdbscan.Rd | 95 +++++++++------- dbscan-1.1-10/dbscan/man/jpclust.Rd | 1 dbscan-1.1-10/dbscan/man/kNN.Rd | 1 dbscan-1.1-10/dbscan/man/kNNdist.Rd | 1 dbscan-1.1-10/dbscan/man/optics.Rd | 54 ++++++--- dbscan-1.1-10/dbscan/man/reachability_plot.Rd |only dbscan-1.1-10/dbscan/man/sNN.Rd | 1 dbscan-1.1-10/dbscan/man/sNNclust.Rd | 3 dbscan-1.1-10/dbscan/src/R_comps.cpp |only dbscan-1.1-10/dbscan/src/R_mrd.cpp |only dbscan-1.1-10/dbscan/src/RcppExports.cpp | 65 ++++++----- dbscan-1.1-10/dbscan/tests/testthat/Rplots.pdf |binary dbscan-1.1-10/dbscan/tests/testthat/test-dbscan.R | 3 dbscan-1.1-10/dbscan/tests/testthat/test-predict.R | 57 ++++++++- dbscan-1.1-10/dbscan/vignettes/dbscan.Rnw | 3 dbscan-1.1-10/dbscan/vignettes/hdbscan.Rmd | 2 dbscan-1.1-9/dbscan/man/predict.Rd |only dbscan-1.1-9/dbscan/man/reachability.Rd |only dbscan-1.1-9/dbscan/src/mrd.cpp |only 44 files changed, 604 insertions(+), 374 deletions(-)
Title: Inference for Spatiotemporal Partially Observed Markov Processes
Description: Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. To do so, it relies on and extends a number of facilities that the 'pomp' package provides for inference on time series data using partially-observed Markov process (POMP) models. Implemented methods include filtering and inference methods in Park and Ionides (2020) <doi:10.1007/s11222-020-09957-3>, Rebeschini and van Handel (2015) <doi:10.1214/14-AAP1061>, Evensen and van Leeuwen (1996) <doi:10.1029/94JC00572> and Ionides et al. (2021) <arXiv:2002.05211v2>. Pre-print statistical software article: Asfaw et al. (2021) <arXiv:2101.01157>.
Author: Kidus Asfaw [aut, cre],
Aaron A. King [aut],
Edward Ionides [aut],
Joonha Park [ctb],
Allister Ho [ctb]
Maintainer: Kidus Asfaw <kasfaw@umich.edu>
Diff between spatPomp versions 0.28.0.0 dated 2021-09-04 and 0.29.0.0 dated 2022-01-15
DESCRIPTION | 13 - MD5 | 42 ++-- R/abf.R | 229 ++++++++++----------- R/abfir.R | 258 ++++++++++++------------ R/as_data_frame.R | 3 R/bpfilter.R | 4 R/enkf.R | 103 ++++----- R/eunit_measure.R | 2 R/girf.R | 409 +++++++++++++++++++-------------------- R/ienkf.R | 148 ++++++-------- R/igirf.R | 354 +++++++++++++++++---------------- R/iubf.R | 148 ++++++-------- R/lorenz.R | 43 ++-- R/measles.R | 4 R/munit_measure.R | 2 R/simulate.R | 57 ++--- R/spatPomp.R | 284 +++++++++++++-------------- R/vunit_measure.R | 2 src/bpfilter.c | 7 src/iabf.c | 8 src/init.c | 10 tests/testthat/test_bm_methods.R | 14 - 22 files changed, 1055 insertions(+), 1089 deletions(-)
Title: Spatial Probit Models
Description: Bayesian Estimation of Spatial Probit and Tobit Models.
Author: Stefan Wilhelm <wilhelm@financial.com> and Miguel Godinho de Matos <miguelgodinhomatos@cmu.edu>
Maintainer: Stefan Wilhelm <wilhelm@financial.com>
Diff between spatialprobit versions 0.9-11 dated 2015-09-17 and 1.0 dated 2022-01-15
DESCRIPTION | 13 MD5 | 69 - NAMESPACE | 9 NEWS | 48 - R/LeSagePaceExperiment.R | 52 - R/SpatialProbit-MCMC.R | 155 ++-- R/kNearestNeighbors.R | 50 - R/marginal.effects.R | 4 R/metropolis-hastings-rho.R | 134 +-- R/rtnorm.R | 20 R/sar_base.r | 736 ++++++++++---------- R/sarorderedprobit.R | 790 +++++++++++----------- R/sarprobit.R |only R/sartobit.R | 1336 +++++++++++++++++++------------------- R/sem.R | 510 +++++++------- R/semprobit.R | 1188 ++++++++++++++++----------------- inst/tests/test-In-rW.R | 122 +-- inst/tests/test-impacts.R | 58 - inst/tests/test-qr-solve.R | 72 +- man/CKM.Rd | 4 man/Katrina.Rd | 4 man/LeSagePaceExperiment.Rd | 130 +-- man/c.sarprobit.Rd | 130 +-- man/coef.sarprobit.Rd | 60 - man/fitted.Rd | 58 - man/kNearestNeighbors.Rd | 90 +- man/logLik.Rd | 60 - man/marginal.effects.sarprobit.Rd | 426 ++++++------ man/plot.sarprobit.Rd | 156 ++-- man/sar_eigs.Rd | 76 +- man/sar_lndet.Rd | 4 man/sarorderedprobit.Rd | 402 +++++------ man/sarprobit.Rd | 314 ++++---- man/sartobit.Rd | 376 +++++----- man/semprobit.Rd | 322 ++++----- man/summary.Rd | 82 +- 36 files changed, 4049 insertions(+), 4011 deletions(-)
Title: 'ggplot2' Based Tool to Facilitate Diagnostic Plots for NLME
Models
Description: At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling
activities in order to reduce the overall effort required for generating such plots.
For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox,
called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient
for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP
<doi:10.1002/psp4.12161>.
Author: Amine Gassem [aut],
Bruno Bieth [aut],
Irina Baltcheva [aut],
Thomas Dumortier [aut],
Christian Bartels [aut],
Souvik Bhattacharya [aut],
Inga Ludwig [aut],
Ines Paule [aut],
Didier Renard [aut],
Matthew Fidler [aut, cre] (<https://orcid.org/0000-0001-8538-6691>),
Seid Hamzic [aut],
Benjamin Guiastrennec [ctb],
Kyle T Baron [ctb] (<https://orcid.org/0000-0001-7252-5656>),
Qing Xi Ooi [ctb],
Novartis Pharma AG [cph]
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>
Diff between ggPMX versions 1.2.4 dated 2021-09-20 and 1.2.5 dated 2022-01-15
DESCRIPTION | 9 MD5 | 58 ++-- R/pmx-reader.R | 2 README.md | 266 +++++++++++++------ inst/doc/ggPMX-guide.pdf |binary man/figures/README-illustrate_diagnostic-1.png |binary man/figures/README-illustrate_diagnostic-2.png |binary man/figures/README-illustrate_diagnostic-3.png |binary man/figures/README-illustrate_diagnostic-4.png |binary man/figures/README-illustrate_diagnostic-5.png |binary man/figures/README-settings_cat_labels-1.png |binary man/figures/README-settings_cat_labels2-1.png |binary man/figures/README-settings_cat_labels3-1.png |binary man/figures/README-settings_color_scales_local-1.png |binary man/figures/README-settings_solor_scales-1.png |binary man/figures/README-settings_solor_scales_a-1.png |binary man/figures/README-settings_use.abbrev-1.png |binary man/figures/README-settings_use.finegrid-1.png |binary man/figures/README-shrink_plot_box-1.png |binary man/figures/README-shrink_plot_hist-1.png |binary man/figures/README-shrink_plot_no-1.png |binary man/figures/README-shrink_plot_strat-1.png |binary man/figures/README-shrink_plot_var-1.png |binary man/figures/README-unnamed-chunk-20-1.png |binary man/figures/README-unnamed-chunk-26-1.png |binary man/figures/README-unnamed-chunk-27-1.png |binary man/figures/README-unnamed-chunk-28-1.png |binary man/figures/README-unnamed-chunk-29-1.png |binary man/figures/README-unnamed-chunk-30-1.png |binary man/figures/README-unnamed-chunk-41-1.png |binary 30 files changed, 228 insertions(+), 107 deletions(-)
Title: Efficient and Flexible Data Preprocessing Tools
Description: Efficiently and flexibly preprocess data using a set of data filtering, deletion, and interpolation tools.
These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation and grouping.
Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data.
They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods.
Auto-identification of consecutive NA via run-length based grouping is used in observation deletion, outlier removal, and NA interpolation;
thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed.
Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation.
These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>.
Author: Chun-Sheng Liang <liangchunsheng@lzu.edu.cn>, Hao Wu, Hai-Yan Li, Qiang Zhang, Zhanqing Li, Ke-Bin He, Lanzhou University, Tsinghua University
Maintainer: Chun-Sheng Liang <liangchunsheng@lzu.edu.cn>
Diff between dataprep versions 0.1.4 dated 2021-07-04 and 0.1.5 dated 2022-01-15
DESCRIPTION | 6 +++--- MD5 | 10 +++++----- R/function.R | 18 +++++++++--------- build/vignette.rds |binary inst/doc/vignettes.html | 28 ++++++++++++++-------------- man/obsedele.Rd | 3 ++- 6 files changed, 33 insertions(+), 32 deletions(-)
Title: Get the Category of Content Hosted by a Domain
Description: Get the category of content hosted by a domain. Use Shallalist <http://shalla.de/>,
Virustotal (which provides access to lots of services) <https://www.virustotal.com/>,
Alexa <https://aws.amazon.com/awis/>, DMOZ <https://curlie.org/>, University Domain list
<https://github.com/Hipo/university-domains-list> or validated machine learning
classifiers based on Shallalist data to learn about the kind of content hosted by a domain.
Author: Gaurav Sood [aut, cre]
Maintainer: Gaurav Sood <gsood07@gmail.com>
Diff between rdomains versions 0.2.0 dated 2021-11-04 and 0.2.1 dated 2022-01-15
DESCRIPTION | 8 ++++---- MD5 | 22 +++++++++++----------- NAMESPACE | 1 + NEWS.md | 4 ++++ R/alexa_cat.R | 2 +- R/get_shalla_data.R | 33 +++++---------------------------- R/rdomains.R | 1 + R/shalla_cat.R | 2 +- README.md | 2 +- inst/doc/rdomains.html | 4 ++-- man/alexa_cat.Rd | 2 +- man/get_shalla_data.Rd | 2 ++ 12 files changed, 34 insertions(+), 49 deletions(-)
Title: Combining Tree-Boosting with Gaussian Process and Mixed Effects
Models
Description: An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2020) <arXiv:2004.02653> and Sigrist (2021) <arXiv:2105.08966> for more information on the methodology.
Author: Fabio Sigrist [aut, cre],
Benoit Jacob [cph],
Gael Guennebaud [cph],
Nicolas Carre [cph],
Pierre Zoppitelli [cph],
Gauthier Brun [cph],
Jean Ceccato [cph],
Jitse Niesen [cph],
Other authors of Eigen for the included version of Eigen [ctb, cph],
Timothy A. Davis [cph],
Guolin Ke [ctb],
Damien Soukhavong [ctb],
James Lamb [ctb],
Other authors of LightGBM for the included version of LightGBM [ctb],
Microsoft Corporation [cph],
Dropbox, Inc. [cph],
Jay Loden [cph],
Dave Daeschler [cph],
Giampaolo Rodola [cph],
Alberto Ferreira [ctb],
Daniel Lemire [ctb],
Victor Zverovich [cph],
IBM Corporation [ctb],
Keith O'Hara [cph],
Stephen L. Moshier [cph]
Maintainer: Fabio Sigrist <fabiosigrist@gmail.com>
Diff between gpboost versions 0.7.0 dated 2021-12-09 and 0.7.1 dated 2022-01-15
DESCRIPTION | 8 MD5 | 60 R/GPModel.R | 124 - configure.ac | 2 demo/GPBoost_algorithm.R | 30 demo/classification_non_Gaussian_data.R | 19 demo/generalized_linear_Gaussian_process_mixed_effects_models.R | 4 man/GPModel.Rd | 32 man/GPModel_shared_params.Rd | 77 man/fit.GPModel.Rd | 20 man/fit.Rd | 20 man/fitGPModel.Rd | 52 man/predict.GPModel.Rd | 25 man/predict.gpb.Booster.Rd | 22 man/set_prediction_data.GPModel.Rd | 19 man/set_prediction_data.Rd | 19 src/gpboost_R.h | 2 src/include/GPBoost/likelihoods.h | 17 src/include/GPBoost/re_comp.h | 254 +- src/include/GPBoost/re_model.h | 2 src/include/GPBoost/re_model_template.h | 1152 +++++----- src/include/LightGBM/c_api.h | 2 src/include/optim.hpp | 18 src/include/unconstrained/bfgs.hpp | 31 src/include/unconstrained/nm.hpp | 14 src/network/socket_wrapper.hpp | 4 tests/testthat/test_GPBoost_algorithm.R | 16 tests/testthat/test_GPBoost_algorithm_non_Gaussian_data.R | 29 tests/testthat/test_GPModel_gaussian_process.R | 16 tests/testthat/test_GPModel_grouped_random_effects.R | 104 tests/testthat/test_GPModel_non_Gaussian_data.R | 41 31 files changed, 1442 insertions(+), 793 deletions(-)
Title: Interactive Analysis of UCSC Xena Data
Description: Provides functions and a Shiny application for downloading,
analyzing and visualizing datasets from UCSC Xena
(<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public
databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Author: Shixiang Wang [aut, cre] (<https://orcid.org/0000-0001-9855-7357>),
Yi Xiong [aut] (<https://orcid.org/0000-0002-4370-9824>),
Longfei Zhao [aut] (<https://orcid.org/0000-0002-6277-0137>),
Kai Gu [aut] (<https://orcid.org/0000-0002-0177-0774>),
Yin Li [aut],
Fei Zhao [aut]
Maintainer: Shixiang Wang <w_shixiang@163.com>
Diff between UCSCXenaShiny versions 1.1.4 dated 2021-12-13 and 1.1.5 dated 2022-01-15
DESCRIPTION | 6 +++--- MD5 | 11 ++++++----- NEWS.md | 4 ++++ build/vignette.rds |binary inst/doc/api.html | 6 +++--- inst/extdata/ccle_drug_response_extend.rda |only inst/shinyapp/modules/modules-ga-surv-analysis.R | 18 ++++++++++++++---- 7 files changed, 30 insertions(+), 15 deletions(-)
Previous versions (as known to CRANberries) which should be available via the Archive link are:
2021-11-12 1.1.0
2021-09-13 1.0