Title: Easy Web Scraping
Description: The goal of 'ralger' is to facilitate web scraping in R.
The user has the ability to extract a vector with scrap(), a tidy dataframe using tidy_scrap(), a table with table_scrap() and web links with weblink_scrap().
Author: Mohamed El Fodil Ihaddaden [aut, cre],
Ezekiel Ogundepo [ctb],
Romain François [ctb]
Maintainer: Mohamed El Fodil Ihaddaden <ihaddaden.fodeil@gmail.com>
Diff between ralger versions 2.0.1 dated 2020-07-24 and 2.1.0 dated 2020-10-09
DESCRIPTION | 14 +-- MD5 | 38 ++++----- NAMESPACE | 1 NEWS.md | 5 + R/paragraphs_scrap.R | 73 +++++------------ R/scrap.R | 48 +++++------ R/table_scrap.R | 46 +++++------ R/tidy_scrap.R | 71 ++++++++--------- R/titles_scrap.R | 76 +++++++----------- R/weblink_scrap.R | 56 +++++-------- README.md | 134 ++++++++++++++++----------------- inst/doc/ralger.html | 60 ++++++++------ man/figures/hex.png |binary man/paragraphs_scrap.Rd | 12 ++ man/titles_scrap.Rd | 8 + man/weblink_scrap.Rd | 4 tests/testthat/test-paragraphs_scrap.R |only tests/testthat/test-scrap.R | 26 ++++++ tests/testthat/test-table_scrap.R | 25 ++++++ tests/testthat/test-titles_scrap.R |only tests/testthat/test-weblink_scrap.R | 22 +++++ 21 files changed, 379 insertions(+), 340 deletions(-)
Title: Tidy Structural Equation Modeling
Description: A tidy workflow for generating, estimating, reporting,
and plotting structural equation models using 'lavaan' or 'Mplus'.
Throughout this workflow, elements of syntax, results, and graphs are
represented as 'tidy' data, making them easy to customize.
Author: Caspar J. van Lissa [aut, cre]
(<https://orcid.org/0000-0002-0808-5024>)
Maintainer: Caspar J. van Lissa <c.j.vanlissa@uu.nl>
Diff between tidySEM versions 0.1.2 dated 2020-06-25 and 0.1.3 dated 2020-10-09
DESCRIPTION | 12 ++-- MD5 | 24 ++++---- R/descriptives.R | 10 ++- R/plot-plot_sem.R | 38 +++++++++---- R/plot_ellipses.R | 6 +- R/utilities.R | 40 ++++++++++++++ README.md | 2 build/vignette.rds |binary inst/doc/Generating_syntax.html | 10 +-- inst/doc/Plotting_graphs.html | 106 +++++++++++++++++++-------------------- inst/doc/Tabulating_results.html | 2 man/graph_sem.Rd | 18 +++++- man/prepare_graph.Rd | 23 +++++++- 13 files changed, 191 insertions(+), 100 deletions(-)
Title: Summarising Multi Well Plate Cellular Assay
Description: The goal is to help users to analyse data from multi wells with minimum effort. Using these functions several plates can be analyzed automatically.
Author: Anwar Azad Palakkan [aut, cre],
Jamie Davies [aut]
Maintainer: Anwar Azad Palakkan <bioanwar@gmail.com>
Diff between bioassays versions 0.1.0 dated 2020-05-29 and 1.0.1 dated 2020-10-09
DESCRIPTION | 10 MD5 | 96 +- NEWS.md | 17 R/bioassays.R | 33 R/data2plateformat.R | 42 R/data_DF1.R | 23 R/dfsummary.R | 52 + R/estimate.R | 45 R/extract_filename.R | 31 R/heatplate.R | 48 + R/matrix96.R | 44 R/metafile384.R | 41 R/metafile96.R | 42 R/plate2df.R | 42 R/plate_metadata.R | 35 R/pvalue.R | 37 R/rawdata24.R | 21 R/rawdata384.R | 19 R/rawdata96.R | 18 R/reduceblank.R | 42 R/rmodd_summary.R | 51 + README.md | 59 - build/vignette.rds |binary inst/doc/bioassays-examples.R | 538 +++++------ inst/doc/bioassays-examples.Rmd | 913 +++++++++---------- inst/doc/bioassays-examples.html | 1716 ++++++++++++++++++------------------- inst/doc/bioassays-vignette.R | 230 ++-- inst/doc/bioassays-vignette.html | 1264 +++++++++++++-------------- inst/exdata/384.csv |only inst/exdata/L_HEPG2_P3_72HRS.csv |only inst/exdata/metafile.csv |only inst/exdata/metafile_384_plate.csv |only man/bioassays.Rd | 65 - man/data2plateformat.Rd | 78 - man/data_DF1.Rd | 7 man/dfsummary.Rd | 106 +- man/estimate.Rd | 110 +- man/extract_filename.Rd | 70 - man/heatplate.Rd | 99 +- man/matrix96.Rd | 93 -- man/metafile384.Rd | 25 man/metafile96.Rd | 25 man/plate2df.Rd | 91 - man/plate_metadata.Rd | 85 - man/pvalue.Rd | 91 - man/rawdata24.Rd | 6 man/rawdata384.Rd | 4 man/rawdata96.Rd | 4 man/reduceblank.Rd | 94 -- man/rmodd_summary.Rd | 104 +- vignettes/bioassays-examples.Rmd | 913 +++++++++---------- 51 files changed, 4062 insertions(+), 3517 deletions(-)
Title: RDF Library Bindings in R
Description: Provides methods to parse, query and serialize information
stored in the Resource Description Framework (RDF). RDF is described at <https://www.w3.org/TR/rdf-primer/>.
This package supports RDF by implementing an R interface to the Redland RDF C library,
described at <http://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph
consisting of Statements composed of Subject, Predicate, and Object Nodes.
Author: Matthew B. Jones [aut, cre],
Peter Slaughter [aut],
Jeroen Ooms [aut],
Carl Boettiger [aut],
Scott Chamberlain [aut],
David Beckett [cph],
University of Bristol [cph],
Regents of the University of California [cph]
Maintainer: Matthew B. Jones <jones@nceas.ucsb.edu>
Diff between redland versions 1.0.17-12 dated 2020-09-25 and 1.0.17-13 dated 2020-10-09
DESCRIPTION | 8 ++-- MD5 | 8 ++-- NEWS | 9 +++- configure | 35 ++++++++++-------- inst/doc/redland_overview.html | 76 +++++++++++++++++++++++++++++++++++++++-- 5 files changed, 108 insertions(+), 28 deletions(-)
Title: Elastic Functional Data Analysis
Description: Performs alignment, PCA, and modeling of multidimensional and
unidimensional functions using the square-root velocity framework
(Srivastava et al., 2011 <arXiv:1103.3817> and
Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework
allows for elastic analysis of functional data through phase and
amplitude separation.
Author: J. Derek Tucker <jdtuck@sandia.gov>
Maintainer: J. Derek Tucker <jdtuck@sandia.gov>
Diff between fdasrvf versions 1.9.3 dated 2020-03-07 and 1.9.4 dated 2020-10-09
fdasrvf-1.9.3/fdasrvf/data/datalist |only fdasrvf-1.9.4/fdasrvf/DESCRIPTION | 10 - fdasrvf-1.9.4/fdasrvf/MD5 | 41 ++++---- fdasrvf-1.9.4/fdasrvf/NAMESPACE | 3 fdasrvf-1.9.4/fdasrvf/NEWS | 5 fdasrvf-1.9.4/fdasrvf/R/curve_functions.R | 4 fdasrvf-1.9.4/fdasrvf/R/curve_karcher_mean.R | 32 ++++-- fdasrvf-1.9.4/fdasrvf/R/elastic.depth.R |only fdasrvf-1.9.4/fdasrvf/R/fdasrvf-package.r | 1 fdasrvf-1.9.4/fdasrvf/R/jointfPCA.R | 4 fdasrvf-1.9.4/fdasrvf/README.md | 7 - fdasrvf-1.9.4/fdasrvf/man/beta.Rd | 30 +++-- fdasrvf-1.9.4/fdasrvf/man/curve_karcher_mean.Rd | 74 +++++++------- fdasrvf-1.9.4/fdasrvf/man/elastic.depth.Rd |only fdasrvf-1.9.4/fdasrvf/man/fdasrvf.Rd | 98 +++++++++---------- fdasrvf-1.9.4/fdasrvf/man/growth_vel.Rd | 28 ++--- fdasrvf-1.9.4/fdasrvf/man/im.Rd | 28 ++--- fdasrvf-1.9.4/fdasrvf/man/jointFPCA.Rd | 121 ++++++++++++------------ fdasrvf-1.9.4/fdasrvf/man/simu_data.Rd | 42 ++++---- fdasrvf-1.9.4/fdasrvf/man/simu_warp.Rd | 42 ++++---- fdasrvf-1.9.4/fdasrvf/man/simu_warp_median.Rd | 42 ++++---- fdasrvf-1.9.4/fdasrvf/man/toy_data.Rd | 36 +++---- fdasrvf-1.9.4/fdasrvf/man/toy_warp.Rd | 36 +++---- 23 files changed, 368 insertions(+), 316 deletions(-)
Title: Two-Steps Benchmarks for Time Series Disaggregation
Description: The twoStepsBenchmark() function and its wrappers allow you to disaggregate a low frequency time serie with time series of higher frequency, using the French National Accounts methodology.
The aggregated sum of the resulting time-serie is strictly equal to the low-frequency serie within the benchmarking window.
Typically, the low frequency serie is an annual one, unknown for the last year, and the high frequency is either quarterly or mensual.
See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8).
Author: Arnaud Feldmann
Maintainer: Arnaud Feldmann <arnaud.feldmann@gmail.com>
Diff between disaggR versions 0.1.6 dated 2020-08-25 and 0.1.7 dated 2020-10-09
disaggR-0.1.6/disaggR/R/ggplot.R |only disaggR-0.1.6/disaggR/R/praislm-methods.R |only disaggR-0.1.6/disaggR/R/twoStepsBenchmark-methods.R |only disaggR-0.1.6/disaggR/tests/testthat/test-ggplot.R |only disaggR-0.1.6/disaggR/tests/testthat/test-praislm-methods.R |only disaggR-0.1.6/disaggR/tests/testthat/test-twoStepsBenchmark-methods.R |only disaggR-0.1.7/disaggR/DESCRIPTION | 14 disaggR-0.1.7/disaggR/MD5 | 60 ++- disaggR-0.1.7/disaggR/NAMESPACE | 2 disaggR-0.1.7/disaggR/NEWS.md |only disaggR-0.1.7/disaggR/R/bflSmooth.R | 151 +++++++--- disaggR-0.1.7/disaggR/R/disaggR.R | 2 disaggR-0.1.7/disaggR/R/methods.R |only disaggR-0.1.7/disaggR/R/plot.R | 67 +++- disaggR-0.1.7/disaggR/R/praislm.R | 88 +++-- disaggR-0.1.7/disaggR/R/twoStepsBenchmark.R | 101 +++--- disaggR-0.1.7/disaggR/R/utils.R |only disaggR-0.1.7/disaggR/README.md | 5 disaggR-0.1.7/disaggR/man/bflSmooth.Rd | 17 - disaggR-0.1.7/disaggR/man/construction.Rd | 40 +- disaggR-0.1.7/disaggR/man/disaggR-package.Rd | 7 disaggR-0.1.7/disaggR/man/model.list.Rd | 52 +-- disaggR-0.1.7/disaggR/man/prais.Rd | 2 disaggR-0.1.7/disaggR/man/reUseBenchmark.Rd | 74 ++-- disaggR-0.1.7/disaggR/man/rho.Rd | 46 +-- disaggR-0.1.7/disaggR/man/se.Rd | 40 +- disaggR-0.1.7/disaggR/man/smoothed.part.Rd | 56 +-- disaggR-0.1.7/disaggR/man/turnover.Rd | 40 +- disaggR-0.1.7/disaggR/man/twoStepsBenchmark.Rd | 6 disaggR-0.1.7/disaggR/src |only disaggR-0.1.7/disaggR/tests/figs/plot/plot-of-insample.svg | 90 ++--- disaggR-0.1.7/disaggR/tests/testthat/outputs/summary-prais.txt |only disaggR-0.1.7/disaggR/tests/testthat/test-bflSmooth.R | 84 +++++ disaggR-0.1.7/disaggR/tests/testthat/test-methods.R |only disaggR-0.1.7/disaggR/tests/testthat/test-plot.R | 14 disaggR-0.1.7/disaggR/tests/testthat/test-praislm.R | 6 disaggR-0.1.7/disaggR/tests/testthat/test-utils.R |only 37 files changed, 655 insertions(+), 409 deletions(-)
Title: Exploring Biological Relevance of Environmental Chemistry
Observations
Description: Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell et al. (2017) <doi:10.1021/acs.est.7b01613>.
Author: Laura DeCicco [aut, cre] (<https://orcid.org/0000-0002-3915-9487>),
Steven Corsi [aut] (<https://orcid.org/0000-0003-0583-5536>),
Daniel Villeneuve [aut] (<https://orcid.org/0000-0003-2801-0203>),
Brett Blackwell [aut] (<https://orcid.org/0000-0003-1296-4539>),
Gerald Ankley [aut] (<https://orcid.org/0000-0002-9937-615X>),
Alison Appling [rev] (Reviewed for USGS),
Dalma Martinovic [rev] (Reviewed for USGS)
Maintainer: Laura DeCicco <ldecicco@usgs.gov>
Diff between toxEval versions 1.1.0 dated 2019-11-26 and 1.2.0 dated 2020-10-09
toxEval-1.1.0/toxEval/inst/CITATION |only toxEval-1.1.0/toxEval/man/figures/unnamed-chunk-4-1.png |only toxEval-1.1.0/toxEval/man/figures/unnamed-chunk-4-2.png |only toxEval-1.1.0/toxEval/man/figures/unnamed-chunk-4-3.png |only toxEval-1.2.0/toxEval/DESCRIPTION | 10 toxEval-1.2.0/toxEval/MD5 | 125 +- toxEval-1.2.0/toxEval/NAMESPACE | 2 toxEval-1.2.0/toxEval/NEWS | 4 toxEval-1.2.0/toxEval/R/explore_endpoints.R | 4 toxEval-1.2.0/toxEval/R/filter_endPoint_info.R | 18 toxEval-1.2.0/toxEval/R/get_ACC.R | 6 toxEval-1.2.0/toxEval/R/get_chemical_summary.R | 30 toxEval-1.2.0/toxEval/R/plot_group_boxplots.R | 5 toxEval-1.2.0/toxEval/R/plot_heat_chemical.R | 19 toxEval-1.2.0/toxEval/R/plot_tox_endpoints.R | 5 toxEval-1.2.0/toxEval/R/sysdata.rda |binary toxEval-1.2.0/toxEval/R/toxEval.R | 21 toxEval-1.2.0/toxEval/README.md | 137 -- toxEval-1.2.0/toxEval/build/vignette.rds |binary toxEval-1.2.0/toxEval/inst/doc/Introduction.html | 157 ++- toxEval-1.2.0/toxEval/inst/doc/PrepareData.Rmd | 2 toxEval-1.2.0/toxEval/inst/doc/PrepareData.html | 40 toxEval-1.2.0/toxEval/inst/doc/basicWorkflow.Rmd | 2 toxEval-1.2.0/toxEval/inst/doc/basicWorkflow.html | 461 +++++----- toxEval-1.2.0/toxEval/inst/doc/shinyApp.html | 139 ++- toxEval-1.2.0/toxEval/inst/shiny/boxPlot.R | 2 toxEval-1.2.0/toxEval/inst/shiny/endpointGraph.R | 4 toxEval-1.2.0/toxEval/inst/shiny/heatMap.R | 7 toxEval-1.2.0/toxEval/inst/shiny/hitTable.R | 6 toxEval-1.2.0/toxEval/inst/shiny/hitsTableEP.R | 6 toxEval-1.2.0/toxEval/inst/shiny/mapStuff.R | 158 +-- toxEval-1.2.0/toxEval/inst/shiny/server.R | 100 +- toxEval-1.2.0/toxEval/inst/shiny/stackPlot.R | 5 toxEval-1.2.0/toxEval/inst/shiny/tableGroupSumm.R | 6 toxEval-1.2.0/toxEval/inst/shiny/tableSum.R | 2 toxEval-1.2.0/toxEval/inst/shiny/ui.R | 56 - toxEval-1.2.0/toxEval/inst/shiny/updateUI.R | 14 toxEval-1.2.0/toxEval/man/ToxCast_ACC.Rd | 58 - toxEval-1.2.0/toxEval/man/clean_endPoint_info.Rd | 66 - toxEval-1.2.0/toxEval/man/createLink.Rd | 32 toxEval-1.2.0/toxEval/man/create_toxEval.Rd | 162 +-- toxEval-1.2.0/toxEval/man/end_point_info.Rd | 2 toxEval-1.2.0/toxEval/man/endpoint_hits_DT.Rd | 168 +-- toxEval-1.2.0/toxEval/man/explore_endpoints.Rd | 43 toxEval-1.2.0/toxEval/man/figures/README-unnamed-chunk-4-1.png |only toxEval-1.2.0/toxEval/man/figures/README-unnamed-chunk-4-2.png |only toxEval-1.2.0/toxEval/man/figures/README-unnamed-chunk-4-3.png |only toxEval-1.2.0/toxEval/man/filter_groups.Rd | 99 +- toxEval-1.2.0/toxEval/man/get_ACC.Rd | 60 - toxEval-1.2.0/toxEval/man/get_chemical_summary.Rd | 150 +-- toxEval-1.2.0/toxEval/man/get_concentration_summary.Rd | 112 +- toxEval-1.2.0/toxEval/man/graph_data_prep.Rd | 198 ++-- toxEval-1.2.0/toxEval/man/helperToxEval.Rd | 60 - toxEval-1.2.0/toxEval/man/hits_by_groupings_DT.Rd | 154 +-- toxEval-1.2.0/toxEval/man/hits_summary_DT.Rd | 156 +-- toxEval-1.2.0/toxEval/man/make_tox_map.Rd | 140 +-- toxEval-1.2.0/toxEval/man/plot_tox_boxplots.Rd | 275 ++--- toxEval-1.2.0/toxEval/man/plot_tox_endpoints.Rd | 196 ++-- toxEval-1.2.0/toxEval/man/plot_tox_heatmap.Rd | 229 ++-- toxEval-1.2.0/toxEval/man/plot_tox_stacks.Rd | 192 ++-- toxEval-1.2.0/toxEval/man/rank_sites_DT.Rd | 150 +-- toxEval-1.2.0/toxEval/man/remove_flags.Rd | 97 +- toxEval-1.2.0/toxEval/man/tox_chemicals.Rd | 50 - toxEval-1.2.0/toxEval/tests/testthat/tests_endpoint.R | 3 toxEval-1.2.0/toxEval/tests/testthat/tests_summary.R | 126 +- toxEval-1.2.0/toxEval/vignettes/PrepareData.Rmd | 2 toxEval-1.2.0/toxEval/vignettes/basicWorkflow.Rmd | 2 67 files changed, 2398 insertions(+), 2137 deletions(-)
Title: Workflow for Open Reproducible Code in Science
Description: Create reproducible and transparent research projects in 'R', with
a minimal amount of code. This package is based on the Workflow for Open
Reproducible Code in Science (WORCS), a step-by-step procedure based on best
practices for
Open Science. It includes an 'RStudio' project template, several
convenience functions, and all dependencies required to make your project
reproducible and transparent. WORCS is explained in the tutorial paper
by Van Lissa, Brandmaier, Brinkman, Lamprecht, Struiksma, & Vreede (2020).
<doi:10.17605/OSF.IO/ZCVBS>.
Author: Caspar J. van Lissa [aut, cre]
(<https://orcid.org/0000-0002-0808-5024>),
Aaron Peikert [aut] (<https://orcid.org/0000-0001-7813-818X>),
Andreas M. Brandmaier [aut] (<https://orcid.org/0000-0001-8765-6982>)
Maintainer: Caspar J. van Lissa <c.j.vanlissa@uu.nl>
Diff between worcs versions 0.1.4 dated 2020-09-29 and 0.1.5 dated 2020-10-09
DESCRIPTION | 11 MD5 | 35 +-- NAMESPACE | 2 R/codebook.R | 9 R/descriptives.R | 28 +- R/github.R | 24 +- R/save_load.R | 20 + R/synthetic.R | 2 R/worcs_project.R | 137 +++++++++--- inst/rstudio/templates/project/resources/pss.Rmd |only inst/rstudio/templates/project/resources/pssr_template.html |only inst/rstudio/templates/project/resources/secondary.Rmd |only man/add_manuscript.Rd | 5 man/add_preregistration.Rd |only man/codebook.Rd | 6 man/git_update.Rd | 134 +++++------ man/git_user.Rd | 5 man/load_data.Rd | 10 man/worcs_project.Rd | 10 tests/testthat/test-add_preregistration.R |only tests/testthat/test-worcs_project.R | 2 21 files changed, 268 insertions(+), 172 deletions(-)
Title: Question Generation in the 'MyLearn' XML Format
Description: Randomized multiple-select and single-select
question generation for the 'MyLearn' teaching and learning
platform. Question templates
in the form of the R/exams package (see <http://www.r-exams.org/>)
are transformed into XML format required by 'MyLearn'.
Author: Darjus Hosszejni [aut, cre] (<https://orcid.org/0000-0002-3803-691X>)
Maintainer: Darjus Hosszejni <darjus.hosszejni@wu.ac.at>
Diff between exams.mylearn versions 1.1 dated 2020-05-25 and 1.2 dated 2020-10-09
DESCRIPTION | 14 MD5 | 16 - R/example_paths.R | 18 + R/exams.mylearn-package.R | 18 + R/exams2mylearn.R | 18 + build/vignette.rds |binary inst/doc/workflow.Rmd | 17 - inst/doc/workflow.html | 667 ++++++++++++++++++++++++++-------------------- vignettes/workflow.Rmd | 17 - 9 files changed, 452 insertions(+), 333 deletions(-)
Title: Graphics in the Context of Analyzing High-Throughput Data
Description: Additional options for making graphics in the context of analyzing high-throughput data are available here.
This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots,
automatic checking for optimal location of legends in plots, small histograms to insert as legends,
histograms re-transforming axis labels to linear when plotting log2-transformed data,
a violin-plot <doi:10.1080/00031305.1998.10480559> function for a wide variety of input-formats,
principal components analysis (PCA) <doi:10.1080/14786440109462720> with bag-plots <doi:10.1080/00031305.1999.10474494> to highlight and compare the center areas for groups of samples,
generic MA-plots (differential- versus average-value plots) <doi:10.1093/nar/30.4.e15>,
staggered count plots and generation of mouse-over interactive html pages.
Author: Wolfgang Raffelsberger [aut, cre]
Maintainer: Wolfgang Raffelsberger <w.raffelsberger@unistra.fr>
Diff between wrGraph versions 1.0.4 dated 2020-09-08 and 1.0.5 dated 2020-10-09
DESCRIPTION | 6 MD5 | 18 +- NAMESPACE | 1 R/VolcanoPlotW.R |only R/vioplotW.R | 74 ++++++--- inst/doc/wrGraphVignette1.R | 36 ++++ inst/doc/wrGraphVignette1.Rmd | 59 +++++++ inst/doc/wrGraphVignette1.html | 336 +++++++++++++++++++++++++---------------- man/VolcanoPlotW.Rd |only man/vioplotW.Rd | 28 ++- vignettes/wrGraphVignette1.Rmd | 59 +++++++ 11 files changed, 448 insertions(+), 169 deletions(-)
Title: Approximate String Matching, Fuzzy Text Search, and String
Distance Functions
Description: Implements an approximate string matching version of R's native
'match' function. Also offers fuzzy text search based on various string
distance measures. Can calculate various string distances based on edits
(Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q-
gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An
implementation of soundex is provided as well. Distances can be computed between
character vectors while taking proper care of encoding or between integer
vectors representing generic sequences. This package is built for speed and
runs in parallel by using 'openMP'. An API for C or C++ is exposed as well.
Reference: MPJ van der Loo (2014) <doi:10.32614/RJ-2014-011>.
Author: Mark van der Loo [aut, cre] (<https://orcid.org/0000-0002-9807-4686>),
Jan van der Laan [ctb],
R Core Team [ctb],
Nick Logan [ctb],
Chris Muir [ctb],
Johannes Gruber [ctb]
Maintainer: Mark van der Loo <mark.vanderloo@gmail.com>
Diff between stringdist versions 0.9.6 dated 2020-07-16 and 0.9.6.3 dated 2020-10-09
DESCRIPTION | 9 +++++---- MD5 | 20 ++++++++++---------- NEWS | 15 ++++++++++++++- R/doc_metrics.R | 2 +- R/phonetic.R | 2 +- README.md | 2 +- inst/doc/RJournal_6_111-122-2014.pdf |binary inst/doc/stringdist_C-Cpp_api.pdf |binary man/phonetic.Rd | 2 +- man/stringdist-metrics.Rd | 2 +- src/Rstringdist.c | 2 +- 11 files changed, 35 insertions(+), 21 deletions(-)
Title: Ensemble Algorithms for Time Series Forecasting with Modeltime
Description: A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging,
weighted averaging, and stacking. These techniques are popular methods
to improve forecast accuracy and stability. Refer to papers such as
"Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
Author: Matt Dancho [aut, cre],
Business Science [cph]
Maintainer: Matt Dancho <mdancho@business-science.io>
Diff between modeltime.ensemble versions 0.1.0 dated 2020-10-07 and 0.2.0 dated 2020-10-09
modeltime.ensemble-0.1.0/modeltime.ensemble/vignettes/stacking_multi_level.jpg |only modeltime.ensemble-0.2.0/modeltime.ensemble/DESCRIPTION | 6 modeltime.ensemble-0.2.0/modeltime.ensemble/LICENSE | 4 modeltime.ensemble-0.2.0/modeltime.ensemble/MD5 | 78 modeltime.ensemble-0.2.0/modeltime.ensemble/NAMESPACE | 113 modeltime.ensemble-0.2.0/modeltime.ensemble/NEWS.md | 17 modeltime.ensemble-0.2.0/modeltime.ensemble/R/00_global_variables.R | 8 modeltime.ensemble-0.2.0/modeltime.ensemble/R/00_imports.R | 10 modeltime.ensemble-0.2.0/modeltime.ensemble/R/dev-model_descriptions.R | 168 - modeltime.ensemble-0.2.0/modeltime.ensemble/R/ensemble_average.R | 202 - modeltime.ensemble-0.2.0/modeltime.ensemble/R/ensemble_linear_stack.R | 732 +++--- modeltime.ensemble-0.2.0/modeltime.ensemble/R/ensemble_model_spec.R | 845 +++---- modeltime.ensemble-0.2.0/modeltime.ensemble/R/ensemble_weighted.R | 252 +- modeltime.ensemble-0.2.0/modeltime.ensemble/R/modeltime_calibrate.R | 26 modeltime.ensemble-0.2.0/modeltime.ensemble/R/modeltime_fit_resamples.R | 316 +- modeltime.ensemble-0.2.0/modeltime.ensemble/R/modeltime_forecast.R | 372 +-- modeltime.ensemble-0.2.0/modeltime.ensemble/R/modeltime_refit.R | 127 - modeltime.ensemble-0.2.0/modeltime.ensemble/R/tibble-type_sum.R | 22 modeltime.ensemble-0.2.0/modeltime.ensemble/R/utils-pipe.R | 22 modeltime.ensemble-0.2.0/modeltime.ensemble/R/utils-tidy-eval.R | 94 modeltime.ensemble-0.2.0/modeltime.ensemble/README.md | 302 +- modeltime.ensemble-0.2.0/modeltime.ensemble/build/vignette.rds |binary modeltime.ensemble-0.2.0/modeltime.ensemble/inst/doc/getting-started-with-modeltime-ensemble.R | 240 +- modeltime.ensemble-0.2.0/modeltime.ensemble/inst/doc/getting-started-with-modeltime-ensemble.Rmd | 488 ++-- modeltime.ensemble-0.2.0/modeltime.ensemble/inst/doc/getting-started-with-modeltime-ensemble.html | 1133 ++++------ modeltime.ensemble-0.2.0/modeltime.ensemble/man/ensemble_average.Rd | 117 - modeltime.ensemble-0.2.0/modeltime.ensemble/man/ensemble_model_spec.Rd | 306 +- modeltime.ensemble-0.2.0/modeltime.ensemble/man/ensemble_weighted.Rd | 119 - modeltime.ensemble-0.2.0/modeltime.ensemble/man/figures/README-unnamed-chunk-6-1.png |binary modeltime.ensemble-0.2.0/modeltime.ensemble/man/mdl_time_fit_resamples.Rd | 38 modeltime.ensemble-0.2.0/modeltime.ensemble/man/modeltime_fit_resamples.Rd | 81 modeltime.ensemble-0.2.0/modeltime.ensemble/man/pipe.Rd | 24 modeltime.ensemble-0.2.0/modeltime.ensemble/man/tidyeval.Rd | 102 modeltime.ensemble-0.2.0/modeltime.ensemble/man/type_sum.mdl_time_ensemble.Rd | 36 modeltime.ensemble-0.2.0/modeltime.ensemble/tests/testthat.R | 26 modeltime.ensemble-0.2.0/modeltime.ensemble/tests/testthat/test-ensemble_average.R | 208 - modeltime.ensemble-0.2.0/modeltime.ensemble/tests/testthat/test-ensemble_linear_stack.R | 206 - modeltime.ensemble-0.2.0/modeltime.ensemble/tests/testthat/test-ensemble_model_spec.R | 448 ++- modeltime.ensemble-0.2.0/modeltime.ensemble/tests/testthat/test-ensemble_weighted.R | 198 - modeltime.ensemble-0.2.0/modeltime.ensemble/vignettes/getting-started-with-modeltime-ensemble.Rmd | 488 ++-- modeltime.ensemble-0.2.0/modeltime.ensemble/vignettes/stacking.jpg |only 41 files changed, 4100 insertions(+), 3874 deletions(-)
More information about modeltime.ensemble at CRAN
Permanent link
Title: Meta-Analysis of RNA-Seq Data
Description: Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
Author: Guillemette Marot [aut, cre], Andrea Rau [aut, cre], Florence Jaffrezic [aut], Samuel Blanck [ctb]
Maintainer: Guillemette Marot <guillemette.marot@univ-lille.fr>
Diff between metaRNASeq versions 1.0.2 dated 2015-01-26 and 1.0.3 dated 2020-10-09
DESCRIPTION | 21 ++++------ MD5 | 20 ++++----- NAMESPACE | 3 - build/vignette.rds |binary data/dispFuncs.rda |binary data/param.rda |binary data/rawpval.rda |binary inst/doc/metaRNASeq.R | 96 +++++++++++++++-------------------------------- inst/doc/metaRNASeq.Rnw | 94 ++++++++++++---------------------------------- inst/doc/metaRNASeq.pdf |binary vignettes/metaRNASeq.Rnw | 94 ++++++++++++---------------------------------- 11 files changed, 106 insertions(+), 222 deletions(-)
Title: Multiple Criteria Optimization Algorithms and Related Functions
Description: A collection of function to solve multiple criteria optimization problems
using genetic algorithms (NSGA-II). Also included is a collection of test functions.
Author: Olaf Mersmann [aut, cre],
Heike Trautmann [ctb],
Detlef Steuer [ctb],
Bernd Bischl [ctb],
Kalyanmoy Deb [cph]
Maintainer: Olaf Mersmann <olafm@p-value.net>
Diff between mco versions 1.0-15.1 dated 2014-11-29 and 1.15.6 dated 2020-10-09
DESCRIPTION | 18 ++++++++++-------- MD5 | 19 +++++++++++-------- NAMESPACE | 3 +++ R/nsga2.R | 5 ++--- build/mco.pdf |binary man/generationalDistance.Rd | 4 ++-- src/eps_ind.c | 2 ++ src/extern.h |only src/hv.c | 1 + src/mco.c |only src/nsga2.c | 11 ++++++----- tests/testthat/test-constraints.R |only 12 files changed, 37 insertions(+), 26 deletions(-)
Title: Tools for Spatial Data
Description: For curve, surface and function fitting with an emphasis
on splines, spatial data, geostatistics, and spatial statistics. The major methods
include cubic, and thin plate splines, Kriging, and compactly supported
covariance functions for large data sets. The splines and Kriging methods are
supported by functions that can determine the smoothing parameter
(nugget and sill variance) and other covariance function parameters by cross
validation and also by restricted maximum likelihood. For Kriging
there is an easy to use function that also estimates the correlation
scale (range parameter). A major feature is that any covariance function
implemented in R and following a simple format can be used for
spatial prediction. There are also many useful functions for plotting
and working with spatial data as images. This package also contains
an implementation of sparse matrix methods for large spatial data
sets and currently requires the sparse matrix (spam) package. Use
help(fields) to get started and for an overview. The fields source
code is deliberately commented and provides useful explanations of
numerical details as a companion to the manual pages. The commented
source code can be viewed by expanding the source code version
and looking in the R subdirectory. The reference for fields can be generated
by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development
of this package was supported in part by the National Science Foundation Grant
1417857, the National Center for Atmospheric Research, and Colorado School of Mines.
See the Fields URL
for a vignette on using this package and some background on spatial statistics.
Author: Douglas Nychka [aut, cre],
Reinhard Furrer [aut],
John Paige [aut],
Stephan Sain [aut],
Florian Gerber [aut],
Matthew Iverson [aut],
University Corporation for Atmospheric Research [cph]
Maintainer: Douglas Nychka <douglasnychka@gmail.com>
Diff between fields versions 11.5 dated 2020-10-04 and 11.6 dated 2020-10-09
DESCRIPTION | 8 +- MD5 | 8 +- R/splint.R | 12 --- tests/mKrigMLETest.Rout.save | 6 - tests/sreg.test.Rout.save | 137 ------------------------------------------- 5 files changed, 13 insertions(+), 158 deletions(-)
Title: Delineating Temporal Dataset Shifts in Electronic Health Records
Description: Functions to delineate temporal dataset shifts in Electronic Health
Records through the projection and visualization of dissimilarities
among data temporal batches. This is done through the estimation of
data statistical distributions over time and their projection in
non-parametric statistical manifolds, uncovering the patterns of the
data latent temporal variability. 'EHRtemporalVariability' is
particularly suitable for multi-modal data and categorical variables
with a high number of values, common features of biomedical data where
traditional statistical process control or time-series methods may not
be appropriate. 'EHRtemporalVariability' allows you to explore and
identify dataset shifts through visual analytics formats such as
Data Temporal heatmaps and Information Geometric Temporal (IGT) plots.
An additional 'EHRtemporalVariability' Shiny app can be used to load
and explore the package results and even to allow the use of these
functions to those users non-experienced in R coding. (Sáez et al. 2020)
<doi:10.1093/gigascience/giaa079>.
Author: Carlos Sáez [aut, cre],
Alba Gutiérrez-Sacristán [aut],
Isaac Kohane [aut],
Juan M García-Gómez [aut],
Paul Avillach [aut],
Biomedical Data Science Lab, Universitat Politècnica de València
(Spain) [cph],
Department of Biomedical Informatics, Harvard Medical School [cph]
Maintainer: Carlos Sáez <carsaesi@upv.es>
Diff between EHRtemporalVariability versions 1.1.2 dated 2020-10-08 and 1.1.2.1 dated 2020-10-09
DESCRIPTION | 6 MD5 | 8 inst/doc/EHRtemporalVariability.Rmd | 7 inst/doc/EHRtemporalVariability.html | 1338 ++++++++++++++++++++++------------- vignettes/EHRtemporalVariability.Rmd | 7 5 files changed, 884 insertions(+), 482 deletions(-)
More information about EHRtemporalVariability at CRAN
Permanent link
Title: Interface to Data from the SAFE Project
Description: The SAFE Project (<https://www.safeproject.net/>) is a large
scale ecological experiment in Malaysian Borneo that explores the impact
of habitat fragmentation and conversion on ecosystem function and services.
Data collected at the SAFE Project is made available under a common format
through the Zenodo data repository and this package makes it easy to
discover and load that data into R.
Author: Andy Aldersley [aut],
David Orme [aut, cre] (<https://orcid.org/0000-0002-7005-1394>)
Maintainer: David Orme <d.orme@imperial.ac.uk>
Diff between safedata versions 1.0.7 dated 2020-08-03 and 1.0.8 dated 2020-10-09
DESCRIPTION | 8 MD5 | 36 +-- NAMESPACE | 2 R/index.R | 99 ++++----- R/load_safe.R | 10 R/metadata.R | 35 +++ R/search_safe.R | 12 + R/taxa.R | 2 inst/doc/overview.Rmd | 2 inst/doc/overview.html | 2 inst/doc/using_safe_data.R | 29 ++ inst/doc/using_safe_data.Rmd | 45 +++- inst/doc/using_safe_data.html | 231 +++++++++++++++++---- inst/safedata_example_dir/safedata_example_dir.zip |binary man/get_taxa.Rd | 2 man/search_safe.Rd | 10 man/validate_record_ids.Rd | 14 + vignettes/overview.Rmd | 2 vignettes/using_safe_data.Rmd | 45 +++- 19 files changed, 465 insertions(+), 121 deletions(-)
Title: Incremental Calculation of Dynamic Time Warping
Description: The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>.
Author: Maximilian Leodolter
Maintainer: Maximilian Leodolter <maximilian.leodolter@gmail.com>
Diff between IncDTW versions 1.1.3.1 dated 2020-04-01 and 1.1.4.2 dated 2020-10-09
DESCRIPTION | 6 MD5 | 35 ++-- NAMESPACE | 2 R/RcppExports.R | 16 +- R/rundtw.R | 68 +++++++++ build/vignette.rds |binary data/brush_teeth.rda |binary data/drink_glass.rda |binary data/walk.rda |binary inst/doc/Theory_and_Applications_for_the_R_Package_IncDTW.pdf.asis | 1 man/DBA.Rd | 2 man/lowerbound.Rd |only src/RcppExports.cpp | 57 +++++++ src/init.c | 10 + src/rundtw.cpp | 42 +++++ tests/testthat/test_cm.R | 2 tests/testthat/test_dtw.R | 13 + tests/testthat/test_lowerbound.R | 72 +++++++++- vignettes/Theory_and_Applications_for_the_R_Package_IncDTW.pdf.asis | 1 19 files changed, 287 insertions(+), 40 deletions(-)
Title: A Dipping Sauce for Data Analysis and Visualizations
Description: Works as an "add-on" to packages like 'shiny', 'future', as well as
'rlang', and provides utility functions. Just like dipping sauce adding
flavors to potato chips or pita bread, 'dipsaus' for data analysis and
visualizations adds handy functions and enhancements to popular packages.
The goal is to provide simple solutions that are frequently asked for
online, such as how to synchronize 'shiny' inputs without freezing the app,
or how to get memory size on 'Linux' or 'MacOS' system. The enhancements
roughly fall into these four categories: 1. 'shiny' input widgets; 2.
high-performance computing using 'RcppParallel' and 'future' package; 3.
modify R calls and convert among numbers, strings, and other objects. 4.
utility functions to get system information such like CPU chip-set, memory
limit, etc.
Author: Zhengjia Wang [aut, cre]
Maintainer: Zhengjia Wang <zhengjia.wang@rice.edu>
Diff between dipsaus versions 0.1.0 dated 2020-08-12 and 0.1.1 dated 2020-10-09
DESCRIPTION | 6 +- MD5 | 29 ++++++----- NAMESPACE | 1 NEWS.md | 14 +++++ R/parallels-future.R | 37 +++++++++----- R/parallels-rscripts.R |only R/shiny-progress.R | 6 ++ R/utils-package.R | 15 ++++++ R/utils-rstudio.R | 12 ++++ inst/doc/async_evaluator.html | 72 ++++++++++++++++++++++++++++ inst/doc/r_expr_addons.html | 82 ++++++++++++++++++++++++++++++--- inst/doc/shiny_customized_widgets.html | 72 ++++++++++++++++++++++++++++ inst/doc/utility_functions.html | 74 +++++++++++++++++++++++++++++ man/async_works.Rd |only man/attached_packages.Rd |only man/lapply_async2.Rd | 3 - man/rs_exec.Rd | 11 ++++ 17 files changed, 395 insertions(+), 39 deletions(-)
Title: R Bindings to 'PyTorch'
Description: 'R' implementation and interface of the Machine Learning platform
'PyTorch' <https://pytorch.org/> developed in 'Python'. It requires a 'conda'
environment with 'torch' and 'torchvision' to provide 'PyTorch' functions,
methods and classes. The key object in 'PyTorch' is the tensor which is in
essence a multidimensional array. These tensors are fairly flexible to perform
calculations in CPUs as well as 'GPUs' to accelerate the process.
Author: Alfonso R. Reyes [aut, cre, cph]
Maintainer: Alfonso R. Reyes <alfonso.reyes@oilgainsanalytics.com>
Diff between rTorch versions 0.0.3 dated 2019-08-05 and 0.4.0 dated 2020-10-09
rTorch-0.0.3/rTorch/R/package_logger.R |only rTorch-0.0.3/rTorch/R/package_validation.R |only rTorch-0.0.3/rTorch/R/packages_import.R |only rTorch-0.0.3/rTorch/R/reticulate.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/ReLU_modular.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/ReLU_modular.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/basic_operations.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/basic_operations.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/linear_regression_modular-XY.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/linear_regression_modular-XY.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/linear_regression_modular.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/linear_regression_modular.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/logistic_regression_modular.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/logistic_regression_modular.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/neural-net_modular.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/neural-net_modular.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/awesome_model.pkl |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/linear_regression_rainfall.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/linear_regression_rainfall.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/logistic_regression_mnist_digits_idx.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/logistic_regression_mnist_digits_idx.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/model.ckpt |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/png_images_mnist_digits.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/png_images_mnist_digits.html |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/two_layer_neural_network.R |only rTorch-0.0.3/rTorch/inst/old_vignettes/vignettes/two_layer_neural_network.html |only rTorch-0.0.3/rTorch/inst/python/torchtools/__pycache__ |only rTorch-0.0.3/rTorch/man/conda-tools.Rd |only rTorch-0.0.3/rTorch/man/torch_getLogger.Rd |only rTorch-0.0.3/rTorch/tests/testthat/tensor_functions.R |only rTorch-0.0.3/rTorch/tests/testthat/test_mnist_digits_png_full.R |only rTorch-0.0.3/rTorch/tests/testthat/utils.R |only rTorch-0.4.0/rTorch/DESCRIPTION | 25 rTorch-0.4.0/rTorch/MD5 | 171 - rTorch-0.4.0/rTorch/NAMESPACE | 42 rTorch-0.4.0/rTorch/NEWS.md | 240 + rTorch-0.4.0/rTorch/R/extract.R | 89 rTorch-0.4.0/rTorch/R/generics.R | 311 +- rTorch-0.4.0/rTorch/R/help.R |only rTorch-0.4.0/rTorch/R/install.R | 175 - rTorch-0.4.0/rTorch/R/package.R | 143 - rTorch-0.4.0/rTorch/R/properties.R | 48 rTorch-0.4.0/rTorch/R/types.R | 56 rTorch-0.4.0/rTorch/R/utils.R | 17 rTorch-0.4.0/rTorch/README.md | 1303 +++++++--- rTorch-0.4.0/rTorch/build |only rTorch-0.4.0/rTorch/inst/doc |only rTorch-0.4.0/rTorch/inst/not-used |only rTorch-0.4.0/rTorch/inst/test_mnist_digits_png_full.R |only rTorch-0.4.0/rTorch/man/all.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/all_dims.Rd | 17 rTorch-0.4.0/rTorch/man/any.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/as_boolean.Rd |only rTorch-0.4.0/rTorch/man/dim.torch.Tensor.Rd | 7 rTorch-0.4.0/rTorch/man/dot-torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/equals-.torch.Tensor.Rd | 8 rTorch-0.4.0/rTorch/man/grapes-.-times-grapes.Rd | 9 rTorch-0.4.0/rTorch/man/grapes-grapes-.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/grapes-times-times-grapes.Rd | 2 rTorch-0.4.0/rTorch/man/greater-than-.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/greater-than-equals-.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/install_pytorch.Rd | 67 rTorch-0.4.0/rTorch/man/install_torch_extras.Rd | 9 rTorch-0.4.0/rTorch/man/is_tensor.Rd |only rTorch-0.4.0/rTorch/man/length.torch.Tensor.Rd | 7 rTorch-0.4.0/rTorch/man/less-than-.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/less-than-equals-.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/log.torch.Tensor.Rd | 10 rTorch-0.4.0/rTorch/man/log10.torch.Tensor.Rd | 5 rTorch-0.4.0/rTorch/man/log2.torch.Tensor.Rd | 5 rTorch-0.4.0/rTorch/man/logical_and.Rd | 8 rTorch-0.4.0/rTorch/man/logical_not.Rd | 4 rTorch-0.4.0/rTorch/man/logical_or.Rd | 8 rTorch-0.4.0/rTorch/man/make_copy.Rd |only rTorch-0.4.0/rTorch/man/modules.Rd | 8 rTorch-0.4.0/rTorch/man/not_equal_to.Rd | 8 rTorch-0.4.0/rTorch/man/one_tensor_op.Rd | 2 rTorch-0.4.0/rTorch/man/plus-.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/rTorch.Rd | 5 rTorch-0.4.0/rTorch/man/reexports.Rd | 2 rTorch-0.4.0/rTorch/man/slash-.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/sub-.torch.Tensor.Rd | 82 rTorch-0.4.0/rTorch/man/tensor_ops.Rd | 8 rTorch-0.4.0/rTorch/man/times-.torch.Tensor.Rd | 2 rTorch-0.4.0/rTorch/man/torch_config.Rd | 6 rTorch-0.4.0/rTorch/man/torch_extract_opts.Rd | 39 rTorch-0.4.0/rTorch/tests/testthat/helper_utils.R |only rTorch-0.4.0/rTorch/tests/testthat/run_quick_test.R | 53 rTorch-0.4.0/rTorch/tests/testthat/test-extract.R | 125 rTorch-0.4.0/rTorch/tests/testthat/test-generic-methods.R | 67 rTorch-0.4.0/rTorch/tests/testthat/test-install_rtorch_dryrun.R |only rTorch-0.4.0/rTorch/tests/testthat/test-install_rtorch_live.R |only rTorch-0.4.0/rTorch/tests/testthat/test-install_rtorch_parse_version.R |only rTorch-0.4.0/rTorch/tests/testthat/test-tensor_comparison.R |only rTorch-0.4.0/rTorch/tests/testthat/test-uint8_boolean.R |only rTorch-0.4.0/rTorch/tests/testthat/test_generics.R | 149 + rTorch-0.4.0/rTorch/tests/testthat/test_info.R | 22 rTorch-0.4.0/rTorch/tests/testthat/test_matrix_like_ops.R | 5 rTorch-0.4.0/rTorch/tests/testthat/test_numpy_logical.R | 50 rTorch-0.4.0/rTorch/tests/testthat/test_py_object_slicing.R | 13 rTorch-0.4.0/rTorch/tests/testthat/test_r_torch_share_objects.R | 38 rTorch-0.4.0/rTorch/tests/testthat/test_rw_tensors_by_index.R | 3 rTorch-0.4.0/rTorch/tests/testthat/test_tensor_dim.R | 18 rTorch-0.4.0/rTorch/tests/testthat/test_tensor_reshape.R | 12 rTorch-0.4.0/rTorch/tests/testthat/test_tensor_slicing.R | 18 rTorch-0.4.0/rTorch/tests/testthat/test_torch_core.R | 217 + rTorch-0.4.0/rTorch/tests/testthat/test_types.R | 35 rTorch-0.4.0/rTorch/vignettes |only 108 files changed, 2663 insertions(+), 1130 deletions(-)
Title: Multi-Task Prediction using Stacking Algorithms
Description: Simultaneous multiple outcomes prediction based on revised stacking algorithms, which enables the integration of information from predictions of individual models. An implementation of methodologies proposed in our paper: Li Xing, Mary L Lesperance, Xuekui Zhang. (2019) Bioinformatics, "Simultaneous prediction of multiple outcomes using revised stacking algorithms" <doi:10.1093/bioinformatics/btz531>.
Author: Li Xing [aut, cre],
Yuying Huang [aut],
Peijie Xie [ctb],
Mary Lesperance [aut],
Xuekui Zhang [aut]
Maintainer: Li Xing <sfulxing@gmail.com>
Diff between MTPS versions 0.1.9 dated 2020-02-06 and 1.0.1 dated 2020-10-09
DESCRIPTION | 10 ++++++---- MD5 | 4 ++-- inst/doc/Guide.html | 4 ++-- 3 files changed, 10 insertions(+), 8 deletions(-)
Title: Analyzing High-Throughput Single Cell Sequencing Data
Description: A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq and CITE-Seq. Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
Author: Alireza Khodadadi-Jamayran [aut, cre]
(<https://orcid.org/0000-0003-2495-7504>),
Joseph Pucella [aut, ctb] (<https://orcid.org/0000-0003-0875-8046>),
Hua Zhou [aut, ctb] (<https://orcid.org/0000-0003-1822-1306>),
Nicole Doudican [aut, ctb] (<https://orcid.org/0000-0003-3827-9644>),
John Carucci [aut, ctb] (<https://orcid.org/0000-0001-6817-9439>),
Adriana Heguy [aut, ctb],
Boris Reizis [aut, ctb] (<https://orcid.org/0000-0003-1140-7853>),
Aristotelis Tsirigos [aut, ctb]
(<https://orcid.org/0000-0002-7512-8477>)
Maintainer: Alireza Khodadadi-Jamayran <alireza.khodadadi.j@gmail.com>
Diff between iCellR versions 1.5.5 dated 2020-07-16 and 1.5.8 dated 2020-10-09
DESCRIPTION | 6 +- MD5 | 22 ++++----- R/F0022.R | 5 +- R/F0031.R | 112 ++++++++++++++++++++++++++++++++++-------------- R/F0041.R | 126 +++++++++++++++++++++++++++++++++++++++--------------- R/F0058.R | 7 +-- R/F0060.R | 5 +- man/clono.plot.Rd | 15 +++++- man/gene.plot.Rd | 7 ++- man/iclust.Rd | 4 - man/run.impute.Rd | 2 man/run.knetl.Rd | 4 - 12 files changed, 218 insertions(+), 97 deletions(-)
Title: Model Elemental Fluxes in Fishes
Description: Model fluxes of carbon, nitrogen, and phosphorus with the use of a coupled
bioenergetics and stoichiometric model that incorporates flexible elemental limitation.
Additional functions to help the user to find parameters are included. Finally, functions to
extract and visualize results are available as well. For an introduction, see vignette.
For more information on the theoretical background of this model,
see Schiettekatte et al. (2020) <doi:10.1111/1365-2435.13618>.
Author: Nina M. D. Schiettekatte [aut, cre],
Diego Barneche [aut]
Maintainer: Nina M. D. Schiettekatte <nina.schiettekatte@gmail.com>
Diff between fishflux versions 0.0.1.1 dated 2020-07-08 and 0.0.1.2 dated 2020-10-09
DESCRIPTION | 6 MD5 | 23 +-- NEWS.md |only README.md | 66 ++++----- inst/doc/intro_to_fishflux.html | 198 ++++++++++++++--------------- src/Makevars | 10 - src/Makevars.win | 11 - tests/testthat/helper_fishflux.R | 20 ++ tests/testthat/test-aspect_ratio.R | 3 tests/testthat/test-growth-params.R | 5 tests/testthat/test-model_parameters.R | 7 - tests/testthat/test-name_error_functions.R | 2 tests/testthat/test-trophic_level.R | 4 13 files changed, 181 insertions(+), 174 deletions(-)
Previous versions (as known to CRANberries) which should be available via the Archive link are:
2020-09-24 3.1-2
2020-05-26 3.1-1
2019-07-25 3.0-0
2018-01-12 2.2-0
2017-04-13 2.1-0