Sat, 15 Oct 2022

Package PKNCA updated to version 0.10.0 with previous version 0.9.5 dated 2021-10-29

Title: Perform Pharmacokinetic Non-Compartmental Analysis
Description: Compute standard Non-Compartmental Analysis (NCA) parameters for typical pharmacokinetic analyses and summarize them.
Author: Bill Denney [aut, cre] , Clare Buckeridge [aut], Sridhar Duvvuri [ctb]
Maintainer: Bill Denney <wdenney@humanpredictions.com>

Diff between PKNCA versions 0.9.5 dated 2021-10-29 and 0.10.0 dated 2022-10-15

 PKNCA-0.10.0/PKNCA/DESCRIPTION                                                              |   16 
 PKNCA-0.10.0/PKNCA/MD5                                                                      |  474 +++---
 PKNCA-0.10.0/PKNCA/NAMESPACE                                                                |  103 -
 PKNCA-0.10.0/PKNCA/NEWS.md                                                                  |   55 
 PKNCA-0.10.0/PKNCA/R/001-add.interval.col.R                                                 |  112 -
 PKNCA-0.10.0/PKNCA/R/002-pk.business.rules.R                                                |   44 
 PKNCA-0.10.0/PKNCA/R/AIC.list.R                                                             |   22 
 PKNCA-0.10.0/PKNCA/R/PKNCA.R                                                                |   10 
 PKNCA-0.10.0/PKNCA/R/PKNCA.options.R                                                        |   71 
 PKNCA-0.10.0/PKNCA/R/auc.R                                                                  |  169 +-
 PKNCA-0.10.0/PKNCA/R/aucint.R                                                               |   36 
 PKNCA-0.10.0/PKNCA/R/auciv.R                                                                |only
 PKNCA-0.10.0/PKNCA/R/check.intervals.R                                                      |   51 
 PKNCA-0.10.0/PKNCA/R/choose.intervals.R                                                     |   67 
 PKNCA-0.10.0/PKNCA/R/class-PKNCAconc.R                                                      |  170 +-
 PKNCA-0.10.0/PKNCA/R/class-PKNCAdata.R                                                      |  117 +
 PKNCA-0.10.0/PKNCA/R/class-PKNCAdose.R                                                      |   86 -
 PKNCA-0.10.0/PKNCA/R/class-PKNCAresults.R                                                   |  193 +-
 PKNCA-0.10.0/PKNCA/R/class-general.R                                                        |   39 
 PKNCA-0.10.0/PKNCA/R/cleaners.R                                                             |   50 
 PKNCA-0.10.0/PKNCA/R/dplyr.R                                                                |only
 PKNCA-0.10.0/PKNCA/R/exclude.R                                                              |   32 
 PKNCA-0.10.0/PKNCA/R/exclude_nca.R                                                          |    4 
 PKNCA-0.10.0/PKNCA/R/general.functions.R                                                    |   52 
 PKNCA-0.10.0/PKNCA/R/half.life.R                                                            |  165 +-
 PKNCA-0.10.0/PKNCA/R/impute.R                                                               |only
 PKNCA-0.10.0/PKNCA/R/interp_extrap_helpers.R                                                |only
 PKNCA-0.10.0/PKNCA/R/interpolate.conc.R                                                     |  245 +--
 PKNCA-0.10.0/PKNCA/R/parseFormula.R                                                         |   47 
 PKNCA-0.10.0/PKNCA/R/pk.calc.all.R                                                          |  550 ++++---
 PKNCA-0.10.0/PKNCA/R/pk.calc.c0.R                                                           |   33 
 PKNCA-0.10.0/PKNCA/R/pk.calc.simple.R                                                       |  278 ++-
 PKNCA-0.10.0/PKNCA/R/pk.calc.urine.R                                                        |   18 
 PKNCA-0.10.0/PKNCA/R/prepare_data.R                                                         |  168 +-
 PKNCA-0.10.0/PKNCA/R/provenance.R                                                           |   14 
 PKNCA-0.10.0/PKNCA/R/sparse.R                                                               |only
 PKNCA-0.10.0/PKNCA/R/superposition.R                                                        |   85 -
 PKNCA-0.10.0/PKNCA/R/time.above.R                                                           |   52 
 PKNCA-0.10.0/PKNCA/R/time_calc.R                                                            |   29 
 PKNCA-0.10.0/PKNCA/R/tss.R                                                                  |   46 
 PKNCA-0.10.0/PKNCA/R/tss.monoexponential.R                                                  |  180 +-
 PKNCA-0.10.0/PKNCA/R/tss.stepwise.linear.R                                                  |   56 
 PKNCA-0.10.0/PKNCA/R/unit-support.R                                                         |only
 PKNCA-0.10.0/PKNCA/R/zzz-pk.calc.dn.R                                                       |only
 PKNCA-0.10.0/PKNCA/README.md                                                                |   28 
 PKNCA-0.10.0/PKNCA/build/vignette.rds                                                       |binary
 PKNCA-0.10.0/PKNCA/inst/WORDLIST                                                            |only
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 PKNCA-0.10.0/PKNCA/man/AIC.list.Rd                                                          |   58 
 PKNCA-0.10.0/PKNCA/man/PKNCA.Rd                                                             |    4 
 PKNCA-0.10.0/PKNCA/man/PKNCA.choose.option.Rd                                               |   62 
 PKNCA-0.10.0/PKNCA/man/PKNCA.options.Rd                                                     |    6 
 PKNCA-0.10.0/PKNCA/man/PKNCA_impute_fun_list.Rd                                             |only
 PKNCA-0.10.0/PKNCA/man/PKNCA_impute_method.Rd                                               |only
 PKNCA-0.10.0/PKNCA/man/PKNCAconc.Rd                                                         |  179 +-
 PKNCA-0.10.0/PKNCA/man/PKNCAdata.Rd                                                         |   35 
 PKNCA-0.10.0/PKNCA/man/PKNCAdose.Rd                                                         |   44 
 PKNCA-0.10.0/PKNCA/man/PKNCAresults.Rd                                                      |    2 
 PKNCA-0.10.0/PKNCA/man/add.interval.col.Rd                                                  |   54 
 PKNCA-0.10.0/PKNCA/man/addProvenance.Rd                                                     |    6 
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 PKNCA-0.10.0/PKNCA/man/any_sparse_dense_in_interval.Rd                                      |only
 PKNCA-0.10.0/PKNCA/man/as.data.frame.PKNCAresults.Rd                                        |   48 
 PKNCA-0.10.0/PKNCA/man/as_sparse_pk.Rd                                                      |only
 PKNCA-0.10.0/PKNCA/man/business.mean.Rd                                                     |  138 -
 PKNCA-0.10.0/PKNCA/man/check.conc.time.Rd                                                   |   74 
 PKNCA-0.10.0/PKNCA/man/check.conversion.Rd                                                  |   46 
 PKNCA-0.10.0/PKNCA/man/check.interval.deps.Rd                                               |   62 
 PKNCA-0.10.0/PKNCA/man/check.interval.specification.Rd                                      |   76 
 PKNCA-0.10.0/PKNCA/man/checkProvenance.Rd                                                   |    2 
 PKNCA-0.10.0/PKNCA/man/choose.auc.intervals.Rd                                              |  120 -
 PKNCA-0.10.0/PKNCA/man/choose_interp_extrap_method.Rd                                       |only
 PKNCA-0.10.0/PKNCA/man/clean.conc.blq.Rd                                                    |   16 
 PKNCA-0.10.0/PKNCA/man/clean.conc.na.Rd                                                     |    6 
 PKNCA-0.10.0/PKNCA/man/cov_holder.Rd                                                        |only
 PKNCA-0.10.0/PKNCA/man/exclude.Rd                                                           |   24 
 PKNCA-0.10.0/PKNCA/man/exclude_nca.Rd                                                       |   10 
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 PKNCA-0.10.0/PKNCA/man/find.tau.Rd                                                          |   84 -
 PKNCA-0.10.0/PKNCA/man/findOperator.Rd                                                      |   66 
 PKNCA-0.10.0/PKNCA/man/fit_half_life.Rd                                                     |   14 
 PKNCA-0.10.0/PKNCA/man/formula.PKNCAconc.Rd                                                 |   44 
 PKNCA-0.10.0/PKNCA/man/formula.parseFormula.Rd                                              |   62 
 PKNCA-0.10.0/PKNCA/man/geomean.Rd                                                           |   86 -
 PKNCA-0.10.0/PKNCA/man/get.best.model.Rd                                                    |   46 
 PKNCA-0.10.0/PKNCA/man/get.first.model.Rd                                                   |   38 
 PKNCA-0.10.0/PKNCA/man/get.interval.cols.Rd                                                 |   60 
 PKNCA-0.10.0/PKNCA/man/get.parameter.deps.Rd                                                |    4 
 PKNCA-0.10.0/PKNCA/man/getAttributeColumn.Rd                                                |    2 
 PKNCA-0.10.0/PKNCA/man/getColumnValueOrNot.Rd                                               |   16 
 PKNCA-0.10.0/PKNCA/man/getDataName.Rd                                                       |   92 -
 PKNCA-0.10.0/PKNCA/man/getDepVar.Rd                                                         |   54 
 PKNCA-0.10.0/PKNCA/man/getGroups.PKNCAconc.Rd                                               |   22 
 PKNCA-0.10.0/PKNCA/man/getIndepVar.Rd                                                       |   54 
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 PKNCA-0.10.0/PKNCA/man/group_vars.PKNCAconc.Rd                                              |    4 
 PKNCA-0.10.0/PKNCA/man/inner_join.PKNCAresults.Rd                                           |only
 PKNCA-0.10.0/PKNCA/man/interp.extrap.conc.Rd                                                |    8 
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 PKNCA-0.10.0/PKNCA/man/is_sparse_pk.Rd                                                      |only
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 PKNCA-0.10.0/PKNCA/man/parseFormula.Rd                                                      |    2 
 PKNCA-0.10.0/PKNCA/man/pk.calc.ae.Rd                                                        |   60 
 PKNCA-0.10.0/PKNCA/man/pk.calc.aucint.Rd                                                    |   12 
 PKNCA-0.10.0/PKNCA/man/pk.calc.auciv.Rd                                                     |only
 PKNCA-0.10.0/PKNCA/man/pk.calc.aucpext.Rd                                                   |    6 
 PKNCA-0.10.0/PKNCA/man/pk.calc.auxc.Rd                                                      |   34 
 PKNCA-0.10.0/PKNCA/man/pk.calc.c0.Rd                                                        |  152 -
 PKNCA-0.10.0/PKNCA/man/pk.calc.cav.Rd                                                       |   48 
 PKNCA-0.10.0/PKNCA/man/pk.calc.ceoi.Rd                                                      |    2 
 PKNCA-0.10.0/PKNCA/man/pk.calc.cl.Rd                                                        |    8 
 PKNCA-0.10.0/PKNCA/man/pk.calc.clast.obs.Rd                                                 |   46 
 PKNCA-0.10.0/PKNCA/man/pk.calc.clr.Rd                                                       |   58 
 PKNCA-0.10.0/PKNCA/man/pk.calc.cmax.Rd                                                      |   58 
 PKNCA-0.10.0/PKNCA/man/pk.calc.ctrough.Rd                                                   |   44 
 PKNCA-0.10.0/PKNCA/man/pk.calc.deg.fluc.Rd                                                  |   48 
 PKNCA-0.10.0/PKNCA/man/pk.calc.dn.Rd                                                        |    2 
 PKNCA-0.10.0/PKNCA/man/pk.calc.f.Rd                                                         |   50 
 PKNCA-0.10.0/PKNCA/man/pk.calc.fe.Rd                                                        |   56 
 PKNCA-0.10.0/PKNCA/man/pk.calc.half.life.Rd                                                 |   24 
 PKNCA-0.10.0/PKNCA/man/pk.calc.mrt.Rd                                                       |   78 -
 PKNCA-0.10.0/PKNCA/man/pk.calc.mrt.md.Rd                                                    |    8 
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 PKNCA-0.10.0/PKNCA/man/pk.calc.swing.Rd                                                     |   46 
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 PKNCA-0.10.0/PKNCA/man/pk.calc.tlag.Rd                                                      |   46 
 PKNCA-0.10.0/PKNCA/man/pk.calc.tlast.Rd                                                     |   62 
 PKNCA-0.10.0/PKNCA/man/pk.calc.tmax.Rd                                                      |   74 
 PKNCA-0.10.0/PKNCA/man/pk.calc.vd.Rd                                                        |   10 
 PKNCA-0.10.0/PKNCA/man/pk.nca.Rd                                                            |   70 
 PKNCA-0.10.0/PKNCA/man/pk.nca.interval.Rd                                                   |  184 +-
 PKNCA-0.10.0/PKNCA/man/pk.nca.intervals.Rd                                                  |    9 
 PKNCA-0.10.0/PKNCA/man/pk.tss.Rd                                                            |   60 
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 PKNCA-0.10.0/PKNCA/man/reexports.Rd                                                         |   12 
 PKNCA-0.10.0/PKNCA/man/roundString.Rd                                                       |   74 
 PKNCA-0.10.0/PKNCA/man/roundingSummarize.Rd                                                 |   44 
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 PKNCA-0.10.0/PKNCA/man/setDuration.Rd                                                       |   58 
 PKNCA-0.10.0/PKNCA/man/setExcludeColumn.Rd                                                  |    2 
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 PKNCA-0.10.0/PKNCA/man/superposition.Rd                                                     |  204 +-
 PKNCA-0.10.0/PKNCA/man/time_calc.Rd                                                         |   64 
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 PKNCA-0.10.0/PKNCA/tests/testthat/test-AIC.list.R                                           |   26 
 PKNCA-0.10.0/PKNCA/tests/testthat/test-PKNCA.options.R                                      |    5 
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 PKNCA-0.10.0/PKNCA/tests/testthat/test-class-PKNCAconc.R                                    |  115 +
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 PKNCA-0.10.0/PKNCA/tests/testthat/test-class-PKNCAdose.R                                    |   67 
 PKNCA-0.10.0/PKNCA/tests/testthat/test-class-PKNCAresults.R                                 |  291 +++
 PKNCA-0.10.0/PKNCA/tests/testthat/test-class-general.R                                      |   10 
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 PKNCA-0.10.0/PKNCA/tests/testthat/test-exclude_nca.R                                        |   12 
 PKNCA-0.10.0/PKNCA/tests/testthat/test-general.functions.R                                  |   86 -
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 PKNCA-0.10.0/PKNCA/tests/testthat/test-interp_extrap_helpers.R                              |only
 PKNCA-0.10.0/PKNCA/tests/testthat/test-interpolate.conc.R                                   |  769 +++++-----
 PKNCA-0.10.0/PKNCA/tests/testthat/test-parseFormula.R                                       |   88 -
 PKNCA-0.10.0/PKNCA/tests/testthat/test-pk.business.rules.R                                  |   24 
 PKNCA-0.10.0/PKNCA/tests/testthat/test-pk.calc.all.R                                        |  214 ++
 PKNCA-0.10.0/PKNCA/tests/testthat/test-pk.calc.c0.R                                         |   14 
 PKNCA-0.10.0/PKNCA/tests/testthat/test-pk.calc.dn.R                                         |    6 
 PKNCA-0.10.0/PKNCA/tests/testthat/test-pk.calc.simple.R                                     |  153 +
 PKNCA-0.10.0/PKNCA/tests/testthat/test-pk.calc.urine.R                                      |    4 
 PKNCA-0.10.0/PKNCA/tests/testthat/test-prepare_data.R                                       |   43 
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Package startup updated to version 0.19.0 with previous version 0.18.0 dated 2022-05-14

Title: Friendly R Startup Configuration
Description: Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files. This makes it possible to keep private / secret environment variables separate from other environment variables. It also makes it easier to share specific startup settings by simply copying a file to a directory.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package simET updated to version 1.0.1 with previous version 1.0.0 dated 2022-09-30

Title: Evapotranspiration Simulation and Soil Water Balance under Field
Description: Supports the calculation of meteorological characteristics in evapotranspiration research and reference crop evapotranspiration, and offers three models to simulate crop evapotranspiration and soil water balance in the field, including single crop coefficient and dual crop coefficient, as well as the Shuttleworth-Wallace model. These calculations main refer to Allen et al.(1998, ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and Liu et al.(2006) <doi:10.1016/j.agwat.2006.01.018>.
Author: Minguo Liu [aut, cre], Huimin Yang [dtc, fnd]
Maintainer: Minguo Liu <liumg@cau.edu.cn>

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Package expss updated to version 0.11.2 with previous version 0.11.1 dated 2022-01-07

Title: Tables, Labels and Some Useful Functions from Spreadsheets and 'SPSS' Statistics
Description: Package computes and displays tables with support for 'SPSS'-style labels, multiple and nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in 'knitr', 'Shiny', '*.xlsx' files, R and 'Jupyter' notebooks. Methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package brings popular data transformation functions from 'SPSS' Statistics and 'Excel': 'RECODE', 'COUNT', 'COUNTIF', 'VLOOKUP' and etc. These functions are very useful for data processing in marketing research surveys. Package intended to help people to move data processing from 'Excel' and 'SPSS' to R.
Author: Gregory Demin [aut, cre], Sebastian Jeworutzki [ctb]
Maintainer: Gregory Demin <gdemin@gmail.com>

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Package xts updated to version 0.12.2 with previous version 0.12.1 dated 2020-09-09

Title: eXtensible Time Series
Description: Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
Author: Jeffrey A. Ryan [aut, cph], Joshua M. Ulrich [cre, aut], Ross Bennett [ctb], Corwin Joy [ctb]
Maintainer: Joshua M. Ulrich <josh.m.ulrich@gmail.com>

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Package timeSeries updated to version 4021.105 with previous version 4021.104 dated 2022-07-16

Title: Financial Time Series Objects (Rmetrics)
Description: 'S4' classes and various tools for financial time series: Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions.
Author: Diethelm Wuertz [aut] , Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] , Georgi N. Boshnakov [cre, ctb]
Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk>

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Package tidyhydat updated to version 0.5.7 with previous version 0.5.6 dated 2022-08-20

Title: Extract and Tidy Canadian 'Hydrometric' Data
Description: Provides functions to access historical and real-time national 'hydrometric' data from Water Survey of Canada data sources (<https://dd.weather.gc.ca/hydrometric/csv/> and <https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.
Author: Sam Albers [aut, cre] , David Hutchinson [ctb], Dewey Dunnington [ctb], Ryan Whaley [ctb], Province of British Columbia [cph], Government of Canada [dtc], Luke Winslow [rev] , Laura DeCicco [rev]
Maintainer: Sam Albers <sam.albers@gmail.com>

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Package threeBrain updated to version 0.2.7 with previous version 0.2.6 dated 2022-08-25

Title: 3D Brain Visualization
Description: A fast, interactive cross-platform, and easy to share 'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or 'AFNI/SUMA' surfaces. The viewer widget can be either standalone or embedded into 'R-shiny' applications. The standalone version only require a web browser with 'WebGL2' support (for example, 'Chrome', 'Firefox', 'Safari'), and can be inserted into any websites. The 'R-shiny' support allows the 3D viewer to be dynamically generated from reactive user inputs. This feature has been fully adopted by 'RAVE' <https://openwetware.org/wiki/RAVE>, an interactive toolbox to analyze 'iEEG' data. Documentation about 'threeBrain' is provided by <https://dipterix.org/threeBrain/> and several vignettes included in this package. To cite the package, please check our 'NeuroImage' paper by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>), or see 'citation("threeBrain")' for details.
Author: Zhengjia Wang [aut, cre, cph], John Magnotti [aut], Xiang Zhang [ctb, res], Brian Metzger [res], Elizabeth Nesbitt [res], Meng Li [ths], Michael Beauchamp [aut, dtc]
Maintainer: Zhengjia Wang <dipterix.wang@gmail.com>

Diff between threeBrain versions 0.2.6 dated 2022-08-25 and 0.2.7 dated 2022-10-15

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Package shidashi updated to version 0.1.4 with previous version 0.1.3 dated 2022-08-06

Title: A Shiny Dashboard Template System
Description: A template system based on 'AdminLTE3' (<https://adminlte.io/themes/v3/>) theme. Comes with default theme that can be easily customized. Developers can upload modified templates on 'Github', and users can easily download templates with 'RStudio' project wizard. The key features of the default template include light and dark theme switcher, resizing graphs, synchronizing inputs across sessions, new notification system, fancy progress bars, and card-like flip panels with back sides, as well as various of 'HTML' tool widgets.
Author: Zhengjia Wang [cph, aut, cre] , ColorlibHQ [cph] , Bootstrap contributors [ctb] , Twitter, Inc [cph] , Ivan Sagalaev [ctb, cph] , Rene Haas [ctb, cph] , Zeno Rocha [ctb, cph]
Maintainer: Zhengjia Wang <dipterix.wang@gmail.com>

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Package recipes updated to version 1.0.2 with previous version 1.0.1 dated 2022-07-07

Title: Preprocessing and Feature Engineering Steps for Modeling
Description: A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.
Author: Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]
Maintainer: Max Kuhn <max@rstudio.com>

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Package Rborist updated to version 0.3-2 with previous version 0.3-1 dated 2022-10-09

Title: Extensible, Parallelizable Implementation of the Random Forest Algorithm
Description: Scalable implementation of classification and regression forests, as described by Breiman (2001), <DOI:10.1023/A:1010933404324>.
Author: Mark Seligman
Maintainer: Mark Seligman <mseligman@suiji.org>

Diff between Rborist versions 0.3-1 dated 2022-10-09 and 0.3-2 dated 2022-10-15

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Package photobiology updated to version 0.10.14 with previous version 0.10.13 dated 2022-10-05

Title: Photobiological Calculations
Description: Definitions of classes, methods, operators and functions for use in photobiology and radiation meteorology and climatology. Calculation of effective (weighted) and not-weighted irradiances/doses, fluence rates, transmittance, reflectance, absorptance, absorbance and diverse ratios and other derived quantities from spectral data. Local maxima and minima: peaks, valleys and spikes. Conversion between energy-and photon-based units. Wavelength interpolation. Astronomical calculations related solar angles and day length. Colours and vision. This package is part of the 'r4photobiology' suite, Aphalo, P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Author: Pedro J. Aphalo [aut, cre] , Titta K. Kotilainen [ctb] , Glenn Davis [ctb], Agnese Fazio [ctb]
Maintainer: Pedro J. Aphalo <pedro.aphalo@helsinki.fi>

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Package IRISSeismic updated to version 1.6.6 with previous version 1.6.5 dated 2022-04-10

Title: Classes and Methods for Seismic Data Analysis
Description: Provides classes and methods for seismic data analysis. The base classes and methods are inspired by the python code found in the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and methods support data returned by web services provided by the 'IRIS DMC' <http://service.iris.edu/>.
Author: Jonathan Callahan [aut], Rob Casey [aut], Gillian Sharer [aut, cre], Mary Templeton [aut], Chad Trabant [ctb]
Maintainer: Gillian Sharer <gillian@iris.washington.edu>

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Package ggpmisc updated to version 0.5.1 with previous version 0.5.0 dated 2022-08-12

Title: Miscellaneous Extensions to 'ggplot2'
Description: Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Statistics: locate and tag peaks and valleys; label plot with the equation of a fitted polynomial or other types of models; labels with P-value, R^2 or adjusted R^2 or information criteria for fitted models; label with ANOVA table for fitted models; label with summary for fitted models. Model fit classes for which suitable methods are provided by package 'broom' and 'broom.mixed' are supported. Scales and stats to build volcano and quadrant plots based on outcomes, fold changes, p-values and false discovery rates.
Author: Pedro J. Aphalo [aut, cre] , Kamil Slowikowski [ctb] , Samer Mouksassi [ctb]
Maintainer: Pedro J. Aphalo <pedro.aphalo@helsinki.fi>

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Package gginnards updated to version 0.1.1 with previous version 0.1.0-1 dated 2021-07-30

Title: Explore the Innards of 'ggplot2' Objects
Description: Extensions to 'ggplot2' providing low-level debug tools: statistics and geometries echoing their data argument. Layer manipulation: deletion, insertion, extraction and reordering of layers. Deletion of unused variables from the data object embedded in "ggplot" objects.
Author: Pedro J. Aphalo [aut, cre]
Maintainer: Pedro J. Aphalo <pedro.aphalo@helsinki.fi>

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Package gghighlight updated to version 0.4.0 with previous version 0.3.3 dated 2022-06-06

Title: Highlight Lines and Points in 'ggplot2'
Description: Make it easier to explore data with highlights.
Author: Hiroaki Yutani [aut, cre]
Maintainer: Hiroaki Yutani <yutani.ini@gmail.com>

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Package ggbreak updated to version 0.1.1 with previous version 0.1.0 dated 2022-06-01

Title: Set Axis Break for 'ggplot2'
Description: An implementation of scale functions for setting axis breaks of a 'gg' plot.
Author: Guangchuang Yu [aut, cre, cph] , Shuangbin Xu [aut]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

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Package geocmeans updated to version 0.3.1 with previous version 0.2.2 dated 2022-06-16

Title: Implementing Methods for Spatial Fuzzy Unsupervised Classification
Description: Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed. The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).
Author: Jeremy Gelb [aut, cre], Philippe Apparicio [ctb]
Maintainer: Jeremy Gelb <jeremy.gelb@ucs.inrs.ca>

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Package Ecdat updated to version 0.4-2 with previous version 0.4-1 dated 2022-07-05

Title: Data Sets for Econometrics
Description: Data sets for econometrics, including political science.
Author: Yves Croissant <yves.croissant@let.ish-lyon.cnrs.fr> and Spencer Graves
Maintainer: Spencer Graves <spencer.graves@effectivedefense.org>

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Package tracee updated to version 0.0.2 with previous version 0.0.1 dated 2022-05-30

Title: Easily Save Output and Trace it Back to Code
Description: Simple but crucial functionality related to creation of reproducible and traceable output (plots) back to code. It has a graphics saver with simple automation of plot stamping with source, destination and creation time, a selection of dimensions for use in presentations. A list of plots can be saved at once.
Author: Philip Delff [aut, cre]
Maintainer: Philip Delff <philip@delff.dk>

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Package ravedash updated to version 0.1.2 with previous version 0.1.1 dated 2022-06-23

Title: Dashboard System for Reproducible Visualization of 'iEEG'
Description: Dashboard system to display the analysis results produced by 'RAVE' (Magnotti J.F., Wang Z., Beauchamp M.S. (2020), R analysis and visualizations of 'iEEG' <doi:10.1016/j.neuroimage.2020.117341>). Provides infrastructure to integrate customized analysis pipelines into dashboard modules, including file structures, front-end widgets, and event handlers.
Author: Zhengjia Wang [aut, cre, cph]
Maintainer: Zhengjia Wang <dipterix.wang@gmail.com>

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Package photobiologyInOut updated to version 0.4.26 with previous version 0.4.25 dated 2022-08-15

Title: Read Spectral and Logged Data from Foreign Files
Description: Functions for reading, and in some cases writing, foreign files containing spectral data from spectrometers and their associated software, output from daylight simulation models in common use, and some spectral data repositories. As well as functions for exchange of spectral data with other R packages. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Author: Pedro J. Aphalo [aut, cre] , Titta K. Kotilainen [ctb] , Glenn Davis [ctb]
Maintainer: Pedro J. Aphalo <pedro.aphalo@helsinki.fi>

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Package ggsurvfit updated to version 0.2.0 with previous version 0.1.0 dated 2022-08-27

Title: Flexible Time-to-Event Figures
Description: Ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper 'ggplot2' geoms or stat methods, allowing the functions from this package to be combined with any function or customization from 'ggplot2' and other 'ggplot2' extension packages.
Author: Daniel D. Sjoberg [aut, cre, cph] , Mark Baillie [aut], Steven Haesendonckx [aut] , Tim Treis [aut]
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

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New package fwildclusterboot with initial version 0.12
Package: fwildclusterboot
Title: Fast Wild Cluster Bootstrap Inference for Linear Models
Version: 0.12
Description: Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Further, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' [...truncated...]
URL: https://s3alfisc.github.io/fwildclusterboot/
BugReports: https://github.com/s3alfisc/fwildclusterboot/issues/
License: GPL-3
Imports: collapse, dreamerr, Formula, generics, dqrng, gtools, Matrix, JuliaConnectoR, MASS, Rcpp, summclust
Suggests: fixest, lfe, ivreg, clubSandwich, lmtest, data.table, fabricatr, covr, knitr, rmarkdown, broom, modelsummary, bench, testthat (>= 3.0.0), tibble, sandwich
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp,RcppArmadillo, RcppEigen
VignetteBuilder: knitr
Language: en-US
SystemRequirements: Julia (>= 1.7), WildBootTests.jl (>=0.8.1)
NeedsCompilation: yes
Packaged: 2022-10-13 08:43:37 UTC; alexa
Author: Alexander Fischer [aut, cre], David Roodman [aut], Achim Zeileis [ctb] , Nathaniel Graham [ctb] , Susanne Koell [ctb] , Laurent Berge [ctb] , Sebastian Krantz [ctb]
Maintainer: Alexander Fischer <alexander-fischer1801@t-online.de>
Repository: CRAN
Date/Publication: 2022-10-15 23:02:34 UTC

More information about fwildclusterboot at CRAN
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Package deforestable updated to version 3.1.1 with previous version 3.1.0 dated 2022-10-10

Title: Classify RGB Images into Forest or Non-Forest
Description: Implements two out-of box classifiers presented in <doi:10.48550/arXiv.2112.01063> for distinguishing forest and non-forest terrain images. Under these algorithms, there are frequentist approaches: one parametric, using stable distributions, and another one- non-parametric, using the squared Mahalanobis distance. The package also contains functions for data handling and building of new classifiers as well as some test data set.
Author: Jesper Muren [aut] , Dmitry Otryakhin [aut, cre]
Maintainer: Dmitry Otryakhin <d.otryakhin.acad@protonmail.ch>

Diff between deforestable versions 3.1.0 dated 2022-10-10 and 3.1.1 dated 2022-10-15

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Package UnifiedDoseFinding updated to version 0.1.10 with previous version 0.1.9 dated 2022-02-03

Title: Dose-Finding Methods for Non-Binary Outcomes
Description: In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. This package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess design’s operating characteristics under various scenarios. Three dose finding designs are included in this package: unified phase I design (Ivanova et al. (2009) <doi:10.1111/j.1541-0420.2008.01045.x>), Quasi-CRM/Robust-Quasi-CRM (Yuan et al. (2007) <doi:10.1111/j.1541-0420.2006.00666.x>, Pan et al. (2014) <doi:10.1371/journal.pone.0098147>) and generalized BOIN design (Mu et al. (2018) <doi:10.1111/rssc.12263>). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ET [...truncated...]
Author: Chia-Wei Hsu, Haitao Pan, Rongji Mu
Maintainer: Chia-Wei Hsu <Chia-Wei.Hsu@stjude.org>

Diff between UnifiedDoseFinding versions 0.1.9 dated 2022-02-03 and 0.1.10 dated 2022-10-15

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Package roger updated to version 1.2-0 with previous version 1.1-0 dated 2022-09-26

Title: Automated Grading of R Scripts
Description: Tools for grading the coding style and documentation of R scripts. This is the R component of Roger the Omni Grader, an automated grading system for computer programming projects based on Unix shell scripts; see <https://gitlab.com/roger-project>. The package also provides an R interface to the shell scripts. Inspired by the lintr package.
Author: Vincent Goulet [aut, cre], Samuel Frechette [aut], Jean-Christophe Langlois [aut], Jim Hester [ctb]
Maintainer: Vincent Goulet <vincent.goulet@act.ulaval.ca>

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Package opencv updated to version 0.2.3 with previous version 0.2.2 dated 2022-04-28

Title: Bindings to 'OpenCV' Computer Vision Library
Description: Exposes some of the available 'OpenCV' <https://opencv.org/> algorithms, such as edge, body or face detection. These can either be applied to analyze static images, or to filter live video footage from a camera device.
Author: Jeroen Ooms [aut, cre] , Jan Wijffels [aut]
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between opencv versions 0.2.2 dated 2022-04-28 and 0.2.3 dated 2022-10-15

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Package gRim updated to version 0.2.10 with previous version 0.2.9 dated 2022-05-12

Title: Graphical Interaction Models
Description: Provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models. Documentation about 'gRim' is provided by vignettes included in this package and the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>); see 'citation("gRim")' for details.
Author: Soeren Hoejsgaard <sorenh@math.aau.dk>
Maintainer: Soeren Hoejsgaard <sorenh@math.aau.dk>

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Package googleAnalyticsR updated to version 1.1.0 with previous version 1.0.1 dated 2021-10-07

Title: Google Analytics API into R
Description: Interact with the Google Analytics APIs <https://developers.google.com/analytics/>, including the Core Reporting API (v3 and v4), Management API, User Activity API GA4's Data API and Admin API and Multi-Channel Funnel API.
Author: Mark Edmondson [aut, cre] , Artem Klevtsov [ctb], Johann deBoer [ctb], David Watkins [ctb], Olivia Brode-Roger [ctb], Jas Sohi [ctb], Zoran Selinger [ctb], Octavian Corlade [ctb], Maegan Whytock [ctb]
Maintainer: Mark Edmondson <m@sunholo.com>

Diff between googleAnalyticsR versions 1.0.1 dated 2021-10-07 and 1.1.0 dated 2022-10-15

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Package familiar updated to version 1.3.0 with previous version 1.2.0 dated 2022-08-16

Title: End-to-End Automated Machine Learning and Model Evaluation
Description: Single unified interface for end-to-end modelling of regression, categorical and time-to-event (survival) outcomes. Models created using familiar are self-containing, and their use does not require additional information such as baseline survival, feature clustering, or feature transformation and normalisation parameters. Model performance, calibration, risk group stratification, (permutation) variable importance, individual conditional expectation, partial dependence, and more, are assessed automatically as part of the evaluation process and exported in tabular format and plotted, and may also be computed manually using export and plot functions. Where possible, metrics and values obtained during the evaluation process come with confidence intervals.
Author: Alex Zwanenburg [aut, cre] , Steffen Loeck [aut], Stefan Leger [ctb], Iram Shahzadi [ctb], Asier Rabasco Meneghetti [ctb], Sebastian Starke [ctb], Technische Universitaet Dresden [cph], German Cancer Research Center [cph]
Maintainer: Alex Zwanenburg <alexander.zwanenburg@nct-dresden.de>

Diff between familiar versions 1.2.0 dated 2022-08-16 and 1.3.0 dated 2022-10-15

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 R/Familiar.R                                                                      |   31 
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 R/FamiliarDataComputationICE.R                                                    |    7 
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 R/FamiliarDataElement.R                                                           |    3 
 R/FamiliarModel.R                                                                 |   17 
 R/FamiliarNoveltyDetector.R                                                       |    8 
 R/FamiliarObjectUpdate.R                                                          |    6 
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 R/LearnerS4Cox.R                                                                  |   10 
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 R/LearnerS4GLMnet.R                                                               |   86 +
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 R/LearnerSurvivalGrouping.R                                                       |   19 
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 R/TrainS4Methods.R                                                                |   10 
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 R/VimpS4Regression.R                                                              |    3 
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 man/dot-parse_general_settings.Rd                                                 |   11 
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 man/precompute_data_assignment.Rd                                                 |   24 
 man/precompute_feature_info.Rd                                                    |   24 
 man/precompute_vimp.Rd                                                            |   24 
 man/summon_familiar.Rd                                                            |   26 
 man/train_familiar.Rd                                                             |   24 
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Package ContRespPP updated to version 0.4.2 with previous version 0.4.1 dated 2022-09-27

Title: Predictive Probability for a Continuous Response with an ANOVA Structure
Description: A Bayesian approach to using predictive probability in an ANOVA construct with a continuous normal response, when threshold values must be obtained for the question of interest to be evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). The Bayesian Mission Mean (BMM) is used to evaluate a question of interest (that is, a mean that randomly selects combination of factor levels based on their probability of occurring instead of averaging over the factor levels, as in the grand mean). Under this construct, in contrast to a Gibbs sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is required. The nested sampler determines the conditional posterior distribution of the model parameters, given Y, and the outside sampler determines the marginal posterior distribution of Y (also commonly called the predictive distribution for Y). This approach provides a sample from the joint posterior distribution of Y and the model parameters, while also [...truncated...]
Author: Victoria Sieck [aut, cre] , Joshua Clifford [aut] , Fletcher Christensen [aut]
Maintainer: Victoria Sieck <vcarrillo314@gmail.com>

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Package AutoScore updated to version 1.0.0 with previous version 0.3.0 dated 2022-04-08

Title: An Interpretable Machine Learning-Based Automatic Clinical Score Generator
Description: A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The The original AutoScore structure is described in the research paper<doi:10.2196/21798>. A full tutorial can be found here<https://nliulab.github.io/AutoScore/>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.
Author: Feng Xie [aut, cre] , Yilin Ning [aut] , Han Yuan [aut] , Mingxuan Liu [aut] , Seyed Ehsan Saffari [aut] , Siqi Li [aut] , Bibhas Chakraborty [aut] , Nan Liu [aut]
Maintainer: Feng Xie <xief@u.duke.nus.edu>

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Package gerbil (with last version 0.1.5) was removed from CRAN

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2021-03-23 0.1.5

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Package tidyndr (with last version 0.2.1) was removed from CRAN

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

2022-04-08 0.2.1
2022-01-04 0.2.0
2021-10-15 0.1.1
2021-03-23 0.1.0

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Package TREXr (with last version 1.0.0) was removed from CRAN

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2021-03-24 1.0.0

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