Title: Simultaneous Penalized Linear Mixed Effects Models
Description: Contains functions that fit linear mixed-effects models
for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection.
The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation
is based on the R package 'lmmlasso'.
Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.
Author: Luoying Yang, Tong Tong Wu
Maintainer: Luoying Yang <lyang19@u.rochester.edu>
Diff between splmm versions 1.1.1 dated 2020-10-05 and 1.1.2 dated 2020-11-21
DESCRIPTION | 11 ++++++----- MD5 | 4 ++-- build/partial.rdb |binary 3 files changed, 8 insertions(+), 7 deletions(-)
Title: Generalized Mortality Estimator
Description: Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.
Author: Daniel Dalthorp [aut, cre],
Juniper Simonis [aut],
Lisa Madsen [aut],
Manuela Huso [aut],
Paul Rabie [aut],
Jeffrey Mintz [aut],
Robert Wolpert [aut],
Jared Studyvin [aut],
Franzi Korner-Nievergelt [aut]
Maintainer: Daniel Dalthorp <ddalthorp@protonmail.com>
Diff between GenEst versions 1.4.4 dated 2020-06-08 and 1.4.5 dated 2020-11-21
DESCRIPTION | 12 ++--- MD5 | 16 +++--- R/carcass_persistence_functions.R | 5 +- inst/doc/GenEstGUI.html | 4 - inst/doc/command-line-example.html | 86 ++++++++++++++++++------------------- inst/doc/solar-examples.html | 4 - inst/doc/wind-examples.Rmd | 3 + inst/doc/wind-examples.html | 6 +- vignettes/wind-examples.Rmd | 3 + 9 files changed, 73 insertions(+), 66 deletions(-)
Title: All Hierarchical or Graphical Models for Generalized Linear
Model
Description: Find all hierarchical models of specified generalized linear
model with information criterion (AIC, BIC, or AICc) within specified
cutoff of minimum value. Alternatively, find all such graphical models.
Use branch and bound algorithm so we do not have to fit all models.
Author: Charles J. Geyer <charlie@stat.umn.edu>.
Maintainer: Charles J. Geyer <charlie@stat.umn.edu>
Diff between glmbb versions 0.4 dated 2020-08-16 and 0.5-1 dated 2020-11-21
ChangeLog |only DESCRIPTION | 8 ++++---- MD5 | 7 +++++-- R/glmbb.R | 2 +- tests/soo.R |only tests/soo.Rout.save |only 6 files changed, 10 insertions(+), 7 deletions(-)
Title: Fast and Simple 'MongoDB' Client for R
Description: High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'.
Includes support for aggregation, indexing, map-reduce, streaming, encryption,
enterprise authentication, and GridFS. The online user manual provides an overview
of the available methods in the package: <https://jeroen.github.io/mongolite/>.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>),
MongoDB, Inc [cph] (Bundled mongo-c-driver, see AUTHORS file)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>
Diff between mongolite versions 2.2.0 dated 2020-03-17 and 2.2.1 dated 2020-11-21
DESCRIPTION | 8 ++++---- MD5 | 20 ++++++++++---------- NEWS | 3 +++ R/client.R | 6 ++++-- R/mongo.R | 10 +++++----- configure | 2 +- inst/CITATION | 4 ++-- man/gridfs.Rd | 2 +- man/mongo.Rd | 10 +++++----- man/ssl_options.Rd | 2 +- src/bson.c | 3 ++- 11 files changed, 38 insertions(+), 32 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 1.0.5 dated 2020-10-09 and 1.1.0 dated 2020-11-21
DESCRIPTION | 6 MD5 | 22 + NAMESPACE | 1 R/VolcanoPlotW.R | 29 +- R/imageW.R |only R/legendHist.R | 24 +- build/vignette.rds |binary inst/doc/wrGraphVignette1.R | 7 inst/doc/wrGraphVignette1.Rmd | 82 ++++--- inst/doc/wrGraphVignette1.html | 460 +++++++++++++++++++++++------------------ man/VolcanoPlotW.Rd | 1 man/imageW.Rd |only vignettes/wrGraphVignette1.Rmd | 82 ++++--- 13 files changed, 406 insertions(+), 308 deletions(-)
Title: Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate
Estimation, and Non-Parametric Phase Modelling
Description: Enables quick calibration of radiocarbon dates under various
calibration curves (including user generated ones); age-depth modelling
as per the algorithm of Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level
rate estimation incorporating time uncertainty in polynomial regression
models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; non-parametric phase modelling via
Gaussian mixtures as a means to determine the activity of a site
(and as an alternative to the Oxcal function SUM; currently
unpublished), and reverse calibration of dates from calibrated into
un-calibrated years (also unpublished).
Author: Andrew Parnell
Maintainer: Andrew Parnell <Andrew.Parnell@mu.ie>
Diff between Bchron versions 4.7.2 dated 2020-09-01 and 4.7.3 dated 2020-11-21
Bchron-4.7.2/Bchron/R/SampleAges.R |only Bchron-4.7.3/Bchron/DESCRIPTION | 8 Bchron-4.7.3/Bchron/MD5 | 36 Bchron-4.7.3/Bchron/NAMESPACE | 1 Bchron-4.7.3/Bchron/NEWS.md | 6 Bchron-4.7.3/Bchron/R/choosePositions.BchronologyRun.R | 2 Bchron-4.7.3/Bchron/R/dateInfluence.BchronologyRun.R | 4 Bchron-4.7.3/Bchron/R/hdr.R | 4 Bchron-4.7.3/Bchron/R/plot.BchronCalibratedDates.R | 344 ++++++--- Bchron-4.7.3/Bchron/R/sampleAges.R |only Bchron-4.7.3/Bchron/R/summary.BchronologyRun.R | 4 Bchron-4.7.3/Bchron/build/vignette.rds |binary Bchron-4.7.3/Bchron/inst/doc/Bchron.R | 13 Bchron-4.7.3/Bchron/inst/doc/Bchron.Rmd | 28 Bchron-4.7.3/Bchron/inst/doc/Bchron.html | 511 +++++++------- Bchron-4.7.3/Bchron/man/plot.BchronCalibratedDates.Rd | 12 Bchron-4.7.3/Bchron/man/sampleAges.Rd | 2 Bchron-4.7.3/Bchron/tests/testthat.R | 2 Bchron-4.7.3/Bchron/tests/testthat/test_BchronCalibrate.R | 27 Bchron-4.7.3/Bchron/vignettes/Bchron.Rmd | 28 20 files changed, 655 insertions(+), 377 deletions(-)
Title: Dirichlet-Multinomial Modelling of Relative Abundance Data
Description: Implements Dirichlet multinomial modelling of relative abundance data using functionality provided by the 'Stan' software. The purpose of this package is to provide a user friendly way to interface with 'Stan' that is suitable for those new to modelling. For more regarding the modelling mathematics and computational techniques we use see our publication in Molecular Ecology Resources titled "Dirichlet multinomial modelling outperforms alternatives for analysis of ecological count data" (Harrison et al. 2020 <doi:10.1111/1755-0998.13128>).
Author: Joshua Harrison [aut, cre] (<https://orcid.org/0000-0003-2524-0273>),
Vivaswat Shastry [aut] (<https://orcid.org/0000-0002-7294-5607>),
W. John Calder [aut] (<https://orcid.org/0000-0002-8923-1803>),
C. Alex Buerkle [aut] (<https://orcid.org/0000-0003-4222-8858>)
Maintainer: Joshua Harrison <joshua.grant.harrison@gmail.com>
Diff between CNVRG versions 0.1 dated 2020-09-16 and 0.2 dated 2020-11-21
CNVRG-0.1/CNVRG/inst/doc |only CNVRG-0.1/CNVRG/man/varHMC.Rd |only CNVRG-0.1/CNVRG/man/varInf.Rd |only CNVRG-0.2/CNVRG/DESCRIPTION | 9 CNVRG-0.2/CNVRG/MD5 | 20 CNVRG-0.2/CNVRG/NAMESPACE | 5 CNVRG-0.2/CNVRG/R/convrg_func.R | 542 +++++++++++++++++--------- CNVRG-0.2/CNVRG/man/cnvrg_HMC.Rd |only CNVRG-0.2/CNVRG/man/cnvrg_VI.Rd |only CNVRG-0.2/CNVRG/man/diff_abund.Rd | 22 - CNVRG-0.2/CNVRG/man/diversity_calc.Rd | 24 - CNVRG-0.2/CNVRG/man/extract_point_estimate.Rd | 19 CNVRG-0.2/CNVRG/man/indexer.Rd |only CNVRG-0.2/CNVRG/man/isd_transform.Rd | 38 + 14 files changed, 457 insertions(+), 222 deletions(-)
Previous versions (as known to CRANberries) which should be available via the Archive link are:
2016-05-18 1.0.1
2010-10-18 0.99
2010-08-12 0.98.1
2010-07-30 0.98
2009-02-12 0.96
2009-01-15 0.95
2008-11-18 0.93
2008-11-03 0.91
2008-10-21 0.9
2008-10-06 0.8
2008-07-09 0.5
Previous versions (as known to CRANberries) which should be available via the Archive link are:
2013-05-03 2.0
2007-01-14 1.0
Title: A Tool Kit for Working with Time Series in R
Description: Easy visualization, wrangling, and feature engineering of time series data for
forecasting and machine learning prediction. Consolidates and extends time series functionality
from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.
Author: Matt Dancho [aut, cre],
Davis Vaughan [aut]
Maintainer: Matt Dancho <mdancho@business-science.io>
Diff between timetk versions 2.5.0 dated 2020-10-22 and 2.6.0 dated 2020-11-21
timetk-2.5.0/timetk/vignettes/temp |only timetk-2.6.0/timetk/DESCRIPTION | 14 timetk-2.6.0/timetk/MD5 | 409 +- timetk-2.6.0/timetk/NAMESPACE | 737 +-- timetk-2.6.0/timetk/NEWS.md | 710 +-- timetk-2.6.0/timetk/R/00_global_vars.R | 22 timetk-2.6.0/timetk/R/augment-tk_augment_differences.R | 328 - timetk-2.6.0/timetk/R/augment-tk_augment_fourier.R | 328 - timetk-2.6.0/timetk/R/augment-tk_augment_holiday_signature.R | 342 - timetk-2.6.0/timetk/R/augment-tk_augment_lags.R | 373 + timetk-2.6.0/timetk/R/augment-tk_augment_slidify.R | 352 - timetk-2.6.0/timetk/R/augment-tk_augment_timeseries.R | 342 - timetk-2.6.0/timetk/R/coersion-tk_tbl.R | 558 +- timetk-2.6.0/timetk/R/coersion-tk_ts.R | 372 - timetk-2.6.0/timetk/R/coersion-tk_xts.R | 456 +- timetk-2.6.0/timetk/R/coersion-tk_zoo.R | 198 - timetk-2.6.0/timetk/R/coersion-tk_zooreg.R | 416 +- timetk-2.6.0/timetk/R/data-bike_sharing.R | 78 timetk-2.6.0/timetk/R/data-m4_daily.R | 44 timetk-2.6.0/timetk/R/data-m4_hourly.R | 44 timetk-2.6.0/timetk/R/data-m4_monthly.R | 44 timetk-2.6.0/timetk/R/data-m4_quarterly.R | 44 timetk-2.6.0/timetk/R/data-m4_weekly.R | 46 timetk-2.6.0/timetk/R/data-m4_yearly.R | 46 timetk-2.6.0/timetk/R/data-taylor_30_min.R | 40 timetk-2.6.0/timetk/R/data-walmart_sales_weekly.R | 98 timetk-2.6.0/timetk/R/data-wikipedia_traffic_daily.R | 54 timetk-2.6.0/timetk/R/diagnostics-tk_acf_diagnostics.R | 492 +- timetk-2.6.0/timetk/R/diagnostics-tk_anomaly_diagnostics.R | 618 +-- timetk-2.6.0/timetk/R/diagnostics-tk_seasonal_diagnostics.R | 488 +- timetk-2.6.0/timetk/R/diagnostics-tk_stl_diagnostics.R | 328 - timetk-2.6.0/timetk/R/diagnostics-tk_summary_diagnostics.R | 232 - timetk-2.6.0/timetk/R/dplyr-add_time.R | 422 +- timetk-2.6.0/timetk/R/dplyr-between_time.R | 302 - timetk-2.6.0/timetk/R/dplyr-condense_period.R |only timetk-2.6.0/timetk/R/dplyr-filter_by_time.R | 222 - timetk-2.6.0/timetk/R/dplyr-filter_period.R |only timetk-2.6.0/timetk/R/dplyr-future_frame.R | 501 +- timetk-2.6.0/timetk/R/dplyr-mutate_by_time.R | 285 - timetk-2.6.0/timetk/R/dplyr-pad_by_time.R | 524 +- timetk-2.6.0/timetk/R/dplyr-slice_period.R |only timetk-2.6.0/timetk/R/dplyr-slidify.R | 534 +- timetk-2.6.0/timetk/R/dplyr-summarise_by_time.R | 325 - timetk-2.6.0/timetk/R/get-tk_get_frequency.R | 574 +- timetk-2.6.0/timetk/R/get-tk_get_holiday_signature.R | 566 +- timetk-2.6.0/timetk/R/get-tk_get_time_scale_template.R | 122 timetk-2.6.0/timetk/R/get-tk_get_timeseries.R | 416 +- timetk-2.6.0/timetk/R/index-tk_index.R | 788 +-- timetk-2.6.0/timetk/R/lubridate-date_parsers.R | 182 timetk-2.6.0/timetk/R/make-tk_make_holiday_sequences.R | 372 - timetk-2.6.0/timetk/R/make-tk_make_timeseries.R | 820 ++-- timetk-2.6.0/timetk/R/make-tk_make_timeseries_future.R | 1972 +++++----- timetk-2.6.0/timetk/R/plot-acf_diagnostics.R | 754 +-- timetk-2.6.0/timetk/R/plot-anomaly_diagnostics.R | 708 +-- timetk-2.6.0/timetk/R/plot-seasonal_diagnostics.R | 704 +-- timetk-2.6.0/timetk/R/plot-stl_diagnostics.R | 554 +- timetk-2.6.0/timetk/R/plot-time_series.R | 1016 ++--- timetk-2.6.0/timetk/R/plot-time_series_regression.R | 298 - timetk-2.6.0/timetk/R/recipes-step_box_cox.R | 470 +- timetk-2.6.0/timetk/R/recipes-step_diff.R | 422 +- timetk-2.6.0/timetk/R/recipes-step_fourier.R | 608 +-- timetk-2.6.0/timetk/R/recipes-step_holiday_signature.R | 620 +-- timetk-2.6.0/timetk/R/recipes-step_log_interval.R | 496 +- timetk-2.6.0/timetk/R/recipes-step_slidify.R | 618 +-- timetk-2.6.0/timetk/R/recipes-step_slidify_augment.R | 480 +- timetk-2.6.0/timetk/R/recipes-step_smooth.R | 642 +-- timetk-2.6.0/timetk/R/recipes-step_timeseries_signature.R | 504 +- timetk-2.6.0/timetk/R/recipes-step_ts_clean.R | 446 +- timetk-2.6.0/timetk/R/recipes-step_ts_impute.R | 454 +- timetk-2.6.0/timetk/R/recipes-step_ts_pad.R | 492 +- timetk-2.6.0/timetk/R/rsample-plot_time_series_cv_plan.R | 350 - timetk-2.6.0/timetk/R/rsample-time_series_cv.R | 881 ++-- timetk-2.6.0/timetk/R/rsample-time_series_split.R | 248 - timetk-2.6.0/timetk/R/rsample-tk_time_series_cv_plan.R | 213 - timetk-2.6.0/timetk/R/tidyquant-theme-compat.R | 378 - timetk-2.6.0/timetk/R/timetk-internal.R | 20 timetk-2.6.0/timetk/R/timetk-package.R | 52 timetk-2.6.0/timetk/R/utils-bind_cols_overwrite.R |only timetk-2.6.0/timetk/R/utils-dates.R | 272 - timetk-2.6.0/timetk/R/utils-parse-time.R | 580 +- timetk-2.6.0/timetk/R/utils-parse_period.R | 256 - timetk-2.6.0/timetk/R/utils-tidy-eval.R | 94 timetk-2.6.0/timetk/R/vec-box_cox.R | 226 - timetk-2.6.0/timetk/R/vec-diff.R | 370 - timetk-2.6.0/timetk/R/vec-fourier.R | 340 - timetk-2.6.0/timetk/R/vec-lag.R | 169 timetk-2.6.0/timetk/R/vec-log_interval.R | 264 - timetk-2.6.0/timetk/R/vec-normalize.R | 198 - timetk-2.6.0/timetk/R/vec-slidify.R | 400 +- timetk-2.6.0/timetk/R/vec-smooth.R | 236 - timetk-2.6.0/timetk/R/vec-standardize.R | 202 - timetk-2.6.0/timetk/R/vec-ts_clean.R | 176 timetk-2.6.0/timetk/R/vec-ts_impute.R | 208 - timetk-2.6.0/timetk/R/zzz.R | 230 - timetk-2.6.0/timetk/README.md | 460 +- timetk-2.6.0/timetk/build/vignette.rds |binary timetk-2.6.0/timetk/inst/doc/TK04_Plotting_Time_Series.R | 142 timetk-2.6.0/timetk/inst/doc/TK04_Plotting_Time_Series.Rmd | 354 - timetk-2.6.0/timetk/inst/doc/TK04_Plotting_Time_Series.html | 1082 ++--- timetk-2.6.0/timetk/inst/doc/TK07_Time_Series_Data_Wrangling.R | 210 - timetk-2.6.0/timetk/inst/doc/TK07_Time_Series_Data_Wrangling.Rmd | 534 +- timetk-2.6.0/timetk/inst/doc/TK07_Time_Series_Data_Wrangling.html | 1340 +++--- timetk-2.6.0/timetk/man/between_time.Rd | 220 - timetk-2.6.0/timetk/man/bike_sharing_daily.Rd | 104 timetk-2.6.0/timetk/man/box_cox_vec.Rd | 184 timetk-2.6.0/timetk/man/condense_period.Rd |only timetk-2.6.0/timetk/man/diff_vec.Rd | 254 - timetk-2.6.0/timetk/man/filter_by_time.Rd | 182 timetk-2.6.0/timetk/man/filter_period.Rd |only timetk-2.6.0/timetk/man/fourier_vec.Rd | 214 - timetk-2.6.0/timetk/man/future_frame.Rd | 306 - timetk-2.6.0/timetk/man/is_date_class.Rd | 52 timetk-2.6.0/timetk/man/lag_vec.Rd | 150 timetk-2.6.0/timetk/man/log_interval_vec.Rd | 172 timetk-2.6.0/timetk/man/m4_daily.Rd | 72 timetk-2.6.0/timetk/man/m4_hourly.Rd | 72 timetk-2.6.0/timetk/man/m4_monthly.Rd | 72 timetk-2.6.0/timetk/man/m4_quarterly.Rd | 72 timetk-2.6.0/timetk/man/m4_weekly.Rd | 72 timetk-2.6.0/timetk/man/m4_yearly.Rd | 74 timetk-2.6.0/timetk/man/mutate_by_time.Rd | 199 - 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timetk-2.6.0/timetk/man/step_slidify.Rd | 392 - timetk-2.6.0/timetk/man/step_slidify_augment.Rd | 336 - timetk-2.6.0/timetk/man/step_smooth.Rd | 416 +- timetk-2.6.0/timetk/man/step_timeseries_signature.Rd | 248 - timetk-2.6.0/timetk/man/step_ts_clean.Rd | 288 - timetk-2.6.0/timetk/man/step_ts_impute.Rd | 280 - timetk-2.6.0/timetk/man/step_ts_pad.Rd | 296 - timetk-2.6.0/timetk/man/summarise_by_time.Rd | 263 - timetk-2.6.0/timetk/man/taylor_30_min.Rd | 66 timetk-2.6.0/timetk/man/tidyeval.Rd | 104 timetk-2.6.0/timetk/man/time_arithmetic.Rd | 180 timetk-2.6.0/timetk/man/time_series_cv.Rd | 342 - timetk-2.6.0/timetk/man/time_series_split.Rd | 270 - timetk-2.6.0/timetk/man/timetk.Rd | 44 timetk-2.6.0/timetk/man/timetk_internal.Rd | 156 timetk-2.6.0/timetk/man/tk_acf_diagnostics.Rd | 202 - timetk-2.6.0/timetk/man/tk_anomaly_diagnostics.Rd | 254 - timetk-2.6.0/timetk/man/tk_augment_differences.Rd | 142 timetk-2.6.0/timetk/man/tk_augment_fourier.Rd | 124 timetk-2.6.0/timetk/man/tk_augment_holiday.Rd | 220 - timetk-2.6.0/timetk/man/tk_augment_lags.Rd | 132 timetk-2.6.0/timetk/man/tk_augment_slidify.Rd | 164 timetk-2.6.0/timetk/man/tk_augment_timeseries.Rd | 112 timetk-2.6.0/timetk/man/tk_get_frequency.Rd | 200 - timetk-2.6.0/timetk/man/tk_get_holiday.Rd | 218 - timetk-2.6.0/timetk/man/tk_get_timeseries.Rd | 148 timetk-2.6.0/timetk/man/tk_get_timeseries_unit_frequency.Rd | 42 timetk-2.6.0/timetk/man/tk_get_timeseries_variables.Rd | 64 timetk-2.6.0/timetk/man/tk_index.Rd | 164 timetk-2.6.0/timetk/man/tk_make_future_timeseries.Rd | 300 - timetk-2.6.0/timetk/man/tk_make_holiday_sequence.Rd | 266 - timetk-2.6.0/timetk/man/tk_make_timeseries.Rd | 360 - timetk-2.6.0/timetk/man/tk_seasonal_diagnostics.Rd | 180 timetk-2.6.0/timetk/man/tk_stl_diagnostics.Rd | 168 timetk-2.6.0/timetk/man/tk_summary_diagnostics.Rd | 98 timetk-2.6.0/timetk/man/tk_tbl.Rd | 226 - timetk-2.6.0/timetk/man/tk_time_scale_template.Rd | 90 timetk-2.6.0/timetk/man/tk_time_series_cv_plan.Rd | 102 timetk-2.6.0/timetk/man/tk_ts.Rd | 240 - timetk-2.6.0/timetk/man/tk_xts.Rd | 182 timetk-2.6.0/timetk/man/tk_zoo.Rd | 188 timetk-2.6.0/timetk/man/tk_zooreg.Rd | 258 - timetk-2.6.0/timetk/man/ts_clean_vec.Rd | 162 timetk-2.6.0/timetk/man/ts_impute_vec.Rd | 166 timetk-2.6.0/timetk/man/walmart_sales_weekly.Rd | 126 timetk-2.6.0/timetk/man/wikipedia_traffic_daily.Rd | 76 timetk-2.6.0/timetk/tests/testthat.R | 26 timetk-2.6.0/timetk/tests/testthat/test-recipes-step_timeseries_signature.R | 96 timetk-2.6.0/timetk/tests/testthat/test-rsample-time_series_cv.R | 320 - timetk-2.6.0/timetk/tests/testthat/test-tk_augment_timeseries.R | 120 timetk-2.6.0/timetk/tests/testthat/test-tk_get_timeseries.R | 316 - timetk-2.6.0/timetk/tests/testthat/test-tk_index.R | 666 +-- timetk-2.6.0/timetk/tests/testthat/test-tk_make_timeseries.R | 158 timetk-2.6.0/timetk/tests/testthat/test-tk_make_timeseries_future-OLD.R | 906 ++-- timetk-2.6.0/timetk/tests/testthat/test-tk_make_timeseries_future.R | 794 ++-- timetk-2.6.0/timetk/tests/testthat/test-tk_tbl.R | 298 - timetk-2.6.0/timetk/tests/testthat/test-tk_ts.R | 156 timetk-2.6.0/timetk/tests/testthat/test-tk_xts.R | 164 timetk-2.6.0/timetk/tests/testthat/test-tk_zoo.R | 122 timetk-2.6.0/timetk/tests/testthat/test-tk_zooreg.R | 134 timetk-2.6.0/timetk/vignettes/TK04_Plotting_Time_Series.Rmd | 354 - timetk-2.6.0/timetk/vignettes/TK07_Time_Series_Data_Wrangling.Rmd | 534 +- timetk-2.6.0/timetk/vignettes/temp_archive |only 204 files changed, 30229 insertions(+), 29782 deletions(-)
Title: Graphical User Interface ('shiny' App) for 'brms'
Description: A graphical user interface (GUI) for fitting Bayesian
regression models using the package 'brms' which in turn relies on
'Stan' (<https://mc-stan.org/>). The 'shinybrms' GUI is a 'shiny'
app.
Author: Frank Weber [aut, cre] (<https://orcid.org/0000-0002-4842-7922>),
Thomas Park [ctb, cph] ('Bootswatch' theme "United"),
Twitter, Inc. [ctb, cph] ('Bootstrap' (basis for the 'Bootswatch' theme
"United")),
Google, LLC [ctb, cph] ("Open Sans" font)
Maintainer: Frank Weber <fweber144@protonmail.com>
Diff between shinybrms versions 1.4.1 dated 2020-09-29 and 1.5.0 dated 2020-11-21
DESCRIPTION | 20 LICENSE | 490 MD5 | 185 NAMESPACE | 6 NEWS.md | 477 R/launch_shinybrms.R | 112 README.md | 288 inst/CITATION |only inst/shinybrms_app/app.R | 5250 +++++----- inst/shinybrms_app/tests/shinytest.R | 6 inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/001.json | 10 inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/002.json | 10 inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/003.json | 195 inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/004.json | 165 inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/005.json | 179 inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/006.json | 181 inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/007.json |only inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/008.json |only inst/shinybrms_app/tests/shinytest/bacteria_run_linux-expected/009.json |only inst/shinybrms_app/tests/shinytest/bacteria_run_linux.R | 105 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inst/shinybrms_app/tests/shinytest/interactions-expected/012.json | 11 inst/shinybrms_app/tests/shinytest/interactions-expected/013.json | 11 inst/shinybrms_app/tests/shinytest/interactions-expected/014.json | 11 inst/shinybrms_app/tests/shinytest/interactions-expected/015.json | 11 inst/shinybrms_app/tests/shinytest/interactions-expected/016.json | 11 inst/shinybrms_app/tests/shinytest/interactions-expected/017.json | 11 inst/shinybrms_app/tests/shinytest/interactions.R | 206 inst/shinybrms_app/tests/shinytest/priors-expected/001.json | 11 inst/shinybrms_app/tests/shinytest/priors-expected/002.json | 11 inst/shinybrms_app/tests/shinytest/priors-expected/003.json | 11 inst/shinybrms_app/tests/shinytest/priors-expected/004.json | 11 inst/shinybrms_app/tests/shinytest/priors-expected/005.json | 11 inst/shinybrms_app/tests/shinytest/priors-expected/006.json | 11 inst/shinybrms_app/tests/shinytest/priors-expected/007.json | 11 inst/shinybrms_app/tests/shinytest/priors.R | 86 inst/shinybrms_app/tests/shinytest/priors_autoSetEmpty-expected/001.json | 11 inst/shinybrms_app/tests/shinytest/priors_autoSetEmpty-expected/002.json | 11 inst/shinybrms_app/tests/shinytest/priors_autoSetEmpty.R | 44 inst/shinybrms_app/tests/shinytest/priors_switchClass-expected/001.json | 11 inst/shinybrms_app/tests/shinytest/priors_switchClass-expected/002.json | 11 inst/shinybrms_app/tests/shinytest/priors_switchClass.R | 44 inst/shinybrms_app/tests/shinytest/switchData-data.csv | 82 inst/shinybrms_app/tests/shinytest/switchData_outcome-expected/001.json | 11 inst/shinybrms_app/tests/shinytest/switchData_outcome-expected/002.json | 11 inst/shinybrms_app/tests/shinytest/switchData_outcome.R | 32 inst/shinybrms_app/tests/shinytest/switchData_pred-data.csv | 82 inst/shinybrms_app/tests/shinytest/switchData_pred-expected/001.json | 11 inst/shinybrms_app/tests/shinytest/switchData_pred-expected/002.json | 11 inst/shinybrms_app/tests/shinytest/switchData_pred-expected/003.json | 11 inst/shinybrms_app/tests/shinytest/switchData_pred.R | 50 inst/shinybrms_app/tests/shinytest/switchData_same-expected/001.json | 11 inst/shinybrms_app/tests/shinytest/switchData_same-expected/002.json | 11 inst/shinybrms_app/tests/shinytest/switchData_same-expected/003.json | 11 inst/shinybrms_app/tests/shinytest/switchData_same-expected/004.json | 11 inst/shinybrms_app/tests/shinytest/switchData_same.R | 66 inst/shinybrms_app/tests/shinytest/switchData_value-data.csv | 82 inst/shinybrms_app/tests/shinytest/switchData_value-expected/001.json | 11 inst/shinybrms_app/tests/shinytest/switchData_value-expected/002.json | 11 inst/shinybrms_app/tests/shinytest/switchData_value-expected/003.json | 11 inst/shinybrms_app/tests/shinytest/switchData_value-expected/004.json | 11 inst/shinybrms_app/tests/shinytest/switchData_value.R | 66 inst/shinybrms_app/www/united_mod.min.css | 40 man/figures/logo.svg | 460 man/launch_shinybrms.Rd | 96 tests/testthat.R | 8 tests/testthat/test-bacteria.R | 50 tests/testthat/test-interactions.R | 20 tests/testthat/test-priors.R | 56 tests/testthat/test-react.R | 148 97 files changed, 5706 insertions(+), 4517 deletions(-)
Title: Clustering of Datasets
Description: Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package.
Author: Fabien Llobell [aut, cre] (Oniris/XLSTAT),
Evelyne Vigneau [ctb] (Oniris),
Veronique Cariou [ctb] (Oniris),
El Mostafa Qannari [ctb] (Oniris)
Maintainer: Fabien Llobell <fllobell@hotmail.fr>
Diff between ClustBlock versions 2.3.0 dated 2020-11-16 and 2.3.1 dated 2020-11-21
ClustBlock-2.3.0/ClustBlock/R/firstEig.R |only ClustBlock-2.3.0/ClustBlock/data/straw.RData |only ClustBlock-2.3.1/ClustBlock/DESCRIPTION | 8 ++-- ClustBlock-2.3.1/ClustBlock/MD5 | 35 +++++++++----------- ClustBlock-2.3.1/ClustBlock/NEWS | 2 - ClustBlock-2.3.1/ClustBlock/R/choc.R | 2 - ClustBlock-2.3.1/ClustBlock/R/clustatis_kmeans.R | 1 ClustBlock-2.3.1/ClustBlock/R/plot.catatis.R | 2 + ClustBlock-2.3.1/ClustBlock/R/plot.cluscata.R | 2 - ClustBlock-2.3.1/ClustBlock/R/plot.clustatis.R | 4 +- ClustBlock-2.3.1/ClustBlock/R/plot.statis.R | 4 +- ClustBlock-2.3.1/ClustBlock/R/vp_rv_Wi.R | 2 - ClustBlock-2.3.1/ClustBlock/data/straw.rda |only ClustBlock-2.3.1/ClustBlock/inst/CITATION | 4 +- ClustBlock-2.3.1/ClustBlock/man/choc.Rd | 2 - ClustBlock-2.3.1/ClustBlock/man/clustatis_kmeans.Rd | 1 ClustBlock-2.3.1/ClustBlock/man/plot.catatis.Rd | 2 + ClustBlock-2.3.1/ClustBlock/man/plot.cluscata.Rd | 2 - ClustBlock-2.3.1/ClustBlock/man/plot.clustatis.Rd | 4 +- ClustBlock-2.3.1/ClustBlock/man/plot.statis.Rd | 4 +- 20 files changed, 47 insertions(+), 34 deletions(-)
Title: Enrichment Analysis Utilizing Active Subnetworks
Description: Enrichment analysis enables researchers to uncover mechanisms
underlying a phenotype. However, conventional methods for enrichment
analysis do not take into account protein-protein interaction information,
resulting in incomplete conclusions. pathfindR is a tool for enrichment
analysis utilizing active subnetworks. The main function identifies active
subnetworks in a protein-protein interaction network using a user-provided
list of genes and associated p values. It then performs enrichment analyses
on the identified subnetworks, identifying enriched terms (i.e. pathways or,
more broadly, gene sets) that possibly underlie the phenotype of interest.
pathfindR also offers functionalities to cluster the enriched terms and
identify representative terms in each cluster, to score the enriched terms
per sample and to visualize analysis results. The enrichment, clustering and
other methods implemented in pathfindR are described in detail in
Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR: An R Package for
Comprehensive Identification of Enriched Pathways in Omics Data Through
Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
Author: Ege Ulgen [cre, cph] (<https://orcid.org/0000-0003-2090-3621>),
Ozan Ozisik [aut] (<https://orcid.org/0000-0001-5980-8002>)
Maintainer: Ege Ulgen <egeulgen@gmail.com>
Diff between pathfindR versions 1.5.1 dated 2020-09-20 and 1.6.0 dated 2020-11-21
DESCRIPTION | 8 MD5 | 72 +- NEWS.md | 13 R/active_snw_functions.R | 20 R/clustering_functions.R | 48 - R/core_functions.R | 94 +- R/data_generation.R | 21 R/enrichment_functions.R | 30 R/visualization_functions.R | 5 build/vignette.rds |binary inst/doc/comparing_results.html | 52 + inst/doc/intro_vignette.R | 5 inst/doc/intro_vignette.Rmd | 7 inst/doc/intro_vignette.html | 1031 ++++++++++++++++++++++++----- inst/doc/manual_execution.R | 4 inst/doc/manual_execution.Rmd | 6 inst/doc/manual_execution.html | 58 + inst/doc/non_hs_analysis.Rmd | 2 inst/doc/non_hs_analysis.html | 336 ++++++--- inst/doc/obtain_data.html | 67 + inst/doc/visualization_vignette.html | 60 + man/active_snw_search.Rd | 7 man/enrichment_analyses.Rd | 1 man/filterActiveSnws.Rd | 4 man/get_biogrid_pin.Rd | 4 man/hierarchical_term_clustering.Rd | 10 man/run_pathfindR.Rd | 9 man/summarize_enrichment_results.Rd | 1 man/visualize_active_subnetworks.Rd | 4 tests/testthat/test-active_snw_functions.R | 24 tests/testthat/test-clustering_functions.R | 18 tests/testthat/test-core_functions.R | 56 - tests/testthat/test-data_generation.R | 3 tests/testthat/test-enrichment_functions.R | 11 vignettes/intro_vignette.Rmd | 7 vignettes/manual_execution.Rmd | 6 vignettes/non_hs_analysis.Rmd | 2 37 files changed, 1616 insertions(+), 490 deletions(-)
Previous versions (as known to CRANberries) which should be available via the Archive link are:
2020-11-12 0.2.0
2020-10-28 0.1.1
2020-10-13 0.1.0
Previous versions (as known to CRANberries) which should be available via the Archive link are:
2019-06-09 0.4.1.1
2018-05-16 0.4.1
2017-07-25 0.4.0
2017-06-12 0.1.0
Title: Joint Models for Longitudinal and Competing Risks Data
Description: Fit joint models of continuous or ordinal longitudinal data and time-to-event data with competing risks. For a detailed information, see Robert Elashoff, Gang Li and Ning Li (2016, ISBN:9781439807828); Robert M. Elashoff,Gang Li and Ning Li (2008) <doi:10.1111/j.1541-0420.2007.00952.x> ; Ning Li, Robert Elashoff, Gang Li and Jeffrey Saver (2010) <doi:10.1002/sim.3798> .
Author: Hong Wang [aut, cre], Ning Li [ctb],Shanpeng Li[ctb] and Gang Li [ctb]
Maintainer: Hong Wang <wh@csu.edu.cn>
Diff between JMcmprsk versions 0.9.8 dated 2020-06-19 and 0.9.9 dated 2020-11-21
JMcmprsk-0.9.8/JMcmprsk/R/anova.JMcmprsk.R |only JMcmprsk-0.9.8/JMcmprsk/R/jmc_long.R |only JMcmprsk-0.9.8/JMcmprsk/R/jmo_long.R |only JMcmprsk-0.9.8/JMcmprsk/README.md |only JMcmprsk-0.9.8/JMcmprsk/man/anova.JMcmprsk.Rd |only JMcmprsk-0.9.8/JMcmprsk/man/jmc_long.Rd |only JMcmprsk-0.9.8/JMcmprsk/man/jmo_long.Rd |only JMcmprsk-0.9.9/JMcmprsk/DESCRIPTION | 17 JMcmprsk-0.9.9/JMcmprsk/MD5 | 72 - JMcmprsk-0.9.9/JMcmprsk/NAMESPACE | 10 JMcmprsk-0.9.9/JMcmprsk/R/JMcmprsk.R | 12 JMcmprsk-0.9.9/JMcmprsk/R/RcppExports.R | 4 JMcmprsk-0.9.9/JMcmprsk/R/coef.JMcmprsk.R | 68 - JMcmprsk-0.9.9/JMcmprsk/R/data.R |only JMcmprsk-0.9.9/JMcmprsk/R/jmc.R | 366 +++-- JMcmprsk-0.9.9/JMcmprsk/R/jmc_0.R |only JMcmprsk-0.9.9/JMcmprsk/R/jmo.R | 436 +++--- JMcmprsk-0.9.9/JMcmprsk/R/jmo_0.R |only JMcmprsk-0.9.9/JMcmprsk/R/linearTest.R |only JMcmprsk-0.9.9/JMcmprsk/R/print.JMcmprsk.R | 666 +++++----- JMcmprsk-0.9.9/JMcmprsk/R/summary.JMcmprsk.R |only JMcmprsk-0.9.9/JMcmprsk/build/vignette.rds |binary JMcmprsk-0.9.9/JMcmprsk/data |only JMcmprsk-0.9.9/JMcmprsk/inst/doc/JMcmprsk.R | 97 + JMcmprsk-0.9.9/JMcmprsk/inst/doc/JMcmprsk.Rmd | 403 +++--- JMcmprsk-0.9.9/JMcmprsk/inst/doc/JMcmprsk.html | 421 ++++-- JMcmprsk-0.9.9/JMcmprsk/inst/extdata/fvc621_c.txt | 282 ++-- JMcmprsk-0.9.9/JMcmprsk/inst/extdata/fvc621_y.txt | 1432 +++++++++++----------- JMcmprsk-0.9.9/JMcmprsk/inst/extdata/runfit.RData |binary JMcmprsk-0.9.9/JMcmprsk/man/SimDataC.Rd | 2 JMcmprsk-0.9.9/JMcmprsk/man/SimDataO.Rd | 2 JMcmprsk-0.9.9/JMcmprsk/man/coef.JMcmprsk.Rd | 43 JMcmprsk-0.9.9/JMcmprsk/man/jmc.Rd | 218 +-- JMcmprsk-0.9.9/JMcmprsk/man/jmc_0.Rd |only JMcmprsk-0.9.9/JMcmprsk/man/jmo.Rd | 207 +-- JMcmprsk-0.9.9/JMcmprsk/man/jmo_0.Rd |only JMcmprsk-0.9.9/JMcmprsk/man/linearTest.Rd |only JMcmprsk-0.9.9/JMcmprsk/man/lung.Rd |only JMcmprsk-0.9.9/JMcmprsk/man/ninds.Rd |only JMcmprsk-0.9.9/JMcmprsk/man/print.JMcmprsk.Rd | 4 JMcmprsk-0.9.9/JMcmprsk/man/summary.JMcmprsk.Rd |only JMcmprsk-0.9.9/JMcmprsk/src/SimDataC.cpp | 2 JMcmprsk-0.9.9/JMcmprsk/src/SimDataO.cpp | 8 JMcmprsk-0.9.9/JMcmprsk/src/jmc.cpp | 4 JMcmprsk-0.9.9/JMcmprsk/src/jmo.cpp | 3 JMcmprsk-0.9.9/JMcmprsk/vignettes/JMcmprsk.Rmd | 403 +++--- 46 files changed, 2963 insertions(+), 2219 deletions(-)
Title: Data Manipulation Functions Implemented in C
Description: Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.
Author: Morgan Jacob [aut, cre, cph]
Maintainer: Morgan Jacob <morgan.emailbox@gmail.com>
Diff between kit versions 0.0.4 dated 2020-07-21 and 0.0.5 dated 2020-11-21
DESCRIPTION | 8 MD5 | 28 NAMESPACE | 4 R/call.R | 10 inst/NEWS.Rd | 295 +++++----- man/funique.Rd | 28 man/vswitch.Rd | 45 + src/dup.c | 1644 +++++++++++++++++---------------------------------------- src/dupLen.c |only src/iif.c | 514 ++++++++++++++--- src/init.c | 4 src/kit.h | 26 src/nswitch.c |only src/utils.c | 280 ++++++--- src/vswitch.c | 62 +- tests |only 16 files changed, 1404 insertions(+), 1544 deletions(-)
Title: Gadget is the Globally-Applicable Area Disaggregated General
Ecosystem Toolbox
Description: A statistical ecosystem modelling package, taking many features of
the ecosystem into account. Gadget works by running an internal
model based on many parameters, and then comparing the data from
the output of this model to real data to get a goodness-of-fit
likelihood score. These parameters can then be adjusted, and the
model re-run, until an optimum is found, which corresponds to the
model with the lowest likelihood score. Gadget allows the user to
include a number of features into an ecosystem model: One or more
species, each of which may be split into multiple stocks; multiple
areas with migration between areas; predation between and within
species; maturation; reproduction and recruitment; multiple
commercial and survey fleets taking catches from the populations.
For more details see <https://hafro.github.io/gadget2/>.
This is the C++ Gadget2 runtime, making it available for R.
Author: Bjarki Thor Elvarsson [aut, cre],
James Begley [aut],
Hoskuldur Bjornsson [aut],
Jamie Lentin [ctb],
Gunnar Stefnasson [ctb],
Lorna Taylor [ctb],
Daniel Howell [ctb],
Sigurdur Hannesson [ctb],
Narfi Stefansson [aut],
Hersir Sigurgeirsson [ctb],
Morten Nygard Asnes [ctb],
Kristin Froysa [ctb],
Audbjorg Jakobsdottir [ctb],
Jon Gudmundsson [ctb],
Gudmundur Einarsson [ctb],
Thordis Linda Thorarinsdottir [ctb],
Kristjana Yr Jonsdottir [ctb],
Mark G. Johnson [ctb, cph],
Bill Goffe [ctb, cph],
Marine and Freshwater Research Institute (Iceland) [cph]
Maintainer: Bjarki Thor Elvarsson <bjarki.elvarsson@hafogvatn.is>
Diff between gadget2 versions 2.3.5 dated 2020-03-17 and 2.3.7 dated 2020-11-21
DESCRIPTION | 10 ++--- MD5 | 78 +++++++++++++++++++++---------------------- README.md | 38 +++++++++++++++++++- src/catchdistribution.cc | 72 +++++++++++++++++++-------------------- src/catchinkilos.cc | 4 +- src/catchstatistics.cc | 10 ++--- src/ecosystem.cc | 4 +- src/fleet.cc | 2 - src/formula.cc | 9 ++-- src/grow.cc | 34 ++++++++++-------- src/growermemberfunctions.cc | 4 +- src/growthcalc.cc | 14 +++---- src/initialcond.cc | 10 ++--- src/initialinputfile.cc | 2 - src/maininfo.cc | 6 +-- src/maturity.cc | 10 ++--- src/migration.cc | 8 ++-- src/migrationproportion.cc | 4 +- src/modelvariable.cc | 2 - src/otherfood.cc | 2 - src/predatoroverprinter.cc | 4 +- src/predatorpreyprinter.cc | 6 +-- src/predatorprinter.cc | 6 +-- src/prey.cc | 2 - src/preyoverprinter.cc | 4 +- src/readmain.cc | 18 ++++----- src/recapture.cc | 6 +-- src/recstatistics.cc | 4 +- src/renewal.cc | 16 ++++---- src/sionstep.cc | 2 - src/stock.cc | 8 ++-- src/stockdistribution.cc | 20 +++++------ src/stockmemberfunctions.cc | 2 - src/stockpreyprinter.cc | 6 +-- src/stockprinter.cc | 6 +-- src/stomachcontent.cc | 16 ++++---- src/surveydistribution.cc | 8 ++-- src/surveyindices.cc | 8 ++-- src/tags.cc | 2 - src/totalpredator.cc | 1 40 files changed, 252 insertions(+), 216 deletions(-)
Title: Method Functions for Confidence Intervals and to Distill
Information from an Object
Description: Some very simple method functions for confidence interval calculation, bootstrap resampling aimed at atmospheric science applications, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.
Author: Eric Gilleland
Maintainer: Eric Gilleland <ericg@ucar.edu>
Diff between distillery versions 1.1 dated 2020-08-06 and 1.2 dated 2020-11-21
COPYING |only DESCRIPTION | 10 +++++----- MD5 | 4 +++- inst |only 4 files changed, 8 insertions(+), 6 deletions(-)
Title: Novel Statistical Test for Aberration Enrichment
Description: Testing for heterogeneous effects in a case-control setting.
The aim here to discover an association that is beyond a mean difference
between all cases and all controls. Instead, the signal of interest here
is present in only a proportion of the cases. This test should be more
powerful than a t-test or Wilcoxon test in this heterogeneous setting.
Please cite the corresponding paper: Mezlini et al. (2020)
<doi:10.1101/2020.03.23.002972>.
Author: Aziz M. Mezlini [aut,cre,cph]
Maintainer: Aziz M. Mezlini <mmezlini@mgh.harvard.edu>
Diff between aziztest versions 0.2.0 dated 2020-09-23 and 0.2.1 dated 2020-11-21
DESCRIPTION | 8 ++++---- MD5 | 10 +++++----- R/main.R | 41 +++++++++++++++++++++-------------------- man/aziz.test.Rd | 14 +++++++------- man/calibrate_test.Rd | 4 ++-- man/get_calibrated_pvalues.Rd | 14 +++++++------- 6 files changed, 46 insertions(+), 45 deletions(-)