Mon, 04 Apr 2022

Package rrtable updated to version 0.3.0 with previous version 0.2.1 dated 2020-04-02

Title: Reproducible Research with a Table of R Codes
Description: Makes documents containing plots and tables from a table of R codes. Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code. In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications.
Author: Keon-Woong Moon [aut, cre]
Maintainer: Keon-Woong Moon <cardiomoon@gmail.com>

Diff between rrtable versions 0.2.1 dated 2020-04-02 and 0.3.0 dated 2022-04-04

 DESCRIPTION                        |   10 -
 MD5                                |  149 +++++++++----------
 NAMESPACE                          |   10 +
 NEWS.md                            |   63 ++++++++
 R/add_2ggplots.R                   |   70 +++++----
 R/add_2plots.R                     |   15 -
 R/add_Rcode.R                      |   69 ++++-----
 R/add_flextable.R                  |    4 
 R/add_ggplot.R                     |    9 -
 R/add_plot.R                       |    9 -
 R/chooserEx.R                      |    2 
 R/code2pptx.R                      |   11 +
 R/data2HTML.R                      |   69 ++++++---
 R/data2office.R                    |  230 +++++++++++++++++-------------
 R/data2pdf.R                       |    4 
 R/data2plotzip.R                   |   77 +++++-----
 R/df2flextable.R                   |   10 -
 R/image2pptx.R                     |   15 +
 R/myFlextable.R                    |only
 R/mytable2flextable.R              |   53 ++++--
 R/plot2pptx.R                      |   48 +++---
 R/table2office.R                   |   25 ++-
 README.md                          |    2 
 build/vignette.rds                 |binary
 data/sampleData2.rda               |binary
 inst/doc/Update.html               |  118 ++++++++++-----
 inst/doc/rrtable.html              |  283 ++++++++++++++++++++++++++-----------
 inst/doc_examples/Report.pptx      |binary
 inst/doc_examples/sampleData2.docx |binary
 inst/doc_examples/sampleData2.pdf  |binary
 inst/doc_examples/sampleData2.pptx |binary
 inst/doc_examples/sampleData3.docx |binary
 inst/doc_examples/sampleData3.pdf  |binary
 inst/doc_examples/sampleData3.pptx |binary
 man/Rcode2df.Rd                    |    4 
 man/Rcode2df2.Rd                   |    4 
 man/Rcode2docx.Rd                  |    2 
 man/Rcode2flextable.Rd             |    4 
 man/Rcode2office.Rd                |    2 
 man/Rcode2pptx.Rd                  |    2 
 man/add_2flextables.Rd             |    2 
 man/add_2ggplots.Rd                |   14 -
 man/add_2plots.Rd                  |    3 
 man/add_Rcode.Rd                   |    4 
 man/add_anyplot.Rd                 |    3 
 man/add_flextable.Rd               |    2 
 man/add_ggplot.Rd                  |    4 
 man/add_plot.Rd                    |    4 
 man/add_text.Rd                    |    3 
 man/as.mynumeric.Rd                |only
 man/chooserInput.Rd                |    2 
 man/code2docx.Rd                   |    2 
 man/code2office.Rd                 |    2 
 man/code2pptx.Rd                   |    2 
 man/data2HTML.Rd                   |    7 
 man/data2docx.Rd                   |    2 
 man/data2office.Rd                 |    5 
 man/data2pdf.Rd                    |    2 
 man/data2plotzip.Rd                |    7 
 man/data2pptx.Rd                   |    2 
 man/data2pptx2.Rd                  |    4 
 man/df2flextable.Rd                |    7 
 man/image2docx.Rd                  |    2 
 man/image2office.Rd                |    2 
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 man/plot2docx.Rd                   |    2 
 man/plot2office.Rd                 |    7 
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 man/plotPNG2.Rd                    |    5 
 man/table2docx.Rd                  |    2 
 man/table2office.Rd                |    3 
 man/table2pptx.Rd                  |    2 
 77 files changed, 921 insertions(+), 577 deletions(-)

More information about rrtable at CRAN
Permanent link

Package RcppSpdlog updated to version 0.0.8 with previous version 0.0.7 dated 2021-12-05

Title: 'spdlog' C++ Header Library for Logging
Description: The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides many desirable features. This package bundles these header files for easy use by R packages via a simple 'LinkingTo:' inclusion.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RcppSpdlog versions 0.0.7 dated 2021-12-05 and 0.0.8 dated 2022-04-04

 ChangeLog                                          |    6 
 DESCRIPTION                                        |    8 
 MD5                                                |  131 +-
 build/partial.rdb                                  |binary
 build/vignette.rds                                 |binary
 inst/NEWS.Rd                                       |    6 
 inst/doc/introduction.html                         |  913 ++++++++--------
 inst/include/spdlog/async.h                        |   20 
 inst/include/spdlog/async_logger-inl.h             |    4 
 inst/include/spdlog/common-inl.h                   |    4 
 inst/include/spdlog/common.h                       |  119 +-
 inst/include/spdlog/details/file_helper-inl.h      |   27 
 inst/include/spdlog/details/file_helper.h          |    4 
 inst/include/spdlog/details/fmt_helper.h           |   60 -
 inst/include/spdlog/details/mpmc_blocking_q.h      |    4 
 inst/include/spdlog/details/os-inl.h               |   35 
 inst/include/spdlog/details/os.h                   |    4 
 inst/include/spdlog/details/synchronous_factory.h  |    2 
 inst/include/spdlog/details/tcp_client-windows.h   |   41 
 inst/include/spdlog/details/tcp_client.h           |    3 
 inst/include/spdlog/details/thread_pool-inl.h      |   13 
 inst/include/spdlog/details/thread_pool.h          |    3 
 inst/include/spdlog/details/udp_client-windows.h   |only
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 inst/include/spdlog/fmt/bundled/args.h             |    2 
 inst/include/spdlog/fmt/bundled/chrono.h           | 1181 +++++++++++++++++----
 inst/include/spdlog/fmt/bundled/color.h            |   35 
 inst/include/spdlog/fmt/bundled/compile.h          |   25 
 inst/include/spdlog/fmt/bundled/core.h             |  754 ++++++++-----
 inst/include/spdlog/fmt/bundled/fmt.license.rst    |only
 inst/include/spdlog/fmt/bundled/format-inl.h       |  549 +++++----
 inst/include/spdlog/fmt/bundled/format.h           |  904 ++++++++++------
 inst/include/spdlog/fmt/bundled/os.h               |   48 
 inst/include/spdlog/fmt/bundled/ostream.h          |  108 -
 inst/include/spdlog/fmt/bundled/printf.h           |   19 
 inst/include/spdlog/fmt/bundled/ranges.h           |  547 +++++++--
 inst/include/spdlog/fmt/bundled/xchar.h            |   10 
 inst/include/spdlog/fmt/chrono.h                   |   16 
 inst/include/spdlog/fmt/compile.h                  |   18 
 inst/include/spdlog/fmt/fmt.h                      |    4 
 inst/include/spdlog/fmt/ostr.h                     |   16 
 inst/include/spdlog/fmt/ranges.h                   |only
 inst/include/spdlog/fmt/xchar.h                    |   18 
 inst/include/spdlog/fwd.h                          |    4 
 inst/include/spdlog/logger-inl.h                   |    4 
 inst/include/spdlog/logger.h                       |  118 +-
 inst/include/spdlog/pattern_formatter-inl.h        |   40 
 inst/include/spdlog/pattern_formatter.h            |    5 
 inst/include/spdlog/sinks/ansicolor_sink-inl.h     |    4 
 inst/include/spdlog/sinks/base_sink.h              |    4 
 inst/include/spdlog/sinks/basic_file_sink-inl.h    |    3 
 inst/include/spdlog/sinks/basic_file_sink.h        |   12 
 inst/include/spdlog/sinks/daily_file_sink.h        |   84 +
 inst/include/spdlog/sinks/hourly_file_sink.h       |   18 
 inst/include/spdlog/sinks/mongo_sink.h             |    1 
 inst/include/spdlog/sinks/msvc_sink.h              |    4 
 inst/include/spdlog/sinks/qt_sinks.h               |   83 -
 inst/include/spdlog/sinks/ringbuffer_sink.h        |    4 
 inst/include/spdlog/sinks/rotating_file_sink-inl.h |   33 
 inst/include/spdlog/sinks/rotating_file_sink.h     |   17 
 inst/include/spdlog/sinks/stdout_sinks-inl.h       |    6 
 inst/include/spdlog/sinks/systemd_sink.h           |   44 
 inst/include/spdlog/sinks/udp_sink.h               |only
 inst/include/spdlog/sinks/win_eventlog_sink.h      |   33 
 inst/include/spdlog/spdlog.h                       |   34 
 inst/include/spdlog/stopwatch.h                    |   13 
 inst/include/spdlog/tweakme.h                      |   16 
 inst/include/spdlog/version.h                      |    4 
 69 files changed, 4187 insertions(+), 2125 deletions(-)

More information about RcppSpdlog at CRAN
Permanent link

Package RcppArmadillo updated to version 0.11.0.0.0 with previous version 0.10.8.1.0 dated 2022-01-24

Title: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library
Description: 'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Armadillo requires a C++11 compiler.
Author: Dirk Eddelbuettel, Romain Francois, Doug Bates, Binxiang Ni, and Conrad Sanderson
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RcppArmadillo versions 0.10.8.1.0 dated 2022-01-24 and 0.11.0.0.0 dated 2022-04-04

 RcppArmadillo-0.10.8.1.0/RcppArmadillo/inst/include/armadillo_bits/arma_rng_cxx11.hpp                    |only
 RcppArmadillo-0.10.8.1.0/RcppArmadillo/inst/include/armadillo_bits/arma_rng_cxx98.hpp                    |only
 RcppArmadillo-0.10.8.1.0/RcppArmadillo/inst/include/armadillo_bits/include_atlas.hpp                     |only
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 RcppArmadillo-0.10.8.1.0/RcppArmadillo/inst/include/armadillo_bits/op_inv_meat.hpp                       |only
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/ChangeLog                                                         |   55 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/DESCRIPTION                                                       |    8 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/MD5                                                               |  211 -
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/build/partial.rdb                                                 |binary
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/build/vignette.rds                                                |binary
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/configure                                                         |   18 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/configure.ac                                                      |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/NEWS.Rd                                                      |   40 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/doc/RcppArmadillo-intro.pdf                                  |binary
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/doc/RcppArmadillo-sparseMatrix.pdf                           |binary
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/RcppArmadilloWrap.h                                  |    6 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo                                            |   17 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Base_bones.hpp                        |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Base_meat.hpp                         |    4 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Col_bones.hpp                         |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Col_meat.hpp                          |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Cube_bones.hpp                        |   17 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Cube_meat.hpp                         |   81 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Mat_bones.hpp                         |   17 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Mat_meat.hpp                          |   77 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Row_bones.hpp                         |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/Row_meat.hpp                          |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/SpCol_bones.hpp                       |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/SpCol_meat.hpp                        |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/SpMat_bones.hpp                       |   15 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/SpMat_meat.hpp                        |   43 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/SpRow_bones.hpp                       |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/SpRow_meat.hpp                        |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/arma_config.hpp                       |   34 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/arma_forward.hpp                      |    6 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/arma_rng.hpp                          |   30 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/arma_rng_cxx03.hpp                    |only
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/arma_version.hpp                      |    8 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/arrayops_meat.hpp                     |   11 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/auxlib_bones.hpp                      |   21 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/auxlib_meat.hpp                       | 1089 +++-------
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/compiler_setup.hpp                    |   10 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/config.hpp                            |   50 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/debug.hpp                             |   98 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/def_arpack.hpp                        |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/def_atlas.hpp                         |  165 -
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/def_blas.hpp                          |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/def_lapack.hpp                        |   16 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/field_bones.hpp                       |   14 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/field_meat.hpp                        |   58 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_as_scalar.hpp                      |    6 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_eig_sym.hpp                        |    7 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_inv.hpp                            |   49 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_inv_sympd.hpp                      |only
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_randperm.hpp                       |    1 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_solve.hpp                          |   58 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/fn_spsolve.hpp                        |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_mvnrnd_meat.hpp                  |    6 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_quantile_meat.hpp                |    4 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_solve_bones.hpp                  |   29 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_solve_meat.hpp                   |  161 -
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/glue_times_meat.hpp                   |  150 -
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/mul_gemm.hpp                          |    8 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/mul_gemv.hpp                          |   14 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/mul_herk.hpp                          |    8 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/mul_syrk.hpp                          |    8 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/newarp_GenEigsSolver_bones.hpp        |   44 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/newarp_GenEigsSolver_meat.hpp         |   46 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/newarp_SymEigsSolver_bones.hpp        |   45 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/newarp_SymEigsSolver_meat.hpp         |   79 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/newarp_TridiagEigen_meat.hpp          |   45 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/newarp_UpperHessenbergEigen_bones.hpp |   20 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/newarp_UpperHessenbergEigen_meat.hpp  |   71 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_chol_meat.hpp                      |   13 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_cond_meat.hpp                      |   43 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_det_bones.hpp                      |    8 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_det_meat.hpp                       |  206 +
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_dot_meat.hpp                       |    6 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_expmat_meat.hpp                    |  174 -
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_inv_gen_bones.hpp                  |only
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_inv_gen_meat.hpp                   |only
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_inv_spd_bones.hpp                  |only
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_inv_spd_meat.hpp                   |only
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_log_det_meat.hpp                   |   58 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_logmat_meat.hpp                    |   51 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_median_bones.hpp                   |   14 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_median_meat.hpp                    |  281 --
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_pinv_bones.hpp                     |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_pinv_meat.hpp                      |  171 -
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_powmat_meat.hpp                    |   10 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_rank_meat.hpp                      |   24 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_shuffle_meat.hpp                   |    6 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/op_sqrtmat_meat.hpp                   |   48 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/sp_auxlib_meat.hpp                    |  100 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/spglue_max_meat.hpp                   |    4 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/spglue_min_meat.hpp                   |    4 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/strip.hpp                             |   20 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/sympd_helper.hpp                      |   36 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/traits.hpp                            |   41 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/translate_arpack.hpp                  |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/translate_atlas.hpp                   |  269 --
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/translate_blas.hpp                    |    2 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/translate_lapack.hpp                  |   14 
 RcppArmadillo-0.11.0.0.0/RcppArmadillo/inst/include/armadillo_bits/trimat_helper.hpp                     |   52 
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Package NNS updated to version 0.8.61 with previous version 0.8.5 dated 2022-03-08

Title: Nonlinear Nonparametric Statistics
Description: Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).
Author: Fred Viole
Maintainer: Fred Viole <ovvo.financial.systems@gmail.com>

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Package ib updated to version 0.2.0 with previous version 0.1.0 dated 2020-12-16

Title: Bias Correction via Iterative Bootstrap
Description: An implementation of the iterative bootstrap procedure of Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This procedure has better bias correction properties than the bootstrap bias correction technique.
Author: Samuel Orso [aut, cre], Stephane Guerrier [ctb], Yuming Zhang [ctb]
Maintainer: Samuel Orso <Samuel.Orso@unige.ch>

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Package elbird updated to version 0.1.3 with previous version 0.1.1 dated 2022-03-31

Title: Blazing Fast Morphological Analyzer Based on Kiwi(Korean Intelligent Word Identifier)
Description: This is the R wrapper package Kiwi(Korean Intelligent Word Identifier), a blazing fast speed morphological analyzer for Korean. It supports configuration of user dictionary and detection of unregistered nouns based on frequency.
Author: Chanyub Park [aut, cre]
Maintainer: Chanyub Park <mrchypark@gmail.com>

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Package BIFIEsurvey updated to version 3.4-15 with previous version 3.3-12 dated 2019-06-12

Title: Tools for Survey Statistics in Educational Assessment
Description: Contains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>, <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, <doi:10.1080/02331889708802523>). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria).
Author: BIFIE [aut], Alexander Robitzsch [aut, cre], Konrad Oberwimmer [aut]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

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Package transforEmotion updated to version 0.1.0 with previous version 0.0.1 dated 2022-03-01

Title: Sentiment Analysis for Text and Qualitative Data
Description: Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines. The default pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> trained on the Stanford Natural Language Inference <https://nlp.stanford.edu/projects/snli/> and Multi-Genre Natural Language Inference <https://huggingface.co/datasets/multi_nli> datasets. Using similar models, zero-shot classification transformers have demonstrated superior performance relative to other natural language processing models <arXiv:1909.00161>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>}.
Author: Alexander Christensen [aut, cre] , Hudson Golino [aut]
Maintainer: Alexander Christensen <alexpaulchristensen@gmail.com>

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Package SEL updated to version 1.0-3 with previous version 1.0-2 dated 2010-05-23

Title: Semiparametric Elicitation
Description: Implements a method for fitting a bounded probability distribution to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009) for details. For this purpose B-splines are used, and the density is obtained by penalized least squares based on a Brier entropy penalty. The package provides methods for fitting the distribution as well as methods for evaluating the underlying density and cdf. In addition methods for plotting the distribution, drawing random numbers and calculating quantiles of the obtained distribution are provided.
Author: Bjoern Bornkamp
Maintainer: Bjoern Bornkamp <bbnkmp@mail.de>

Diff between SEL versions 1.0-2 dated 2010-05-23 and 1.0-3 dated 2022-04-04

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Package medExtractR updated to version 0.4 with previous version 0.3 dated 2021-06-06

Title: Extraction of Medication Information from Clinical Text
Description: Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) <doi: 10.1093/jamia/ocz207>. The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition.
Author: Leena Choi [aut, cre] , Cole Beck [aut] , Hannah Weeks [aut]
Maintainer: Leena Choi <leena.choi@vanderbilt.edu>

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Package klausuR updated to version 0.12-14 with previous version 0.12-13 dated 2022-03-01

Title: Multiple Choice Test Evaluation
Description: A set of functions designed to quickly generate results of a multiple choice test. Generates detailed global results, lists for anonymous feedback and personalised result feedback (in LaTeX and/or PDF format), as well as item statistics like Cronbach's alpha or disciminatory power. 'klausuR' also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for its basic features. The respective R package 'rkward' cannot be installed directly from a repository, as it is a part of RKWard. To make full use of this feature, please install RKWard from <https://rkward.kde.org> (plugins are detected automatically). Due to some restrictions on CRAN, the full package sources are only available from the project homepage.
Author: m.eik michalke [aut, cre]
Maintainer: m.eik michalke <meik.michalke@hhu.de>

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Package crossdes updated to version 1.1-2 with previous version 1.1-1 dated 2013-03-18

Title: Construction of Crossover Designs
Description: Contains functions for the construction of carryover balanced crossover designs. In addition contains functions to check given designs for balance.
Author: Martin Oliver Sailer
Maintainer: Bjoern Bornkamp <bbnkmp@mail.de>

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Package pacs updated to version 0.4.4 with previous version 0.4.3 dated 2022-03-03

Title: Supplementary Tools for R Packages Developers
Description: Supplementary utils for CRAN maintainers and R packages developers. Validating the library, packages and lock files. Exploring a complexity of a specific package like evaluating its size in bytes with all dependencies. The shiny app complexity could be explored too. Assessing the life duration of a specific package version. Checking a CRAN package check page status for any errors and warnings. Retrieving a DESCRIPTION or NAMESPACE file for any package version. Comparing DESCRIPTION or NAMESPACE files between different package versions. Getting a list of all releases for a specific package. The Bioconductor is partly supported.
Author: Maciej Nasinski [aut, cre]
Maintainer: Maciej Nasinski <nasinski.maciej@gmail.com>

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Package irrNA updated to version 0.2.3 with previous version 0.2.2 dated 2021-04-05

Title: Coefficients of Interrater Reliability – Generalized for Randomly Incomplete Datasets
Description: Provides coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi{10.1037/1082-989X.1.1.30} and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi{10.1214/aoms/1177732186}.
Author: Markus Brueckl [aut, cre], Florian Heuer [aut, trl]
Maintainer: Markus Brueckl <markus.brueckl@tu-berlin.de>

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Package exceldata updated to version 0.1.1.2 with previous version 0.1.1.1 dated 2022-03-10

Title: Streamline Data Import, Cleaning and Recoding from 'Excel'
Description: A small group of functions to read in a data dictionary and the corresponding data table from 'Excel' and to automate the cleaning, re-coding and creation of simple calculated variables. This package was designed to be a companion to the macro-enabled 'Excel' template available on the GitHub site, but works with any similarly-formatted 'Excel' data.
Author: Lisa Avery [aut, cre, cph]
Maintainer: Lisa Avery <lisa.avery@uhn.ca>

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Package gifski updated to version 1.6.6 with previous version 1.4.3-1 dated 2021-05-02

Title: Highest Quality GIF Encoder
Description: Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. Converts images to GIF animations using pngquant's efficient cross-frame palettes and temporal dithering with thousands of colors per frame.
Author: Jeroen Ooms [aut, cre] , Kornel Lesiński [cph]
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between gifski versions 1.4.3-1 dated 2021-05-02 and 1.6.6 dated 2022-04-04

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Package chisquare updated to version 0.3 with previous version 0.2 dated 2022-03-20

Title: Chi-Square and G-Square Test of Independence, Residual Analysis, and Measures of Categorical Association
Description: Provides the facility to perform the chi-square and G-square test of independence, calculates permutation-based p value, and provides measures of association such as Phi, odds ratio with 95 percent CI and p value, adjusted contingency coefficient, Cramer's V and 95 percent CI, bias-corrected Cramer's V, Cohen's w, Goodman-Kruskal's lambda, gamma and its p value, and tau, Cohen's k and its 95 percent CI. It also calculates standardized, moment-corrected standardized, and adjusted standardized residuals, and their significance. Different outputs are returned in nicely formatted tables.
Author: Gianmarco Alberti [aut, cre]
Maintainer: Gianmarco Alberti <gianmarcoalberti@gmail.com>

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Package Momocs updated to version 1.4.0 with previous version 1.3.2 dated 2020-10-06

Title: Morphometrics using R
Description: The goal of 'Momocs' is to provide a complete, convenient, reproducible and open-source toolkit for 2D morphometrics. It includes most common 2D morphometrics approaches on outlines, open outlines, configurations of landmarks, traditional morphometrics, and facilities for data preparation, manipulation and visualization with a consistent grammar throughout. It allows reproducible, complex morphometrics analyses and other morphometrics approaches should be easy to plug in, or develop from, on top of this canvas.
Author: Vincent Bonhomme [aut, cre], Julien Claude [aut]
Maintainer: Vincent Bonhomme <bonhomme.vincent@gmail.com>

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Package embryogrowth updated to version 8.4 with previous version 8.3-4 dated 2022-02-17

Title: Tools to Analyze the Thermal Reaction Norm of Embryo Growth
Description: Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.
Author: Marc Girondot <marc.girondot@gmail.com>
Maintainer: Marc Girondot <marc.girondot@gmail.com>

Diff between embryogrowth versions 8.3-4 dated 2022-02-17 and 8.4 dated 2022-04-04

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Package autoMrP updated to version 1.0.2 with previous version 1.0.1 dated 2022-02-07

Title: Improving MrP with Ensemble Learning
Description: A tool that improves the prediction performance of multilevel regression with post-stratification (MrP) by combining a number of machine learning methods. For information on the method, please refer to Broniecki, Wüest, Leemann (2020) ''Improving Multilevel Regression with Post-Stratification Through Machine Learning (autoMrP)'' forthcoming in 'Journal of Politics'. Final pre-print version: <https://lucasleemann.files.wordpress.com/2020/07/automrp-r2pa.pdf>.
Author: Reto Wueest [aut] , Lucas Leemann [aut] , Philipp Broniecki [aut, cre] , Hadley Wickham [ctb]
Maintainer: Philipp Broniecki <philippbroniecki@gmail.com>

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Package s2dverification updated to version 2.10.2 with previous version 2.10.1 dated 2022-02-22

Title: Set of Common Tools for Forecast Verification
Description: Set of tools to verify forecasts through the computation of typical prediction scores against one or more observational datasets or reanalyses (a reanalysis being a physical extrapolation of observations that relies on the equations from a model, not a pure observational dataset). Intended for seasonal to decadal climate forecasts although can be useful to verify other kinds of forecasts. The package can be helpful in climate sciences for other purposes than forecasting. To find more details, see the review paper Manubens, N.et al. (2018) <doi:10.1016/j.envsoft.2018.01.018>.
Author: BSC-CNS [aut, cph], Virginie Guemas [aut], Nicolau Manubens [aut], An-Chi Ho [ctb, cre], Nuria Perez-Zanon [aut], Javier Garcia-Serrano [aut], Neven Fuckar [aut], Louis-Philippe Caron [aut], Omar Bellprat [aut], Luis Rodrigues [aut], Veronica Torralb [...truncated...]
Maintainer: An-Chi Ho <an.ho@bsc.es>

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Package mlr3viz updated to version 0.5.8 with previous version 0.5.7 dated 2021-10-14

Title: Visualizations for 'mlr3'
Description: Provides visualizations for 'mlr3' objects such as tasks, predictions, resample results or benchmark results via the autoplot() generic of 'ggplot2'. The returned 'ggplot' objects are intended to provide sensible defaults, yet can easily be customized to create camera-ready figures. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves.
Author: Michel Lang [cre, aut] , Patrick Schratz [aut] , Raphael Sonabend [aut] , Marc Becker [aut] , Jakob Richter [aut] , Damir Pulatov [ctb]
Maintainer: Michel Lang <michellang@gmail.com>

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Package microsimulation updated to version 1.3.7 with previous version 1.3.6 dated 2021-11-16

Title: Discrete Event Simulation in R and C++, with Tools for Cost-Effectiveness Analysis
Description: Discrete event simulation using both R and C++ (Karlsson et al 2016; <doi:10.1109/eScience.2016.7870915>). The C++ code is adapted from the SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for both event-oriented and process-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis.
Author: Mark Clements [aut, cre, cph], Alexandra Jauhiainen [aut], Andreas Karlsson [aut], Antonio Carzaniga [cph], University of Colorado [cph], Pierre L'Ecuyer [cph]
Maintainer: Mark Clements <mark.clements@ki.se>

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Package genalg updated to version 0.2.1 with previous version 0.2.0 dated 2015-03-16

Title: R Based Genetic Algorithm
Description: R based genetic algorithm for binary and floating point chromosomes.
Author: Egon Willighagen and Michel Ballings
Maintainer: Michel Ballings <Michel.Ballings@gmail.com>

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Package BoneProfileR updated to version 2.2 with previous version 2.1 dated 2022-03-05

Title: Tools to Study Bone Compactness
Description: Bone Profiler is a scientific method and a software used to model bone section for paleontological and ecological studies. See Girondot and Laurin 2003 <https://www.researchgate.net/publication/280021178_Bone_profiler_A_tool_to_quantify_model_and_statistically_compare_bone-section_compactness_profiles>.
Author: Marc Girondot <marc.girondot@gmail.com>
Maintainer: Marc Girondot <marc.girondot@gmail.com>

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Package AUC updated to version 0.3.2 with previous version 0.3.0 dated 2013-09-30

Title: Threshold Independent Performance Measures for Probabilistic Classifiers
Description: Various functions to compute the area under the curve of selected measures: The area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). Support for visualization and partial areas is included.
Author: Michel Ballings and Dirk Van den Poel
Maintainer: Michel Ballings <Michel.Ballings@gmail.com>

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Package weakARMA updated to version 1.0.3 with previous version 1.0.2 dated 2022-02-07

Title: Tools for the Analysis of Weak ARMA Models
Description: Numerous time series admit autoregressive moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent. These models are called weak ARMA by opposition to the standard ARMA models, also called strong ARMA models, in which the error terms are supposed to be independent and identically distributed (iid). This package allows the study of nonlinear time series models through weak ARMA representations. It determines identification, estimation and validation for ARMA models and for AR and MA models in particular. Functions can also be used in the strong case. This package also works on white noises by omitting arguments 'p', 'q', 'ar' and 'ma'. See Francq, C. and Zakoïan, J. (1998) <doi:10.1016/S0378-3758(97)00139-0> and Boubacar Maïnassara, Y. and Saussereau, B. (2018) <doi:10.1080/01621459.2017.1380030> for more details.
Author: Yacouba Boubacar Maïnassara [aut] , Julien Yves Rolland [aut, cre] , Coraline Parguey [ctb], Vincent Mouillot [ctb]
Maintainer: Julien Yves Rolland <julien.rolland@univ-fcomte.fr>

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Package pubh updated to version 1.2.7 with previous version 1.2.5 dated 2021-10-11

Title: A Toolbox for Public Health and Epidemiology
Description: A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand 'ggplot2' plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.
Author: Josie Athens [aut, cre], Frank Harell [ctb], John Fox [ctb], R-Core [ctb]
Maintainer: Josie Athens <josie.athens@gmail.com>

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Package bbotk updated to version 0.5.2 with previous version 0.5.1 dated 2022-02-25

Title: Black-Box Optimization Toolkit
Description: Provides a common framework for optimization of black-box functions for other packages, e.g. 'mlr3tuning' or 'mlr3fselect'. It offers various optimization methods e.g. grid search, random search, generalized simulated annealing and iterated racing.
Author: Marc Becker [cre, aut] , Jakob Richter [aut] , Michel Lang [aut] , Bernd Bischl [aut] , Martin Binder [aut], Olaf Mersmann [ctb]
Maintainer: Marc Becker <marcbecker@posteo.de>

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Package bamdit updated to version 3.4.0 with previous version 3.3.2 dated 2019-07-09

Title: Bayesian Meta-Analysis of Diagnostic Test Data
Description: Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Author: Pablo Emilio Verde [aut, cre]
Maintainer: Pablo Emilio Verde <pabloemilio.verde@hhu.de>

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Package protViz updated to version 0.7.3 with previous version 0.7.2 dated 2022-03-16

Title: Visualizing and Analyzing Mass Spectrometry Related Data in Proteomics
Description: Helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. The package is developed, tested and used at the Functional Genomics Center Zurich <https://fgcz.ch>. We use this package mainly for prototyping, teaching, and having fun with proteomics data. But it can also be used to do data analysis for small scale data sets.
Author: Christian Panse [aut, cre] , Jonas Grossmann [aut] , Simon Barkow-Oesterreicher [ctb]
Maintainer: Christian Panse <cp@fgcz.ethz.ch>

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Package ISR updated to version 2022.3.28 with previous version 2021.9.24 dated 2021-09-24

Title: The Iterated Score Regression-Based Estimation Algorithm
Description: Algorithm to handle with PCA-based missing data, where ISR is for PCA-based missing data with high correlation and DISR is for distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
Author: Guangbao Guo [aut, cre] , Haoyue Song [aut], Lixing Zhu [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>

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Package BINtools updated to version 0.2.0 with previous version 0.1.0 dated 2021-06-10

Title: Bayesian BIN (Bias, Information, Noise) Model of Forecasting
Description: A recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.
Author: Ville Satopaeae [aut, cre] , Marat Salikhov [aut], Elvira Moreno [aut]
Maintainer: Ville Satopaeae <ville.satopaa@gmail.com>

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Package pcalg updated to version 2.7-6 with previous version 2.7-5 dated 2022-02-22

Title: Methods for Graphical Models and Causal Inference
Description: Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.
Author: Markus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp R [...truncated...]
Maintainer: Markus Kalisch <kalisch@stat.math.ethz.ch>

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Package miceadds updated to version 3.12-26 with previous version 3.11-6 dated 2021-01-21

Title: Some Additional Multiple Imputation Functions, Especially for 'mice'
Description: Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are included. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
Author: Alexander Robitzsch [aut,cre] , Simon Grund [aut] , Thorsten Henke [ctb]
Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

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Package FlexDotPlot updated to version 0.2.2 with previous version 0.2.1 dated 2022-02-07

Title: Flexible Dot Plot
Description: Flexible Dotplot and Pacman plot for multimodal data.
Author: Simon Leonard [aut, cre]
Maintainer: Simon Leonard <simon.leonard@univ-rennes1.fr>

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Package MODISTools updated to version 1.1.2 with previous version 1.1.1 dated 2020-03-05

Title: Interface to the 'MODIS Land Products Subsets' Web Services
Description: Programmatic interface to the Oak Ridge National Laboratories 'MODIS Land Products Subsets' web services (<https://modis.ornl.gov/data/modis_webservice.html>). Allows for easy downloads of 'MODIS' time series directly to your R workspace or your computer.
Author: Hufkens Koen [aut, cre]
Maintainer: Hufkens Koen <koen.hufkens@gmail.com>

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Package Julia updated to version 1.3.3 with previous version 1.3.1 dated 2022-02-02

Title: Fractal Image Data Generator
Description: Generates image data for fractals (Julia and Mandelbrot sets) on the complex plane in the given region and resolution. Benoit B Mandelbrot (1982).
Author: Mehmet Suzen [aut, cre]
Maintainer: Mehmet Suzen <mehmet.suzen@physics.org>

Diff between Julia versions 1.3.1 dated 2022-02-02 and 1.3.3 dated 2022-04-04

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Package BGVAR updated to version 2.4.5 with previous version 2.4.4 dated 2022-04-01

Title: Bayesian Global Vector Autoregressions
Description: Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available.
Author: Maximilian Boeck [aut, cre] , Martin Feldkircher [aut] , Florian Huber [aut] , Darjus Hosszejni [ctb]
Maintainer: Maximilian Boeck <maximilian.boeck@da-vienna.ac.at>

Diff between BGVAR versions 2.4.4 dated 2022-04-01 and 2.4.5 dated 2022-04-04

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New package beyondWhittle with initial version 1.1.3
Package: beyondWhittle
Title: Bayesian Spectral Inference for Stationary Time Series
Version: 1.1.3
Date: 2022-04-03
Maintainer: Alexander Meier <meier.alexander@posteo.de>
Description: Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126> and A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470>. It was supported by DFG grant KI 1443/3-1.
License: GPL (>= 3)
Imports: ltsa (>= 1.4.6), Rcpp (>= 0.12.5), MASS, forecast
LinkingTo: Rcpp, RcppArmadillo, BH
NeedsCompilation: yes
Packaged: 2022-04-03 13:05:14 UTC; alexandermeier
Author: Alexander Meier [aut, cre], Claudia Kirch [aut], Matthew C. Edwards [aut], Renate Meyer [aut]
Repository: CRAN
Date/Publication: 2022-04-04 11:00:02 UTC
Suggests: testthat (>= 3.0.0)

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Package arfima updated to version 1.8-0 with previous version 1.7-0 dated 2018-11-01

Title: Fractional ARIMA (and Other Long Memory) Time Series Modeling
Description: Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used. Bug reports should be done via GitHub (at <https://github.com/JQVeenstra/arfima>), where the development version of this package lives; it can be installed using devtools.
Author: JQ Veenstra [aut, cre], A.I. McLeod [aut]
Maintainer: JQ Veenstra <jqveenstra@gmail.com>

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Package ttutils updated to version 1.0-1.1 with previous version 1.0-1 dated 2010-06-20

Title: Utility Functions
Description: Contains some auxiliary functions.
Author: Thorn Thaler <thorn.thaler@thothal.com>
Maintainer: Thorn Thaler <thorn.thaler@thothal.com>

Diff between ttutils versions 1.0-1 dated 2010-06-20 and 1.0-1.1 dated 2022-04-04

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Package lassoshooting updated to version 0.1.5-1.1 with previous version 0.1.5-1 dated 2012-05-02

Title: L1 Regularized Regression (Lasso) Solver using the Cyclic Coordinate Descent Algorithm aka Lasso Shooting
Description: L1 regularized regression (Lasso) solver using the Cyclic Coordinate Descent algorithm aka Lasso Shooting is fast. This implementation can choose which coefficients to penalize. It support coefficient-specific penalties and it can take X'X and X'y instead of X and y.
Author: Tobias Abenius <Tobias.Abenius@Chalmers.se>
Maintainer: Tobias Abenius <Tobias.Abenius@Chalmers.se>

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Package stppSim updated to version 1.2.3 with previous version 1.2.1 dated 2022-03-27

Title: Spatiotemporal Point Patterns Simulation
Description: Generates artificial spatiotemporal (ST) point patterns through the integration of microsimulation (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) and agent-based models (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>). Allows a user to define the behaviours of a set of 'walkers' (agents, objects, persons, etc.) whose interactions with the spatial (landscape) (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>) and the temporal domains produce new point events. The resulting ST patterns from the point cloud can be measured and utilized for spatial and/or temporal model testings and evaluations. Application: With increasingly limited availability of fine-grained spatially and temporally stamped point data, the package provides an alternative source of data for a wide range of research in social and life sciences.
Author: Monsuru Adepeju [cre, aut]
Maintainer: Monsuru Adepeju <monsuur2010@yahoo.com>

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Package EventWinRatios updated to version 1.0.0 with previous version 0.1.0 dated 2021-10-03

Title: Event-Specific Win Ratios for Terminal and Non-Terminal Events
Description: Provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets.
Author: Daewoo Pak [aut, cre], Song Yang [aut]
Maintainer: Daewoo Pak <dpak@yonsei.ac.kr>

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Package dataframeexplorer updated to version 1.0.2 with previous version 1.0.0 dated 2020-11-10

Title: Familiarity with Dataframes Before Data Manipulation
Description: Real life data is muddy, fuzzy and unpredictable. This makes data manipulations tedious and bringing the data to right shape alone is a major chunk of work. Functions in this package help us get an understanding of dataframes to dramatically reduces data coding time.
Author: Ashrith Reddy [aut, cre]
Maintainer: Ashrith Reddy <ashrithssreddy@gmail.com>

Diff between dataframeexplorer versions 1.0.0 dated 2020-11-10 and 1.0.2 dated 2022-04-04

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Package wBoot (with last version 1.0.3) was removed from CRAN

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2016-01-27 1.0.3
2015-09-22 1.0.1

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Package reliaR (with last version 0.01) was removed from CRAN

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2015-06-21 0.1.4
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2014-08-28 0.1.2
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Package kzft (with last version 0.17) was removed from CRAN

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2007-09-24 0.17
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Package orQA (with last version 0.2.1) was removed from CRAN

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2010-12-16 0.2.1
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Package HYRISK (with last version 1.2) was removed from CRAN

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2017-04-04 1.2

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2021-02-17 0.0.2
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Package EMC (with last version 1.3) was removed from CRAN

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2011-12-11 1.3

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2019-06-02 1.5.0.2
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2016-09-23 1.0.1.5
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Package scrapeR (with last version 0.1.6) was removed from CRAN

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2010-01-22 0.1.4
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Package vortexR (with last version 1.1.7) was removed from CRAN

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2020-04-10 1.1.7
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2018-05-14 1.1.5
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2022-02-17 0.1.0

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Package hydroroute updated to version 0.1.1 with previous version 0.1.0 dated 2022-03-23

Title: Trace Longitudinal Hydropeaking Waves
Description: Implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022, accepted with minor revisions). The identification of associated events is based on the event detection implemented in 'hydropeak'.
Author: Bettina Gruen [cre, ctb] , Julia Haider [aut], Franz Greimel [ctb]
Maintainer: Bettina Gruen <Bettina.Gruen@R-project.org>

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Package iheiddown (with last version 0.8.6) was removed from CRAN

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2021-11-17 0.8.6
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2021-09-21 3.1.6
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Package PRSim (with last version 1.4-1) was removed from CRAN

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Package RFCCA (with last version 1.0.7) was removed from CRAN

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2021-11-25 1.0.7
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Package causalCmprsk (with last version 1.0.2) was removed from CRAN

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Package dmacs (with last version 0.1.0) was removed from CRAN

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Package Data2LD (with last version 3.2.1) was removed from CRAN

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

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Package ffbase (with last version 0.13.3) was removed from CRAN

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2020-03-18 0.12.8
2018-11-06 0.12.7
2018-09-24 0.12.5
2016-03-22 0.12.3
2015-06-05 0.12.1
2015-05-25 0.12.0
2014-03-08 0.11.3
2014-01-18 0.11.1
2014-01-17 0.11
2013-11-18 0.10.1
2013-07-02 0.9
2013-04-15 0.8
2013-01-02 0.7
2012-12-04 0.6-3
2012-11-26 0.6-2
2012-11-08 0.6-1
2012-06-04 0.5
2012-04-28 0.4-1
2012-04-27 0.4
2011-07-25 0.2-1
2011-04-07 0.2

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Package MRPC (with last version 3.0.0) was removed from CRAN

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

2021-12-16 3.0.0
2021-05-02 2.3.1
2021-03-14 2.3.0
2021-01-24 2.2.2
2020-10-06 2.2.1
2019-11-16 2.2.0
2019-05-09 2.1.0
2018-10-13 2.0.0
2018-08-14 1.0.0

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

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

2016-10-22 1.0.0
2016-06-13 0.1.0

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Package drtmle (with last version 1.1.0) was removed from CRAN

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

2021-06-02 1.1.0
2020-01-09 1.0.5
2018-12-18 1.0.4
2018-07-02 1.0.3
2018-02-05 1.0.2

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Package SOPC updated to version 0.0.2 with previous version 0.0.1 dated 2021-09-17

Title: The Sparse Online Principal Component Estimation Algorithm
Description: The sparse online principal component can not only process the real-time updated data set and stream data set, but also obtain the sparse solution of the updated data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>.
Author: Guangbao Guo [aut, cre], Chunjie Wei [aut], Guoqi Qian [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>

Diff between SOPC versions 0.0.1 dated 2021-09-17 and 0.0.2 dated 2022-04-04

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More information about SOPC at CRAN
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Package revss updated to version 1.0.4 with previous version 1.0.3 dated 2022-01-19

Title: Robust Estimation in Very Small Samples
Description: Implements the estimation techniques described in Rousseeuw & Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for the location and scale of very small samples.
Author: Avraham Adler [aut, cph, cre]
Maintainer: Avraham Adler <Avraham.Adler@gmail.com>

Diff between revss versions 1.0.3 dated 2022-01-19 and 1.0.4 dated 2022-04-04

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 10 files changed, 82 insertions(+), 38 deletions(-)

More information about revss at CRAN
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Package Rsagacmd updated to version 0.2.0 with previous version 0.1.2 dated 2021-12-04

Title: Linking R with the Open-Source 'SAGA-GIS' Software
Description: Provides an R scripting interface to the open-source 'SAGA-GIS' (System for Automated Geoscientific Analyses Geographical Information System) software. 'Rsagacmd' dynamically generates R functions for every 'SAGA-GIS' geoprocessing tool based on the user's currently installed 'SAGA-GIS' version. These functions are contained within an S3 object and are accessed as a named list of libraries and tools. This structure facilitates an easier scripting experience by organizing the large number of 'SAGA-GIS' geoprocessing tools (>700) by their respective library. Interactive scripting can fully take advantage of code autocompletion tools (e.g. in 'Rstudio'), allowing for each tools syntax to be quickly recognized. Furthermore, the most common types of spatial data (via the 'raster', 'terra', 'sp', and 'sf' packages) along with non-spatial data are automatically passed from R to the 'SAGA-GIS' command line tool for geoprocessing operations, and the results are loaded as the appropriate R object. Outputs from individual 'SAGA-GIS' tools can also be chained using pipes from the 'magrittr' and 'dplyr' packages to combine complex geoprocessing operations together in a single statement. 'SAGA-GIS' is available under a GPLv2 / LGPLv2 licence from <https://sourceforge.net/projects/saga-gis/> including Windows x86/x64 binaries. SAGA-GIS is also included in Debian/Ubuntu default software repositories and is available for macOS using homebrew (<https://brew.sh/>) from the osgeo/osgeo4mac (<https://github.com/OSGeo/homebrew-osgeo4mac>) formula tap, as well as being bundled within the 'QGIS' application bundle for macOS. Rsagacmd has currently been tested on 'SAGA-GIS' versions from 2.3.1 to 8.0.1 on Windows, Linux and macOS.
Author: Steven Pawley [aut, cre]
Maintainer: Steven Pawley <dr.stevenpawley@gmail.com>

Diff between Rsagacmd versions 0.1.2 dated 2021-12-04 and 0.2.0 dated 2022-04-04

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 98 files changed, 576 insertions(+), 296 deletions(-)

More information about Rsagacmd at CRAN
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