Title: Augments 'ASReml-R' in Fitting Mixed Models and Packages
Generally in Exploring Prediction Differences
Description: Assists in automating the selection of terms to include in mixed models when
'asreml' is used to fit the models. Also used to display, in tables and graphs, predictions
obtained using any model fitting function and to explore differences between predictions.
The content falls into the following natural groupings: (i) Data, (ii) Object
manipulation functions, (iii) Model modification functions, (iv) Model testing functions,
(v) Model diagnostics functions, (vi) Prediction production and presentation functions,
(vii) Response transformation functions, and (viii) Miscellaneous functions (for further
details see 'asremlPlus-package' in help). A history of
the fitting of a sequence of models is kept in a data frame. Procedures are available for
choosing models that conform to the hierarchy or marginality principle and for displaying
predictions for significant terms in tables and graphs. The 'asreml' package provides a
computationally efficient algorithm for fitting mixed models using Residual Maximum
Likelihood. It is a commercial package that can be purchased from
'VSNi' <http://www.vsni.co.uk/> as 'asreml-R', who will supply a zip file for local
installation/updating (see <http://asreml.org/>). It is not needed for functions that are
methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be
installed from <http://chris.brien.name/rpackages/>.
Author: Chris Brien [aut, cre] (<https://orcid.org/0000-0003-0581-1817>)
Maintainer: Chris Brien <chris.brien@adelaide.edu.au>
Diff between asremlPlus versions 4.2-18 dated 2020-03-16 and 4.2-21 dated 2020-07-11
DESCRIPTION | 12 +-- MD5 | 42 +++++------ R/REMLRTIC.v3.r | 16 ++-- R/asremlPlusUtilities.r | 66 +++++++++++++----- R/reml4.v8.r | 130 +++++++++++++++++++------------------ build/partial.rdb |binary build/vignette.rds |binary inst/NEWS.Rd | 25 +++++-- inst/doc/Ladybird.asreml.pdf |binary inst/doc/Ladybird.lm.pdf |binary inst/doc/Wheat.analysis.pdf |binary inst/doc/Wheat.infoCriteria.pdf |binary inst/doc/asremlPlus-manual.pdf |binary man/addBacktransforms.alldiffs.Rd | 18 ++--- man/getFormulae.asreml.Rd | 5 + man/plotPvalues.alldiffs.Rd | 8 +- man/printFormulae.asreml.Rd | 5 + man/redoErrorIntervals.alldiffs.Rd | 2 man/sort.alldiffs.Rd | 10 +- tests/testthat/test3REMLRTIC.r | 68 +++++++++++++------ tests/testthat/test4REMLRTIC.r | 83 ++++++++++++++++------- tests/testthat/test4Selection.r | 11 +-- 22 files changed, 309 insertions(+), 192 deletions(-)
Title: Bayesian Package for Network Changepoint Analysis
Description: Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn 2020). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
Author: Jong Hee Park [aut,cre], Yunkyu Sohn [aut]
Maintainer: Jong Hee Park <jongheepark@snu.ac.kr>
Diff between NetworkChange versions 0.6 dated 2020-02-06 and 0.7 dated 2020-07-11
DESCRIPTION | 14 +++++--- MD5 | 66 +++++++++++++++++++++++------------------- R/BreakDiagnostic.R | 3 - R/BreakPointLoss.R | 4 +- R/MarginalCompare.R | 4 +- R/NetworkChange.r | 4 +- R/NetworkChangeRobust.r | 4 +- R/NetworkStatic.R | 4 +- R/WaicCompare.R | 3 + R/drawpostanalysis.R | 2 - R/drawregimeraw.R | 6 +-- R/plotU.R | 10 ++---- R/plotnetarray.R | 4 +- build |only inst |only man/BreakDiagnostic.Rd | 19 +++++++++--- man/BreakPointLoss.Rd | 4 +- man/MajorAlly.Rd | 6 ++- man/MakeBlockNetworkChange.Rd | 14 +++++++- man/MarginalCompare.Rd | 4 +- man/NetworkChange.Rd | 48 ++++++++++++++++++++++-------- man/NetworkChangeRobust.Rd | 37 ++++++++++++++++++----- man/NetworkStatic.Rd | 47 +++++++++++++++++++++-------- man/PostwarAlly.Rd | 6 ++- man/WaicCompare.Rd | 3 + man/drawPostAnalysis.Rd | 12 +++++-- man/drawRegimeRaw.Rd | 4 +- man/plotContour.Rd | 7 +--- man/plotU.Rd | 17 +++------- man/plotV.Rd | 4 +- man/plotnetarray.Rd | 15 +++++++-- man/updateS.Rd | 16 ++++++++-- vignettes |only 33 files changed, 254 insertions(+), 137 deletions(-)
Title: Neat and Painless Statistical Reporting
Description: User-friendly, clear and simple statistics, primarily for
publication in psychological science. The main functions are wrappers for
other packages, but there are various additions as well. Every relevant step
from data aggregation to reportable printed statistics is covered for basic
experimental designs.
Author: Gáspár Lukács [aut, cre] (<https://orcid.org/0000-0001-9401-4830>),
Bennett Kleinberg [ctb] (<https://orcid.org/0000-0003-1658-9086>),
Johnny van Doorn [ctb] (<https://orcid.org/0000-0003-0270-096X>)
Maintainer: Gáspár Lukács <lkcsgaspar@gmail.com>
Diff between neatStats versions 1.2.0 dated 2020-05-10 and 1.4.2 dated 2020-07-11
DESCRIPTION | 17 - MD5 | 38 +-- NAMESPACE | 1 R/aggr_neat.R | 4 R/anova_neat.R | 8 R/corr_neat.R | 111 +++++++-- R/dems_neat.R | 30 +- R/excl_neat.R |only R/internal.R | 566 ++++++++++++++++++++++++++++++++++++++++++++++- R/ro.R | 14 - R/t_neat.R | 214 +++++++++++++++-- R/table_neat.R | 30 +- README.md | 14 - build/partial.rdb |binary man/anova_neat.Rd | 4 man/corr_neat.Rd | 31 +- man/dems_neat.Rd | 8 man/excl_neat.Rd |only man/neatStats-package.Rd | 1 man/ro.Rd | 5 man/t_neat.Rd | 53 +++- 21 files changed, 1017 insertions(+), 132 deletions(-)
Title: Simulated Maximum Likelihood Estimation of Mixed Logit Models
for Large Datasets
Description: Specification and estimation of multinomial logit
models. Large datasets and complex models are supported, with an
intuitive syntax. Multinomial Logit Models, Mixed models, random
coefficients and Hybrid Choice are all supported. For more
information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.
Author: Joseph Molloy [aut, cre]
Maintainer: Joseph Molloy <joseph.molloy@ivt.baug.ethz.ch>
Diff between mixl versions 1.2.0 dated 2020-07-10 and 1.2.1 dated 2020-07-11
DESCRIPTION | 8 MD5 | 99 - R/compile_posterior.R | 184 +-- R/compiler.R | 198 +-- R/examples/av_matrix.R | 10 R/examples/extract_av_cols.R | 10 R/examples/extract_indiv_data.R | 10 R/examples/generate_default_availabilities.R | 6 R/examples/model_stats.R | 40 R/examples/posteriors.R | 54 R/examples/probabilities.R | 50 R/examples/specify_model.R | 38 R/helper_functions.R | 138 +- R/mle.R | 344 ++--- R/model_stats.R | 358 ++--- R/preprocessor.R | 334 ++--- R/probabilities.R | 210 +-- R/tex.R | 188 +-- build/vignette.rds |binary inst/MIXL_OPENMP_FLAG |only inst/doc/user-guide.R | 12 inst/doc/user-guide.Rmd | 280 ++-- inst/doc/user-guide.html | 736 +++++------- inst/include/MIXL_OPENMP_FLAG | 1 man/av_matrix.Rd | 54 man/check_inputs.Rd | 62 - man/compileUtilityFunction.Rd | 32 man/create_halton_draws.Rd | 52 man/estimate.Rd | 156 +- man/extract_av_cols.Rd | 54 man/extract_indiv_data.Rd | 54 man/generate_default_availabilities.Rd | 50 man/mixl-package.Rd | 108 - man/posteriors.Rd | 102 - man/print.mixl.Rd | 80 - man/print.summary.mixl.Rd | 78 - man/probabilities.Rd | 126 +- man/specify_model.Rd | 124 +- man/summary.mixl.Rd | 84 - man/summary_tex.Rd | 34 src/Makefile | 2 tests/testthat.R | 10 tests/testthat/23_wtp_pooled_hybrid_ol_dalynorm_utilities.R | 200 +-- tests/testthat/test_hybrid_choice.R | 266 ++-- tests/testthat/test_maxlikelihood.R | 274 ++-- tests/testthat/test_posteriors.R | 374 +++--- tests/testthat/test_prediction.R | 212 +-- tests/testthat/test_variable_identification.R | 248 ++-- tests/testthat/test_variable_replacement.R | 158 +- tests/testthat/test_variable_validation.R | 114 - vignettes/user-guide.Rmd | 280 ++-- 51 files changed, 3340 insertions(+), 3356 deletions(-)
Title: Simulation of Chromosomal Regions Shared by Family Members
Description: Simulation of segments shared identical-by-descent (IBD) by
pedigree members. Using sex specific recombination rates along the human
genome (Halldorsson et al. (2019) <doi:10.1126/science.aau1043>), phased chromosomes
are simulated for all pedigree members. Additional features include calculation
of realised IBD coefficients and plots of IBD segment distributions.
Author: Magnus Dehli Vigeland [aut, cre]
(<https://orcid.org/0000-0002-9134-4962>)
Maintainer: Magnus Dehli Vigeland <m.d.vigeland@medisin.uio.no>
Diff between ibdsim2 versions 1.1 dated 2020-06-28 and 1.2 dated 2020-07-11
DESCRIPTION | 8 MD5 | 66 +-- NAMESPACE | 11 NEWS.md | 23 + R/estimateCoeffs.R | 281 ++++++++----- R/genedrop.R | 3 R/haploDraw.R |only R/ibdsim.R | 91 ++-- R/ibdsim2-package.R | 18 R/map_utils.R | 460 +++++++++++++++++----- R/meiosis.R | 2 R/plotSegmentDistribution.R | 43 +- R/profileSimIBD.R | 78 ++- R/realisedCoeffs.R | 120 ++++- R/segment_utils.R | 26 + R/sysdata.rda |binary R/utils.R | 28 - README.md | 25 - build/partial.rdb |binary man/customMap.Rd |only man/estimateCoeffs.Rd | 126 +++--- man/figures/README-ibdsim2-example-distplot-1.png |binary man/haploDraw.Rd |only man/ibdsim.Rd | 42 -- man/ibdsim2.Rd | 14 man/loadMap.Rd |only man/plotSegmentDistribution.Rd | 30 - man/profileSimIBD.Rd | 50 +- man/realised.Rd | 50 +- man/uniformMap.Rd | 10 src/ibdsim2.cpp | 55 +- tests/testthat/test-ibdsim.R | 2 tests/testthat/test-inbred-founders.R | 4 tests/testthat/test-maps.R |only tests/testthat/test-meisosis.R | 6 tests/testthat/test-plotSegmentDistribution.R | 4 tests/testthat/test-segments.R |only 37 files changed, 1128 insertions(+), 548 deletions(-)
Title: Gaussian Graphical Models with Non-Convex Penalties
Description: Estimate Gaussian graphical models with non-convex penalties, including
the atan Wang and Zhu (2016) <doi:10.1155/2016/6495417>,
seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>,
exponential Wang, Fan, and Zhu <doi:10.1007/s10463-016-0588-3>,
smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>,
logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>,
Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>,
and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions
for computing variable inclusion probabilities, multiple regression coefficients, and
statistical inference <doi:10.1214/15-EJS1031>.
Author: Donald Williams [aut, cre]
Maintainer: Donald Williams <drwwilliams@ucdavis.edu>
Diff between GGMncv versions 1.0.0 dated 2020-07-06 and 1.1.0 dated 2020-07-11
DESCRIPTION | 16 - MD5 | 41 ++-- NAMESPACE | 16 + R/coef.ggmncv.R | 11 - R/datasets.R | 24 ++ R/desparsify.R |only R/ggm_compare.R |only R/ggmncv.R | 289 +++++++++++++++++++++++------ R/helpers.R | 171 ++++++++++++++++- R/htf.R | 6 R/inference.R |only R/predict.R |only README.md | 437 +++++++++++++++++++++++++++++++++++++++------ build/partial.rdb |binary data/Sachs.rda |only inst/REFERENCES.bib | 88 +++++++++ man/GGMncv.Rd | 365 ++++++++++++++++++++----------------- man/Sachs.Rd |only man/coef.ggmncv.Rd | 4 man/constrained.Rd | 2 man/desparsify.Rd |only man/figures/atan_path.png |only man/figures/lasso_path.png |only man/figures/pen_func.png |only man/ggm_compare.Rd |only man/inference.Rd |only man/plot.ggmncv.Rd | 18 + man/predict.ggmncv.Rd |only 28 files changed, 1168 insertions(+), 320 deletions(-)
Title: Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
Description: Text mining for word processing and sentiment analysis using
'dplyr', 'ggplot2', and other tidy tools.
Author: Gabriela De Queiroz [ctb],
Colin Fay [ctb] (<https://orcid.org/0000-0001-7343-1846>),
Emil Hvitfeldt [ctb],
Os Keyes [ctb] (<https://orcid.org/0000-0001-5196-609X>),
Kanishka Misra [ctb],
Tim Mastny [ctb],
Jeff Erickson [ctb],
David Robinson [aut],
Julia Silge [aut, cre] (<https://orcid.org/0000-0002-3671-836X>)
Maintainer: Julia Silge <julia.silge@gmail.com>
Diff between tidytext versions 0.2.4 dated 2020-04-17 and 0.2.5 dated 2020-07-11
DESCRIPTION | 14 ++-- MD5 | 39 ++++++------ NAMESPACE | 1 NEWS.md | 7 ++ R/corpus_tidiers.R | 2 R/lda_tidiers.R | 2 R/reorder_within.R | 17 ++++- R/stm_tidiers.R | 20 +++++- R/unnest_tokens.R | 2 README.md | 19 ++--- build/vignette.rds |binary inst/doc/tf_idf.html | 20 +++++- inst/doc/tidying_casting.html | 20 +++++- inst/doc/tidytext.html | 24 ++++++- inst/doc/topic_modeling.html | 20 +++++- man/reorder_within.Rd | 11 +++ man/stm_tidiers.Rd | 11 +++ tests/figs/reorder-within/reordered-multi-facet-boxplot.svg |only tests/testthat/test-reorder-within.R | 19 +++++ tests/testthat/test-stm-tidiers.R | 12 +++ tests/testthat/test-unnest-tokens.R | 4 - 21 files changed, 209 insertions(+), 55 deletions(-)
Title: Explaining and Visualizing Random Forests in Terms of Variable
Importance
Description: A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).
Author: Aleksandra Paluszynska [aut],
Przemyslaw Biecek [aut, ths],
Yue Jiang [aut, cre] (<https://orcid.org/0000-0002-9798-5517>)
Maintainer: Yue Jiang <rivehill@gmail.com>
Diff between randomForestExplainer versions 0.10.0 dated 2019-09-18 and 0.10.1 dated 2020-07-11
DESCRIPTION | 8 +-- MD5 | 39 ++++++++-------- R/explain_forest.R | 13 +++-- README.md | 18 ++----- build/vignette.rds |binary inst/doc/randomForestExplainer.R | 38 +++++++-------- inst/doc/randomForestExplainer.Rmd | 6 +- inst/doc/randomForestExplainer.html | 27 ++++------- inst/templates/Explain_forest_template.Rmd | 4 - inst/templates/Explain_forest_template_interactions.Rmd | 2 man/explain_forest.Rd | 17 +++++- man/figures |only man/important_variables.Rd | 7 ++ man/min_depth_interactions.Rd | 7 ++ man/plot_importance_ggpairs.Rd | 7 ++ man/plot_importance_rankings.Rd | 7 ++ man/plot_min_depth_distribution.Rd | 12 +++- man/plot_min_depth_interactions.Rd | 8 ++- man/plot_multi_way_importance.Rd | 13 +++-- man/plot_predict_interaction.Rd | 13 ++++- vignettes/randomForestExplainer.Rmd | 6 +- 21 files changed, 145 insertions(+), 107 deletions(-)
More information about randomForestExplainer at CRAN
Permanent link
Title: Exploratory Data Analysis System
Description: Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.
Author: Oldemar Rodriguez R. with contributions from Diego Jimenez A. and Andres Navarro D.
Maintainer: Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr>
Diff between discoveR versions 1.2.7 dated 2020-06-26 and 1.2.8 dated 2020-07-11
DESCRIPTION | 6 MD5 | 27 NAMESPACE | 28 R/init_discover.R | 40 inst/application/biblioteca.R | 3660 +++++++++++++++---------------- inst/application/global.R | 2564 ++++++++++----------- inst/application/server.R | 2364 +++++++++----------- inst/application/ui.R | 1387 +++++------ inst/application/www/crear_diccionario.R |only inst/application/www/diccionario.csv | 332 +- inst/application/www/myscript.js | 274 +- inst/application/www/style_promidat.css | 976 ++++---- inst/application/www/translation.bin |binary inst/rstudio/addins.dcf | 8 man/init_discover.Rd | 54 15 files changed, 5827 insertions(+), 5893 deletions(-)
Title: Historical Retail Data from the 'Trundler' API
Description: A wrapper around the 'Trundler' API, which gives access to
historical retail product and pricing data, and can be found at
<https://api.trundler.dev/>.
Author: Andrew B. Collier [aut, cre],
Megan Beckett [aut, pbl]
Maintainer: Andrew B. Collier <andrew@exegetic.biz>
Diff between trundler versions 0.1.14 dated 2020-07-05 and 0.1.15 dated 2020-07-11
DESCRIPTION | 12 ++++++------ MD5 | 6 +++--- R/product.R | 24 ++++++++++++++++++------ tests/testthat/setup-files.R | 2 +- 4 files changed, 28 insertions(+), 16 deletions(-)
Title: Imputation of Financial Time Series with Missing Values and/or
Outliers
Description: Missing values often occur in financial data due to a variety
of reasons (errors in the collection process or in the processing stage,
lack of asset liquidity, lack of reporting of funds, etc.). However,
most data analysis methods expect complete data and cannot be employed
with missing values. One convenient way to deal with this issue without
having to redesign the data analysis method is to impute the missing
values. This package provides an efficient way to impute the missing
values based on modeling the time series with a random walk or an
autoregressive (AR) model, convenient to model log-prices and log-volumes
in financial data. In the current version, the imputation is
univariate-based (so no asset correlation is used). In addition,
outliers can be detected and removed.
The package is based on the paper:
J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of
Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on
Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.
Author: Daniel P. Palomar [cre, aut],
Junyan Liu [aut]
Maintainer: Daniel P. Palomar <daniel.p.palomar@gmail.com>
Diff between imputeFin versions 0.1.0 dated 2019-12-13 and 0.1.1 dated 2020-07-11
imputeFin-0.1.0/imputeFin/tests/testthat/Gaussian_data.RData |only imputeFin-0.1.0/imputeFin/tests/testthat/t_data.RData |only imputeFin-0.1.1/imputeFin/DESCRIPTION | 32 imputeFin-0.1.1/imputeFin/MD5 | 76 - imputeFin-0.1.1/imputeFin/NEWS.md | 13 imputeFin-0.1.1/imputeFin/R/estimate_impute_AR1_Gaussian.R | 436 +++++----- imputeFin-0.1.1/imputeFin/R/estimate_impute_AR1_t.R | 429 +++++---- imputeFin-0.1.1/imputeFin/R/imputeFin-package.R | 6 imputeFin-0.1.1/imputeFin/R/misc.R |only imputeFin-0.1.1/imputeFin/R/outliers.R |only imputeFin-0.1.1/imputeFin/R/plot_imputed.R | 88 +- imputeFin-0.1.1/imputeFin/R/ts_AR1_Gaussian.R | 4 imputeFin-0.1.1/imputeFin/R/ts_AR1_t.R | 6 imputeFin-0.1.1/imputeFin/README.md | 110 +- imputeFin-0.1.1/imputeFin/build/vignette.rds |binary imputeFin-0.1.1/imputeFin/data/ts_AR1_Gaussian.RData |binary imputeFin-0.1.1/imputeFin/data/ts_AR1_t.RData |binary imputeFin-0.1.1/imputeFin/inst/CITATION | 4 imputeFin-0.1.1/imputeFin/inst/doc/ImputeFinancialTimeSeries.html | 434 ++++++--- imputeFin-0.1.1/imputeFin/man/figures/README-unnamed-chunk-6-1.png |binary imputeFin-0.1.1/imputeFin/man/figures/README-unnamed-chunk-6-2.png |only imputeFin-0.1.1/imputeFin/man/figures/README-unnamed-chunk-7-1.png |only imputeFin-0.1.1/imputeFin/man/figures/README-unnamed-chunk-7-2.png |only imputeFin-0.1.1/imputeFin/man/fit_AR1_Gaussian.Rd | 31 imputeFin-0.1.1/imputeFin/man/fit_AR1_t.Rd | 24 imputeFin-0.1.1/imputeFin/man/imputeFin-package.Rd | 6 imputeFin-0.1.1/imputeFin/man/impute_AR1_Gaussian.Rd | 35 imputeFin-0.1.1/imputeFin/man/impute_AR1_t.Rd | 35 imputeFin-0.1.1/imputeFin/man/plot_imputed.Rd | 14 imputeFin-0.1.1/imputeFin/man/ts_AR1_Gaussian.Rd | 10 imputeFin-0.1.1/imputeFin/man/ts_AR1_t.Rd | 12 imputeFin-0.1.1/imputeFin/tests/testthat/estimation_Gaussian_check.RData |binary imputeFin-0.1.1/imputeFin/tests/testthat/estimation_Gaussian_random_walk_check.RData |binary imputeFin-0.1.1/imputeFin/tests/testthat/estimation_Gaussian_zero_mean_check.RData |binary imputeFin-0.1.1/imputeFin/tests/testthat/estimation_t_check.RData |binary imputeFin-0.1.1/imputeFin/tests/testthat/estimation_t_random_walk_check.RData |binary imputeFin-0.1.1/imputeFin/tests/testthat/estimation_t_zero_mean_check.RData |binary imputeFin-0.1.1/imputeFin/tests/testthat/test-condMeanCov.R | 12 imputeFin-0.1.1/imputeFin/tests/testthat/test-fit_AR1_Gaussian.R | 106 +- imputeFin-0.1.1/imputeFin/tests/testthat/test-fit_AR1_t.R | 120 +- imputeFin-0.1.1/imputeFin/tests/testthat/test-impute_AR1_Gaussian.R | 42 imputeFin-0.1.1/imputeFin/tests/testthat/test-impute_AR1_t.R | 43 imputeFin-0.1.1/imputeFin/tests/testthat/test-misc.R |only 43 files changed, 1281 insertions(+), 847 deletions(-)
Title: Random Projection with Classification
Description: Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.
Author: Aghila G, Siddharth R
Maintainer: Siddharth R <r.siddharthcse@gmail.com>
Diff between RandPro versions 0.2.0 dated 2018-01-10 and 0.2.1 dated 2020-07-11
DESCRIPTION | 8 ++++---- MD5 | 15 ++++++++------- R/classify.R | 2 +- R/dimension.R | 2 +- R/form_matrix.R | 4 ++-- README.md |only man/classify.Rd | 20 +++++++++++++------- man/dimension.Rd | 12 +++--------- man/form_matrix.Rd | 15 +++++++-------- 9 files changed, 39 insertions(+), 39 deletions(-)
Title: Data Import, Cleaning, and Conversions for Swimming Results
Description: There are two goals for 'SwimmeR' as presently constructed. The first is reading in swimming results from html or pdf sources and returning tidy dataframes. The second is working with the resulting data. To this end 'SwimmeR' converts swimming times (performances) between the computationally useful
format of seconds, reported to the 100ths place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community, as well as providing tools for assigning team names etc.
Additionally 'SwimmeR' has functions for drawing single-elimination brackets and also converts times between the various pool sizes used in competitive swimming, namely 50m length (LCM), 25m length (SCM)
and 25y length (SCY).
Author: Greg Pilgrim [aut, cre] (<https://orcid.org/0000-0001-7831-442X>),
Caitlin Baldwin [ctb]
Maintainer: Greg Pilgrim <gpilgrim2670@gmail.com>
Diff between SwimmeR versions 0.3.0 dated 2020-07-10 and 0.3.1 dated 2020-07-11
DESCRIPTION | 10 LICENSE | 4 MD5 | 86 NAMESPACE | 90 NEWS.md | 29 R/Course_Convert.R | 182 R/Course_Convert_DF.R | 208 R/King200Breast.R | 28 R/Read_Results.R | 106 R/SwimR.R | 12 R/Swim_Parse.R | 2237 ++++----- R/draw_bracket.R | 632 +- R/fold.R | 60 R/get_mode.R | 92 R/globals.R | 46 R/mmss_format.R | 52 R/relay_aggregate.R | 18 R/sec_format.R | 56 R/sec_format_helper.R | 56 R/test_sets.R | 4 R/utils-pipe.R | 22 README.md | 224 build/vignette.rds |binary inst/doc/SwimmeR.R | 186 inst/doc/SwimmeR.Rmd | 330 - inst/doc/SwimmeR.html | 1020 ++-- inst/extdata/Swim_Parse_Testing.R | 2264 ++++----- inst/extdata/df_test.csv | 7688 +++++++++++++++---------------- inst/extdata/larger_Swim_Parse_test.R | 104 man/King200Breast.Rd | 38 man/Read_Results.Rd | 64 man/Swim_Parse.Rd | 104 man/SwimmeR.Rd | 32 man/get_mode.Rd | 82 man/mmss_format.Rd | 60 man/sec_format.Rd | 4 man/sec_format_helper.Rd | 34 tests/testthat.R | 8 tests/testthat/test-Course_Convert.R | 6 tests/testthat/test-Course_Convert_DF.R | 22 tests/testthat/test-Read_Results_works.R | 38 tests/testthat/test-Swim_Parse_works.R | 248 - tests/testthat/test-get_mode.R | 20 vignettes/SwimmeR.Rmd | 330 - 44 files changed, 8459 insertions(+), 8477 deletions(-)
Previous versions (as known to CRANberries) which should be available via the Archive link are:
2020-04-17 0.3.4
2020-02-05 0.3.3