Title: Extra String Manipulation Functions
Description: There are some things that I wish were easier with the
'stringr' or 'stringi' packages. The foremost of these is the
extraction of numbers from strings. 'stringr' and 'stringi' make you
figure out the regular expression for yourself; 'strex' takes care of
this for you. There are many other handy functionalities in 'strex'.
Contributions to this package are encouraged: it is intended as a
miscellany of string manipulation functions that cannot be found in
'stringi' or 'stringr'.
Author: Rory Nolan [aut, cre]
Maintainer: Rory Nolan <rorynoolan@gmail.com>
Diff between strex versions 1.4.2 dated 2021-04-18 and 1.4.3 dated 2022-07-24
DESCRIPTION | 19 - MD5 | 22 - NEWS.md | 6 build/partial.rdb |binary build/vignette.rds |binary inst/WORDLIST | 39 --- inst/doc/alphordering-numbers.html | 114 +++++--- inst/doc/argument-matching.html | 196 ++++++++------- inst/doc/before-and-after.html | 135 ++++++---- inst/doc/important-miscellany.html | 240 ++++++++++-------- inst/doc/numbers-in-strings.html | 478 +++++++++++++++++++------------------ man/str_nth_number_before_mth.Rd | 7 12 files changed, 692 insertions(+), 564 deletions(-)
Title: Random Walk Clustering on Weighted Graphs
Description: Implements the random walk clustering algorithm for weighted graphs as found in Harel and Koren (2001) <https://link.springer.com/chapter/10.1007/3-540-45294-X_3>.
Author: Carson Sprock [aut, cre]
Maintainer: Carson Sprock <csprock@gmail.com>
Diff between Rwclust versions 0.0.1 dated 2022-05-17 and 0.1.0 dated 2022-07-24
Rwclust-0.0.1/Rwclust/man/rwclust_.Rd |only Rwclust-0.1.0/Rwclust/DESCRIPTION | 8 +- Rwclust-0.1.0/Rwclust/MD5 | 34 +++++--- Rwclust-0.1.0/Rwclust/NAMESPACE | 13 +++ Rwclust-0.1.0/Rwclust/NEWS.md | 7 + Rwclust-0.1.0/Rwclust/R/matrix_functions.R | 4 - Rwclust-0.1.0/Rwclust/R/metrics.R | 6 + Rwclust-0.1.0/Rwclust/R/rwclust.R | 76 ++++++++------------ Rwclust-0.1.0/Rwclust/R/user_input.R | 1 Rwclust-0.1.0/Rwclust/R/utils.R |only Rwclust-0.1.0/Rwclust/README.md | 13 +++ Rwclust-0.1.0/Rwclust/inst/doc/basic_usage.R | 12 +-- Rwclust-0.1.0/Rwclust/inst/doc/basic_usage.Rmd | 12 +-- Rwclust-0.1.0/Rwclust/inst/doc/basic_usage.html | 19 ++--- Rwclust-0.1.0/Rwclust/man/adjacency.Rd |only Rwclust-0.1.0/Rwclust/man/new_rwclust.Rd |only Rwclust-0.1.0/Rwclust/man/plot.rwclust.Rd |only Rwclust-0.1.0/Rwclust/man/rwclust.Rd | 14 ++- Rwclust-0.1.0/Rwclust/tests/testthat/test-rwclust.R | 3 Rwclust-0.1.0/Rwclust/tests/testthat/test-utils.R |only Rwclust-0.1.0/Rwclust/vignettes/basic_usage.Rmd | 12 +-- 21 files changed, 125 insertions(+), 109 deletions(-)
Title: Retrieve and Analyze Clinical Trials in Public Registers
Description: A system for querying, retrieving and analyzing
protocol- and results-related information on clinical trials from
three public registers, the 'European Union Clinical Trials Register'
('EUCTR', <https://www.clinicaltrialsregister.eu/>),
'ClinicalTrials.gov' ('CTGOV', <https://clinicaltrials.gov/>) and
the 'ISRCTN' (<http://www.isrctn.com/>).
Trial information is downloaded, converted and stored in a database
('PostgreSQL', 'SQLite' or 'MongoDB'; via package 'nodbi').
Functions are included to identify de-duplicated records,
to easily find and extract variables (fields) of interest even
from complex nesting as used by the registers, and
to update previous queries.
The package can be used for meta-analysis and trend-analysis of
the design and conduct as well as for results of clinical trials.
Author: Ralf Herold [aut, cre]
Maintainer: Ralf Herold <ralf.herold@mailbox.org>
Diff between ctrdata versions 1.10.0 dated 2022-07-06 and 1.10.1 dated 2022-07-24
DESCRIPTION | 8 ++++---- MD5 | 16 ++++++++-------- NEWS.md | 5 +++++ R/ctrdata-registers.R | 4 ++-- README.md | 13 ++++++------- build/vignette.rds |binary inst/tinytest/ctrdata_euctr.R | 2 +- inst/tinytest/test_ctrdata_other_functions.R | 2 +- man/ctrdata-registers.Rd | 4 ++-- 9 files changed, 29 insertions(+), 25 deletions(-)
Title: Artificial Intelligence Systems and Observer Performance
Description: Analyzing the performance of artificial intelligence
(AI) systems/algorithms characterized by a 'search-and-report'
strategy. Historically observer performance has dealt with
measuring radiologists' performances in search tasks, e.g., searching
for lesions in medical images and reporting them, but the implicit
location information has been ignored. The implemented methods apply
to analyzing the absolute and relative performances of AI systems,
comparing AI performance to a group of human readers or optimizing the
reporting threshold of an AI system. In addition to performing historical
receiver operating receiver operating characteristic (ROC) analysis
(localization information ignored), the software also performs
free-response receiver operating characteristic (FROC)
analysis, where lesion localization information is used. A book
using the software has been published: Chakraborty DP: Observer
Performance Methods for Diagnostic Imaging - Foundations, Modeling,
and Applications with R-Based Examples, Taylor-Francis LLC; 2017.
Online updates to this book, which use the software, are at
<https://dpc10ster.github.io/RJafrocQuickStart/>,
<https://dpc10ster.github.io/RJafrocRocBook/> and at
<https://dpc10ster.github.io/RJafrocFrocBook/>. Supported data
collection paradigms are the ROC, FROC and the location ROC (LROC).
ROC data consists of single ratings per images, where a rating is
the perceived confidence level that the image is that of a diseased
patient. An ROC curve is a plot of true positive fraction vs. false
positive fraction. FROC data consists of a variable number (zero or
more) of mark-rating pairs per image, where a mark is the location
of a reported suspicious region and the rating is the confidence
level that it is a real lesion. LROC data consists of a rating and a
location of the most suspicious region, for every image. Four models
of observer performance, and curve-fitting software, are implemented:
the binormal model (BM), the contaminated binormal model (CBM), the
correlated contaminated binormal model (CORCBM), and the radiological
search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM
predict 'proper' ROC curves that do not inappropriately cross the
chance diagonal. Additionally, RSM parameters are related to search
performance (not measured in conventional ROC analysis) and
classification performance. Search performance refers to finding
lesions, i.e., true positives, while simultaneously not finding false
positive locations. Classification performance measures the ability to
distinguish between true and false positive locations. Knowing these
separate performances allows principled optimization of reader or AI
system performance. This package supersedes Windows JAFROC (jackknife
alternative FROC) software V4.2.1,
<https://github.com/dpc10ster/WindowsJafroc>. Package functions are
organized as follows. Data file related function names are preceded
by 'Df', curve fitting functions by 'Fit', included data sets by 'dataset',
plotting functions by 'Plot', significance testing functions by 'St',
sample size related functions by 'Ss', data simulation functions by
'Simulate' and utility functions by 'Util'. Implemented are figures of
merit (FOMs) for quantifying performance and functions for visualizing
empirical or fitted operating characteristics: e.g., ROC, FROC, alternative
FROC (AFROC) and weighted AFROC (wAFROC) curves. For fully crossed study
designs significance testing of reader-averaged FOM differences between
modalities is implemented via either Dorfman-Berbaum-Metz or the
Obuchowski-Rockette methods. Also implemented is single treatment analysis,
which allows comparison of performance of a group of radiologists to a
specified value, or comparison of AI to a group of radiologists interpreting
the same cases. Crossed-modality analysis is implemented wherein there are
two crossed treatment factors and the aim is to determined performance in
each treatment factor averaged over all levels of the second factor. Sample
size estimation tools are provided for ROC and FROC studies; these use
estimates of the relevant variances from a pilot study to predict required
numbers of readers and cases in a pivotal study to achieve the desired power.
Utility and data file manipulation functions allow data to be read in any of
the currently used input formats, including Excel, and the results of the
analysis can be viewed in text or Excel output files. The methods are
illustrated with several included datasets from the author's collaborations.
This update includes improvements to the code, some as a result of
user-reported bugs and new feature requests, and others discovered during
ongoing testing and code simplification.
Author: Dev Chakraborty [cre, aut, cph],
Peter Phillips [ctb],
Xuetong Zhai [aut]
Maintainer: Dev Chakraborty <dpc10ster@gmail.com>
Diff between RJafroc versions 2.0.1 dated 2020-12-15 and 2.1.0 dated 2022-07-24
RJafroc-2.0.1/RJafroc/R/Compare3ProperRocFits.R |only RJafroc-2.0.1/RJafroc/R/MyFom_ij.R |only RJafroc-2.0.1/RJafroc/R/UtilLesionDistr.R |only RJafroc-2.0.1/RJafroc/R/UtilLesionWeightsDistr.R |only RJafroc-2.0.1/RJafroc/inst/ANALYZED |only RJafroc-2.0.1/RJafroc/inst/MRMCRuns |only RJafroc-2.0.1/RJafroc/man/Compare3ProperRocFits.Rd |only RJafroc-2.0.1/RJafroc/man/UtilLesionDistr.Rd |only RJafroc-2.0.1/RJafroc/man/UtilLesionWeightsDistr.Rd |only RJafroc-2.1.0/RJafroc/DESCRIPTION | 128 +- RJafroc-2.1.0/RJafroc/MD5 | 555 ++++++++-- RJafroc-2.1.0/RJafroc/NAMESPACE | 18 RJafroc-2.1.0/RJafroc/NEWS.md | 150 ++ RJafroc-2.1.0/RJafroc/R/ChisqrGoodnessOfFit.R | 17 RJafroc-2.1.0/RJafroc/R/DfBinDataset.R | 1 RJafroc-2.1.0/RJafroc/R/DfExtractDataset.R | 19 RJafroc-2.1.0/RJafroc/R/DfReadCrossedModalities.R | 13 RJafroc-2.1.0/RJafroc/R/DfReadDataFile.R | 69 - RJafroc-2.1.0/RJafroc/R/DfReadIowaFormats.R | 13 RJafroc-2.1.0/RJafroc/R/DfReadLrocDataFile.R |only RJafroc-2.1.0/RJafroc/R/DfSaveDataFile.R | 202 +-- RJafroc-2.1.0/RJafroc/R/DfWriteExcelDataFile.R |only RJafroc-2.1.0/RJafroc/R/FitRsmRoc.R | 32 RJafroc-2.1.0/RJafroc/R/PlotRsmOperatingCharacteristics.R | 75 - RJafroc-2.1.0/RJafroc/R/ReadJAFROCNewFormat.R | 39 RJafroc-2.1.0/RJafroc/R/ReadJAFROCOldFormat.R | 4 RJafroc-2.1.0/RJafroc/R/SimulateFrocDataset.R | 1 RJafroc-2.1.0/RJafroc/R/SimulateFrocFromLrocDataset.R | 1 RJafroc-2.1.0/RJafroc/R/SimulateLrocDataset.R | 1 RJafroc-2.1.0/RJafroc/R/SimulateRocDataset.R | 1 RJafroc-2.1.0/RJafroc/R/SsFrocNhRsmModel.R | 123 +- RJafroc-2.1.0/RJafroc/R/StSignificanceTesting.R | 7 RJafroc-2.1.0/RJafroc/R/StSignificanceTestingCadVsRad.R | 28 RJafroc-2.1.0/RJafroc/R/StSignificanceTestingCrossedModalities.R | 7 RJafroc-2.1.0/RJafroc/R/UtilAnalyticalAucsRSM.R | 65 - RJafroc-2.1.0/RJafroc/R/UtilFigureOfMerit.R | 1 RJafroc-2.1.0/RJafroc/R/UtilFigureOfMerit_ij.R |only RJafroc-2.1.0/RJafroc/R/UtilIntrinsic2PhysicalRSM.R | 1 RJafroc-2.1.0/RJafroc/R/UtilLesionDistrVector.R |only RJafroc-2.1.0/RJafroc/R/UtilLesionWeightsMatrix.R |only RJafroc-2.1.0/RJafroc/R/UtilOutputReport.R | 22 RJafroc-2.1.0/RJafroc/R/UtilPhysical2IntrinsicRSM.R | 1 RJafroc-2.1.0/RJafroc/R/datasets.R | 11 RJafroc-2.1.0/RJafroc/R/rsmFormulae.R | 321 +++++ RJafroc-2.1.0/RJafroc/R/test2Functions.R |only RJafroc-2.1.0/RJafroc/inst/cranSubmission/cranSubmission.R | 86 + RJafroc-2.1.0/RJafroc/inst/extdata/CadFrocData.xlsx |only RJafroc-2.1.0/RJafroc/inst/extdata/CrossedModalities.xlsx |only RJafroc-2.1.0/RJafroc/inst/extdata/JT.xlsx |only RJafroc-2.1.0/RJafroc/inst/extdata/JT2Rdrs.xlsx |only RJafroc-2.1.0/RJafroc/inst/extdata/NicoRadRoc.xlsx |only RJafroc-2.1.0/RJafroc/inst/extdata/findings.txt |only RJafroc-2.1.0/RJafroc/inst/extdata/toyFiles |only RJafroc-2.1.0/RJafroc/inst/temp |only RJafroc-2.1.0/RJafroc/man/ChisqrGoodnessOfFit.Rd | 18 RJafroc-2.1.0/RJafroc/man/DfBinDataset.Rd | 3 RJafroc-2.1.0/RJafroc/man/DfReadCrossedModalities.Rd | 17 RJafroc-2.1.0/RJafroc/man/DfReadDataFile.Rd | 44 RJafroc-2.1.0/RJafroc/man/DfReadLrocDataFile.Rd |only RJafroc-2.1.0/RJafroc/man/DfSaveDataFile.Rd | 17 RJafroc-2.1.0/RJafroc/man/DfWriteExcelDataFile.Rd |only RJafroc-2.1.0/RJafroc/man/FitRsmRoc.Rd | 29 RJafroc-2.1.0/RJafroc/man/PlotRsmOperatingCharacteristics.Rd | 22 RJafroc-2.1.0/RJafroc/man/RJafroc-package.Rd | 88 - RJafroc-2.1.0/RJafroc/man/RSM_erf.Rd |only RJafroc-2.1.0/RJafroc/man/RSM_pdfD.Rd |only RJafroc-2.1.0/RJafroc/man/RSM_pdfN.Rd |only RJafroc-2.1.0/RJafroc/man/RSM_xFROC.Rd |only RJafroc-2.1.0/RJafroc/man/RSM_xROC.Rd |only RJafroc-2.1.0/RJafroc/man/RSM_yFROC.Rd |only RJafroc-2.1.0/RJafroc/man/RSM_yROC.Rd |only RJafroc-2.1.0/RJafroc/man/SimulateFrocDataset.Rd | 3 RJafroc-2.1.0/RJafroc/man/SimulateFrocFromLrocDataset.Rd | 1 RJafroc-2.1.0/RJafroc/man/SimulateLrocDataset.Rd | 3 RJafroc-2.1.0/RJafroc/man/SimulateRocDataset.Rd | 3 RJafroc-2.1.0/RJafroc/man/SsFrocNhRsmModel.Rd | 48 RJafroc-2.1.0/RJafroc/man/StSignificanceTesting.Rd | 7 RJafroc-2.1.0/RJafroc/man/StSignificanceTestingCadVsRad.Rd | 24 RJafroc-2.1.0/RJafroc/man/StSignificanceTestingCrossedModalities.Rd | 7 RJafroc-2.1.0/RJafroc/man/UtilAnalyticalAucsRSM.Rd | 23 RJafroc-2.1.0/RJafroc/man/UtilFigureOfMerit.Rd | 1 RJafroc-2.1.0/RJafroc/man/UtilIntrinsic2PhysicalRSM.Rd | 3 RJafroc-2.1.0/RJafroc/man/UtilLesionDistrVector.Rd |only RJafroc-2.1.0/RJafroc/man/UtilLesionWeightsMatrix.Rd |only RJafroc-2.1.0/RJafroc/man/UtilOutputReport.Rd | 21 RJafroc-2.1.0/RJafroc/man/UtilPhysical2IntrinsicRSM.Rd | 3 RJafroc-2.1.0/RJafroc/man/dataset05.Rd | 6 RJafroc-2.1.0/RJafroc/man/dataset10.Rd | 2 RJafroc-2.1.0/RJafroc/man/datasetCrossedModality.Rd | 2 RJafroc-2.1.0/RJafroc/man/funs.Rd |only RJafroc-2.1.0/RJafroc/src/CommonFuncs.h | 11 RJafroc-2.1.0/RJafroc/src/RcppExports.cpp | 25 RJafroc-2.1.0/RJafroc/src/RsmFuncs.cpp | 141 -- RJafroc-2.1.0/RJafroc/tests |only 94 files changed, 1824 insertions(+), 760 deletions(-)
Title: Credit Risk Scorecard
Description: The `scorecard` package makes the development of credit risk scorecard
easier and efficient by providing functions for some common tasks,
such as data partition, variable selection, woe binning, scorecard scaling,
performance evaluation and report generation. These functions can also used
in the development of machine learning models.
The references including:
1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard:
Development and Implementation Using SAS.
2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards.
Developing and Implementing Intelligent Credit Scoring.
Author: Shichen Xie [aut, cre]
Maintainer: Shichen Xie <xie@shichen.name>
Diff between scorecard versions 0.3.8 dated 2022-07-09 and 0.3.9 dated 2022-07-24
DESCRIPTION | 6 +-- MD5 | 15 ++++--- NEWS.md | 5 ++ R/condition_fun.R | 10 ++++- R/report.R | 11 ++++- R/var_filter.R | 102 ++++++++++++++++++++++++++++++++++++---------------- R/woebin.R | 24 ++++++------ inst/extdata/demo.R |only man/var_filter.Rd | 10 +++-- 9 files changed, 124 insertions(+), 59 deletions(-)
Title: Prediction Performance Metrics
Description: A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.
Author: Adrian A. Correndo [cre, cph] ,
Adrian A. Correndo [aut] ,
Luiz H. Moro Rosso [aut] ,
Rai Schwalbert [aut] ,
Carlos Hernandez [aut] ,
Leonardo M. Bastos [aut] ,
Luciana Nieto [aut] ,
Dean Holzworth [aut],
Ignacio A. Ciampitti [aut]
Maintainer: Adrian A. Correndo <correndo@ksu.edu>
Diff between metrica versions 2.0.0 dated 2022-07-05 and 2.0.1 dated 2022-07-24
DESCRIPTION | 16 ++-- MD5 | 42 +++++----- NEWS.md | 30 +++++-- R/class_confusion_matrix.R | 52 +++++++------ R/plot_density.R | 4 - R/plot_scatter.R | 4 - R/plot_tiles.R | 4 - R/reg_d1r.R | 3 README.md | 53 ++++++++++--- inst/doc/apsim_open.Rmd | 2 inst/doc/apsim_open.html | 8 +- inst/doc/available_metrics_classification.html | 4 - inst/doc/available_metrics_regression.html | 4 - inst/doc/classification_case.Rmd | 2 inst/doc/classification_case.html | 8 +- inst/doc/regression_case.html | 100 ++++++++++++------------- man/figures/README-unnamed-chunk-12-1.png |binary man/figures/README-unnamed-chunk-5-1.png |binary man/figures/README-unnamed-chunk-6-1.png |binary man/metrica-package.Rd | 4 - vignettes/apsim_open.Rmd | 2 vignettes/classification_case.Rmd | 2 22 files changed, 197 insertions(+), 147 deletions(-)