Sat, 02 Jul 2022

Package xefun updated to version 0.1.1 with previous version 0.1.0 dated 2022-05-17

Title: X-Engineering or Supporting Functions
Description: Miscellaneous functions used for x-engineering (feature engineering) or for supporting in other packages maintained by 'Shichen Xie'.
Author: Shichen Xie [aut, cre]
Maintainer: Shichen Xie <xie@shichen.name>

Diff between xefun versions 0.1.0 dated 2022-05-17 and 0.1.1 dated 2022-07-02

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 NAMESPACE       |    1 
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Package emplik updated to version 1.2 with previous version 1.1-1 dated 2020-05-20

Title: Empirical Likelihood Ratio for Censored/Truncated Data
Description: Empirical likelihood ratio tests for means/quantiles/hazards from possibly censored and/or truncated data. Now does regression too. This version contains some C code.
Author: Mai Zhou. . Yifan Yang for some C code.)
Maintainer: Mai Zhou <maizhou@gmail.com>

Diff between emplik versions 1.1-1 dated 2020-05-20 and 1.2 dated 2022-07-02

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Package gcKrig updated to version 1.1.8 with previous version 1.1.7 dated 2021-10-14

Title: Analysis of Geostatistical Count Data using Gaussian Copulas
Description: Provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations. Description of the method is available from: Han and DeOliveira (2018) <doi:10.18637/jss.v087.i13>.
Author: Zifei Han
Maintainer: Zifei Han <hanzifei1@gmail.com>

Diff between gcKrig versions 1.1.7 dated 2021-10-14 and 1.1.8 dated 2022-07-02

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Package cir updated to version 2.2.1 with previous version 2.2.0 dated 2021-08-23

Title: Centered Isotonic Regression and Dose-Response Utilities
Description: Isotonic regression (IR) and its improvement: centered isotonic regression (CIR). CIR is recommended in particular with small samples. Also, interval estimates for both, and additional utilities such as plotting dose-response data.
Author: Assaf P. Oron [cre, aut]
Maintainer: Assaf P. Oron <assaf.oron@gmail.com>

Diff between cir versions 2.2.0 dated 2021-08-23 and 2.2.1 dated 2022-07-02

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New package ANTs with initial version 0.0.16
Package: ANTs
Date: 2022-06-29
Title: Animal Network Toolkit Software
Version: 0.0.16
Description: How animals interact and develop social relationships in face of sociodemographic and ecological pressures is of great interest. New methodologies, in particular Social Network Analysis (SNA), allow us to elucidate these types of questions. However, the different methodologies developed to that end and the speed at which they emerge make their use difficult. Moreover, the lack of communication between the different software developed to provide an answer to the same/different research questions is a source of confusion. The R package Animal Network Toolkit 'ANTs' was developed with the aim of implementing in one package the different social network analysis techniques currently used in the study of animal social networks. Hence, ANT is a toolkit for animal research allowing among other things to: 1) measure global, dyadic and nodal networks metrics; 2) perform data randomization: pre- and post-network (node and link permutations); 3) perform statistical permutation tests as correlation test (<doi:10.2307/2332226>), t-test (<doi:10.1037/h0041412>), General Linear Model (<doi:10.2307/2346786>), General Linear Mixed Model (<doi:10.2307/2346786>), deletion simulation (<doi:10.1098/rsbl.2003.0057>), 'Matrix TauKr correlations' (<doi:10.1016/S0022-5193(05)80036-0>). The package is partially coded in C++ using the R package 'Rcpp' for an optimal coding speed. The package gives researchers a workflow from the raw data to the achievement of statistical analyses, allowing for a multilevel approach (<doi:10.1007/978-3-319-47829-6_1882-1>): from the individual's position and role within the network, to the identification of interaction patterns, and the study of the overall network properties. Furthermore, ANT also provides a guideline on the SNA techniques used: 1) from the appropriate randomization technique according to the data collected; 2) to the choice, the meaning, the limitations and advantages of the network metrics to apply, 3) and the type of statistical tests to run. The ANT project is multi-collaborative, aiming to provide access to advanced social network analysis techniques and to create new ones that meet researchers' needs in future versions. The ANT project is multi-collaborative, aiming to provide access to advanced social network analysis techniques and to create new ones that meet researchers' needs in future versions.
License: GPL (>= 3)
URL: www.s-sosa.com/softwares or https://github.com/SebastianSosa/ANTs
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: lme4,methods,Kendall,gtools,rstudioapi
LinkingTo: Rcpp,RcppArmadillo,RcppEigen
SystemRequirements: C++11
BugReports: https://github.com/SebastianSosa/ANTs
Suggests: testthat, knitr, rmarkdown, markdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2022-06-29 16:29:29 UTC; SSosa
Author: Sosa Sebastian [aut, cre], Puga-Gonzalez Ivan [aut], Hu Feng He [aut], Pansanel Jerome [aut], Xiaohua Xie [aut], Sueur Cedric [aut]
Maintainer: Sosa Sebastian <s.sosa@live.fr>
Repository: CRAN
Date/Publication: 2022-07-02 22:20:02 UTC

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Package WaveletArima updated to version 0.1.2 with previous version 0.1.1 dated 2018-06-01

Title: Wavelet-ARIMA Model for Time Series Forecasting
Description: Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.
Author: Dr. Ranjit Kumar Paul [aut, cre], Mr. Sandipan Samanta [aut], Dr. Md Yeasin [aut]
Maintainer: Dr. Ranjit Kumar Paul <ranjitstat@gmail.com>

Diff between WaveletArima versions 0.1.1 dated 2018-06-01 and 0.1.2 dated 2022-07-02

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Package eefAnalytics updated to version 1.1.0 with previous version 1.0.11 dated 2021-03-16

Title: Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs
Description: Analysing data from evaluations of educational interventions using a randomised controlled trial design. Various analytical tools to perform sensitivity analysis using different methods are supported (e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The included commands can be used for simple randomised trials, cluster randomised trials and multisite trials. The methods can also be used more widely beyond education trials. This package can be used to evaluate other intervention designs using Frequentist and Bayesian multilevel models.
Author: Germaine Uwimpuhwe, Akansha Singh, Dimitris Vallis, Steve Higgins, ZhiMin Xiao, Ewoud De Troyer and Adetayo Kasim
Maintainer: Dimitris Vallis <dimitris.vallis@durham.ac.uk>

Diff between eefAnalytics versions 1.0.11 dated 2021-03-16 and 1.1.0 dated 2022-07-02

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Package caviarpd updated to version 0.2.32 with previous version 0.2.28 dated 2022-03-21

Title: Cluster Analysis via Random Partition Distributions
Description: Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates. An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids clustering methods. The method is currently under peer review but an earlier draft is available in Dahl, Andros, Carter <doi:10.48550/arXiv.2106.02760>.
Author: David B. Dahl [aut, cre] , Jacob Andros [aut] , J. Brandon Carter [aut]
Maintainer: David B. Dahl <dahl@stat.byu.edu>

Diff between caviarpd versions 0.2.28 dated 2022-03-21 and 0.2.32 dated 2022-07-02

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Package embed updated to version 1.0.0 with previous version 0.2.0 dated 2022-04-13

Title: Extra Recipes for Encoding Predictors
Description: Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <arXiv:1611.09477> or nonlinear models <arXiv:1604.06737> can be used. There are also functions for dimension reduction and other approaches.
Author: Emil Hvitfeldt [aut, cre] , Max Kuhn [aut] , RStudio [cph]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>

Diff between embed versions 0.2.0 dated 2022-04-13 and 1.0.0 dated 2022-07-02

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New package vrtest with initial version 1.0
Package: vrtest
Title: Variance Ratio Tests and Other Tests for Martingale Difference Hypothesis
Version: 1.0
Date: 2022-06-29
Author: Jae H. Kim
Maintainer: Jae H. Kim <jaekim8080@gmail.com>
Description: A collection of statistical tests for martingale difference hypothesis, including automatic portmanteau test (Escansiano and Lobato, 2009) <doi:10.1016/j.jeconom.2009.03.001> and automatic variance ratio test (Kim, 2009) <doi:10.1016/j.frl.2009.04.003>.
License: GPL-2
NeedsCompilation: no
Packaged: 2022-07-02 12:28:56 UTC; jh808
Repository: CRAN
Date/Publication: 2022-07-02 15:00:02 UTC

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New package WLogit with initial version 1.0
Package: WLogit
Title: Whitening Logistic Regression for Variable Selection
Version: 1.0
Date: 2022-07-01
Author: Wencan Zhu
Maintainer: Wencan Zhu <wencan.zhu@agroparistech.fr>
Description: It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion. For further details we refer the reader to the paper Zhu et al. (2022) <arXiv:2206.14850>.
License: GPL-2
Imports: cvCovEst, genlasso, tibble, MASS, ggplot2, Matrix, glmnet, corpcor
VignetteBuilder: knitr
Suggests: knitr
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2022-07-02 06:05:53 UTC; mmip
Repository: CRAN
Date/Publication: 2022-07-02 12:20:02 UTC

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New package pedbp with initial version 1.0.0
Package: pedbp
Title: Pediatric Blood Pressure
Version: 1.0.0
Description: Data and utilities for estimating pediatric blood pressure percentiles by sex, age, and optionally height (stature). Blood pressure percentiles for children under one year of age come from Gemelli et.al. (1990) <doi:10.1007/BF02171556>. Estimates of blood pressure percentiles for children at least one year of age are informed by data from the National Heart, Lung, and Blood Institute (NHLBI) and the Centers for Disease Control and Prevention (CDC) <doi:10.1542/peds.2009-2107C> or from Lo et.al. (2013) <doi:10.1542/peds.2012-1292>. The flowchart for selecting the informing data source comes from Martin et.al. (2022) <doi:10.1542/hpeds.2021-005998>.
Depends: R (>= 3.5.0)
License: GPL-2
Encoding: UTF-8
URL: https://github.com/dewittpe/pedbp/
Language: en-us
LazyData: true
Imports: ggplot2, scales
Suggests: covr, data.table, DT, gridExtra, knitr, rmarkdown, shiny, shinydashboard
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-07-01 18:10:18 UTC; peterdewitt
Author: Peter DeWitt [aut, cre] , Blake Martin [ctb] , David Albers [ctb] , Tell Bennett [ctb]
Maintainer: Peter DeWitt <peter.dewitt@cuanschutz.edu>
Repository: CRAN
Date/Publication: 2022-07-02 10:10:02 UTC

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New package Missplot with initial version 0.1.0
Package: Missplot
Title: Missing Plot Technique in Design of Experiment
Version: 0.1.0
Description: A system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-07-01 19:07:07 UTC; USER
Author: Shantanu Nayek [cre, aut], Saheli Datta [aut]
Maintainer: Shantanu Nayek <shantanuashis@gmail.com>
Repository: CRAN
Date/Publication: 2022-07-02 10:10:06 UTC

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New package ixplorer with initial version 0.2.2
Package: ixplorer
Title: Easy DataOps for R Users
Version: 0.2.2
Description: Create and view tickets in 'gitea', a self-hosted git service <https://gitea.io>, using an 'RStudio' addin, and use helper functions to publish documentation and use git.
License: AGPL (>= 3)
Depends: R (>= 3.4)
Imports: dplyr (>= 0.8.1), gitear (>= 0.0.2), kableExtra (>= 1.1.0), lubridate (>= 1.7.4), miniUI (>= 0.1.1.1), RColorBrewer (>= 1.1.2), shiny.i18n (>= 0.2.0), shiny (>= 1.3.2), stringr (>= 1.4.0), shinyWidgets (>= 0.2.1), purrr (>= 0.3.4), gert (>= 1.5.0), keyring (>= 1.3.0), methods
Suggests: knitr (>= 1.23), git2r (>= 0.30.1), here (>= 1.0.1), tibble (>= 3.1.6), readr (>= 1.3.1), rmarkdown (>= 1.12), rstudioapi (>= 0.10), testthat (>= 3.0.0), tidyr (>= 0.8.3),
VignetteBuilder: knitr
Encoding: UTF-8
URL: https://github.com/ixpantia/ixplorer
BugReports: https://github.com/ixpantia/ixplorer/issues
NeedsCompilation: no
Packaged: 2022-07-01 19:54:01 UTC; frans
Author: ixpantia, SRL [cph], Frans van Dunne [cre, aut] , Ronny Hernandez Mora [aut] , Daniel Granados Campos [ctb], Nayib Vargas Zuniga [ctb]
Maintainer: Frans van Dunne <frans@ixpantia.com>
Repository: CRAN
Date/Publication: 2022-07-02 10:20:02 UTC

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New package GPCERF with initial version 0.1.0
Package: GPCERF
Title: Gaussian Processes for Estimating Causal Exposure Response Curves
Version: 0.1.0
Maintainer: Naeem Khoshnevis <nkhoshnevis@g.harvard.edu>
Description: Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint <arXiv:2105.03454>.
License: GPL (>= 3)
Language: en-US
URL: https://github.com/NSAPH-Software/GPCERF
BugReports: https://github.com/NSAPH-Software/GPCERF/issues
Copyright: Harvard University
Imports: parallel, data.table, xgboost, stats, MASS, spatstat.geom, logger, Rcpp, ggplot2, rlang, Matrix
Encoding: UTF-8
Depends: R (>= 3.5.0)
Suggests: rmarkdown, knitr, testthat (>= 3.0.0)
VignetteBuilder: knitr
LinkingTo: RcppArmadillo, Rcpp
NeedsCompilation: yes
Packaged: 2022-06-30 14:33:22 UTC; nak443
Author: Naeem Khoshnevis [aut, cre] , Boyu Ren [aut] , Tanujt Dey [ctb] , Danielle Braun [aut]
Repository: CRAN
Date/Publication: 2022-07-02 09:50:02 UTC

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Package TNRS (with last version 0.3.1) was removed from CRAN

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

2022-06-22 0.3.1
2022-02-22 0.3.0

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Package textrecipes updated to version 1.0.0 with previous version 0.5.2 dated 2022-05-03

Title: Extra 'Recipes' for Text Processing
Description: Converting text to numerical features requires specifically created procedures, which are implemented as steps according to the 'recipes' package. These steps allows for tokenization, filtering, counting (tf and tfidf) and feature hashing.
Author: Emil Hvitfeldt [aut, cre]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>

Diff between textrecipes versions 0.5.2 dated 2022-05-03 and 1.0.0 dated 2022-07-02

 DESCRIPTION                                                        |   10 
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 NAMESPACE                                                          |    1 
 NEWS.md                                                            |    4 
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 R/tf.R                                                             |    6 
 R/tfidf.R                                                          |    8 
 R/tokenfilter.R                                                    |    6 
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 R/tokenize_bpe.R                                                   |    6 
 R/tokenize_sentencepiece.R                                         |    6 
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 README.md                                                          |    5 
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 tests/testthat/_snaps/textfeature.md                               |   18 
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 tests/testthat/_snaps/tokenize.md                                  |   18 
 tests/testthat/_snaps/tokenmerge.md                                |   36 +
 tests/testthat/_snaps/untokenize.md                                |   19 
 tests/testthat/_snaps/wordpiece_tokenize.md                        |   18 
 tests/testthat/test-bpe_tokenize.R                                 |   12 
 tests/testthat/test-clean_levels.R                                 |   14 
 tests/testthat/test-clean_names.R                                  |    1 
 tests/testthat/test-embeddings.R                                   |   19 
 tests/testthat/test-hashing.R                                      |   19 
 tests/testthat/test-hashing_dummy.R                                |   14 
 tests/testthat/test-lda.R                                          |   20 
 tests/testthat/test-lemma.R                                        |   20 
 tests/testthat/test-ngram.R                                        |   19 
 tests/testthat/test-pos_filter.R                                   |   21 +
 tests/testthat/test-sentencepiece_tokenize.R                       |   12 
 tests/testthat/test-sequence_onehot.R                              |   19 
 tests/testthat/test-stem.R                                         |   18 
 tests/testthat/test-stopwords.R                                    |   19 
 tests/testthat/test-text_normalization.R                           |   15 
 tests/testthat/test-textfeature.R                                  |   13 
 tests/testthat/test-tf.R                                           |   18 
 tests/testthat/test-tfidf.R                                        |   19 
 tests/testthat/test-tokenfilter.R                                  |   19 
 tests/testthat/test-tokenize.R                                     |   13 
 tests/testthat/test-tokenmerge.R                                   |   26 +
 tests/testthat/test-untokenize.R                                   |   19 
 tests/testthat/test-wordpiece_tokenize.R                           |   13 
 102 files changed, 1361 insertions(+), 323 deletions(-)

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Package themis updated to version 1.0.0 with previous version 0.2.2 dated 2022-05-11

Title: Extra Recipes Steps for Dealing with Unbalanced Data
Description: A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 <arXiv:1106.1813>, BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008 <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the number of majority cases using NearMiss 2003 <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
Author: Emil Hvitfeldt [aut, cre]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>

Diff between themis versions 0.2.2 dated 2022-05-11 and 1.0.0 dated 2022-07-02

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Package Rwhois updated to version 1.0.11 with previous version 1.0.10 dated 2022-06-22

Title: WHOIS Server Querying
Description: Queries data from WHOIS servers.
Author: Brad Cable
Maintainer: Brad Cable <brad@bcable.net>

Diff between Rwhois versions 1.0.10 dated 2022-06-22 and 1.0.11 dated 2022-07-02

 DESCRIPTION   |    8 ++++----
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Package itp updated to version 1.1.0 with previous version 1.0.1 dated 2022-06-14

Title: The Interpolate, Truncate, Project (ITP) Root-Finding Algorithm
Description: Implements the Interpolate, Truncate, Project (ITP) root-finding algorithm developed by Oliveira and Takahashi (2021) <doi:10.1145/3423597>. The user provides the function, from the real numbers to the real numbers, and an interval with the property that the values of the function at its endpoints have different signs. If the function is continuous over this interval then the ITP method estimates the value at which the function is equal to zero. If the function is discontinuous then a point of discontinuity at which the function changes sign may be found. The function can be supplied using either an R function or an external pointer to a C++ function. Tuning parameters of the ITP algorithm can be set by the user. Default values are set based on arguments in Oliveira and Takahashi (2021).
Author: Paul J. Northrop [aut, cre, cph]
Maintainer: Paul J. Northrop <p.northrop@ucl.ac.uk>

Diff between itp versions 1.0.1 dated 2022-06-14 and 1.1.0 dated 2022-07-02

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More information about itp at CRAN
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