Sun, 06 Nov 2022

Package mcr (with last version 1.3.0) was removed from CRAN

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

2022-10-23 1.3.0
2021-04-23 1.2.2
2014-02-12 1.2.1
2014-02-05 1.2
2012-07-24 1.1

Permanent link
Package xpectr (with last version 0.4.2) was removed from CRAN

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

2022-01-19 0.4.2
2021-10-03 0.4.1
2020-06-18 0.4.0
2020-03-31 0.3.0
2020-03-15 0.2.0
2020-03-07 0.1.1
2020-02-29 0.1.0

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Package SCpubr updated to version 1.0.3 with previous version 1.0.2 dated 2022-10-25

Title: Generate Publication Ready Visualizations of Single Cell Transcriptomics Data
Description: A system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a “publication ready” format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.
Author: Enrique Blanco-Carmona [cre, aut]
Maintainer: Enrique Blanco-Carmona <scpubr@gmail.com>

Diff between SCpubr versions 1.0.2 dated 2022-10-25 and 1.0.3 dated 2022-11-06

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Package sasLM updated to version 0.9.3 with previous version 0.9.2 dated 2022-10-05

Title: 'SAS' Linear Model
Description: This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, FREQ, and UNIVARIATE. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).
Author: Kyun-Seop Bae [aut]
Maintainer: Kyun-Seop Bae <k@acr.kr>

Diff between sasLM versions 0.9.2 dated 2022-10-05 and 0.9.3 dated 2022-11-06

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More information about sasLM at CRAN
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Package RecordTest updated to version 2.1.1 with previous version 2.1.0 dated 2021-08-08

Title: Inference Tools in Time Series Based on Record Statistics
Description: Statistical tools based on the probabilistic properties of the record occurrence in a sequence of independent and identically distributed continuous random variables. In particular, tools to prepare a time series as well as distribution-free trend and change-point tests and graphical tools to study the record occurrence.
Author: Jorge Castillo-Mateo [aut, cre, cph] , Ana C. Cebrian [ths] , Jesus Asin [ths]
Maintainer: Jorge Castillo-Mateo <jorgecastillomateo@gmail.com>

Diff between RecordTest versions 2.1.0 dated 2021-08-08 and 2.1.1 dated 2022-11-06

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More information about RecordTest at CRAN
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Package ggrepel updated to version 0.9.2 with previous version 0.9.1 dated 2021-01-15

Title: Automatically Position Non-Overlapping Text Labels with 'ggplot2'
Description: Provides text and label geoms for 'ggplot2' that help to avoid overlapping text labels. Labels repel away from each other and away from the data points.
Author: Kamil Slowikowski [aut, cre] , Alicia Schep [ctb] , Sean Hughes [ctb] , Trung Kien Dang [ctb] , Saulius Lukauskas [ctb], Jean-Olivier Irisson [ctb] , Zhian N Kamvar [ctb] , Thompson Ryan [ctb] , Dervieux Christophe [ctb] , Yutani Hiroaki [ctb], Pierr [...truncated...]
Maintainer: Kamil Slowikowski <kslowikowski@gmail.com>

Diff between ggrepel versions 0.9.1 dated 2021-01-15 and 0.9.2 dated 2022-11-06

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Package robfilter updated to version 4.1.3 with previous version 4.1.2 dated 2019-11-21

Title: Robust Time Series Filters
Description: Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> and the slope comparing adaptive repeated median by Borowski and Fried (2013) <doi:10.1007/s11222-013-9391-7> choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393> for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter w [...truncated...]
Author: Roland Fried [aut, cre], Karen Schettlinger [aut], Matthias Borowski [aut], Robin Nunkesser [ctb], Thorsten Bernholt [ctb]
Maintainer: Roland Fried <fried@statistik.tu-dortmund.de>

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Package polyRAD updated to version 2.0.0 with previous version 1.6 dated 2022-02-15

Title: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids
Description: Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. 'polyRAD' is described by Clark et al. (2019) <doi:10.1534/g3.118.200913>, and the Hind/He statistic for marker filtering is described by Clark et al. (2022) <doi:10.1186/s12859-022-04635-9>. A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020, Version 1) <doi:10.1101/2020.01.11.902890>.
Author: Lindsay V. Clark [aut, cre] , U.S. National Science Foundation [fnd]
Maintainer: Lindsay V. Clark <Lindsay.Clark@seattlechildrens.org>

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Package MKinfer updated to version 0.8 with previous version 0.7 dated 2022-09-06

Title: Inferential Statistics
Description: Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), <doi:10.1016/S0167-7152(97)00043-6>), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) and multiple imputation (Barnard and Rubin (1999), <doi:10.1093/biomet/86.4.948>) t-test. Graphical visualization by volcano plots.
Author: Matthias Kohl [aut, cre]
Maintainer: Matthias Kohl <Matthias.Kohl@stamats.de>

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Package sendgridr updated to version 0.6.1 with previous version 0.5.1 dated 2022-08-12

Title: Mail Sender Using 'Sendgrid' Service
Description: Send email using 'Sendgrid' <https://sendgrid.com/> mail API(v3) <https://docs.sendgrid.com/api-reference/how-to-use-the-sendgrid-v3-api/authentication>.
Author: Chanyub Park [aut, cre]
Maintainer: Chanyub Park <mrchypark@gmail.com>

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Package RprobitB updated to version 1.1.2 with previous version 1.1.1 dated 2022-08-11

Title: Bayesian Probit Choice Modeling
Description: Bayes estimation of probit choice models, both in the cross-sectional and panel setting. The package can analyze binary, multivariate, ordered, and ranked choices, and places a special focus on modeling heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Markov chain Monte Carlo methods, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating scheme. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method see Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.
Author: Lennart Oelschläger [aut, cre] , Dietmar Bauer [aut] , Sebastian Buescher [ctb], Manuel Batram [ctb]
Maintainer: Lennart Oelschläger <oelschlaeger.lennart@gmail.com>

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Package ijtiff updated to version 2.2.9 with previous version 2.2.8 dated 2022-08-31

Title: Comprehensive TIFF I/O with Full Support for 'ImageJ' TIFF Files
Description: General purpose TIFF file I/O for R users. Currently the only such package with read and write support for TIFF files with floating point (real-numbered) pixels, and the only package that can correctly import TIFF files that were saved from 'ImageJ' and write TIFF files than can be correctly read by 'ImageJ' <https://imagej.nih.gov/ij/>. Also supports text image I/O.
Author: Rory Nolan [aut, cre] , Kent Johnson [aut], Simon Urbanek [ctb], Sergi Padilla-Parra [ths] , Jeroen Ooms [rev, ctb] , Jon Clayden [rev]
Maintainer: Rory Nolan <rorynoolan@gmail.com>

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Package ggmulti updated to version 1.0.6 with previous version 1.0.5 dated 2022-10-08

Title: High Dimensional Data Visualization
Description: It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.
Author: Zehao Xu [aut, cre], R. Wayne Oldford [aut]
Maintainer: Zehao Xu <z267xu@uwaterloo.ca>

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Package future updated to version 1.29.0 with previous version 1.28.0 dated 2022-09-02

Title: Unified Parallel and Distributed Processing in R for Everyone
Description: The purpose of this package is to provide a lightweight and unified Future API for sequential and parallel processing of R expression via futures. The simplest way to evaluate an expression in parallel is to use `x %<-% { expression }` with `plan(multisession)`. This package implements sequential, multicore, multisession, and cluster futures. With these, R expressions can be evaluated on the local machine, in parallel a set of local machines, or distributed on a mix of local and remote machines. Extensions to this package implement additional backends for processing futures via compute cluster schedulers, etc. Because of its unified API, there is no need to modify any code in order switch from sequential on the local machine to, say, distributed processing on a remote compute cluster. Another strength of this package is that global variables and functions are automatically identified and exported as needed, making it straightforward to tweak existing code to make use of futures.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

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Package discgolf (with last version 0.2.0) was removed from CRAN

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

2018-01-03 0.2.0
2016-04-21 0.1.0

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Package RcppCCTZ updated to version 0.2.12 with previous version 0.2.11 dated 2022-08-06

Title: 'Rcpp' Bindings for the 'CCTZ' Library
Description: 'Rcpp' Access to the 'CCTZ' timezone library is provided. 'CCTZ' is a C++ library for translating between absolute and civil times using the rules of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License, is included in this package. See <https://github.com/google/cctz> for more details.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package tabula updated to version 2.0.0 with previous version 1.8.0 dated 2022-09-08

Title: Analysis and Visualization of Archaeological Count Data
Description: An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). The package make it easy to visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams.
Author: Nicolas Frerebeau [aut, cre] , Brice Lebrun [ctb] , Matthew Peeples [ctb] , Ben Marwick [ctb] , Anne Philippe [ctb] , Jean-Baptiste Fourvel [ctb]
Maintainer: Nicolas Frerebeau <nicolas.frerebeau@u-bordeaux-montaigne.fr>

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More information about tabula at CRAN
Permanent link

Package RcmdrPlugin.EZR updated to version 1.61 with previous version 1.60 dated 2022-10-06

Title: R Commander Plug-in for the EZR (Easy R) Package
Description: EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-26158-1, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 3,000 scientific articles.
Author: Yoshinobu Kanda
Maintainer: Yoshinobu Kanda <ycanda-tky@umin.ac.jp>

Diff between RcmdrPlugin.EZR versions 1.60 dated 2022-10-06 and 1.61 dated 2022-11-06

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More information about RcmdrPlugin.EZR at CRAN
Permanent link

New package psfmi with initial version 1.1.0
Package: psfmi
Depends: R (>= 4.0.0),
Imports: ggplot2 (>= 3.3.6), norm (>= 1.0-10.0), survival (>= 3.4-0), mitools (>= 2.4), pROC (>= 1.18.0), rms (>= 6.3-0), ResourceSelection (>= 0.3-5), magrittr (>= 2.0.3), rsample (>= 1.1.0), mice (>= 3.14.0), mitml (>= 0.4-3), cvAUC (>= 1.1.4), dplyr (>= 1.0.10), purrr (>= 0.3.5), tidyr (>= 1.2.1), tibble (>= 3.1.8), stringr (>= 1.4.1), lme4 (>= 1.1-30), car(>= 3.1-1)
Suggests: foreign (>= 0.8-80), knitr, rmarkdown, testthat, bookdown, readr, gtools, covr
Title: Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets
Version: 1.1.0
Description: Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using bootstrapping and cluster bootstrapping. The package further contains functions to pool the model performance as ROC/AUC, R-squares, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate l [...truncated...]
Encoding: UTF-8
LazyData: true
License: GPL (>= 2)
URL: https://mwheymans.github.io/psfmi/
BugReports: https://github.com/mwheymans/psfmi/issues/
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-11-06 15:02:51 UTC; mwhey
Author: Martijn Heymans [cre, aut] , Iris Eekhout [ctb]
Maintainer: Martijn Heymans <mw.heymans@amsterdamumc.nl>
Repository: CRAN
Date/Publication: 2022-11-06 15:50:02 UTC

More information about psfmi at CRAN
Permanent link

Package mongolite updated to version 2.7.0 with previous version 2.6.2 dated 2022-06-14

Title: Fast and Simple 'MongoDB' Client for R
Description: High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS. The online user manual provides an overview of the available methods in the package: <https://jeroen.github.io/mongolite/>.
Author: Jeroen Ooms [aut, cre] , MongoDB, Inc [cph]
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between mongolite versions 2.6.2 dated 2022-06-14 and 2.7.0 dated 2022-11-06

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More information about mongolite at CRAN
Permanent link

Package CORElearn updated to version 1.57.1 with previous version 1.56.0 dated 2021-03-23

Title: Classification, Regression and Feature Evaluation
Description: A suite of machine learning algorithms written in C++ with the R interface contains several learning techniques for classification and regression. Predictive models include e.g., classification and regression trees with optional constructive induction and models in the leaves, random forests, kNN, naive Bayes, and locally weighted regression. All predictions obtained with these models can be explained and visualized with the 'ExplainPrediction' package. This package is especially strong in feature evaluation where it contains several variants of Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, and DKM. These methods can be used for feature selection or discretization of numeric attributes. The OrdEval algorithm and its visualization is used for evaluation of data sets with ordinal features and class, enabling analysis according to the Kano model of customer satisfaction. Several algorithms support parallel multithreaded executi [...truncated...]
Author: Marko Robnik-Sikonja and Petr Savicky
Maintainer: "Marko Robnik-Sikonja" <marko.robnik@fri.uni-lj.si>

Diff between CORElearn versions 1.56.0 dated 2021-03-23 and 1.57.1 dated 2022-11-06

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More information about CORElearn at CRAN
Permanent link

Package CompoundEvents updated to version 0.2.0 with previous version 0.1.0 dated 2020-02-28

Title: Statistical Modeling of Compound Events
Description: Tools for extracting occurrences, assessing potential driving factors, predicting occurrences, and quantifying impacts of compound events in hydrology and climatology. Please see Hao Zengchao et al. (2019) <doi:10.1088/1748-9326/ab4df5>.
Author: Zengchao Hao
Maintainer: Zengchao Hao <z.hao4univ@gmail.com>

Diff between CompoundEvents versions 0.1.0 dated 2020-02-28 and 0.2.0 dated 2022-11-06

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More information about CompoundEvents at CRAN
Permanent link

Package adlift updated to version 1.4-4 with previous version 1.4-3 dated 2022-04-06

Title: An Adaptive Lifting Scheme Algorithm
Description: Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.
Author: Matt Nunes [aut, cre], Marina Knight [aut], Guy Nason [ctb, ths]
Maintainer: Matt Nunes <nunesrpackages@gmail.com>

Diff between adlift versions 1.4-3 dated 2022-04-06 and 1.4-4 dated 2022-11-06

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More information about adlift at CRAN
Permanent link

Package manymome updated to version 0.1.6 with previous version 0.1.4.3 dated 2022-09-09

Title: Mediation, Moderation and Moderated-Mediation After Model Fitting
Description: Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax. Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by structural equation modeling using lavaan() or regression using lm().
Author: Shu Fai Cheung [aut, cre] , Sing-Hang Cheung [aut]
Maintainer: Shu Fai Cheung <shufai.cheung@gmail.com>

Diff between manymome versions 0.1.4.3 dated 2022-09-09 and 0.1.6 dated 2022-11-06

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More information about manymome at CRAN
Permanent link

Package flextable updated to version 0.8.3 with previous version 0.8.2 dated 2022-09-26

Title: Functions for Tabular Reporting
Description: Create pretty tables for 'HTML', 'PDF', 'Microsoft Word', 'Microsoft PowerPoint' documents from 'R Markdown' and as 'Grid Graphics' objects. Functions are provided to let users create tables, modify and format their content. It also extends package 'officer' that does not contain any feature for customized tabular reporting.
Author: David Gohel [aut, cre], Clementine Jager [ctb], Panagiotis Skintzos [aut], Quentin Fazilleau [ctb], Maxim Nazarov [ctb] , Titouan Robert [ctb], Michael Barrowman [ctb] , Atsushi Yasumoto [ctb] , Paul Julian [ctb] , Sean Browning [ctb] , Remi Theriaul [...truncated...]
Maintainer: David Gohel <david.gohel@ardata.fr>

Diff between flextable versions 0.8.2 dated 2022-09-26 and 0.8.3 dated 2022-11-06

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Package DTSEA updated to version 0.0.3 with previous version 0.0.2 dated 2022-10-18

Title: Drug Target Set Enrichment Analysis
Description: It is a novel tool used to identify the candidate drugs against a particular disease based on the drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. (See Gómez-Carballa et al. (2022) <doi: 10.1016/j.envres.2022.112890> and Feng et al. (2022) <doi: 10.7150/ijms.67815> for disease expression profiles; see Wishart et al. (2018) <doi: 10.1093/nar/gkx1037> and Gaulton et al. (2017) <doi: 10.1093/nar/gkw1074> for drug target information; see Kanehisa et al. (2021) <doi: 10.1093/nar/gkaa970> for the details of KEGG database.)
Author: Junwei Han [aut, cre, cph], Yinchun Su [aut]
Maintainer: Junwei Han <hanjunwei1981@163.com>

Diff between DTSEA versions 0.0.2 dated 2022-10-18 and 0.0.3 dated 2022-11-06

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Package biostat3 updated to version 0.1.8 with previous version 0.1.6 dated 2021-11-03

Title: Utility Functions, Datasets and Extended Examples for Survival Analysis
Description: Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".
Author: Annika Tillander [ctb], Andreas Karlsson [aut], Johan Zetterqvist [ctb], Peter Strom [ctb], Benedicte Delcoigne [ctb], Mark Clements [aut, cre]
Maintainer: Mark Clements <mark.clements@ki.se>

Diff between biostat3 versions 0.1.6 dated 2021-11-03 and 0.1.8 dated 2022-11-06

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Package HMDHFDplus updated to version 1.9.19 with previous version 1.9.18 dated 2022-06-18

Title: Read Human Mortality Database and Human Fertility Database Data from the Web
Description: Utilities for reading data from the Human Mortality Database (<https://www.mortality.org>), Human Fertility Database (<https://www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<https://www.fertilitydata.org>), The Japanese Mortality Database (<https://www.ipss.go.jp/p-toukei/JMD/index-en.html>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
Author: Tim Riffe [aut, cre], Carl Boe [aut], Jason Hilton [aut], Josh Goldstein [ctb], Stephen Holzman [ctb]
Maintainer: Tim Riffe <tim.riffe@gmail.com>

Diff between HMDHFDplus versions 1.9.18 dated 2022-06-18 and 1.9.19 dated 2022-11-06

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Package psfmi (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:

2021-09-23 1.0.0
2021-01-13 0.7.1
2020-09-24 0.5.0
2020-02-03 0.2.0
2019-05-16 0.1.0

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Package allhomes (with last version 0.3.0) was removed from CRAN

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

2022-09-09 0.3.0

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Package Spillover (with last version 0.1.0.2) was removed from CRAN

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

2022-10-16 0.1.0.2
2021-06-17 0.1.0.1
2020-09-29 0.1

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Package legco (with last version 0.1.4) was removed from CRAN

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

2021-10-16 0.1.4
2020-03-31 0.1.3

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Package MOEADr (with last version 1.1.1) was removed from CRAN

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

2020-02-17 1.1.1
2017-10-24 1.1.0
2017-08-20 1.0.0
2017-03-20 0.2.1
2017-03-16 0.2.0

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Package DynForest (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:

2022-09-20 1.0.0

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Package skedastic updated to version 2.0.1 with previous version 1.0.4 dated 2022-02-21

Title: Handling Heteroskedasticity in the Linear Regression Model
Description: Implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The wild bootstrap employed here follows the description i [...truncated...]
Author: Thomas Farrar [aut, cre] , University of the Western Cape [cph]
Maintainer: Thomas Farrar <tjfarrar@alumni.uwaterloo.ca>

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Package lubridate updated to version 1.9.0 with previous version 1.8.0 dated 2021-10-07

Title: Make Dealing with Dates a Little Easier
Description: Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The 'lubridate' package has a consistent and memorable syntax that makes working with dates easy and fun.
Author: Vitalie Spinu [aut, cre], Garrett Grolemund [aut], Hadley Wickham [aut], Davis Vaughan [ctb], Ian Lyttle [ctb], Imanuel Costigan [ctb], Jason Law [ctb], Doug Mitarotonda [ctb], Joseph Larmarange [ctb], Jonathan Boiser [ctb], Chel Hee Lee [ctb]
Maintainer: Vitalie Spinu <spinuvit@gmail.com>

Diff between lubridate versions 1.8.0 dated 2021-10-07 and 1.9.0 dated 2022-11-06

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Package DEoptim updated to version 2.2-7 with previous version 2.2-6 dated 2021-05-05

Title: Global Optimization by Differential Evolution
Description: Implements the Differential Evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector as described in Mullen et al. (2011) <doi:10.18637/jss.v040.i06>.
Author: David Ardia [aut] , Katharine Mullen [aut, cre], Brian Peterson [aut], Joshua Ulrich [aut], Kris Boudt [ctb]
Maintainer: Katharine Mullen <mullenkate@gmail.com>

Diff between DEoptim versions 2.2-6 dated 2021-05-05 and 2.2-7 dated 2022-11-06

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Permanent link

Package mmodely updated to version 0.2.2 with previous version 0.2.0 dated 2022-11-04

Title: Modeling Multivariate Origins Determinants - Evolutionary Lineages in Ecology
Description: Perform multivariate modeling of evolved traits, with special attention to understanding the interplay of the multi-factorial determinants of their origins in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>). This software primarily concentrates on phylogenetic regression analysis, enabling implementation of tree transformation averaging and visualization functionality. Functions additionally support information theoretic approaches (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>; Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>) such as model averaging and selection of phylogenetic models. There are other numerous functions for visualizing confounded variables, plotting phylogenetic trees, as well as reporting and exporting modeling results. Lastly, as challenges to ecology are inherently multifarious, and therefore often multi-dataset, this package features several functions to support the identification, interpolation, merging, an [...truncated...]
Author: David M Schruth
Maintainer: David M Schruth <dschruth@anthropoidea.org>

Diff between mmodely versions 0.2.0 dated 2022-11-04 and 0.2.2 dated 2022-11-06

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Permanent link

Package gluedown updated to version 1.0.6 with previous version 1.0.5 dated 2022-11-04

Title: Wrap Vectors in Markdown Formatting
Description: Ease the transition between R vectors and markdown text. With 'gluedown' and 'rmarkdown', users can create traditional vectors in R, glue those strings together with the markdown syntax, and print those formatted vectors directly to the document. This package primarily uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous CommonMark specification by John MacFarlane (2019) <https://spec.commonmark.org/>.
Author: Kiernan Nicholls [aut, cre]
Maintainer: Kiernan Nicholls <kiernann@protonmail.com>

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Package ctsem updated to version 3.7.2 with previous version 3.7.1 dated 2022-08-08

Title: Continuous Time Structural Equation Modelling
Description: Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Mod [...truncated...]
Author: Charles Driver [aut, cre, cph], Manuel Voelkle [aut, cph], Han Oud [aut, cph], Trustees of Columbia University [cph]
Maintainer: Charles Driver <charles.driver@ibe.uzh.ch>

Diff between ctsem versions 3.7.1 dated 2022-08-08 and 3.7.2 dated 2022-11-06

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Package Boom updated to version 0.9.11 with previous version 0.9.9 dated 2022-05-27

Title: Bayesian Object Oriented Modeling
Description: A C++ library for Bayesian modeling, with an emphasis on Markov chain Monte Carlo. Although boom contains a few R utilities (mainly plotting functions), its primary purpose is to install the BOOM C++ library on your system so that other packages can link against it.
Author: Steven L. Scott is the sole author and creator of the BOOM project. Some code in the BOOM libraries has been modified from other open source projects. These include Cephes , NEWUOA , and a modified version of the R math libraries . Original copyrig [...truncated...]
Maintainer: Steven L. Scott <steve.the.bayesian@gmail.com>

Diff between Boom versions 0.9.9 dated 2022-05-27 and 0.9.11 dated 2022-11-06

 Boom-0.9.11/Boom/DESCRIPTION                                                                            |   11 
 Boom-0.9.11/Boom/MD5                                                                                    |  184 +++---
 Boom-0.9.11/Boom/inst/include/LinAlg/ArrayIterator.hpp                                                  |   41 -
 Boom-0.9.11/Boom/inst/include/LinAlg/Eigen.hpp                                                          |   14 
 Boom-0.9.11/Boom/inst/include/LinAlg/Matrix.hpp                                                         |   26 
 Boom-0.9.11/Boom/inst/include/LinAlg/Selector.hpp                                                       |   20 
 Boom-0.9.11/Boom/inst/include/LinAlg/SpdMatrix.hpp                                                      |    2 
 Boom-0.9.11/Boom/inst/include/LinAlg/VectorViewIterator.hpp                                             |   34 -
 Boom-0.9.11/Boom/inst/include/Models/BetaBinomialModel.hpp                                              |   51 +
 Boom-0.9.11/Boom/inst/include/Models/ConstrainedVectorParams.hpp                                        |   80 ++
 Boom-0.9.11/Boom/inst/include/Models/DataTypes.hpp                                                      |    3 
 Boom-0.9.11/Boom/inst/include/Models/Glm/Glm.hpp                                                        |    2 
 Boom-0.9.11/Boom/inst/include/Models/Glm/GlmCoefs.hpp                                                   |    4 
 Boom-0.9.11/Boom/inst/include/Models/Glm/PosteriorSamplers/RegressionConjSampler.hpp                    |    3 
 Boom-0.9.11/Boom/inst/include/Models/Glm/RegressionModel.hpp                                            |    6 
 Boom-0.9.11/Boom/inst/include/Models/IRT/PartialCreditModel.hpp                                         |    6 
 Boom-0.9.11/Boom/inst/include/Models/Mixtures/BetaBinomialMixture.hpp                                   |only
 Boom-0.9.11/Boom/inst/include/Models/Mixtures/PosteriorSamplers/BetaBinomialMixturePosteriorSampler.hpp |only
 Boom-0.9.11/Boom/inst/include/Models/MultinomialModel.hpp                                               |    4 
 Boom-0.9.11/Boom/inst/include/Models/ParamTypes.hpp                                                     |    8 
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 Boom-0.9.11/Boom/inst/include/Models/PosteriorSamplers/GenericStudentSampler.hpp                        |    2 
 Boom-0.9.11/Boom/inst/include/Models/StateSpace/Filters/ConditionalIidKalmanFilter.hpp                  |    8 
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 Boom-0.9.11/Boom/inst/include/Models/StateSpace/Filters/KalmanFilterBase.hpp                            |    9 
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 Boom-0.9.11/Boom/inst/include/Models/StateSpace/Filters/ScalarKalmanFilter.hpp                          |    2 
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 Boom-0.9.11/Boom/inst/include/Models/StateSpace/Multivariate/tests/mv_framework.hpp                     |   20 
 Boom-0.9.11/Boom/inst/include/Models/StateSpace/StateModels/ArStateModel.hpp                            |    2 
 Boom-0.9.11/Boom/inst/include/Models/StateSpace/StateModels/GeneralSeasonalStateModel.hpp               |    2 
 Boom-0.9.11/Boom/inst/include/Models/StateSpace/StateModels/LocalLinearTrend.hpp                        |    2 
 Boom-0.9.11/Boom/inst/include/Models/StateSpace/StateModels/StateModel.hpp                              |    2 
 Boom-0.9.11/Boom/inst/include/TargetFun/JacobianChecker.hpp                                             |    5 
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 Boom-0.9.11/Boom/inst/include/TargetFun/Transformation.hpp                                              |   48 -
 Boom-0.9.11/Boom/inst/include/distributions.hpp                                                         |    3 
 Boom-0.9.11/Boom/inst/include/distributions/Markov.hpp                                                  |   31 -
 Boom-0.9.11/Boom/inst/include/stats/ChiSquareTest.hpp                                                   |   23 
 Boom-0.9.11/Boom/inst/include/stats/FreqDist.hpp                                                        |   11 
 Boom-0.9.11/Boom/inst/include/stats/logit.hpp                                                           |    6 
 Boom-0.9.11/Boom/src/LinAlg/Eigen.cpp                                                                   |   33 +
 Boom-0.9.11/Boom/src/LinAlg/Givens.cpp                                                                  |    1 
 Boom-0.9.11/Boom/src/LinAlg/LU.cpp                                                                      |    4 
 Boom-0.9.11/Boom/src/LinAlg/Matrix.cpp                                                                  |   51 +
 Boom-0.9.11/Boom/src/LinAlg/Selector.cpp                                                                |   24 
 Boom-0.9.11/Boom/src/LinAlg/SpdMatrix.cpp                                                               |   22 
 Boom-0.9.11/Boom/src/Models/BetaBinomialModel.cpp                                                       |  203 +++++-
 Boom-0.9.11/Boom/src/Models/ConstrainedVectorParams.cpp                                                 |  116 ++-
 Boom-0.9.11/Boom/src/Models/DataTypes.cpp                                                               |    1 
 Boom-0.9.11/Boom/src/Models/Glm/GlmCoefs.cpp                                                            |   24 
 Boom-0.9.11/Boom/src/Models/IRT/PartialCreditModel.cpp                                                  |   22 
 Boom-0.9.11/Boom/src/Models/MarkovModel.cpp                                                             |    1 
 Boom-0.9.11/Boom/src/Models/Mixtures/BetaBinomialMixture.cpp                                            |only
 Boom-0.9.11/Boom/src/Models/Mixtures/PosteriorSamplers/BetaBinomialMixturePosteriorSampler.cpp          |only
 Boom-0.9.11/Boom/src/Models/ModelTypes.cpp                                                              |    4 
 Boom-0.9.11/Boom/src/Models/MultinomialModel.cpp                                                        |   17 
 Boom-0.9.11/Boom/src/Models/PosteriorSamplers/BetaBinomialPosteriorSampler.cpp                          |  160 +----
 Boom-0.9.11/Boom/src/Models/PosteriorSamplers/DirichletPosteriorSampler.cpp                             |  153 -----
 Boom-0.9.11/Boom/src/Models/PosteriorSamplers/GenericStudentSampler.cpp                                 |    2 
 Boom-0.9.11/Boom/src/Models/SpdData.cpp                                                                 |   20 
 Boom-0.9.11/Boom/src/Models/StateSpace/Filters/ConditionalIidKalmanFilter.cpp                           |  103 +++
 Boom-0.9.11/Boom/src/Models/StateSpace/Filters/ConditionallyIndependentKalmanFilter.cpp                 |  125 +++-
 Boom-0.9.11/Boom/src/Models/StateSpace/Filters/MultivariateKalmanFilterBase.cpp                         |  294 ++++++++--
 Boom-0.9.11/Boom/src/Models/StateSpace/Filters/SparseMatrix.cpp                                         |  254 +++++++-
 Boom-0.9.11/Boom/src/Models/StateSpace/Filters/SparseVector.cpp                                         |    1 
 Boom-0.9.11/Boom/src/Models/StateSpace/Multivariate/MultivariateStateSpaceModelBase.cpp                 |   81 ++
 Boom-0.9.11/Boom/src/Models/StateSpace/Multivariate/MultivariateStateSpaceRegressionModel.cpp           |   73 --
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 Boom-0.9.11/Boom/src/Models/StateSpace/Multivariate/StateModels/ScalarStateModelAdapter.cpp             |  192 ++----
 Boom-0.9.11/Boom/src/Models/StateSpace/StateModels/StateModel.cpp                                       |   15 
 Boom-0.9.11/Boom/src/Models/StateSpace/StateModels/test_utils/ArStateModelTestModule.cpp                |    4 
 Boom-0.9.11/Boom/src/Models/ZeroMeanGaussianModel.cpp                                                   |    4 
 Boom-0.9.11/Boom/src/Samplers/Gilks/arms.cpp                                                            |   57 -
 Boom-0.9.11/Boom/src/TargetFun/JacobianChecker.cpp                                                      |   60 +-
 Boom-0.9.11/Boom/src/TargetFun/LogTransform.cpp                                                         |only
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 Boom-0.9.11/Boom/src/TargetFun/MultinomialLogitTransform.cpp                                            |only
 Boom-0.9.11/Boom/src/TargetFun/Transformation.cpp                                                       |   52 -
 Boom-0.9.11/Boom/src/cpputil/LongString.cpp                                                             |   16 
 Boom-0.9.11/Boom/src/cpputil/report_error.cpp                                                           |    4 
 Boom-0.9.11/Boom/src/distributions/Markov.cpp                                                           |   19 
 Boom-0.9.11/Boom/src/math/cephes/planck.cpp                                                             |    2 
 Boom-0.9.11/Boom/src/numopt/NelderMead.cpp                                                              |    1 
 Boom-0.9.11/Boom/src/numopt/bfgs.cpp                                                                    |    1 
 Boom-0.9.11/Boom/src/numopt/max_nd.cpp                                                                  |    1 
 Boom-0.9.11/Boom/src/numopt/newton.cpp                                                                  |    1 
 Boom-0.9.11/Boom/src/stats/ChiSquareTest.cpp                                                            |   38 +
 Boom-0.9.11/Boom/src/stats/DataTable.cpp                                                                |    1 
 Boom-0.9.11/Boom/src/stats/FreqDist.cpp                                                                 |   12 
 Boom-0.9.9/Boom/inst/include/cpputil/date_utils.hpp                                                     |only
 Boom-0.9.9/Boom/src/cpputil/get_date.cpp                                                                |only
 100 files changed, 2287 insertions(+), 1312 deletions(-)

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Package abess updated to version 0.4.6 with previous version 0.4.5 dated 2022-03-22

Title: Fast Best Subset Selection
Description: Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <arXiv:2104.12576> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.
Author: Jin Zhu [aut, cre] , Liyuan Hu [aut], Junhao Huang [aut], Kangkang Jiang [aut], Yanhang Zhang [aut], Zezhi Wang [aut], Borui Tang [aut], Shiyun Lin [aut], Junxian Zhu [aut], Canhong Wen [aut], Heping Zhang [aut] , Xueqin Wang [aut] , spectra contribu [...truncated...]
Maintainer: Jin Zhu <zhuj37@mail2.sysu.edu.cn>

Diff between abess versions 0.4.5 dated 2022-03-22 and 0.4.6 dated 2022-11-06

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 abess-0.4.6/abess/src/AlgorithmGLM.h            |    4 
 abess-0.4.6/abess/src/Metric.h                  |    4 
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New package ICSsmoothing with initial version 1.2.6
Package: ICSsmoothing
Title: Data Smoothing by Interpolating Cubic Splines
Version: 1.2.6
Author: Juraj Hudak, Lubomir Antoni, Csaba Torok
Maintainer: Lubomir Antoni <lubomir.antoni@upjs.sk>
Description: We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing.
Depends: R (>= 3.5.0), polynom, ggplot2
License: GPL-2
Encoding: UTF-8
LazyData: true
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-10-23 19:51:44 UTC; xy
Repository: CRAN
Date/Publication: 2022-11-06 05:00:02 UTC

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