Sun, 08 Sep 2019

Package sjstats updated to version 0.17.6 with previous version 0.17.5 dated 2019-06-04

Title: Collection of Convenient Functions for Common Statistical Computations
Description: Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), or for which currently no functions available. Second, another focus lies on weighted variants of common statistical measures and tests like weighted standard error, mean, t-test, correlation, and more.
Author: Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>)
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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Package npsf updated to version 0.5.2 with previous version 0.5.1 dated 2019-04-12

Title: Nonparametric and Stochastic Efficiency and Productivity Analysis
Description: Provides a variety of tools for nonparametric and parametric efficiency measurement.
Author: Oleg Badunenko [aut, cre], Yaryna Kolomiytseva [aut], Pavlo Mozharovskyi [aut]
Maintainer: Oleg Badunenko <oleg.badunenko@port.ac.uk>

Diff between npsf versions 0.5.1 dated 2019-04-12 and 0.5.2 dated 2019-09-08

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Package hansard updated to version 0.7.1 with previous version 0.7.0 dated 2019-05-21

Title: Provides Easy Downloading Capabilities for the UK Parliament API
Description: Provides functions to download data from the <http://www.data.parliament.uk/> APIs. Because of the structure of the API, there is a named function for each type of available data for ease of use, as well as some functions designed to retrieve specific pieces of commonly used data. Functions for each new API will be added as and when they become available.
Author: Evan Odell [aut, cre] (<https://orcid.org/0000-0003-1845-808X>)
Maintainer: Evan Odell <evanodell91@gmail.com>

Diff between hansard versions 0.7.0 dated 2019-05-21 and 0.7.1 dated 2019-09-08

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Package checkpoint updated to version 0.4.7 with previous version 0.4.6 dated 2019-07-27

Title: Install Packages from Snapshots on the Checkpoint Server for Reproducibility
Description: The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint's checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) Microsoft refreshes the Austria CRAN mirror on the "Microsoft R Archived Network" server (<https://mran.microsoft.com/>). Immediately after completion of the rsync mirror process, the process takes a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17.
Author: Hong Ooi [aut, cre], Microsoft [cph]
Maintainer: Hong Ooi <hongooi@microsoft.com>

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Package AzureAuth updated to version 1.2.1 with previous version 1.2.0 dated 2019-08-21

Title: Authentication Services for Azure Active Directory
Description: Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud <https://azure.microsoft.com/>. Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages.
Author: Hong Ooi [aut, cre], httr development team [ctb] (Original OAuth listener code), Scott Holden [ctb] (Advice on AAD authentication), Chris Stone [ctb] (Advice on AAD authentication), Microsoft [cph]
Maintainer: Hong Ooi <hongooi@microsoft.com>

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Package pinp updated to version 0.0.8 with previous version 0.0.7 dated 2019-01-11

Title: 'pinp' is not 'PNAS'
Description: A 'PNAS'-alike style for 'rmarkdown', derived from the 'Proceedings of the National Academy of Sciences of the United States of America' ('PNAS', see <https://www.pnas.org>) 'LaTeX' style, and adapted for use with 'markdown' and 'pandoc'.
Author: Dirk Eddelbuettel and James Balamuta
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package GGIR updated to version 1.10-3 with previous version 1.10-1 dated 2019-08-23

Title: Raw Accelerometer Data Analysis
Description: A tool to process and analyse data collected with wearable raw acceleration sensors as described in van Hees and colleagues (2014) <doi: 10.1152/japplphysiol.00421.2014> and (2015) <doi: 10.1371/journal.pone.0142533>. The package has been developed and tested for binary data from 'GENEActiv' <https://www.activinsights.com/> and GENEA devices (not for sale), .csv-export data from 'Actigraph' <http://actigraphcorp.com> devices, and .cwa and .wav-format data from 'Axivity' <https://axivity.com/product/ax3>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format and has either no header or a two column header.
Author: Vincent T van Hees [aut, cre], Zhou Fang [ctb], Jing Hua Zhao [ctb], Joe Heywood [ctb], Evgeny Mirkes [ctb], Severine Sabia [ctb], Joan Capdevila Pujol [ctb], Jairo H Migueles [ctb]
Maintainer: Vincent T van Hees <vincentvanhees@gmail.com>

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Package funModeling updated to version 1.9.2 with previous version 1.9 dated 2019-08-26

Title: Exploratory Data Analysis and Data Preparation Tool-Box
Description: Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
Author: Pablo Casas [aut, cre]
Maintainer: Pablo Casas <pcasas.biz@gmail.com>

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Package cna updated to version 2.2.1 with previous version 2.2.0 dated 2019-04-13

Title: Causal Modeling with Coincidence Analysis
Description: Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is related to Qualitative Comparative Analysis (QCA), but contrary to the latter, it is custom-built for uncovering causal structures with multiple outcomes and it builds causal models from the bottom up by gradually combining single factors to complex dependency structures until the requested thresholds of model fit are met. The new functionalities provided by this package version include functions for evaluating and benchmarking the correctness of CNA's output, a function determining whether a solution is an INUS model, a function bringing non-INUS expressions into INUS form, and a function for identifying cyclic models. The package vignette has been updated accordingly.
Author: Mathias Ambuehl [aut, cre, cph], Michael Baumgartner [aut, cph], Ruedi Epple [ctb], Veli-Pekka Parkkinen [ctb], Alrik Thiem [ctb]
Maintainer: Mathias Ambuehl <mathias.ambuehl@consultag.ch>

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Package RSCABS (with last version 0.9.3) was removed from CRAN

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

2018-04-03 0.9.3
2017-04-26 0.9.2
2016-12-12 0.9.01
2016-08-12 0.9.0

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Package StatCharrms (with last version 0.90.92) was removed from CRAN

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

2019-02-11 0.90.92
2018-06-10 0.90.91

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Package phaseR (with last version 2.0) was removed from CRAN

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

2018-08-20 2.0
2014-07-16 1.3
2013-10-09 1.2

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Package nlsr updated to version 2019.9.7 with previous version 2018.1.28 dated 2018-01-31

Title: Functions for Nonlinear Least Squares Solutions
Description: Provides tools for working with nonlinear least squares problems. It is intended to eventually supersede the 'nls()' function in the R distribution. For example, 'nls()' specifically does NOT deal with small or zero residual problems as its Gauss-Newton method frequently stops with 'singular gradient' messages. 'nlsr' is based on the now-deprecated package 'nlmrt', and has refactored functions and R-language symbolic derivative features.
Author: John C Nash [aut, cre], Duncan Murdoch [aut]
Maintainer: John C Nash <nashjc@uottawa.ca>

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Package kernelPSI updated to version 1.1.0 with previous version 1.0.0 dated 2019-06-24

Title: Post-Selection Inference for Nonlinear Variable Selection
Description: Different post-selection inference strategies for kernel selection, as described in "kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection", Slim et al., Proceedings of Machine Learning Research, 2019, <http://proceedings.mlr.press/v97/slim19a/slim19a.pdf>. The strategies rest upon quadratic kernel association scores to measure the association between a given kernel and an outcome of interest. The inference step tests for the joint effect of the selected kernels on the outcome. A fast constrained sampling algorithm is proposed to derive empirical p-values for the test statistics.
Author: Lotfi Slim [aut, cre], Clément Chatelain [ctb], Chloé-Agathe Azencott [ctb], Jean-Philippe Vert [ctb]
Maintainer: Lotfi Slim <lotfi.slim@mines-paristech.fr>

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Package FunChisq updated to version 2.4.8-1 with previous version 2.4.5-3 dated 2018-12-06

Title: Model-Free Functional Chi-Squared and Exact Tests
Description: Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-squared tests ('Zhang & Song' 2013) <arXiv:1311.2707> and an exact functional test ('Zhong & Song' 2019) <doi:10.1109/TCBB.2018.2809743>. The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges ('Hill et al' 2016) <doi:10.1038/nmeth.3773>. A function index ('Zhong & Song' in press) ('Kumar et al' 2018) <doi:10.1109/BIBM.2018.8621502> derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests.
Author: Yang Zhang [aut], Hua Zhong [aut] (<https://orcid.org/0000-0003-1962-2603>), Hien Nguyen [aut], Ruby Sharma [aut], Sajal Kumar [aut], Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>)
Maintainer: Joe Song <joemsong@cs.nmsu.edu>

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Package epiGWAS updated to version 1.0.2 with previous version 1.0.1 dated 2019-05-21

Title: Robust Methods for Epistasis Detection
Description: Functions to perform robust epistasis detection in genome-wide association studies, as described in Slim et al. (2018) <doi:10.1101/442749>. The implemented methods identify pairwise interactions between a particular target variant and the rest of the genotype, using a propensity score approach. The propensity score models the linkage disequilibrium between the target and the rest of the genotype. All methods are penalized regression approaches, which differently incorporate the propensity score to only recover the synergistic effects between the target and the genotype.
Author: Lotfi Slim [aut, cre], Clément Chatelain [ctb], Chloé-Agathe Azencott [ctb], Jean-Philippe Vert [ctb]
Maintainer: Lotfi Slim <lotfi.slim@mines-paristech.fr>

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Package plot3logit updated to version 1.0.2 with previous version 1.0.1 dated 2019-02-20

Title: Ternary Plots for Trinomial Regression Models
Description: An implementation of the ternary plot for interpreting regression coefficients of trinomial regression models, as proposed in Santi, Dickson and Espa (2019) <doi:10.1080/00031305.2018.1442368>. Ternary plots can be drawn using either 'ggtern' package (based on 'ggplot2') or 'Ternary' package (based on standard graphics).
Author: Flavio Santi [cre, aut] (<https://orcid.org/0000-0002-2014-1981>), Maria Michela Dickson [aut] (<https://orcid.org/0000-0002-4307-0469>), Giuseppe Espa [aut] (<https://orcid.org/0000-0002-0331-3630>)
Maintainer: Flavio Santi <flavio.santi@univr.it>

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Package pder updated to version 1.0-1 with previous version 1.0-0 dated 2017-09-28

Title: Panel Data Econometrics with R
Description: Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.
Author: Yves Croissant [aut, cre], Giovanni Millo [aut]
Maintainer: Yves Croissant <yves.croissant@univ-reunion.fr>

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Package collidr updated to version 0.1.2 with previous version 0.1.1 dated 2019-07-18

Title: Check for Namespace Collisions with Other Packages and Functions on CRAN
Description: Check for namespace collisions between a string input (your function or package name) and a quarter of a million packages and functions on CRAN.
Author: Steve Condylios [aut, cre] (<https://orcid.org/0000-0003-0599-844X>)
Maintainer: Steve Condylios <steve.condylios@gmail.com>

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Package pammtools updated to version 0.1.14 with previous version 0.1.11 dated 2019-04-18

Title: Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis
Description: The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and Scheipl (2018) <doi: 10.1177/1471082X17748083>) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2018) <doi:10.1093/biostatistics/kxy003>. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.
Author: Andreas Bender [aut, cre] (<https://orcid.org/0000-0001-5628-8611>), Fabian Scheipl [aut]
Maintainer: Andreas Bender <andreas.bender@stat.uni-muenchen.de>

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Package joint.Cox updated to version 3.5 with previous version 3.4 dated 2019-07-18

Title: Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis
Description: Perform likelihood estimation and dynamic prediction under joint frailty-copula models for tumour progression and death in meta-analysis. A penalized likelihood method is employed for estimating model parameters, where the baseline hazard functions are modeled by smoothing splines. The methods are applicable for meta-analytic data combining several studies. The methods can analyze data having information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). See Emura et al. (2017) <doi:10.1177/0962280215604510> for likelihood estimation, and Emura et al. (2018) <doi:10.1177/0962280216688032> for dynamic prediction. More details on these methods can also be found in a book of Emura et al. (2019) <10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.
Author: Takeshi Emura
Maintainer: Takeshi Emura <takeshiemura@gmail.com>

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Package dynr (with last version 0.1.14-9) was removed from CRAN

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

2019-04-02 0.1.14-9

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Package changepointsHD (with last version 0.3.2) was removed from CRAN

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

2019-09-06 0.3.2
2019-03-16 0.3.1
2017-11-07 0.3.0

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Package Phase12Compare (with last version 1.1) was removed from CRAN

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

2019-09-04 1.1
2019-08-30 1.0

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Package mhde (with last version 1.0-1) was removed from CRAN

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

2015-10-23 1.0-1

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Package OptSig updated to version 2.0 with previous version 1.0 dated 2017-12-21

Title: Optimal Level of Significance for Regression and Other Statistical Tests
Description: Calculates the optimal level of significance based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim, Jae H. and Choi, In, 2019, Choosing the Level of Significance: A Decision-Theoretic Approach, Abacus. See also Kim and Ji (2015) <doi:10.1016/j.jempfin.2015.08.006>.
Author: Jae H. Kim <J.Kim@latrobe.edu.au>
Maintainer: Jae H. Kim <J.Kim@latrobe.edu.au>

Diff between OptSig versions 1.0 dated 2017-12-21 and 2.0 dated 2019-09-08

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Package circlize updated to version 0.4.8 with previous version 0.4.7 dated 2019-08-21

Title: Circular Visualization
Description: Circular layout is an efficient way for the visualization of huge amounts of information. Here this package provides an implementation of circular layout generation in R as well as an enhancement of available software. The flexibility of the package is based on the usage of low-level graphics functions such that self-defined high-level graphics can be easily implemented by users for specific purposes. Together with the seamless connection between the powerful computational and visual environment in R, it gives users more convenience and freedom to design figures for better understanding complex patterns behind multiple dimensional data.
Author: Zuguang Gu
Maintainer: Zuguang Gu <z.gu@dkfz.de>

Diff between circlize versions 0.4.7 dated 2019-08-21 and 0.4.8 dated 2019-09-08

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Package welchADF updated to version 0.3.2 with previous version 0.3.1 dated 2019-05-11

Title: Welch-James Statistic for Robust Hypothesis Testing under Heterocedasticity and Non-Normality
Description: Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://supp.apa.org/psycarticles/supplemental/met_13_2_110/SAS_Program.pdf> and published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.
Author: Pablo J. Villacorta <pjvi@decsai.ugr.es>
Maintainer: Pablo J. Villacorta <pjvi@decsai.ugr.es>

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Package blockRAR updated to version 1.0.1 with previous version 1.0.0 dated 2019-04-27

Title: Block Design for Response-Adaptive Randomization
Description: Computes power for response-adaptive randomization with a block design that captures both the time and treatment effect. T. Chandereng, R. Chappell (2019) <arXiv:1904.07758>.
Author: Thevaa Chandereng [aut, cre, cph] (<https://orcid.org/0000-0003-4078-9176>), Rick Chapppell [aut, cph]
Maintainer: Thevaa Chandereng <chandereng@wisc.edu>

Diff between blockRAR versions 1.0.0 dated 2019-04-27 and 1.0.1 dated 2019-09-08

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