Tue, 17 Nov 2020

Package MoEClust updated to version 1.3.2 with previous version 1.3.1 dated 2020-05-12

Title: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
Description: Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>)
Maintainer: Keefe Murphy <keefe.murphy@mu.ie>

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Package hpa updated to version 1.1.3 with previous version 1.1.2 dated 2020-08-24

Title: Distributions Hermite Polynomial Approximation
Description: Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.
Author: Potanin Bogdan
Maintainer: Potanin Bogdan <bogdanpotanin@gmail.com>

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Package MAMS updated to version 1.4 with previous version 1.3 dated 2019-01-25

Title: Designing Multi-Arm Multi-Stage Studies
Description: Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.
Author: Thomas Jaki <jaki.thomas@gmail.com>, Dominic Magirr <d.magirr@gmail.com>, Philip Pallmann <p.pallmann@lancaster.ac.uk>
Maintainer: Thomas Jaki <jaki.thomas@gmail.com>

Diff between MAMS versions 1.3 dated 2019-01-25 and 1.4 dated 2020-11-17

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Package DAC (with last version 0.1.1) was removed from CRAN

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

2018-05-06 0.1.1
2017-09-08 0.1.0

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Package stochvol updated to version 3.0.2 with previous version 3.0.1 dated 2020-11-04

Title: Efficient Bayesian Inference for Stochastic Volatility (SV) Models
Description: Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Kastner (2016) <doi:10.18637/jss.v069.i05> and the package vignette.
Author: Darjus Hosszejni [aut, cre] (<https://orcid.org/0000-0002-3803-691X>), Gregor Kastner [aut] (<https://orcid.org/0000-0002-8237-8271>)
Maintainer: Darjus Hosszejni <darjus.hosszejni@wu.ac.at>

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Package netgwas updated to version 1.13 with previous version 1.12 dated 2020-10-21

Title: Network-Based Genome Wide Association Studies
Description: A multi-core R package that contains a set of tools based on copula graphical models for accomplishing the three interrelated goals in genetics and genomics in an unified way: (1) linkage map construction, (2) constructing linkage disequilibrium networks, and (3) exploring high-dimensional genotype-phenotype network and genotype- phenotype-environment interactions networks. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is considerably larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent methodological developments in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <doi:10.1093/bioinformatics/bty777>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.
Author: Pariya Behrouzi <https://orcid.org/0000-0001-6762-5433> and Ernst C. Wit
Maintainer: Pariya Behrouzi <pariya.behrouzi@gmail.com>

Diff between netgwas versions 1.12 dated 2020-10-21 and 1.13 dated 2020-11-17

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Package animaltracker updated to version 0.2.0 with previous version 0.1.0 dated 2020-03-25

Title: Animal Tracker
Description: Utilities for spatial-temporal analysis and visualization of animal (e.g. cattle) tracking data. The core feature is a 'shiny' web application for customized processing of GPS logs, including features for data augmentation (e.g. elevation lookup), data selection, export, plotting, and statistical summaries. A data validation application allows for side-by-side comparison via time series plots and extreme value detection described by J.P. van Brakel <https://stackoverflow.com/questions/22583391/peak-signal-detection-in-realtime-timeseries-data/>.
Author: Joe Champion [aut, cre], Thea Sukianto [aut]
Maintainer: Joe Champion <joechampion@boisestate.edu>

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Package RAthena updated to version 1.11.1 with previous version 1.11.0 dated 2020-11-11

Title: Connect to 'AWS Athena' using 'Boto3' ('DBI' Interface)
Description: Designed to be compatible with the R package 'DBI' (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this 'Python' 'Boto3' Software Development Kit ('SDK') <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html> is used as a driver.
Author: Dyfan Jones [aut, cre]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

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Package noctua updated to version 1.9.1 with previous version 1.9.0 dated 2020-11-11

Title: Connect to 'AWS Athena' using R 'AWS SDK' 'paws' ('DBI' Interface)
Description: Designed to be compatible with the 'R' package 'DBI' (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this the 'R' 'AWS' Software Development Kit ('SDK') 'paws' <https://github.com/paws-r/paws> is used as a driver.
Author: Dyfan Jones [aut, cre]
Maintainer: Dyfan Jones <dyfan.r.jones@gmail.com>

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Package magrittr updated to version 2.0.1 with previous version 1.5 dated 2014-11-22

Title: A Forward-Pipe Operator for R
Description: Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions. For more information, see package vignette. To quote Rene Magritte, "Ceci n'est pas un pipe."
Author: Stefan Milton Bache [aut, cph] (Original author and creator of magrittr), Hadley Wickham [aut], Lionel Henry [cre], RStudio [cph, fnd]
Maintainer: Lionel Henry <lionel@rstudio.com>

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Package geneExpressionFromGEO updated to version 0.3 with previous version 0.2 dated 2020-11-16

Title: Retrieves Gene Expression Dataset and Gene Symbols from GEO Code
Description: A function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optional) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL80, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>.
Author: Davide Chicco [aut, cre]
Maintainer: Davide Chicco <davidechicco@davidechicco.it>

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Package vctrs updated to version 0.3.5 with previous version 0.3.4 dated 2020-08-29

Title: Vector Helpers
Description: Defines new notions of prototype and size that are used to provide tools for consistent and well-founded type-coercion and size-recycling, and are in turn connected to ideas of type- and size-stability useful for analysing function interfaces.
Author: Hadley Wickham [aut], Lionel Henry [aut, cre], Davis Vaughan [aut], data.table team [cph] (Radix sort based on data.table's forder() and their contribution to R's order()), RStudio [cph]
Maintainer: Lionel Henry <lionel@rstudio.com>

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Package usemodels updated to version 0.1.0 with previous version 0.0.1 dated 2020-09-22

Title: Boilerplate Code for 'Tidymodels' Analyses
Description: Code snippets to fit models using the tidymodels framework can be easily created for a given data set.
Author: Max Kuhn [aut, cre] (<https://orcid.org/0000-0003-2402-136X>), RStudio [cph]
Maintainer: Max Kuhn <max@rstudio.com>

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Package DIZutils updated to version 0.0.6 with previous version 0.0.4 dated 2020-06-03

Title: Utilities for 'DIZ' R Package Development
Description: Utility functions used for the R package development infrastructure inside the data integration centers ('DIZ') to standardize and facilitate repetitive tasks such as setting up a database connection or issuing notification messages and to avoid redundancy.
Author: Jonathan M. Mang [aut, cre] (<https://orcid.org/0000-0003-0518-4710>), Lorenz A. Kapsner [aut] (<https://orcid.org/0000-0003-1866-860X>), MIRACUM - Medical Informatics in Research and Care in University Medicine [fnd], Universitätsklinikum Erlangen [cph]
Maintainer: Jonathan M. Mang <jonathan.mang@uk-erlangen.de>

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Package vinereg updated to version 0.7.2 with previous version 0.7.1 dated 2020-10-12

Title: D-Vine Quantile Regression
Description: Implements D-vine quantile regression models with parametric or nonparametric pair-copulas. See Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and Schallhorn et al. (2017) <arXiv:1705.08310>.
Author: Thomas Nagler [aut, cre], Dani Kraus [ctb]
Maintainer: Thomas Nagler <mail@tnagler.com>

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Package treespace updated to version 1.1.4.0 with previous version 1.1.3.2 dated 2019-12-08

Title: Statistical Exploration of Landscapes of Phylogenetic Trees
Description: Tools for the exploration of distributions of phylogenetic trees. This package includes a 'shiny' interface which can be started from R using treespaceServer(). For further details see Jombart et al. (2017) <DOI:10.1111/1755-0998.12676>.
Author: Thibaut Jombart [aut] (<https://orcid.org/0000-0003-2226-8692>), Michelle Kendall [aut, cre] (<https://orcid.org/0000-0001-7344-7071>), Jacob Almagro-Garcia [aut] (<https://orcid.org/0000-0002-0595-7333>), Caroline Colijn [aut] (<https://orcid.org/0000-0001-6097-6708>)
Maintainer: Michelle Kendall <michelle.kendall@warwick.ac.uk>

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Package monoClust updated to version 1.1.0 with previous version 1.0.0 dated 2020-11-06

Title: Perform Monothetic Clustering with Extensions to Circular Data
Description: Implementation of the Monothetic Clustering algorithm (Chavent, 1998 <doi:10.1016/S0167-8655(98)00087-7>) on continuous data sets. A lot of extensions are included in the package, including applying Monothetic clustering on data sets with circular variables, visualizations with the results, and permutation and cross-validation based tests to support the decision on the number of clusters.
Author: Tan Tran [aut, cre] (<https://orcid.org/0000-0001-9881-6339>), Brian McGuire [aut], Mark Greenwood [aut] (<https://orcid.org/0000-0001-6933-1201>)
Maintainer: Tan Tran <vinhtantran@gmail.com>

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Package inti updated to version 0.1.1 with previous version 0.1.0 dated 2020-10-22

Title: Tools and Statistical Procedures in Plant Science
Description: The 'inti' package is part of the 'inkaverse' project for developing different procedures and tools used in plant science and experimental designs. The mean aim of the package is to support researchers during the planning of experiments and data collection (tarpuy()), data analysis and graphics (yupana()) , and technical writing (rtciles()). Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
Author: Flavio Lozano-Isla [aut, cre] (<https://orcid.org/0000-0002-0714-669X>)
Maintainer: Flavio Lozano-Isla <flavjack@gmail.com>

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Package BatchGetSymbols updated to version 2.5.9 with previous version 2.5.7 dated 2020-04-21

Title: Downloads and Organizes Financial Data for Multiple Tickers
Description: Makes it easy to download a large number of trade data from Yahoo Finance <https://finance.yahoo.com/>.
Author: Marcelo Perlin [aut, cre]
Maintainer: Marcelo Perlin <marceloperlin@gmail.com>

Diff between BatchGetSymbols versions 2.5.7 dated 2020-04-21 and 2.5.9 dated 2020-11-17

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Package tiff updated to version 0.1-6 with previous version 0.1-5 dated 2013-09-04

Title: Read and write TIFF images
Description: This package provides an easy and simple way to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors.
Author: Simon Urbanek <Simon.Urbanek@r-project.org>
Maintainer: Simon Urbanek <Simon.Urbanek@r-project.org>

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Package mvord updated to version 1.1.0 with previous version 1.0.1 dated 2020-05-28

Title: Multivariate Ordinal Regression Models
Description: A flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.
Author: Rainer Hirk [aut, cre], Kurt Hornik [aut], Laura Vana [aut], Alan Genz [ctb] (Fortran Code)
Maintainer: Rainer Hirk <rhirk@wu.ac.at>

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Package scoringutils updated to version 0.1.4 with previous version 0.1.0 dated 2020-06-14

Title: Utilities for Scoring and Assessing Predictions
Description: Combines a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) <doi:10.1198/016214506000001437>) with an easy to use wrapper that can be used to automatically evaluate predictions. Apart from proper scoring rules functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) <doi:10.1371/journal.pcbi.1006785>) of forecasts. Several types of predictions can be evaluated: probabilistic forecasts (generally predictive samples generated by Markov Chain Monte Carlo procedures), quantile forecasts or point forecasts. Observed values and predictions can be either continuous, integer, or binary. Users can either choose to apply these rules separately in a vector / matrix format that can be flexibly used within other packages, or they can choose to do an automatic evaluation of their forecasts. This is implemented with 'data.table' and provides a consistent and very efficient framework for evaluating various types of predictions.
Author: Nikos Bosse [aut, cre] (<https://orcid.org/0000-0002-7750-5280>), Sam Abbott [aut] (<https://orcid.org/0000-0001-8057-8037>), Joel Hellewell [ctb] (<https://orcid.org/0000-0003-2683-0849>), Sophie Meakins [ctb], James Munday [ctb], Katharine Sherratt [ctb], Sebastian Funk [aut]
Maintainer: Nikos Bosse <nikosbosse@gmail.com>

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New package dbmss with initial version 2.7-3
Package: dbmss
Type: Package
Title: Distance-Based Measures of Spatial Structures
Version: 2.7-3
Authors@R: c( person("Eric", "Marcon", email="eric.marcon@agroparistech.fr", role=c("aut", "cre"), comment = c(ORCID = "0000-0002-5249-321X")), person("Gabriel", "Lang", email="gabriel.lang@agroparistech.fr", role="aut", comment = c(ORCID = "0000-0002-4325-6044")), person("Stephane", "Traissac", email="stephane.traissac@agroparistech.fr", role="aut", comment = c(ORCID = "0000-0001-9255-1616")), person("Florence", "Puech", email="florence.puech@u-psud.fr", role="aut", comment = c(ORCID = "0000-0002-5279-6878")) )
Description: Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) <doi:10.18637/jss.v067.c03>. Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on 'spatstat' for some core calculation.
URL: https://github.com/EricMarcon/dbmss
BugReports: https://github.com/EricMarcon/dbmss/issues
License: GNU General Public License
Depends: R (>= 3.3.0), spatstat, Rcpp (>= 0.12.14), ggplot2
Imports: cubature, reshape2, stats, RcppParallel, spatstat.utils, tibble
Suggests: testthat, knitr, pkgdown
LinkingTo: Rcpp, RcppParallel
VignetteBuilder: knitr
SystemRequirements: pandoc, GNU make
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Packaged: 2020-11-01 11:40:55 UTC; emarc
Author: Eric Marcon [aut, cre] (<https://orcid.org/0000-0002-5249-321X>), Gabriel Lang [aut] (<https://orcid.org/0000-0002-4325-6044>), Stephane Traissac [aut] (<https://orcid.org/0000-0001-9255-1616>), Florence Puech [aut] (<https://orcid.org/0000-0002-5279-6878>)
Maintainer: Eric Marcon <eric.marcon@agroparistech.fr>
Repository: CRAN
Date/Publication: 2020-11-01 15:50:02 UTC

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Package glm.predict updated to version 4.0-0 with previous version 3.1-0 dated 2019-09-04

Title: Predicted Values and Discrete Changes for GLM
Description: Functions to calculate predicted values and the difference between the two cases with confidence interval for lm() [linear model], glm() [general linear model], glm.nb() [negative binomial model], polr() [ordinal logistic model], multinom() [multinomial model] and tobit() [tobit model] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
Author: Benjamin Schlegel [aut,cre]
Maintainer: Benjamin Schlegel <kontakt@benjaminschlegel.ch>

Diff between glm.predict versions 3.1-0 dated 2019-09-04 and 4.0-0 dated 2020-11-17

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Package funGp updated to version 0.2.0 with previous version 0.1.0 dated 2020-04-22

Title: Gaussian Process Models for Scalar and Functional Inputs
Description: Construction and smart selection of Gaussian process models with emphasis on treatment of functional inputs. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). Metamodeling background is provided in Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>. The algorithm for structural parameter optimization is described in <https://hal.archives-ouvertes.fr/hal-02532713>.
Author: Jose Betancourt [cre, aut], François Bachoc [aut], Thierry Klein [aut], Deborah Idier [ctb], Jeremy Rohmer [ctb]
Maintainer: Jose Betancourt <djbetancourt@uninorte.edu.co>

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Package tune updated to version 0.1.2 with previous version 0.1.1 dated 2020-07-08

Title: Tidy Tuning Tools
Description: The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.
Author: Max Kuhn [aut, cre], RStudio [cph]
Maintainer: Max Kuhn <max@rstudio.com>

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Package tabr updated to version 0.4.4 with previous version 0.4.3 dated 2020-05-05

Title: Music Notation Syntax, Manipulation, Analysis and Transcription in R
Description: Provides a music notation syntax and a collection of music programming functions for generating, manipulating, organizing and analyzing musical information in R. The music notation framework facilitates creating and analyzing music data in notation form. Music data can be viewed, manipulated and analyzed while in different forms of representation based around different data structures: strings and data frames. Each representation offers advantages over the other for different use cases. Music syntax can be entered directly and represented in character strings to minimize the formatting overhead of data entry by using simple data structures, for example when wanting to quickly enter and transcribe short pieces of music to sheet music or tablature. The package contains functions for directly performing various mathematical, logical and organizational operations and musical transformations on special object classes that facilitate working with music data and notation. The same music data can also be organized in tidy data frames, allowing for a more familiar and powerful approach to the analysis of large amounts of structured music data. Functions are available for mapping seamlessly between these data structures and their representations of musical information. The package also provides API wrapper functions for transcribing musical representations in R into guitar tablature ("tabs") and basic sheet music using the 'LilyPond' backend (<http://lilypond.org>). 'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. The package generates 'LilyPond' files from R code and can pass them to 'LilyPond' to be rendered into sheet music pdf files. The package offers nominal MIDI file output support in conjunction with rendering sheet music. The package can read MIDI files and attempts to structure the MIDI data to integrate as best as possible with the data structures and functionality found throughout the package.
Author: Matthew Leonawicz [aut, cre] (<https://orcid.org/0000-0001-9452-2771>)
Maintainer: Matthew Leonawicz <matt_leonawicz@esource.com>

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Package SRS updated to version 0.2.1 with previous version 0.2.0 dated 2020-11-14

Title: Scaling with Ranked Subsampling
Description: Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.
Author: Lukas Beule [aut, cre], Vitor Heidrich [aut], Devon O'rourke [aut], Petr Karlovsky [aut]
Maintainer: Lukas Beule <lukas.beule@agr.uni-goettingen.de>

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Package scTenifoldNet updated to version 1.2.4 with previous version 1.2.3 dated 2020-08-25

Title: Construct and Compare scGRN from Single-Cell Transcriptomic Data
Description: A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Author: Daniel Osorio [aut, cre] (<https://orcid.org/0000-0003-4424-8422>), Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Jianhua Huang [aut, ctb], James Cai [aut, ctb, ths] (<https://orcid.org/0000-0002-8081-6725>)
Maintainer: Daniel Osorio <dcosorioh@tamu.edu>

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New package r.jive with initial version 2.4
Package: r.jive
Type: Package
Title: Perform JIVE Decomposition for Multi-Source Data
Authors@R: c(person("Michael J.", "O'Connell", role=c("aut","cre"), email="oconnemj@miamioh.edu"), person("Eric F.", "Lock", role="aut"), person("Adam", "Kaplan", role="ctb"))
Version: 2.4
Date: 2020-11-11
Author: Michael J. O'Connell [aut, cre], Eric F. Lock [aut], Adam Kaplan [ctb]
Maintainer: Michael J. O'Connell <oconnemj@miamioh.edu>
Description: Performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
License: GPL-3
Imports: gplots, abind, graphics, stats
Suggests: knitr, rmarkdown
Depends: R(>= 2.10.0)
VignetteBuilder: knitr
Packaged: 2020-11-12 02:50:38 UTC; Eric
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2020-11-17 08:10:08 UTC

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Package psychotools updated to version 0.6-0 with previous version 0.5-1 dated 2020-01-28

Title: Psychometric Modeling Infrastructure
Description: Infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination, guessing, upper asymptotes), unified inference and visualizations, and various datasets for illustration. Intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package "psychomix" and trees based on psychometric models in package "psychotree".
Author: Achim Zeileis [aut, cre] (<https://orcid.org/0000-0003-0918-3766>), Carolin Strobl [aut], Florian Wickelmaier [aut], Basil Komboz [aut], Julia Kopf [aut], Lennart Schneider [aut] (<https://orcid.org/0000-0003-4152-5308>), Rudolf Debelak [aut]
Maintainer: Achim Zeileis <Achim.Zeileis@R-project.org>

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Package pcalg updated to version 2.7-0 with previous version 2.6-12 dated 2020-09-22

Title: Methods for Graphical Models and Causal Inference
Description: Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.
Author: Markus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel Schuerch [ctb], Marco Eigenmann [ctb], Leonard Henckel [ctb], Joris Mooij [ctb]
Maintainer: Markus Kalisch <kalisch@stat.math.ethz.ch>

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Package n1qn1 updated to version 6.0.1-10 with previous version 6.0.1-9 dated 2020-07-02

Title: Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGS Optimization
Description: Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.
Author: Matthew Fidler [aut, cre], Wenping Wang [aut], Claude Lemarechal [aut, ctb], Joseph Bonnans [ctb], Jean-Charles Gilbert [ctb], Claudia Sagastizabal [ctb], Stephen L. Campbell, [ctb], Jean-Philippe Chancelier [ctb], Ramine Nikoukhah [ctb], Dirk Eddelbuettel [ctb], Bruno Jofret [ctb], INRIA [cph]
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>

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Package mlr3misc updated to version 0.6.0 with previous version 0.5.0 dated 2020-08-13

Title: Helper Functions for 'mlr3'
Description: Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>)
Maintainer: Michel Lang <michellang@gmail.com>

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Package mlergm updated to version 0.7 with previous version 0.6 dated 2020-07-03

Title: Multilevel Exponential-Family Random Graph Models
Description: Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (2006) <DOI: 10.1198/106186006X133069>. The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.
Author: Jonathan Stewart [cre, aut], Michael Schweinberger [ctb]
Maintainer: Jonathan Stewart <jrstewart@fsu.edu>

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Package exampletestr updated to version 1.6.2 with previous version 1.6.1 dated 2020-04-15

Title: Help for Writing Unit Tests Based on Function Examples
Description: Take the examples written in your documentation of functions and use them to create shells (skeletons which must be manually completed by the user) of test files to be tested with the 'testthat' package. Sort of like python 'doctests' for R.
Author: Rory Nolan [aut, cre] (<https://orcid.org/0000-0002-5239-4043>), Sergi Padilla-Parra [ths] (<https://orcid.org/0000-0002-8010-9481>), Thomas Quinn [rev] (<https://orcid.org/0000-0003-0286-6329>), Laurent Gatto [rev] (<https://orcid.org/0000-0002-1520-2268>)
Maintainer: Rory Nolan <rorynoolan@gmail.com>

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More information about exampletestr at CRAN
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Package dlookr updated to version 0.3.14 with previous version 0.3.13 dated 2020-01-09

Title: Tools for Data Diagnosis, Exploration, Transformation
Description: A collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values and outliers and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and relationship between target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputates missing values and outliers, resolving skewness. And it creates automated reports that support these three tasks.
Author: Choonghyun Ryu [aut, cre]
Maintainer: Choonghyun Ryu <choonghyun.ryu@gmail.com>

Diff between dlookr versions 0.3.13 dated 2020-01-09 and 0.3.14 dated 2020-11-17

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More information about dlookr at CRAN
Permanent link

Package dismo updated to version 1.3-3 with previous version 1.3-2 dated 2020-11-13

Title: Species Distribution Modeling
Description: Methods for species distribution modeling, that is, predicting the environmental similarity of any site to that of the locations of known occurrences of a species.
Author: Robert J. Hijmans, Steven Phillips, John Leathwick and Jane Elith
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

Diff between dismo versions 1.3-2 dated 2020-11-13 and 1.3-3 dated 2020-11-17

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More information about dismo at CRAN
Permanent link

New package CoRpower with initial version 1.0.4
Package: CoRpower
Title: Power Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials
Version: 1.0.4
Authors@R: c(person("Stephanie", "Wu", email = "swu18@uw.edu", role = "aut"), person("Michal", "Juraska", email = "mjuraska@fredhutch.org", role = c("aut", "cre")), person("Peter", "Gilbert", email = "pgilbert@scharp.org", role = "aut"), person("Yunda", "Huang", email = "yunda@scharp.org", role = "aut"))
URL: https://github.com/mjuraska/CoRpower
Description: Calculates power for assessment of intermediate biomarker responses as correlates of risk in the active treatment group in clinical efficacy trials, as described in Gilbert, Janes, and Huang, Power/Sample Size Calculations for Assessing Correlates of Risk in Clinical Efficacy Trials (2016, Statistics in Medicine). The methods differ from past approaches by accounting for the level of clinical treatment efficacy overall and in biomarker response subgroups, which enables the correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection. The methods also account for inter-individual variability of the observed biomarker response that is not biologically relevant (e.g., due to technical measurement error of the laboratory assay used to measure the biomarker response), which is important because power to detect a specified correlate of risk effect size is heavily affected by the biomarker's measurement error. The methods can be used for a general binary clinical endpoint model with a univariate dichotomous, trichotomous, or continuous biomarker response measured in active treatment recipients at a fixed timepoint after randomization, with either case-cohort Bernoulli sampling or case-control without-replacement sampling of the biomarker (a baseline biomarker is handled as a trivial special case). In a specified two-group trial design, the computeN() function can initially be used for calculating additional requisite design parameters pertaining to the target population of active treatment recipients observed to be at risk at the biomarker sampling timepoint. Subsequently, the power calculation employs an inverse probability weighted logistic regression model fitted by the tps() function in the 'osDesign' package. Power results as well as the relationship between the correlate of risk effect size and treatment efficacy can be visualized using various plotting functions. To link power calculations for detecting a correlate of risk and a correlate of treatment efficacy, a baseline immunogenicity predictor (BIP) can be simulated according to a specified classification rule (for dichotomous or trichotomous BIPs) or correlation with the biomarker response (for continuous BIPs), then outputted along with biomarker response data under assignment to treatment, and clinical endpoint data for both treatment and placebo groups.
BugReports: https://github.com/mjuraska/CoRpower/issues
Depends: R (>= 3.5.0)
License: GPL-2
Encoding: UTF-8
Imports: survival, osDesign
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: no
Packaged: 2020-11-17 01:24:39 UTC; mjuraska
Author: Stephanie Wu [aut], Michal Juraska [aut, cre], Peter Gilbert [aut], Yunda Huang [aut]
Maintainer: Michal Juraska <mjuraska@fredhutch.org>
Repository: CRAN
Date/Publication: 2020-11-17 08:10:14 UTC

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Package clordr updated to version 1.6.0 with previous version 1.5.0 dated 2018-12-06

Title: Composite Likelihood Inference and Diagnostics for Replicated Spatial Ordinal Data
Description: Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error. Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.
Author: Ting Fung (Ralph) Ma [cre, aut], Pingping Wang [aut], Jun Zhu [aut], Dipankar Bandyopadhyay [ctb], Yincai Tang [ctb]
Maintainer: Ting Fung (Ralph) Ma <tingfung.ma@wisc.edu>

Diff between clordr versions 1.5.0 dated 2018-12-06 and 1.6.0 dated 2020-11-17

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More information about clordr at CRAN
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Package assignPOP updated to version 1.2.2 with previous version 1.2.1 dated 2020-11-03

Title: Population Assignment using Genetic, Non-Genetic or Integrated Data in a Machine Learning Framework
Description: Use Monte-Carlo and K-fold cross-validation coupled with machine- learning classification algorithms to perform population assignment, with functionalities of evaluating discriminatory power of independent training samples, identifying informative loci, reducing data dimensionality for genomic data, integrating genetic and non-genetic data, and visualizing results.
Author: Kuan-Yu (Alex) Chen [aut, cre], Elizabeth A. Marschall [aut], Michael G. Sovic [aut], Anthony C. Fries [aut], H. Lisle Gibbs [aut], Stuart A. Ludsin [aut]
Maintainer: Kuan-Yu (Alex) Chen <alexkychen@gmail.com>

Diff between assignPOP versions 1.2.1 dated 2020-11-03 and 1.2.2 dated 2020-11-17

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Package QF updated to version 0.0.3 with previous version 0.0.2 dated 2020-11-11

Title: Density, Cumulative and Quantile Functions of Quadratic Forms
Description: The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.
Author: Aldo Gardini [aut, cre], Fedele Greco [aut], Carlo Trivisano [aut]
Maintainer: Aldo Gardini <aldo.gardini2@unibo.it>

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Package neonstore updated to version 0.3.3 with previous version 0.3.2 dated 2020-10-27

Title: NEON Data Store
Description: The National Ecological Observatory Network (NEON) provides access to its numerous data products through its REST API, <https://data.neonscience.org/data-api/>. This package provides a high-level user interface for downloading and storing NEON data products. While each of NEON data products consist of hundreds or thousands of individual files. Unlike 'neonUtilities', this package will avoid repeated downloading, provides persistent storage, and improves performance. 'neonstore' can also construct a local 'duckdb' database of stacked tables, making it possible to work with tables that are far to big to fit into memory.
Author: Carl Boettiger [aut, cre] (<https://orcid.org/0000-0002-1642-628X>), Quinn Thomas [aut] (<https://orcid.org/0000-0003-1282-7825>), Christine Laney [aut] (<https://orcid.org/0000-0002-4944-2083>), Claire Lunch [aut] (<https://orcid.org/0000-0001-8753-6593>), Noam Ross [ctb] (<https://orcid.org/0000-0002-2136-0000>)
Maintainer: Carl Boettiger <cboettig@gmail.com>

Diff between neonstore versions 0.3.2 dated 2020-10-27 and 0.3.3 dated 2020-11-17

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Package lineup updated to version 0.38-3 with previous version 0.37-11 dated 2019-03-06

Title: Lining Up Two Sets of Measurements
Description: Tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. Broman et al. (2015) <doi:10.1534/g3.115.019778>.
Author: Karl W Broman [aut, cre] (<https://orcid.org/0000-0002-4914-6671>)
Maintainer: Karl W Broman <broman@wisc.edu>

Diff between lineup versions 0.37-11 dated 2019-03-06 and 0.38-3 dated 2020-11-17

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Package gateR updated to version 0.1.3 with previous version 0.1.2 dated 2020-11-10

Title: Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation
Description: Estimates statistically significant marker combination values within which one immunologically distinctive group (i.e., disease case) is more associated than another group (i.e., healthy control), successively, using various combinations (i.e., "gates") of markers to examine features of cells that may be different between groups. For a two-group comparison, the 'gateR' package uses the spatial relative risk function that is estimated using the 'sparr' package. Details about the 'sparr' package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Author: Ian D. Buller [aut, cre, cph] (<https://orcid.org/0000-0001-9477-8582>), Elena Hsieh [ctb] (<https://orcid.org/0000-0003-3969-6597>), Debashis Ghosh [ctb] (<https://orcid.org/0000-0001-6618-1316>), Lance A. Waller [ctb] (<https://orcid.org/0000-0001-5002-8886>), NCI [cph]
Maintainer: Ian D. Buller <ian.buller@nih.gov>

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Package TeXCheckR updated to version 0.7.0 with previous version 0.6.0 dated 2018-08-25

Title: Parses LaTeX Documents for Errors
Description: Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.
Author: Hugh Parsonage [aut, cre]
Maintainer: Hugh Parsonage <hugh.parsonage@gmail.com>

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Package rasterdiv updated to version 0.2-2 with previous version 0.2-1 dated 2020-07-18

Title: Diversity Indices for Numerical Matrices
Description: Providing functions to calculate indices of diversity on numerical matrices based on information theory. The rationale behind the package is described in Rocchini, Marcantonio and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039>.
Author: Matteo Marcantonio [aut, cre], Martina Iannacito [aut, ctb], Elisa Marchetto [ctb], Elisa Thouverai [aut, ctb], Daniele Da Re [aut], Clara Tattoni [aut], Giovanni Bacaro [aut], Saverio Vicario [aut, ctb], Carlo Ricotta [aut], Duccio Rocchini [aut, ctb]
Maintainer: Matteo Marcantonio <marcantoniomatteo@gmail.com>

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Package packager updated to version 1.7.0 with previous version 1.6.0 dated 2020-11-05

Title: Create, Build and Maintain Packages
Description: Helper functions for package creation, building and maintenance. Designed to work with a build system such as 'GNU make' or package 'fakemake' to help you to conditionally work through the stages of package development (such as spell checking, linting, testing, before building and checking a package).
Author: Andreas Dominik Cullmann [aut, cre]
Maintainer: Andreas Dominik Cullmann <fvafrcu@mailbox.org>

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Package zdeskR updated to version 0.1.0 with previous version 0.0.1 dated 2020-08-24

Title: Connect to Your 'Zendesk' Data
Description: Facilitates making a connection to the 'Zendesk' API and executing various queries. You can use it to get ticket data and ticket metrics. The 'Zendesk' documentation is available at <https://developer.zendesk.com/rest_api /docs/support/introduction>. This package is not supported by 'Zendesk' (owner of the software).
Author: Chris Umphlett [aut, cre], Avinash Panigrahi [aut]
Maintainer: Chris Umphlett <christopher.umphlett@gmail.com>

Diff between zdeskR versions 0.0.1 dated 2020-08-24 and 0.1.0 dated 2020-11-17

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Package parsemsf (with last version 0.1.1) was removed from CRAN

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

2017-12-09 0.1.1
2017-01-24 0.1.0

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Package tensorBSS (with last version 0.3.6) was removed from CRAN

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

2020-02-27 0.3.6

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Package tsBSS (with last version 0.5.5) was removed from CRAN

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

2019-10-14 0.5.5
2019-10-09 0.5.4
2019-09-03 0.5.3
2018-10-09 0.5.2
2018-06-25 0.5.1
2018-06-07 0.5.0
2018-05-06 0.4.1
2017-11-17 0.4
2017-08-18 0.3.1
2017-07-03 0.3
2016-09-06 0.2
2015-12-04 0.1

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