Mon, 02 Sep 2019

New package warbleR with initial version 1.1.16
Package: warbleR
Type: Package
Title: Streamline Bioacoustic Analysis
Version: 1.1.16
Date: 2019-09-01
Description: Functions aiming to facilitate the analysis of the structure of animal acoustic signals in 'R'. Users can collect open-access avian recordings or enter their own data into a workflow that facilitates spectrographic visualization and measurement of acoustic parameters. 'warbleR' makes use of the basic sound analysis tools from the package, and offers new tools for acoustic structure analysis. The main features of the package are the use of loops to apply tasks through acoustic signals referenced in a selection (annotation) table and the production of spectrograms in image files that allow to organize data and verify acoustic analyzes. The package offers functions to explore, organize and manipulate multiple sound files, explore and download 'XenoCanto' recordings, detect signals automatically, create spectrograms of complete recordings or individual signals, run different measures of acoustic signal structure, evaluate the performance of measurement methods, catalog signals, characterize different structural levels in acoustic signals, run statistical analysis of duet coordination and consolidate databases and annotation tables, among others.
License: GPL (>= 2)
Imports: bitops, dtw, fftw, graphics, grDevices, iterators, jpeg, monitoR, parallel, pbapply, RCurl, rjson, stats, utils, methods, pracma, Sim.DiffProc, soundgen
Depends: R (>= 3.2.1), maps, tuneR, seewave (>= 2.0.1), NatureSounds
LazyData: TRUE
URL: https://marce10.github.io/warbleR
BugReports: https://github.com/maRce10/warbleR/issues
NeedsCompilation: no
Suggests: knitr, ggplot2, rmarkdown, ape, bioacoustics
RoxygenNote: 6.1.1
Packaged: 2019-09-02 19:38:20 UTC; m
Repository: CRAN
Authors@R: c(person("Marcelo", "Araya-Salas", role = c("aut", "cre"), email = "marceloa27@gmail.com", comment = c(ORCID = "0000-0003-3594-619X")), person("Grace", "Smith-Vidaurre", role = c("aut"), email = "gsmithvi@gmail.com"))
Date/Publication: 2019-09-03 00:00:02 UTC
Author: Marcelo Araya-Salas [aut, cre] (<https://orcid.org/0000-0003-3594-619X>), Grace Smith-Vidaurre [aut]
Maintainer: Marcelo Araya-Salas <marceloa27@gmail.com>

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New package scModels with initial version 1.0.1
Package: scModels
Title: Fitting Discrete Distribution Models to Count Data
Version: 1.0.1
DateNote: Previous CRAN version 1.0.0 on 2019-06-13
Maintainer: Lisa Amrhein <amrheinlisa@gmail.com>
Authors@R: c( person("Lisa", "Amrhein", email="amrheinlisa@gmail.com", role=c("aut", "cre")), person("Kumar", "Harsha", email="kumar.harsha@tum.de", role="aut"), person("Christiane", "Fuchs", email="christiane.fuchs@helmholtz-muenchen.de", role="aut"), person("Pavel", "Holoborodko", email="pavel@holoborodko.com", role="ctb", comment="Author and copyright holder of 'mpreal.h'"))
License: GPL-3
Description: Provides functions for fitting discrete distribution models to count data. Included are the Poisson, the negative binomial and, most importantly, a new implementation of the Poisson-beta distribution (density, distribution and quantile functions, and random number generator) together with a needed new implementation of Kummer's function (also: confluent hypergeometric function of the first kind). Three different implementations of the Gillespie algorithm allow data simulation based on the basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for four variants of each of the three aforementioned distributions are also available. The variants include one population and two population mixtures, both with and without zero-inflation. The package depends on the 'MPFR' libraries (<https://www.mpfr.org/>) which need to be installed separately (see description at <https://github.com/fuchslab/scModels>). This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on bioRxiv.
Depends: R (>= 3.1.0)
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, testthat
LinkingTo: Rcpp
Imports: Rcpp
Encoding: UTF-8
SystemRequirements: gmp (>= 4.2.3), mpfr (>= 3.0.0)
SystemRequirementsNote: 'MPFR' (MP Floating-Point Reliable Library, <http://mpfr.org/>) and 'GMP' (GNU Multiple Precision library,<http://gmplib.org/>)
NeedsCompilation: yes
Packaged: 2019-09-02 17:38:05 UTC; kumarharsha
Author: Lisa Amrhein [aut, cre], Kumar Harsha [aut], Christiane Fuchs [aut], Pavel Holoborodko [ctb] (Author and copyright holder of 'mpreal.h')
Repository: CRAN
Date/Publication: 2019-09-03 00:00:09 UTC

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Package spray updated to version 1.0-7 with previous version 1.0-6 dated 2019-04-10

Title: Sparse Arrays and Multivariate Polynomials
Description: Sparse arrays interpreted as multivariate polynomials.
Author: Robin K. S. Hankin [aut, cre] (<https://orcid.org/0000-0001-5982-0415>)
Maintainer: Robin K. S. Hankin <hankin.robin@gmail.com>

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Package fastDummies updated to version 1.5.0 with previous version 1.4.0 dated 2019-06-17

Title: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables
Description: Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().
Author: Jacob Kaplan [aut, cre] (<https://orcid.org/0000-0002-0601-0387>), Benjamin Schlegel [ctb]
Maintainer: Jacob Kaplan <jkkaplan6@gmail.com>

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Package episensr updated to version 0.9.4 with previous version 0.9.3 dated 2018-12-03

Title: Basic Sensitivity Analysis of Epidemiological Results
Description: Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Lash T.L, Fox M.P, and Fink A.K. "Applying Quantitative Bias Analysis to Epidemiologic Data", ('Springer', 2009).
Author: Denis Haine [aut, cre] (<https://orcid.org/0000-0002-6691-7335>)
Maintainer: Denis Haine <denis.haine@gmail.com>

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Package ghibli updated to version 0.3.0 with previous version 0.2.0 dated 2019-03-21

Title: Studio Ghibli Colour Palettes
Description: Colour palettes inspired by Studio Ghibli <https://en.wikipedia.org/wiki/Studio_Ghibli> films, ported to R for your enjoyment.
Author: Ewen Henderson [aut, cre] (<https://orcid.org/0000-0002-4748-4693>), Danielle Desrosiers [ctb]
Maintainer: Ewen Henderson <ewenhenderson@gmail.com>

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Package Rcmdr updated to version 2.6-0 with previous version 2.5-3 dated 2019-05-07

Title: R Commander
Description: A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Author: John Fox [aut, cre], Milan Bouchet-Valat [aut], Liviu Andronic [ctb], Michael Ash [ctb], Theophilius Boye [ctb], Stefano Calza [ctb], Andy Chang [ctb], Philippe Grosjean [ctb], Richard Heiberger [ctb], Kosar Karimi Pour [ctb], G. Jay Kerns [ctb], Renaud Lancelot [ctb], Matthieu Lesnoff [ctb], Uwe Ligges [ctb], Samir Messad [ctb], Martin Maechler [ctb], Robert Muenchen [ctb], Duncan Murdoch [ctb], Erich Neuwirth [ctb], Dan Putler [ctb], Brian Ripley [ctb], Miroslav Ristic [ctb], Peter Wolf [ctb], Kevin Wright [ctb]
Maintainer: John Fox <jfox@mcmaster.ca>

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Package ggalluvial updated to version 0.10.0 with previous version 0.9.1 dated 2018-10-21

Title: Alluvial Diagrams in 'ggplot2'
Description: Alluvial diagrams use x-splines, sometimes augmented with stacked histograms, to visualize multi-dimensional or repeated-measures data with categorical or ordinal variables. They can be viewed as simplified and standardized Sankey diagrams; see Riehmann, Hanfler, and Froehlich (2005) <doi:10.1109/INFVIS.2005.1532152> and Rosvall and Bergstrom (2010) <doi:10.1371/journal.pone.0008694>. This package provides ggplot2 layers to produce alluvial diagrams from tidy data.
Author: Jason Cory Brunson [aut, cre]
Maintainer: Jason Cory Brunson <cornelioid@gmail.com>

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Package fullfact updated to version 1.3 with previous version 1.2 dated 2017-04-12

Title: Full Factorial Breeding Analysis
Description: We facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses. The paper associated with the package including worked examples is: Houde ALS, Pitcher TE (2016) <doi:10.1002/ece3.1943>.
Author: Aimee Lee Houde [aut, cre], Trevor Pitcher [aut]
Maintainer: Aimee Lee Houde <aimee.lee.houde@gmail.com>

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Package jstable updated to version 0.8.6 with previous version 0.8.5 dated 2019-08-03

Title: Create Tables from Different Types of Regression
Description: Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
Author: Jinseob Kim [aut, cre] (<https://orcid.org/0000-0002-9403-605X>), Zarathu [cph, fnd]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>

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Package glmmboot updated to version 0.4.0 with previous version 0.3.0 dated 2018-12-05

Title: Bootstrap Resampling for Mixed Effects and Plain Models
Description: Performs bootstrap resampling for most models that update() works for. There are two primary functions: bootstrap_model() performs block resampling if random effects are present, and case resampling if not; bootstrap_ci() converts output from bootstrap model runs into confidence intervals and p-values. By default, bootstrap_model() calls bootstrap_ci(). Package motivated by Humphrey and Swingley (2018) <arXiv:1805.08670>.
Author: Colman Humphrey [aut, cre]
Maintainer: Colman Humphrey <humphrc@tcd.ie>

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Package FateID updated to version 0.1.8 with previous version 0.1.7 dated 2019-03-28

Title: Quantification of Fate Bias in Multipotent Progenitors
Description: Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Author: Dominic Grün <dominic.gruen@gmail.com>
Maintainer: Dominic Grün <dominic.gruen@gmail.com>

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Package chngpt updated to version 2019.9-2 with previous version 2019.8-28 dated 2019-08-29

Title: Estimation and Hypothesis Testing for Threshold Regression
Description: Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) chngpt: threshold regression model estimation and inference, BMC Bioinformatics, in press, <DOI:10.1186/s12859-017-1863-x>.
Author: Youyi Fong [cre], Tao Yang [aut], Zonglin He [aut], Adam Elder [aut], Hyunju Son [aut]
Maintainer: Youyi Fong <youyifong@gmail.com>

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Package tidylog updated to version 0.2.0 with previous version 0.1.0 dated 2019-03-08

Title: Logging for 'dplyr' and 'tidyr' Functions
Description: Provides feedback about 'dplyr' and 'tidyr' operations.
Author: Benjamin Elbers [aut, cre] (<https://orcid.org/0000-0001-5392-3448>)
Maintainer: Benjamin Elbers <elbersb@gmail.com>

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Package ssr updated to version 0.1.1 with previous version 0.1.0 dated 2019-08-20

Title: Semi-Supervised Regression Methods
Description: An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions.
Author: Enrique Garcia-Ceja [aut, cre] (<https://orcid.org/0000-0001-6864-8557>)
Maintainer: Enrique Garcia-Ceja <e.g.mx@ieee.org>

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Package SparkR updated to version 2.4.4 with previous version 2.4.3 dated 2019-05-09

Title: R Front End for 'Apache Spark'
Description: Provides an R Front end for 'Apache Spark' <https://spark.apache.org>.
Author: Shivaram Venkataraman [aut, cre], Xiangrui Meng [aut], Felix Cheung [aut], The Apache Software Foundation [aut, cph]
Maintainer: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

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Package ggraph updated to version 2.0.0 with previous version 1.0.2 dated 2018-07-07

Title: An Implementation of Grammar of Graphics for Graphs and Networks
Description: The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. ggraph is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer.
Author: Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>), RStudio [cph]
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>

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Package genesysr updated to version 0.9.2 with previous version 0.9.1 dated 2018-06-14

Title: Genesys PGR Client
Description: Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>). Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.
Author: Global Crop Diversity Trust [cph], Matija Obreza [aut, cre], Nora Castaneda [ctb]
Maintainer: Matija Obreza <matija.obreza@croptrust.org>

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Package forecastSNSTS updated to version 1.3-0 with previous version 1.2-0 dated 2017-06-18

Title: Forecasting for Stationary and Non-Stationary Time Series
Description: Methods to compute linear h-step ahead prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean squared and absolute prediction errors for the resulting predictors. Also, functions to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series, and to verify an assumption from Kley et al. (2019), Electronic of Statistics, forthcoming. Preprint <arXiv:1611.04460>.
Author: Tobias Kley [aut, cre], Philip Preuss [aut], Piotr Fryzlewicz [aut]
Maintainer: Tobias Kley <tobias.kley@bristol.ac.uk>

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Package emuR updated to version 2.0.3 with previous version 2.0.0 dated 2019-07-22

Title: Main Package of the EMU Speech Database Management System
Description: Provides the next iteration of the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities.
Author: Raphael Winkelmann [aut, cre], Klaus Jaensch [aut, ctb], Steve Cassidy [aut, ctb], Jonathan Harrington [aut, ctb]
Maintainer: Raphael Winkelmann <raphael@phonetik.uni-muenchen.de>

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Package creditmodel updated to version 1.1.2 with previous version 1.1.1 dated 2019-07-18

Title: Toolkit for Credit Modeling Analysis and Visualization
Description: Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization. Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. 1.Anderson, R. (2007). The credit scoring toolkit: Theory and practice for retail credit risk management and decision automation. 2.Find, S. (2012, ISBN13: 9780230347762). Credit scoring, response modelling and insurance rating:A practical guide to forecasting consumer behaviour.
Author: Dongping Fan [aut, cre]
Maintainer: Dongping Fan <fdp@pku.edu.cn>

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Package sirus updated to version 0.1.2 with previous version 0.1.1 dated 2019-08-28

Title: Stable and Interpretable RUle Set
Description: A classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with the predictivity of random forests for problems with low order interactions. The core aggregation principle of random forests is kept, but instead of aggregating predictions, SIRUS selects the most frequent nodes of the forest to form a stable rule ensemble model. The algorithm is fully described in the following article: Benard C., Biau G., da Veiga S., Scornet E. (2019) <arXiv:1908.06852>. This R package is a fork from the project ranger (<https://github.com/imbs-hl/ranger>).
Author: Clement Benard [aut, cre], Marvin N. Wright [ctb, cph]
Maintainer: Clement Benard <clement.benard@safrangroup.com>

Diff between sirus versions 0.1.1 dated 2019-08-28 and 0.1.2 dated 2019-09-02

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Package RMixtCompIO updated to version 4.0.1 with previous version 4.0.0 dated 2019-08-03

Title: Mixture Models with Heterogeneous and (Partially) Missing Data
Description: Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. It includes models for real, categorical, counting, functional and ranking data. This package contains the minimal R interface of the C++ 'MixtComp' library.
Author: Vincent Kubicki [aut], Christophe Biernacki [aut], Quentin Grimonprez [aut, cre], Serge Iovleff [ctb], Matthieu Marbac-Lourdelle [ctb], Étienne Goffinet [ctb], Patrick Patrick Wieschollek [ctb] (for CppOptimizationLibrary), Tobias Wood [ctb] (for CppOptimizationLibrary)
Maintainer: Quentin Grimonprez <quentin.grimonprez@inria.fr>

Diff between RMixtCompIO versions 4.0.0 dated 2019-08-03 and 4.0.1 dated 2019-09-02

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Package rerddapXtracto updated to version 0.4.2 with previous version 0.4.1 dated 2019-08-06

Title: Extracts Environmental Data from 'ERDDAP' Web Services
Description: Contains three functions that access environmental data from any 'ERDDAP' data web service. The rxtracto() function extracts data along a trajectory for a given "radius" around the point. The rxtracto_3D() function extracts data in a box. The rxtractogon() function extracts data in a polygon. All of those three function use the 'rerddap' package to extract the data, and should work with any 'ERDDAP' server. There are also two functions, plotBBox() and plotTrack() that use the 'plotdap' package to simplify the creation of maps of the data.
Author: Roy Mendelssohn [aut, cre]
Maintainer: Roy Mendelssohn <roy.mendelssohn@noaa.gov>

Diff between rerddapXtracto versions 0.4.1 dated 2019-08-06 and 0.4.2 dated 2019-09-02

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Package RcppArmadillo updated to version 0.9.700.2.0 with previous version 0.9.600.4.0 dated 2019-07-15

Title: 'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library
Description: 'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that Armadillo requires a fairly recent compiler; for the g++ family at least version 4.6.* is required.
Author: Dirk Eddelbuettel, Romain Francois, Doug Bates and Binxiang Ni
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package outliertree updated to version 1.0.4 with previous version 1.0.2 dated 2019-08-28

Title: Explainable Outlier Detection Through Decision Tree Conditioning
Description: Will try to fit decision trees that try to "predict" values for each column based on the values of each other column. Along the way, each time a split is evaluated, it will take the observations that fall into each branch as a homogeneous cluster in which it will search for outliers in the 1-d distribution of the column being predicted. Outliers are determined according to confidence intervals on this 1-d distribution, and need to have a large gap with respect to the next observation in sorted order to be flagged as outliers. Since outliers are searched for in a decision tree branch, it will know the conditions that make it a rare observation compared to others that meet the same conditions, and the conditions will always be correlated with the target variable (as it's being predicted from them). Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>

Diff between outliertree versions 1.0.2 dated 2019-08-28 and 1.0.4 dated 2019-09-02

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Package nomogramFormula updated to version 1.1.0.0 with previous version 1.0.0.0 dated 2019-08-28

Title: Calculate Total Points and Probabilities for Nomogram
Description: A nomogram, which can be carried out in 'rms' package, provides a graphical explanation of a prediction process. However, it is not very easy to draw straight lines, read points and probabilities accurately. Even, it is hard for users to calculate total points and probabilities for all subjects. This package provides formula_rd() and formula_lp() functions to fit the formula of total points with raw data and linear predictors respectively by polynomial regression. Function points_cal() will help you calculate the total points. prob_cal() can be used to calculate the probabilities after lrm(), cph() or psm() regression.
Author: Jing Zhang, Zhi Jin
Maintainer: Jing Zhang<zj391120@163.com>

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Package nilde updated to version 1.1-3 with previous version 1.1-2 dated 2018-08-07

Title: Nonnegative Integer Solutions of Linear Diophantine Equations with Applications
Description: Routines for enumerating all existing nonnegative integer solutions of a linear Diophantine equation. The package provides routines for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; additive partitioning of natural numbers; and one-dimensional bin-packing problem.
Author: Natalya Pya Arnqvist[aut, cre], Vassilly Voinov [aut], Rashid Makarov [aut], Yevgeniy Voinov [aut]
Maintainer: Natalya Pya Arnqvist <nat.pya@gmail.com>

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Package mfbvar updated to version 0.5.1 with previous version 0.4.0 dated 2018-12-27

Title: Mixed-Frequency Bayesian VAR Models
Description: Estimation of mixed-frequency Bayesian vector autoregressive (VAR) models. The package implements a state space-based VAR model that handles mixed frequencies of the data. The model is estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution. Prior distributions that can be used include normal-inverse Wishart and normal-diffuse priors as well as steady-state priors. Stochastic volatility can be handled by common or factor stochastic volatility models.
Author: Sebastian Ankargren [cre, aut] (<https://orcid.org/0000-0003-4415-8734>), Yukai Yang [aut] (<https://orcid.org/0000-0002-2623-8549>)
Maintainer: Sebastian Ankargren <sebastian.ankargren@statistics.uu.se>

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Package gld updated to version 2.6 with previous version 2.5 dated 2019-07-04

Title: Estimation and Use of the Generalised (Tukey) Lambda Distribution
Description: The generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form. This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different types of the distribution, the FKML, RS, GPD and FM5 - see documentation for details. It provides the density function, distribution function, and Quantile-Quantile plots. It implements a variety of estimation methods for the distribution, including diagnostic plots. Estimation methods include the starship (all 4 types), method of L-Moments for the GPD and FKML types, and a number of methods for only the FKML parameterisation. These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, Trimmed L-Moments and Distributional Least Absolutes.
Author: Robert King <Robert.King@newcastle.edu.au>, Benjamin Dean <Benjamin.Dean@uon.edu.au>, Sigbert Klinke, Paul van Staden
Maintainer: Robert King <Robert.King@newcastle.edu.au>

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Package feasts updated to version 0.1.1 with previous version 0.1.0 dated 2019-08-28

Title: Feature Extraction And Statistics for Time Series
Description: Provides a collection of features, decompositions, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.
Author: Mitchell O'Hara-Wild [aut, cre], Rob Hyndman [aut], Earo Wang [aut], Di Cook [ctb], Thiyanga Talagala [ctb] (Correlation features), Leanne Chhay [ctb] (Guerrero's method)
Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com>

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Package SDMtune updated to version 0.2.0 with previous version 0.1.1 dated 2019-07-14

Title: Species Distribution Model Selection
Description: User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution.
Author: Sergio Vignali [aut, cre] (<https://orcid.org/0000-0002-3390-5442>), Arnaud Barras [aut] (<https://orcid.org/0000-0003-0850-6965>), Veronika Braunisch [aut] (<https://orcid.org/0000-0001-7035-4662>), Conservation Biology - University of Bern [fnd]
Maintainer: Sergio Vignali <sergio.vignali@iee.unibe.ch>

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Package dispRity updated to version 1.3.1 with previous version 1.3 dated 2019-08-26

Title: Measuring Disparity
Description: A modular package for measuring disparity from multidimensional matrices. Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics (Guillerme (2018) <doi:10.1111/2041-210X.13022>). The package also provides functions for looking at disparity in a serial way (e.g. disparity through time - Guillerme and Cooper (2018) <doi:10.1111/pala.12364>) or per groups as well as visualising the results. Finally, this package provides several basic statistical tests for disparity analysis.
Author: Thomas Guillerme [aut, cre, cph], Mark N Puttick [aut, cph]
Maintainer: Thomas Guillerme <guillert@tcd.ie>

Diff between dispRity versions 1.3 dated 2019-08-26 and 1.3.1 dated 2019-09-02

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Package rclimateca (with last version 1.0.3) was removed from CRAN

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

2019-08-30 1.0.3
2018-06-11 1.0.2
2018-03-25 1.0.1
2018-01-09 1.0.0
2017-11-13 0.4
2017-02-27 0.2
2016-12-07 0.1

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2019-01-16 0.5.1
2018-11-22 0.5.0
2018-06-25 0.4.0
2018-03-14 0.3.2
2018-03-03 0.3.1

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2019-08-25 0.2.3
2019-03-04 0.2.2
2019-01-16 0.2.1
2018-10-17 0.2.0
2018-09-21 0.1.0

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Package cobalt (with last version 3.7.0) was removed from CRAN

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

2019-05-01 3.7.0
2019-01-16 3.6.1
2018-11-25 3.6.0
2018-10-25 3.5.0
2018-09-15 3.4.1
2018-08-14 3.4.0
2018-06-24 3.3.0
2018-05-06 3.2.3
2018-03-13 3.2.2
2018-02-20 3.2.1

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