Tue, 03 Mar 2020

Package vcr updated to version 0.5.0 with previous version 0.4.0 dated 2019-12-07

Title: Record 'HTTP' Calls to Disk
Description: Record test suite 'HTTP' requests and replays them during future runs. A port of the Ruby gem of the same name (<https://github.com/vcr/vcr/>). Works by hooking into the 'webmockr' R package for matching 'HTTP' requests by various rules ('HTTP' method, 'URL', query parameters, headers, body, etc.), and then caching real 'HTTP' responses on disk in 'cassettes'. Subsequent 'HTTP' requests matching any previous requests in the same 'cassette' use a cached 'HTTP' response.
Author: Scott Chamberlain [aut, cre] (<https://orcid.org/0000-0003-1444-9135>), Aaron Wolen [ctb] (<https://orcid.org/0000-0003-2542-2202>)
Maintainer: Scott Chamberlain <sckott@protonmail.com>

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Package unheadr updated to version 0.2.1 with previous version 0.2.0 dated 2020-02-09

Title: Handle Data with Messy Header Rows and Broken Values
Description: Verb-like functions to work with messy data, often derived from spreadsheets or parsed PDF tables. Includes functions for unwrapping values broken up across rows, relocating embedded grouping values, and to annotate meaningful formatting in spreadsheet files.
Author: Luis D. Verde Arregoitia [aut, cre] (<https://orcid.org/0000-0001-9520-6543>)
Maintainer: Luis D. Verde Arregoitia <luis@liomys.mx>

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Package todor updated to version 0.1.0 with previous version 0.0.5 dated 2019-05-08

Title: Find All TODO Comments and More
Description: This is a simple addin to 'RStudio' that finds all 'TODO', 'FIX ME', 'CHANGED' etc. comments in your project and shows them as a markers list.
Author: Dominik Krzemiński [aut, cre]
Maintainer: Dominik Krzemiński <raymon92@gmail.com>

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Package snahelper updated to version 1.1.0 with previous version 1.0.0 dated 2019-09-03

Title: 'RStudio' Addin for Network Analysis and Visualization
Description: 'RStudio' addin which provides a GUI to visualize and analyse networks. After finishing a session, the code to produce the plot is inserted in the current script. Alternatively, the function SNAhelperGadget() can be used directly from the console. Additional addins include the Netreader() for reading network files and Netbuilder() to create small networks via point and click.
Author: David Schoch [aut, cre]
Maintainer: David Schoch <david.schoch@manchester.ac.uk>

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Package rcrimeanalysis updated to version 0.4.1 with previous version 0.3.1 dated 2020-02-13

Title: An Implementation of Crime Analysis Methods
Description: An implementation of functions for the analysis of crime incident or records management system data. The package implements analysis algorithms scaled for city or regional crime analysis units. The package provides functions for kernel density estimation for crime heat maps, geocoding using the 'Google Maps' API, identification of repeat crime incidents, spatio-temporal map comparison across time intervals, time series analysis (forecasting and decomposition), detection of optimal parameters for the identification of near repeat incidents, and near repeat analysis with crime network linkage.
Author: Jamie Spaulding and Keith Morris
Maintainer: Jamie Spaulding <jspauldi@mix.wvu.edu>

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Package molaR updated to version 4.5 with previous version 4.4 dated 2018-09-18

Title: Dental Surface Complexity Measurement Tools
Description: Surface topography calculations of Dirichlet's normal energy, relief index, surface slope, and orientation patch count for teeth using scans of enamel caps. Importantly, for the relief index and orientation patch count calculations to work, the scanned tooth files must be oriented with the occlusal plane parallel to the x and y axes, and perpendicular to the z axis. The files should also be simplified, and smoothed in some other software prior to uploading into R.
Author: James D. Pampush [aut, cre, cph], Julia M. Winchester [aut, cph], Paul E. Morse [aut, cph], Alexander Q. Vining [aut, cph], Edward Fuselier [aut, cph]
Maintainer: James D. Pampush <jdpampush@gmail.com>

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Package mmcm updated to version 1.2-8 with previous version 1.2-7 dated 2018-02-26

Title: Modified Maximum Contrast Method
Description: An implementation of modified maximum contrast methods (Sato et al. (2009) <doi:10.1038/tpj.2008.17>; Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>) and the maximum contrast method (Yoshimura et al. (1997) <doi:10.1177/009286159703100213>): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method.
Author: Kengo Nagashima [aut, cre] (<https://orcid.org/0000-0003-4529-9045>), Yasunori Sato [aut]
Maintainer: Kengo Nagashima <nshi1201@gmail.com>

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Package gnn updated to version 0.0-2 with previous version 0.0-1 dated 2019-09-30

Title: Generative Neural Networks
Description: Tools to set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided. See Hofert, Prasad, Zhu (2018) <arXiv:1811.00683> for more details.
Author: Marius Hofert [aut, cre] (<https://orcid.org/0000-0001-8009-4665>), Avinash Prasad [aut]
Maintainer: Marius Hofert <marius.hofert@uwaterloo.ca>

Diff between gnn versions 0.0-1 dated 2019-09-30 and 0.0-2 dated 2020-03-03

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 gnn-0.0-2/gnn/NAMESPACE                                                                                |    4 
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 gnn-0.0-2/gnn/R/rda.R                                                                                  |   24 +-
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 gnn-0.0-2/gnn/TODO                                                                                     |   12 +
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Package comperank updated to version 0.1.1 with previous version 0.1.0 dated 2018-05-30

Title: Ranking Methods for Competition Results
Description: Compute ranking and rating based on competition results. Methods of different nature are implemented: with fixed Head-to-Head structure, with variable Head-to-Head structure and with iterative nature. All algorithms are taken from the book 'Who’s #1?: The science of rating and ranking' by Amy N. Langville and Carl D. Meyer (2012, ISBN:978-0-691-15422-0).
Author: Evgeni Chasnovski [aut, cre]
Maintainer: Evgeni Chasnovski <evgeni.chasnovski@gmail.com>

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Package biclique updated to version 1.0.5 with previous version 1.0.4 dated 2019-10-25

Title: Maximal Biclique Enumeration in Bipartite Graphs
Description: A tool for enumerating maximal complete bipartite graphs. The input should be a edge list file or a binary matrix file. The output are maximal complete bipartite graphs. Algorithms used can be found in this paper Y. Lu et al. BMC Res Notes 13, 88 (2020) <doi:10.1186/s13104-020-04955-0>.
Author: Yuping Lu [aut, cre, cph], Charles Phillips [aut], Michael Langston [aut], Department of Computer Science, University of Tennessee, Knoxville [cph]
Maintainer: Yuping Lu <yupinglu89@gmail.com>

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Package stats19 updated to version 1.2.0 with previous version 1.1.0 dated 2019-10-15

Title: Work with Open Road Traffic Casualty Data from Great Britain
Description: Tools to help download, process and analyse the UK road collision data collected using the 'STATS19' form. The data are provided as 'CSV' files with detailed road safety data about the circumstances of car crashes and other incidents on the roads resulting in casualties in Great Britain from 1979, the types (including make and model) of vehicles involved and the consequential casualties. The statistics relate only to personal casualties on public roads that are reported to the police, and subsequently recorded, using the 'STATS19' accident reporting form. See the Department for Transport website <https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data> for more information on these data.
Author: Robin Lovelace [aut, cre] (<https://orcid.org/0000-0001-5679-6536>), Malcolm Morgan [aut] (<https://orcid.org/0000-0002-9488-9183>), Layik Hama [aut] (<https://orcid.org/0000-0003-1912-4890>), Mark Padgham [aut] (<https://orcid.org/0000-0003-2172-5265>), David Ranzolin [rev], Adam Sparks [rev, ctb] (<https://orcid.org/0000-0002-0061-8359>), Ivo Wengraf [ctb], RAC Foundation [fnd]
Maintainer: Robin Lovelace <rob00x@gmail.com>

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Package mcemGLM updated to version 1.1.1 with previous version 1.1 dated 2015-11-29

Title: Maximum Likelihood Estimation for Generalized Linear Mixed Models
Description: Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM. For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005) <DOI:10.1111/j.1467-9868.2005.00499.x>.
Author: Felipe Acosta Archila
Maintainer: Felipe Acosta Archila <acosta@umn.edu>

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Package HEMDAG updated to version 2.6.1 with previous version 2.6.0 dated 2019-11-15

Title: Hierarchical Ensemble Methods for Directed Acyclic Graphs
Description: An implementation of several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs). 'HEMDAG' package: 1) reconciles flat predictions with the topology of the ontology; 2) can enhance the predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; 3) provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; 4) is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but can be safely applied to tree-structured taxonomies as well (as FunCat), since trees are DAGs; 5) scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; 6) provides several utility functions to process and analyze graphs; 7) provides several performance metrics to evaluate HEMs algorithms. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).
Author: Marco Notaro [aut, cre] (<https://orcid.org/0000-0003-4309-2200>), Alessandro Petrini [ctb] (<https://orcid.org/0000-0002-0587-1484>), Giorgio Valentini [aut] (<https://orcid.org/0000-0002-5694-3919>)
Maintainer: Marco Notaro <marco.notaro@unimi.it>

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

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Package smacof updated to version 2.1-0 with previous version 2.0-0 dated 2019-06-06

Title: Multidimensional Scaling
Description: Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Author: Patrick Mair [aut, cre], Jan De Leeuw [aut], Patrick J. F. Groenen [aut], Ingwer Borg [ctb]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

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Package foreign updated to version 0.8-76 with previous version 0.8-75 dated 2020-01-20

Title: Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ...
Description: Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.
Author: R Core Team [aut, cph, cre], Roger Bivand [ctb, cph], Vincent J. Carey [ctb, cph], Saikat DebRoy [ctb, cph], Stephen Eglen [ctb, cph], Rajarshi Guha [ctb, cph], Swetlana Herbrandt [ctb], Nicholas Lewin-Koh [ctb, cph], Mark Myatt [ctb, cph], Michael Nelson [ctb], Ben Pfaff [ctb], Brian Quistorff [ctb], Frank Warmerdam [ctb, cph], Stephen Weigand [ctb, cph], Free Software Foundation, Inc. [cph]
Maintainer: R Core Team <R-core@R-project.org>

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Package eRm updated to version 1.0-1 with previous version 1.0-0 dated 2019-06-06

Title: Extended Rasch Modeling
Description: Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
Author: Patrick Mair [cre, aut], Reinhold Hatzinger [aut], Marco J. Maier [aut], Thomas Rusch [ctb], Rudolf Debelak [ctb]
Maintainer: Patrick Mair <mair@fas.harvard.edu>

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New package SDMPlay with initial version 1.3
Package: SDMPlay
Type: Package
Title: Species Distribution Modelling Playground
Version: 1.3
Date: 2020-03-03
Authors@R: c( person("Guillaumot", "Charlene", email ="charleneguillaumot21@gmail.com",role=c("aut","cre")), person("Martin","Alexis",email = "alexis.martin@mnhn.fr",role = "aut"), person("Eleaume","Marc",email = "marc.eleaume@mnhn.fr",role = "aut"), person("Saucede","Thomas",email = "thomas.saucede@u-bourgogne.fr",role = "aut"))
Maintainer: Guillaumot Charlene <charleneguillaumot21@gmail.com>
Description: Functions provided by this pedagogic package allow to compute models with two popular machine learning approaches, BRT (Boosted Regression Trees) and MaxEnt (Maximum Entropy) applied on sets of marine biological and environmental data. They include the possibility of managing the main parameters for the construction of the models. Classic tools to evaluate model performance are provided (Area Under the Curve, omission rate and confusion matrix, map standard deviation) and are completed with tools to perform null models. The biological dataset includes original occurrences of two species of the class Echinoidea (sea urchins) present on the Kerguelen Plateau and that show contrasted ecological niches. The environmental dataset includes the corresponding statistics for 15 abiotic and biotic descriptors summarized for the Kerguelen Plateau and for different periods in a raster format. The package can be used for practicals to teach and learn the basics of species distribution modelling. Maps of potential distribution can be produced based on the example data included in the package, which brings prior observations of the influence of spatial and temporal heterogeneities on modelling performances. The user can also provide his own datasets to use the modelling functions.
License: GPL-3
LazyData: TRUE
Depends: R (>= 3.1.0)
Imports: raster, dismo, gbm, stats, base
Suggests: maptools, testthat, grDevices, knitr, rmarkdown, ncdf4, rgdal, graphics, sp
RoxygenNote: 7.0.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-03-03 13:04:21 UTC; charlene
Author: Guillaumot Charlene [aut, cre], Martin Alexis [aut], Eleaume Marc [aut], Saucede Thomas [aut]
Repository: CRAN
Date/Publication: 2020-03-03 14:30:11 UTC

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Package restatapi updated to version 0.7.1 with previous version 0.6.9 dated 2020-02-18

Title: Search and Retrieve Data from Eurostat Database
Description: Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX <https://sdmx.org>) Web Services (<https://ec.europa.eu/eurostat/web/sdmx-web-services/about-this-service>) to search and download data from the Eurostat database using the SDMX standard.
Author: Mátyás Mészáros [aut, cre]
Maintainer: Mátyás Mészáros <matyas.meszaros@ec.europa.eu>

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Package RcppSimdJson updated to version 0.0.3 with previous version 0.0.2 dated 2020-02-21

Title: 'Rcpp' Bindings for the 'simdjson' Header-Only Library for 'JSON' Parsing
Description: The 'JSON' format is ubiquitous for data interchange, and the 'simdjson' library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel 'SIMD' instruction manages to parse these files as faster than disk speed. See the <arXiv:1902.08318> paper for more details about 'simdjson'. This package is at present only a very thin and incomplete wrapper and does not aim to replace the existing and excellent 'JSON' packages for R. But it does already validate orders of magnitude faster.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package RBtest updated to version 1.1 with previous version 1.0 dated 2019-05-03

Title: Regression-Based Approach for Testing the Type of Missing Data
Description: The regression-based (RB) approach is a method to test the missing data mechanism. This package contains two functions that test the type of missing data (Missing Completely At Random vs Missing At Random) on the basis of the RB approach. The first function applies the RB approach independently on each variable with missing data, using the completely observed variables only. The second function tests the missing data mechanism globally (on all variables with missing data) with the use of all available information. The algorithm is adapted both to continuous and categorical data.
Author: Serguei Rouzinov and André Berchtold
Maintainer: Serguei Rouzinov <rouzinovs@gmail.com>

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Package plyr updated to version 1.8.6 with previous version 1.8.5 dated 2019-12-10

Title: Tools for Splitting, Applying and Combining Data
Description: A set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics. The development of 'plyr' has been generously supported by 'Becton Dickinson'.
Author: Hadley Wickham [aut, cre]
Maintainer: Hadley Wickham <hadley@rstudio.com>

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Package pcalg updated to version 2.6-10 with previous version 2.6-9 dated 2020-02-11

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]
Maintainer: Markus Kalisch <kalisch@stat.math.ethz.ch>

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Package eaf updated to version 1.9 with previous version 1.8 dated 2018-03-22

Title: Plots of the Empirical Attainment Function
Description: Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. Lopez-Ibanez, L. Paquete, and T. Stuetzle (2010) <doi:10.1007/978-3-642-02538-9_9>.
Author: Manuel López-Ibáñez [aut, cre] (<https://orcid.org/0000-0001-9974-1295>), Marco Chiarandini [aut], Carlos Fonseca [aut], Luis Paquete [aut], Thomas Stützle [aut], Mickaël Binois [ctb]
Maintainer: Manuel López-Ibáñez <manuel.lopez-ibanez@manchester.ac.uk>

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Package DNAtools updated to version 0.2-2 with previous version 0.1-22 dated 2017-02-10

Title: Tools for Analysing Forensic Genetic DNA Data
Description: Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results.
Author: Torben Tvedebrink [aut], James Curran [aut], Mikkel Meyer Andersen [aut, cre]
Maintainer: Mikkel Meyer Andersen <mikl@math.aau.dk>

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Package gWQS updated to version 2.0.1 with previous version 2.0.0 dated 2019-08-27

Title: Generalized Weighted Quantile Sum Regression
Description: Fits Weighted Quantile Sum (WQS) regressions for continuous, binomial, multinomial and count outcomes.
Author: Stefano Renzetti, Paul Curtin, Allan C Just, Ghalib Bello, Chris Gennings
Maintainer: Stefano Renzetti <stefano.renzetti88@gmail.com>

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Package BuyseTest updated to version 1.8.5 with previous version 1.8.4 dated 2020-02-14

Title: Generalized Pairwise Comparisons
Description: Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better than a random observation drawn from the other group. The net benefit and win ratio statistics, i.e. the difference and ratio between the probabilities relative to the intervention and control groups, can then be estimated. Confidence intervals and p-values are obtained using permutations, a non-parametric bootstrap, or the asymptotic theory. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O'Brien test), and corrections to deal with missing values.
Author: Brice Ozenne [aut, cre] (<https://orcid.org/0000-0001-9694-2956>), Julien Peron [ctb], Eva Cantagallo [aut]
Maintainer: Brice Ozenne <brice.mh.ozenne@gmail.com>

Diff between BuyseTest versions 1.8.4 dated 2020-02-14 and 1.8.5 dated 2020-03-03

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Package bgmm updated to version 1.8.4 with previous version 1.8.3 dated 2017-02-27

Title: Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling
Description: Two partially supervised mixture modeling methods: soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.
Author: Przemyslaw Biecek \& Ewa Szczurek
Maintainer: Przemyslaw Biecek <Przemyslaw.Biecek@gmail.com>

Diff between bgmm versions 1.8.3 dated 2017-02-27 and 1.8.4 dated 2020-03-03

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Package SimBIID updated to version 0.1.4 with previous version 0.1.3 dated 2019-11-21

Title: Simulation-Based Inference Methods for Infectious Disease Models
Description: Provides some code to run simulations of state-space models, and then use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle filter based particle Markov chain Monte Carlo (PMCMC) algorithm (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). Also provides functions to plot and summarise the outputs.
Author: Trevelyan J. McKinley [aut, cre], Stefan Widgren [aut] (Author of 'R/mparse.R'), Pavol Bauer [cph] (R/mparse.R), Robin Eriksson [cph] (R/mparse.R), Stefan Engblom [cph] (R/mparse.R)
Maintainer: Trevelyan J. McKinley <t.mckinley@exeter.ac.uk>

Diff between SimBIID versions 0.1.3 dated 2019-11-21 and 0.1.4 dated 2020-03-03

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Package mmand updated to version 1.6.1 with previous version 1.6.0 dated 2020-02-24

Title: Mathematical Morphology in Any Number of Dimensions
Description: Provides tools for performing mathematical morphology operations, such as erosion and dilation, on data of arbitrary dimensionality. Can also be used for finding connected components, resampling, filtering, smoothing and other image processing-style operations.
Author: Jon Clayden
Maintainer: Jon Clayden <code@clayden.org>

Diff between mmand versions 1.6.0 dated 2020-02-24 and 1.6.1 dated 2020-03-03

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New package MM4LMM with initial version 2.0.2
Package: MM4LMM
Type: Package
Title: Inference of Linear Mixed Models Through MM Algorithm
Version: 2.0.2
Date: 2020-02-28
Author: Fabien Laporte, Tristan Mary-Huard
Maintainer: Fabien Laporte <fabien.laporte@pasteur.fr>
Description: The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Hunter, D. R., & Lange, K. (2004) <doi:10.1198/0003130042836>).
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.13), Matrix, parallel, stats, MASS
LinkingTo: Rcpp, RcppEigen
NeedsCompilation: yes
Packaged: 2020-03-03 10:36:47 UTC; fabien
Depends: R (>= 2.10)
Repository: CRAN
Date/Publication: 2020-03-03 12:00:06 UTC

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Package lbfgsb3c updated to version 2020-3.2 with previous version 2020-2.3 dated 2020-02-10

Title: Limited Memory BFGS Minimizer with Bounds on Parameters with optim() 'C' Interface
Description: Interfacing to Nocedal et al. L-BFGS-B.3.0 (See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>) limited memory BFGS minimizer with bounds on parameters. This is a fork of 'lbfgsb3'. This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function (see writing 'R' extensions and source for details). This package also adds more stopping criteria as well as allowing the adjustment of more tolerances.
Author: Matthew L Fidler [aut, cre], John C Nash [aut], Ciyou Zhu [aut], Richard Byrd [aut], Jorge Nocedal [aut], Jose Luis Morales [aut]
Maintainer: Matthew L Fidler <matthew.fidler@gmail.com>

Diff between lbfgsb3c versions 2020-2.3 dated 2020-02-10 and 2020-3.2 dated 2020-03-03

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Package gWQSRS updated to version 1.1.1 with previous version 1.1.0 dated 2020-01-24

Title: Generalized Weighted Quantile Sum Regression Random Subset
Description: Fits Weighted Quantile Sum Random Subset (WQSRS) regressions for continuous, binomial, multinomial and count outcomes. Paul Curtin, Joshua Kellogg, Nadja Cech, Chris Gennings (2019) <doi:10.1080/03610918.2019.1577971>.
Author: Stefano Renzetti, Paul Curtin, Chris Gennings
Maintainer: Stefano Renzetti <stefano.renzetti88@gmail.com>

Diff between gWQSRS versions 1.1.0 dated 2020-01-24 and 1.1.1 dated 2020-03-03

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New package NobBS with initial version 0.1.0
Package: NobBS
Title: Nowcasting by Bayesian Smoothing
Version: 0.1.0
Authors@R: c( person("Sarah", "McGough", email = "sfm341@mail.harvard.edu", role = c("aut", "cre")), person("Nicolas", "Menzies", email = "nmenzies@hsph.harvard.edu", role = "aut"), person("Marc", "Lipsitch", email = "mlipsitc@hsph.harvard.edu", role = "aut"), person("Michael", "Johansson", email = "mjohansson@cdc.gov", role = "aut") )
Description: A Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. 'NobBS' learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2019) <doi:10.1101/663823>.
Depends: R (>= 3.3.0)
SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net/) for analysis of Bayesian hierarchical models
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: dplyr, rjags, coda, magrittr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2020-02-25 03:05:31 UTC; sarah
Author: Sarah McGough [aut, cre], Nicolas Menzies [aut], Marc Lipsitch [aut], Michael Johansson [aut]
Maintainer: Sarah McGough <sfm341@mail.harvard.edu>
Repository: CRAN
Date/Publication: 2020-03-03 10:40:02 UTC

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Package LabourMarketAreas updated to version 3.2.4 with previous version 3.2.3 dated 2020-01-14

Title: Identification, Tuning, Visualisation and Analysis of Labour Market Areas
Description: Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.
Author: Daniela Ichim, Luisa Franconi, Michele D'Alo', Guido van den Heuvel
Maintainer: Luisa Franconi <franconi@istat.it>

Diff between LabourMarketAreas versions 3.2.3 dated 2020-01-14 and 3.2.4 dated 2020-03-03

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New package diverge with initial version 1.0.2
Package: diverge
Version: 1.0.2
Date: 2020-02-10
Title: Evolutionary Trait Divergence Between Sister Species and Other Paired Lineages
Maintainer: Sean A. S. Anderson <seanas.anderson@mail.utoronto.ca>
Author: Sean A. S. Anderson and Jason T. Weir
Description: Compares alternative models of continuous trait differentiation in sister species and other paired lineages. Alternative models include approximations of divergent selection, drift, and stabilizing selection, plus a variety of extensions to facilitate the testing of process-to-pattern hypotheses. Alternative processes are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many species pairs. Users supply species pair trait data and divergence times. The fit of alternative models is compared in a likelihood framework.
Imports: parallel, truncnorm
Depends: R (>= 3.4)
License: GPL (>= 2)
NeedsCompilation: no
LazyData: true
Packaged: 2020-02-24 21:39:59 UTC; Sean
Repository: CRAN
Date/Publication: 2020-03-03 10:20:02 UTC

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Package ThresholdROC updated to version 2.8.2 with previous version 2.8 dated 2020-01-07

Title: Optimum Threshold Estimation
Description: Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. The package also provides functions for sample size determination and estimation of diagnostic accuracy measures. It also includes graphical tools.
Author: Sara Perez-Jaume, Natalia Pallares, Konstantina Skaltsa
Maintainer: Sara Perez-Jaume <spjaume@gmail.com>

Diff between ThresholdROC versions 2.8 dated 2020-01-07 and 2.8.2 dated 2020-03-03

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Package RPESE updated to version 1.0.2 with previous version 1.0.1 dated 2019-12-17

Title: Estimates of Standard Errors for Risk and Performance Measures
Description: Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2019) <https://ssrn.com/abstract=3085672>.
Author: Anthony Christidis <anthony.christidis@stat.ubc.ca>, Xin Chen <chenx26@uw.edu>
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>

Diff between RPESE versions 1.0.1 dated 2019-12-17 and 1.0.2 dated 2020-03-03

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Package RPEIF updated to version 1.0.6 with previous version 1.0.5 dated 2020-03-02

Title: Computation and Plots of Influence Functions for Risk and Performance Measures
Description: Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Zhang and Martin (2017) <https://ssrn.com/abstract=2747179> as well as Chen and Martin (2018) <https://ssrn.com/abstract=3085672>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.
Author: Anthony Christidis <anthony.christidis@stat.ubc.ca>, Shengyu Zhang <syzhang@uw.edu>, Douglas Martin <doug@amath.washington.edu>
Maintainer: Anthony Christidis <anthony.christidis@stat.ubc.ca>

Diff between RPEIF versions 1.0.5 dated 2020-03-02 and 1.0.6 dated 2020-03-03

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Package prophet updated to version 0.6 with previous version 0.5 dated 2019-05-14

Title: Automatic Forecasting Procedure
Description: Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
Author: Sean Taylor [cre, aut], Ben Letham [aut]
Maintainer: Sean Taylor <sjtz@pm.me>

Diff between prophet versions 0.5 dated 2019-05-14 and 0.6 dated 2020-03-03

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Package rel updated to version 1.4.2 with previous version 1.4.1 dated 2020-01-28

Title: Reliability Coefficients
Description: Point estimates with confidence intervals for Bennett et als S, Cohen's kappa, Conger's kappa, Fleiss' kappa, Gwet's AC, intraclass correlation coefficients, Krippendorff's alpha, Scott's pi, the standard error of measurement, and weighted kappa.
Author: Riccardo LoMartire
Maintainer: Riccardo LoMartire <lomartire@kth.se>

Diff between rel versions 1.4.1 dated 2020-01-28 and 1.4.2 dated 2020-03-03

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Package ragtop updated to version 1.1.1 with previous version 1.1.0 dated 2019-05-20

Title: Pricing Equity Derivatives with Extensions of Black-Scholes
Description: Algorithms to price American and European equity options, convertible bonds and a variety of other financial derivatives. It uses an extension of the usual Black-Scholes model in which jump to default may occur at a probability specified by a power-law link between stock price and hazard rate as found in the paper by Takahashi, Kobayashi, and Nakagawa (2001) <doi:10.3905/jfi.2001.319302>. We use ideas and techniques from Andersen and Buffum (2002) <doi:10.2139/ssrn.355308> and Linetsky (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.
Author: Brian K. Boonstra
Maintainer: Brian K. Boonstra <ragtop@boonstra.org>

Diff between ragtop versions 1.1.0 dated 2019-05-20 and 1.1.1 dated 2020-03-03

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Package networkABC updated to version 0.7-0 with previous version 0.6-0 dated 2020-02-07

Title: Network Reverse Engineering with Approximate Bayesian Computation
Description: We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as scale-freeness and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.
Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Khadija Musayeva [ctb], Nicolas Jung [ctb], Université de Strasbourg [cph], CNRS [cph]
Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr>

Diff between networkABC versions 0.6-0 dated 2020-02-07 and 0.7-0 dated 2020-03-03

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 NAMESPACE                                                  |    2 +
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Package LDATS updated to version 0.2.6 with previous version 0.2.4 dated 2019-08-01

Title: Latent Dirichlet Allocation Coupled with Time Series Analyses
Description: Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.
Author: Juniper L. Simonis [aut, cre] (<https://orcid.org/0000-0001-9798-0460>), Erica M. Christensen [aut] (<https://orcid.org/0000-0002-5635-2502>), David J. Harris [aut] (<https://orcid.org/0000-0003-3332-9307>), Renata M. Diaz [aut] (<https://orcid.org/0000-0003-0803-4734>), Hao Ye [aut] (<https://orcid.org/0000-0002-8630-1458>), Ethan P. White [aut] (<https://orcid.org/0000-0001-6728-7745>), S.K. Morgan Ernest [aut] (<https://orcid.org/0000-0002-6026-8530>), Weecology [cph]
Maintainer: Juniper L. Simonis <juniper.simonis@weecology.org>

Diff between LDATS versions 0.2.4 dated 2019-08-01 and 0.2.6 dated 2020-03-03

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 inst/doc/paper-comparison.html    |    4 +-
 inst/doc/rodents-example.html     |    2 -
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 man/sim_TS_data.Rd                |    9 ++---
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More information about LDATS at CRAN
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Package hier.part updated to version 1.0-6 with previous version 1.0-5 dated 2020-02-25

Title: Hierarchical Partitioning
Description: Partitioning of the independent and joint contributions of each variable in a multivariate data set, to a linear regression by hierarchical decomposition of goodness-of-fit measures of regressions using all subsets of predictors in the data set. (i.e., model (1), (2), ..., (N), (1,2), ..., (1,N), ..., (1,2,3,...,N)). A Z-score based estimate of the 'importance' of each predictor is provided by using a randomisation test.
Author: Chris Walsh [aut, cre], Ralph Mac Nally [aut]
Maintainer: Chris Walsh <cwalsh@unimelb.edu.au>

Diff between hier.part versions 1.0-5 dated 2020-02-25 and 1.0-6 dated 2020-03-03

 DESCRIPTION      |    6 ++--
 MD5              |   12 ++++----
 NEWS.md          |   12 ++++++--
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 README.md        |    3 ++
 man/all.regs.Rd  |    4 +-
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 7 files changed, 89 insertions(+), 32 deletions(-)

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Package HH updated to version 3.1-40 with previous version 3.1-39 dated 2019-12-11

Title: Statistical Analysis and Data Display: Heiberger and Holland
Description: Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Author: Richard M. Heiberger
Maintainer: Richard M. Heiberger <rmh@temple.edu>

Diff between HH versions 3.1-39 dated 2019-12-11 and 3.1-40 dated 2020-03-03

 DESCRIPTION                   |    8 
 MD5                           |   14 
 NEWS                          |   21 
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 R/mcp.993.R                   |   22 
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 8 files changed, 577 insertions(+), 669 deletions(-)

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Package heatmap3 updated to version 1.1.7 with previous version 1.1.6 dated 2019-03-22

Title: An Improved Heatmap Package
Description: An improved heatmap package. Completely compatible with the original R function 'heatmap', and provides more powerful and convenient features.
Author: Shilin Zhao, Linlin Yin, Yan Guo, Quanhu Sheng, Yu Shyr
Maintainer: Shilin Zhao <shilin.zhao@vanderbilt.edu>

Diff between heatmap3 versions 1.1.6 dated 2019-03-22 and 1.1.7 dated 2020-03-03

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 8 files changed, 64 insertions(+), 59 deletions(-)

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Package EditImputeCont updated to version 1.1.6 with previous version 1.1.4 dated 2019-05-29

Title: Simultaneous Edit-Imputation for Continuous Microdata
Description: An integrated editing and imputation method for continuous microdata under linear constraints is implemented. It relies on a Bayesian nonparametric hierarchical modeling approach as described in Kim et al. (2015) <doi:10.1080/01621459.2015.1040881>. In this approach, the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
Author: Quanli Wang, Hang J. Kim, Jerome P. Reiter, Lawrence H. Cox and Alan F. Karr
Maintainer: Hang J. Kim <hangkim0@gmail.com>

Diff between EditImputeCont versions 1.1.4 dated 2019-05-29 and 1.1.6 dated 2020-03-03

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 EditImputeCont-1.1.6/EditImputeCont/src/CFeasibilityMap.cpp  |    4 
 EditImputeCont-1.1.6/EditImputeCont/src/bandmat.cpp          |    4 
 EditImputeCont-1.1.6/EditImputeCont/src/hbio.c               |  418 +++++------
 EditImputeCont-1.1.6/EditImputeCont/src/lp_utils.c           |    2 
 EditImputeCont-1.1.6/EditImputeCont/src/lpkit.h              |    2 
 EditImputeCont-1.1.6/EditImputeCont/src/mmio.c               |    2 
 16 files changed, 368 insertions(+), 366 deletions(-)

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Package corto updated to version 1.0.5 with previous version 1.0.2 dated 2020-01-30

Title: Inference of Gene Regulatory Networks
Description: We present 'corto' (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The package implements a similar pipeline as ARACNe-AP (Algorithm for the Reconstruction of Accurate Cellular Networks with Adaptive Partitioning) by Giorgi (2016) <doi:10.1093/bioinformatics/btw216>) with optimizations to run outside a computing cluster (most notably correlation to infer feature dependencies instead of Mutual Information).
Author: Federico M. Giorgi [aut, cre], Daniele Mercatelli [ctb], Gonzalo Lopez-Garcia [ctb]
Maintainer: Federico M. Giorgi <federico.giorgi@gmail.com>

Diff between corto versions 1.0.2 dated 2020-01-30 and 1.0.5 dated 2020-03-03

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 10 files changed, 165 insertions(+), 20 deletions(-)

More information about corto at CRAN
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Package rangeModelMetadata (with last version 0.1.2) was removed from CRAN

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

2019-05-01 0.1.2

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Package MinBAR (with last version 1.1.0) was removed from CRAN

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

2019-07-10 1.1.0

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Package MultNonParam (with last version 1.3.2) was removed from CRAN

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

2020-02-14 1.3.2
2019-11-15 1.3.0
2019-09-07 1.2.8
2018-06-08 1.2.6
2016-10-20 1.2.5
2016-08-28 1.2.3
2015-10-07 1.2.1
2015-10-05 1.2
2015-09-16 1.1
2014-08-25 1.0

Permanent link
Package RWDataPlyr updated to version 0.6.3 with previous version 0.6.2 dated 2018-08-16

Title: Read and Manipulate Data from 'RiverWare'
Description: A tool to read and manipulate data generated from 'RiverWare'(TM) <http://www.riverware.org/> simulations. 'RiverWare' and 'RiverSMART' generate data in "rdf", "csv", and "nc" format. This package provides an interface to read, aggregate, and summarize data from one or more simulations in a 'dplyr' pipeline.
Author: Alan Butler [aut, cre], Cameron Bracken [aut]
Maintainer: Alan Butler <rabutler@usbr.gov>

Diff between RWDataPlyr versions 0.6.2 dated 2018-08-16 and 0.6.3 dated 2020-03-03

 DESCRIPTION                              |    9 
 MD5                                      |  122 ++++-----
 NAMESPACE                                |    2 
 NEWS.md                                  |   36 ++
 R/apply_period.R                         |   12 
 R/combine_rwd_agg.R                      |    7 
 R/period_funs.R                          |    3 
 R/rdf_aggregate.R                        |   11 
 R/read_rdf.R                             |    2 
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 R/rwd_agg.R                              |    4 
 R/rwd_agg_helpers.R                      |    2 
 R/rwslot_aggregations.R                  |   41 +--
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 README.md                                |   21 +
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 man/sysRdf.Rd                            |   44 +--
 man/ym_get_wateryear.Rd                  |   72 ++---
 tests/testthat/test_apply_eval.R         |    7 
 tests/testthat/test_apply_period.R       |    7 
 tests/testthat/test_apply_summary.R      |    7 
 tests/testthat/test_getDataForAllScens.R |   52 ++--
 tests/testthat/test_rdf_aggregate.R      |   16 +
 tests/testthat/test_rw_scen_aggregate.R  |   31 +-
 tests/testthat/test_rwd_agg.R            |   34 ++
 tests/testthat/test_rwtbl_helpers.R      |    5 
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More information about RWDataPlyr at CRAN
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Package mlflow updated to version 1.7.0 with previous version 1.6.0 dated 2020-01-30

Title: Interface to 'MLflow'
Description: R interface to 'MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models.
Author: Matei Zaharia [aut, cre], Javier Luraschi [aut], Kevin Kuo [aut] (<https://orcid.org/0000-0001-7803-7901>), RStudio [cph]
Maintainer: Matei Zaharia <matei@databricks.com>

Diff between mlflow versions 1.6.0 dated 2020-01-30 and 1.7.0 dated 2020-03-03

 DESCRIPTION                 |    6 +++---
 MD5                         |   16 ++++++++--------
 NAMESPACE                   |    2 +-
 R/model-crate.R             |   10 +++++-----
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 R/model.R                   |   38 +++++++++++++++++++++++++-------------
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 9 files changed, 81 insertions(+), 37 deletions(-)

More information about mlflow at CRAN
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Package Eagle updated to version 2.2 with previous version 2.1.1 dated 2019-11-17

Title: Multiple Locus Association Mapping on a Genome-Wide Scale
Description: An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred and outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.
Author: Andrew George [aut, cre], Joshua Bowden [ctb], Ryan Stephenson [ctb], Hyun Kang [ctb], Noah Zaitlen [ctb], Claire Wade [ctb], Andrew Kirby [ctb], David Heckerman [ctb], Mark Daly [ctb], Eleazar Eskin [ctb]
Maintainer: Andrew George <andrew.george@csiro.au>

Diff between Eagle versions 2.1.1 dated 2019-11-17 and 2.2 dated 2020-03-03

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 Eagle-2.2/Eagle/R/RcppExports.R                            |   16 
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 Eagle-2.2/Eagle/R/calcMMt.R                                |    4 
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More information about Eagle at CRAN
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