Sat, 14 Mar 2020

Package tsmp updated to version 0.4.11 with previous version 0.4.8 dated 2020-02-28

Title: Time Series with Matrix Profile
Description: A toolkit implementing the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Author: Francisco Bischoff [aut, cre] (<https://orcid.org/0000-0002-5301-8672>), Michael Yeh [res, ccp, ctb] (<https://orcid.org/0000-0002-9807-2963>), Diego Silva [res, ccp, ctb] (<https://orcid.org/0000-0002-5184-9413>), Yan Zhu [res, ccp, ctb] (<https://orcid.org/0000-0002-5952-2108>), Hoang Dau [res, ccp, ctb] (<https://orcid.org/0000-0003-2439-5185>), Michele Linardi [res, ccp, ctb] (<https://orcid.org/0000-0002-3249-2068>)
Maintainer: Francisco Bischoff <fbischoff@med.up.pt>

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Package suddengains updated to version 0.4.3 with previous version 0.4.2 dated 2020-03-10

Title: Identify Sudden Gains in Longitudinal Data
Description: Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.
Author: Milan Wiedemann [aut, cre] (<https://orcid.org/0000-0003-1991-282X>), Graham M Thew [ctb] (<https://orcid.org/0000-0003-2851-1315>), Richard Stott [ctb] (<https://orcid.org/0000-0003-2533-5504>), Anke Ehlers [ctb, ths] (<https://orcid.org/0000-0002-8742-0192>), Mental Health Research UK [fnd], Wellcome Trust [fnd]
Maintainer: Milan Wiedemann <milan.wiedemann@gmail.com>

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Package ShapePattern updated to version 2.0.2 with previous version 1.0.1 dated 2016-10-26

Title: Tools for Analyzing Shapes and Patterns
Description: This is (1) An evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. The spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking. The spectra of results are returned in graphic and tabular formats. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented. (2) Provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps. Current limitation is for binary (classes 1, 2) maps that are 64x64 cells in extent. (3) Counting the number of each first-order pattern element and converting that information into both frequency and probability vectors. See Remmel (2018) <doi:10.3390/su10103413> and Remmel and Fortin (2013) <doi:10.1007/s10980-013-9905-x>. NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived.
Author: Tarmo K. Remmel [aut, cre] (<https://orcid.org/0000-0001-6251-876X>), Marie-Josee Fortin [ctb], Ferenc Csillag [ctb], Sandor Kabos [ctb]
Maintainer: Tarmo K. Remmel <remmelt@yorku.ca>

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Package r2sundials updated to version 5.0.0-7 with previous version 5.0.0-4 dated 2020-01-08

Title: Wrapper for 'SUNDIALS' Solving ODE and Sensitivity Problem
Description: Wrapper for widely used 'SUNDIALS' software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its 'CVODES' solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made 'R' friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in 'R' and in 'C++' ('RcppArmadillo' flavor). In case of 'C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case 'rmumps' package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as 'R' or 'C++' functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. 'SUNDIALS' library was published in Hindmarsh et al. (2005) <doi:10.1145/1089014.1089020>.
Author: Serguei Sokol [cre, aut], Carol S. Woodward [ctb], Daniel R. Reynolds [ctb], Alan C. Hindmarsh [ctb], David J. Gardner [ctb], Cody J. Balos [ctb], Radu Serban [ctb], Scott D. Cohen [ctb], Peter N. Brown [ctb], George Byrne [ctb], Allan G. Taylor [ctb], Steven L. Lee [ctb], Keith E. Grant [ctb], Aaron Collier [ctb], Lawrence E. Banks [ctb], Steve Smith [ctb], Cosmin Petra [ctb], Slaven Peles [ctb], John Loffeld [ctb], Dan Shumaker [ctb], Ulrike Yang [ctb], James Almgren-Bell [ctb], Shelby L. Lockhart [ctb], Hilari C. Tiedeman [ctb], Ting Yan [ctb], Jean M. Sexton [ctb], Chris White [ctb], Lawrence Livermore National Security [cph], Southern Methodist University [cph], INRAE [cph]
Maintainer: Serguei Sokol <sokol@insa-toulouse.fr>

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Package evgam updated to version 0.1.1 with previous version 0.1.0 dated 2020-03-08

Title: Generalised Additive Extreme Value Models
Description: Methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. For details of distributions see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.
Author: Ben Youngman <b.youngman@exeter.ac.uk> [aut, cre]
Maintainer: Ben Youngman <b.youngman@exeter.ac.uk>

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Package nonnest2 updated to version 0.5-3 with previous version 0.5-2 dated 2018-09-25

Title: Tests of Non-Nested Models
Description: Testing non-nested models via theory supplied by Vuong (1989) <DOI:10.2307/1912557>. Includes tests of model distinguishability and of model fit that can be applied to both nested and non-nested models. Also includes functionality to obtain confidence intervals associated with AIC and BIC. This material is partially based on work supported by the National Science Foundation under Grant Number SES-1061334.
Author: Edgar Merkle [aut, cre], Dongjun You [aut], Lennart Schneider [ctb], Seongho Bae [ctb]
Maintainer: Edgar Merkle <merklee@missouri.edu>

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Package ANN2 updated to version 2.3.3 with previous version 2.3.2 dated 2019-04-13

Title: Artificial Neural Networks for Anomaly Detection
Description: Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning.
Author: Bart Lammers
Maintainer: Bart Lammers <bart.f.lammers@gmail.com>

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Package portfolio updated to version 0.5-0 with previous version 0.4-7 dated 2015-01-29

Title: Analysing Equity Portfolios
Description: Classes for analysing and implementing equity portfolios.
Author: Jeff Enos <jeff@kanecap.com> and David Kane <dave@kanecap.com>, with contributions from Daniel Gerlanc <dgerlanc@gmail.com> and Kyle Campbell <Kyle.W.Campbell@williams.edu>
Maintainer: Daniel Gerlanc <dgerlanc@enplusadvisors.com>

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Package landscapemetrics updated to version 1.4.3 with previous version 1.4.2 dated 2020-01-20

Title: Landscape Metrics for Categorical Map Patterns
Description: Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.umass.edu/landeco/research/fragstats/fragstats.html>) and new ones from the current literature on landscape metrics. This package supports 'raster' spatial objects and takes RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer from the 'raster' package as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.
Author: Maximillian H.K. Hesselbarth [aut, cre] (<https://orcid.org/0000-0003-1125-9918>), Marco Sciaini [aut] (<https://orcid.org/0000-0002-3042-5435>), Jakub Nowosad [aut] (<https://orcid.org/0000-0002-1057-3721>), Sebastian Hanss [aut] (<https://orcid.org/0000-0002-3990-4897>), Laura J. Graham [ctb] (Input on package structure), Jeffrey Hollister [ctb] (Input on package structure), Kimberly A. With [ctb] (Input on package structure), Florian Privé [ctb] (Original author of underlying C++ code for get_nearestneighbour() function), Project Nayuki [ctb] (Original author of underlying C++ code for get_circumscribingcircle and lsm_p_circle), Matt Strimas-Mackey [ctb] (Bugfix in sample_metrics())
Maintainer: Maximillian H.K. Hesselbarth <maximilian.hesselbarth@uni-goettingen.de>

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Package ggeffects updated to version 0.14.2 with previous version 0.14.1 dated 2020-01-28

Title: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
Description: Compute marginal effects from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The main functions are ggpredict(), ggemmeans() and ggeffect(). There is a generic plot()-method to plot the results using 'ggplot2'.
Author: Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>), Frederik Aust [ctb] (<https://orcid.org/0000-0003-4900-788X>)
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>

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Package tidyfst updated to version 0.8.8 with previous version 0.7.7 dated 2020-03-04

Title: Tidy Verbs for Fast Data Manipulation
Description: A toolkit of tidy data manipulation verbs with 'data.table' as the backend. Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of 'data.table'. While enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations.
Author: Tian-Yuan Huang [aut, cre] (<https://orcid.org/0000-0002-3591-4203>)
Maintainer: Tian-Yuan Huang <huang.tian-yuan@qq.com>

Diff between tidyfst versions 0.7.7 dated 2020-03-04 and 0.8.8 dated 2020-03-14

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Package Ropj updated to version 0.3 with previous version 0.2-2 dated 2019-03-14

Title: Import Origin(R) Project Files
Description: Read the data from Origin(R) project files ('*.opj') <https://www.originlab.com/doc/User-Guide/Origin-File-Types>. No write support is planned.
Author: Miquel Garriga [aut, cph], Stefan Gerlach [aut, cph], Ion Vasilief [aut, cph], Alex Kargovsky [aut, cph], Knut Franke [ctb, cph], Alexander Semke [ctb, cph], Tilman Benkert [ctb, cph], Kasper Peeters [ctb, cph], Russell Standish [ctb, cph], Ivan Krylov [cre, cph]
Maintainer: Ivan Krylov <krylov.r00t@gmail.com>

Diff between Ropj versions 0.2-2 dated 2019-03-14 and 0.3 dated 2020-03-14

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Package spacey updated to version 0.1.1 with previous version 0.1.0 dated 2020-02-28

Title: Easily Obtain Spatial Data and Make Better Maps
Description: One of the remaining pain points in making beautiful maps via packages like 'rayshader' is both obtaining and processing spatial data to build from. 'spacey' aims to make it easier to obtain and use this data for locations within the United States, providing utilities to download 'USGS' and 'ESRI' geospatial data and quickly turn it into maps.
Author: Michael Mahoney [aut, cre] (<https://orcid.org/0000-0003-2402-304X>)
Maintainer: Michael Mahoney <mike.mahoney.218@gmail.com>

Diff between spacey versions 0.1.0 dated 2020-02-28 and 0.1.1 dated 2020-03-14

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Package RcppAPT updated to version 0.0.6 with previous version 0.0.5 dated 2018-09-29

Title: 'Rcpp' Interface to the APT Package Manager
Description: The 'APT Package Management System' provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R. This can only work on a system with a suitable 'libapt-pkg-dev' installation so functionality is curtailed if such a library is not found.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

Diff between RcppAPT versions 0.0.5 dated 2018-09-29 and 0.0.6 dated 2020-03-14

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New package languageserversetup with initial version 0.1.0
Package: languageserversetup
Type: Package
Title: Automated Setup and Auto Run for R Language Server
Version: 0.1.0
Authors@R: person("Jozef", "Hajnala", email = "jozef.hajnala@gmail.com", role = c("aut", "cre"))
Maintainer: Jozef Hajnala <jozef.hajnala@gmail.com>
Description: Allows to install the R 'languageserver' with all dependencies into a separate library and use that independent installation automatically when R is instantiated as a language server process. Useful for making language server seamless to use without running into package version conflicts.
License: AGPL-3
Suggests: tinytest
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
SystemRequirements: procps (linux), tasklist (windows)
URL: https://github.com/jozefhajnala/languageserversetup
BugReports: https://github.com/jozefhajnala/languageserversetup/issues
Language: en-US
NeedsCompilation: no
Packaged: 2020-03-06 14:47:30 UTC; root
Author: Jozef Hajnala [aut, cre]
Repository: CRAN
Date/Publication: 2020-03-14 18:50:07 UTC

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New package CatReg with initial version 1.0.0
Package: CatReg
Type: Package
Title: Solution Paths for Linear and Logistic Regression Models with SCOPE Penalty
Version: 1.0.0
Date: 2020-03-05
Authors@R: c(person("Benjamin","Stokell",email="b.stokell@statslab.cam.ac.uk",role=c("aut")), person("Daniel","Grose",email="dan.grose@lancaster.ac.uk",role=c("ctb","cre")), person("Rajen","Shah",email="r.shah@statslab.cam.ac.uk",role=c("ctb")))
Description: Computes solutions for linear and logistic regression models with a nonconvex penalty (SCOPE) in an efficient path-wise fashion (Stokell, Shah and Tibshirani 2020, <arXiv:2002.12606>). The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.1),Rdpack
LinkingTo: Rcpp
NeedsCompilation: yes
RdMacros: Rdpack
RoxygenNote: 7.0.2
Packaged: 2020-03-05 13:39:36 UTC; grosedj
Author: Benjamin Stokell [aut], Daniel Grose [ctb, cre], Rajen Shah [ctb]
Maintainer: Daniel Grose <dan.grose@lancaster.ac.uk>
Repository: CRAN
Date/Publication: 2020-03-14 18:10:05 UTC

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New package prospectr with initial version 0.2.0
Package: prospectr
Type: Package
Title: Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data
Version: 0.2.0
Date: 2020-03-14
Author: Antoine Stevens [aut, cre], Leonardo Ramirez-Lopez [aut, cre]
Maintainer: Leonardo Ramirez-Lopez <ramirez.lopez.leo@gmail.com>
BugReports: https://github.com/l-ramirez-lopez/prospectr/issues
Description: Functions to preprocess spectroscopic data and conduct (representative) sample selection/calibration sampling.
License: GPL (>= 3)
URL: https://github.com/l-ramirez-lopez/prospectr
VignetteBuilder: knitr
Suggests: resemble, knitr, rmarkdown, formatR
LinkingTo: Rcpp, RcppArmadillo
Depends: R (>= 3.5.0),
Imports: foreach, iterators, Rcpp (>= 1.0.1)
RoxygenNote: 7.1.0
NeedsCompilation: yes
LazyData: true
Repository: CRAN
Encoding: UTF-8
Packaged: 2020-03-14 08:35:46 UTC; raml
Date/Publication: 2020-03-14 17:10:02 UTC

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Package nlmixr updated to version 1.1.1-6 with previous version 1.1.1-5 dated 2020-01-27

Title: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics
Description: Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'RxODE' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Author: Matthew Fidler [aut] (<https://orcid.org/0000-0001-8538-6691>), Yuan Xiong [aut], Rik Schoemaker [aut] (<https://orcid.org/0000-0002-7538-3005>), Justin Wilkins [aut] (<https://orcid.org/0000-0002-7099-9396>), Richard Hooijmaijers [aut], Teun Post [aut], Robert Leary [ctb], Wenping Wang [aut, cre], Hadley Wickham [ctb], Dirk Eddelbuettel [cph], Johannes Pfeifer [ctb], Robert B. Schnabel [ctb], Elizabeth Eskow [ctb], Emmanuelle Comets [ctb], Audrey Lavenu [ctb], Marc Lavielle [ctb], David Ardia [cph], Bill Denney [ctb] (<https://orcid.org/0000-0002-5759-428X>), Daniel C. Dillon [ctb], Katharine Mullen [cph], Ben Goodrich [ctb]
Maintainer: Wenping Wang <wwang8198@gmail.com>

Diff between nlmixr versions 1.1.1-5 dated 2020-01-27 and 1.1.1-6 dated 2020-03-14

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Package jsmodule updated to version 1.0.6 with previous version 1.0.5 dated 2020-02-17

Title: 'RStudio' Addins and 'Shiny' Modules for Medical Research
Description: 'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
Author: Jinseob Kim [aut, cre] (<https://orcid.org/0000-0002-9403-605X>), Zarathu [cph, fnd]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>

Diff between jsmodule versions 1.0.5 dated 2020-02-17 and 1.0.6 dated 2020-03-14

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Package helda updated to version 1.0.1 with previous version 1.0.0 dated 2020-02-29

Title: Preprocess Data and Get Better Insights from Machine Learning Models
Description: The main focus is on preprocessing and data visualization of machine learning models performances. Some functions allow to fill in gaps in time series using linear interpolation on panel data, some functions permit to draw lift effect and lift curve in order to benchmark machine learning models or you can even find the optimal number of clusters in agglomerative clustering algorithm.
Author: Simon Corde [aut, cre]
Maintainer: Simon Corde <simon.corde@hotmail.fr>

Diff between helda versions 1.0.0 dated 2020-02-29 and 1.0.1 dated 2020-03-14

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Package FunChisq updated to version 2.4.9.2 with previous version 2.4.9.1 dated 2019-09-24

Title: Model-Free Functional Chi-Squared and Exact Tests
Description: Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-squared tests (Zhang & Song 2013) <arXiv:1311.2707> and an exact functional test (Zhong & Song 2019) <doi:10.1109/TCBB.2018.2809743>. The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al 2016) <doi:10.1038/nmeth.3773>. A function index (Zhong & Song 2019) <doi:10.1186/s12920-019-0565-9> (Kumar et al 2018) <doi:10.1109/BIBM.2018.8621502> derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests.
Author: Yang Zhang [aut], Hua Zhong [aut] (<https://orcid.org/0000-0003-1962-2603>), Hien Nguyen [aut] (<https://orcid.org/0000-0002-7237-4752>), Ruby Sharma [aut], Sajal Kumar [aut] (<https://orcid.org/0000-0003-0930-1582>), Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>)
Maintainer: Joe Song <joemsong@cs.nmsu.edu>

Diff between FunChisq versions 2.4.9.1 dated 2019-09-24 and 2.4.9.2 dated 2020-03-14

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Package daewr updated to version 1.2-3 with previous version 1.2-2 dated 2020-03-11

Title: Design and Analysis of Experiments with R
Description: Contains Data frames and functions used in the book "Design and Analysis of Experiments with R".
Author: John Lawson [aut, cre], Gerhard Krennrich [aut]
Maintainer: John Lawson <lawsonjsl7net@gmail.com>

Diff between daewr versions 1.2-2 dated 2020-03-11 and 1.2-3 dated 2020-03-14

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Package Ckmeans.1d.dp updated to version 4.3.2 with previous version 4.3.0 dated 2019-09-07

Title: Optimal, Fast, and Reproducible Univariate Clustering
Description: Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four types of problem including univariate k-means, k-median, k-segments, and multi-channel weighted k-means are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced at a large number of clusters k. Weighted k-means can also process time series to perform peak calling. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility.
Author: Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>), Hua Zhong [aut] (<https://orcid.org/0000-0003-1962-2603>), Haizhou Wang [aut]
Maintainer: Joe Song <joemsong@cs.nmsu.edu>

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New package sim2Dpredictr with initial version 0.1.0
Package: sim2Dpredictr
Title: Simulate Outcomes Using Spatially Dependent Design Matrices
Version: 0.1.0
Authors@R: person("Justin", "Leach", email = "jleach@uab.edu", role = c("aut", "cre", "cph"))
Description: Provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288> and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: car, ggplot2, MASS, Rdpack, spam (>= 2.2-0), tidyverse, tibble, dplyr, magrittr, matrixcalc
RdMacros: Rdpack
RoxygenNote: 7.0.2
Suggests: knitr, rmarkdown, testthat
URL: http://github.com/jmleach-bst/sim2Dpredictr
BugReports: http://github.com/jmleach-bst/sim2Dpredictr
NeedsCompilation: no
Packaged: 2020-03-06 15:44:38 UTC; Justin
Author: Justin Leach [aut, cre, cph]
Maintainer: Justin Leach <jleach@uab.edu>
Repository: CRAN
Date/Publication: 2020-03-14 16:10:02 UTC

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New package rangeModelMetadata with initial version 0.1.3
Package: rangeModelMetadata
Type: Package
Title: Provides Templates for Metadata Files Associated with Species Range Models
Version: 0.1.3
Author: Cory Merow, Brian Maitner, Hannah Owens, Jamie Kass, Brian Enquist, Rob Guralnick, Damairis Zurrel, Christian Koenig
Maintainer: Cory Merow <cory.merow@gmail.com>
Description: Range Modeling Metadata Standards (RMMS) address three challenges: they (i) are designed for convenience to encourage use, (ii) accommodate a wide variety of applications, and (iii) are extensible to allow the community of range modelers to steer it as needed. RMMS are based on a data dictionary that specifies a hierarchical structure to catalog different aspects of the range modeling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to provide their own values. Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in more detail. Note that users who prefer to use the R package 'ecospat' can obtain it from <https://github.com/ecospat/ecospat>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Depends:
Suggests: BIEN, biomod2, dismo, ecospat, ENMeval, googlesheets, knitr, rmarkdown
Imports: dplyr, jsonlite, MASS, raster, rgbif, rgdal, rgeos, shiny, sp, spatstat, spocc, spThin, utils
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-03-13 13:42:09 UTC; ctg
Repository: CRAN
Date/Publication: 2020-03-14 16:50:03 UTC

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Package iCellR updated to version 1.3.3 with previous version 1.3.1 dated 2020-02-26

Title: Analyzing High-Throughput Single Cell Sequencing Data
Description: A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq and CITE-Seq. Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Li F, et al (2019) <doi:10.1158/2159-8290.CD-19-0780> for more details.
Author: Alireza Khodadadi-Jamayran [aut, cre] (<https://orcid.org/0000-0003-2495-7504>), Joseph Pucella [ctb], Hua Zhou [ctb], Nicole Doudican [ctb], John Carucci [ctb], Adriana Heguy [ctb], Boris Reizis [ctb], Aristotelis Tsirigos [aut, ctb] (<https://orcid.org/0000-0002-7512-8477>)
Maintainer: Alireza Khodadadi-Jamayran <alireza.khodadadi.j@gmail.com>

Diff between iCellR versions 1.3.1 dated 2020-02-26 and 1.3.3 dated 2020-03-14

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Package GillespieSSA2 updated to version 0.2.6.1 with previous version 0.2.5 dated 2019-08-22

Title: Gillespie's Stochastic Simulation Algorithm for Impatient People
Description: A fast, scalable, and versatile framework for simulating large systems with Gillespie's Stochastic Simulation Algorithm ('SSA'). This package is the spiritual successor to the 'GillespieSSA' package originally written by Mario Pineda-Krch. Benefits of this package include major speed improvements (>100x), easier to understand documentation, and many unit tests that try to ensure the package works as intended.
Author: Robrecht Cannoodt [aut, cre] (<https://orcid.org/0000-0003-3641-729X>), Wouter Saelens [aut] (<https://orcid.org/0000-0002-7114-6248>)
Maintainer: Robrecht Cannoodt <rcannood@gmail.com>

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New package subselect with initial version 0.15.2
Package: subselect
Version: 0.15.2
Date: 2020-03-04
Title: Selecting Variable Subsets
Authors@R: c(person("Jorge" ,"Orestes Cerdeira", role="aut", email="jo.cerdeira@fct.unl.pt"), person("Pedro", "Duarte Silva", role="aut", email="psilva@porto.ucp.pt"), person("Jorge", "Cadima", role=c("aut","cre"), email="jcadima@isa.ulisboa.pt"), person("Manuel", "Minhoto", role="aut", email="minhoto@uevora.pt"))
Author: Jorge Orestes Cerdeira [aut], Pedro Duarte Silva [aut], Jorge Cadima [aut, cre], Manuel Minhoto [aut]
Maintainer: Jorge Cadima <jcadima@isa.ulisboa.pt>
Description: A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette "subselect", which includes additional references.
Depends: R (>= 3.5.0)
Imports: MASS, ISwR, corpcor
License: GPL (>= 2)
Repository: CRAN
LazyData: TRUE
NeedsCompilation: yes
Packaged: 2020-03-06 10:02:02 UTC; jcadima
Date/Publication: 2020-03-14 15:30:02 UTC

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New package msd with initial version 0.1.0
Package: msd
Type: Package
Title: Method of Successive Dichotomizations
Version: 0.1.0
Author: Chris Bradley <cbradley05@gmail.com>
Maintainer: Chris Bradley <cbradley05@gmail.com>
Imports: stats
Description: Implements the method of successive dichotomizations by Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>, which estimates item measures, person measures and ordered rating category thresholds given ordinal rating scale data.
License: GPL
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2020-03-06 03:22:34 UTC; chrisbradley
Repository: CRAN
Date/Publication: 2020-03-14 15:20:02 UTC

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New package mdapack with initial version 0.0.1
Package: mdapack
Title: Medical Data Analysis Pack
Version: 0.0.1
Depends: R (>= 2.10)
Authors@R: c( person("Henry","Nanji", email = "uczhn@exmail.nottingham.ac.uk",role = c("aut", "cre")), person("Saisakul ", "Chernbumroong", email = "S.Chernbumroong@bham.ac.uk", role = "aut"))
Description: An implementation of two functions for medical data analysis which perform basic univariate analysis and plot heatmaps of numeric variables.Kirkwood et al. (2003) <doi/abs/10.1002/sim.1961>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Imports: stats, ggplot2, reshape2, devtools, roxygen2, knitr, testthat, rmarkdown, spelling, httr, covr, gh, rlang, openssl, git2r, pkgbuild, utils, curl
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-03-06 13:30:54 UTC; uczhn
Author: Henry Nanji [aut, cre], Saisakul Chernbumroong [aut]
Maintainer: Henry Nanji <uczhn@exmail.nottingham.ac.uk>
Repository: CRAN
Date/Publication: 2020-03-14 15:40:02 UTC

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Package lmomco updated to version 2.3.6 with previous version 2.3.2 dated 2018-09-20

Title: L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions
Description: Extensive functions for L-moments (LMs) and probability-weighted moments (PWMs), parameter estimation for distributions, LM computation for distributions, and L-moment ratio diagrams. Maximum likelihood and maximum product of spacings estimation are also available. LMs for right-tail and left-tail censoring by known or unknown threshold and by indicator variable are available. Asymmetric (asy) trimmed LMs (TL-moments, TLMs) are supported. LMs of residual (resid) and reversed (rev) resid life are implemented along with 13 quantile function operators for reliability and survival analyses. Exact analytical bootstrap estimates of order statistics, LMs, and variances- covariances of LMs are provided. The Harri-Coble Tau34-squared Normality Test is available. Distribution support with "L" (LMs), "TL" (TLMs) and added (+) support for right-tail censoring (RC) encompasses: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L,TL], Gen Logistic [L), Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Resid. Quantile Function [L], Normal [L], 3-p log-Normal [L], Pearson Type III [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice/Rician [L], Slash [TL], 3-p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L]. Multivariate sample L-comoments (LCMs) are implemented to measure asymmetric associations.
Author: William Asquith
Maintainer: William Asquith <william.asquith@ttu.edu>

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New package IRTBEMM with initial version 1.0.2
Package: IRTBEMM
Type: Package
Title: Family of Bayesian EMM Algorithm for Item Response Models
Version: 1.0.2
Date: 2020-02-28
Authors@R: c(person("Shaoyang", "Guo", role = c("aut", "cre", "cph"), email = "syguo1992@outlook.com"), person("Chanjin", "Zheng", role = "aut"), person("Justin L", "Kern", role = "aut"))
Description: Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martín et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the Bayesian Expectation-Maximization-Maximization-Maximization (BE3M) algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Wang (2018) <https:www.ncme.org/news/past-meetings/2018-recap>; (4) the BE3M algorithm for 4PL model proposed by Zhang, Guo, & Zheng (2018) <https:www.ncme.org/news/past-meetings/2018-recap>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
NeedsCompilation: yes
Packaged: 2020-03-06 12:38:06 UTC; DSjiaxu
Encoding: UTF-8
Author: Shaoyang Guo [aut, cre, cph], Chanjin Zheng [aut], Justin L Kern [aut]
Maintainer: Shaoyang Guo <syguo1992@outlook.com>
Repository: CRAN
Date/Publication: 2020-03-14 15:30:07 UTC

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New package HighestMedianRules with initial version 1.0
Package: HighestMedianRules
Type: Package
Title: Implementation of Voting Rules Electing the Candidate with Highest Median Grade
Version: 1.0
Authors@R: person("Adrien", "Fabre", email = "fabre.adri1@gmail.com", role = c("aut", "cre"))
Description: Computes the scores and ranks candidates according to voting rules electing the highest median grade. Based on "Tie-breaking the highest median: alternatives to the majority judgment", A. Fabre, Social Choice & Welfare (forthcoming as of 2020). The paper is available here: <https://github.com/bixiou/highest_median/raw/master/Tie-breaking%20Highest%20Median%20-%20Fabre%202019.pdf>. Functions to plot the voting profiles can be found on github: <https://github.com/bixiou/highest_median/blob/master/packages_functions_data.R>.
License: AGPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2.9000
Imports: RMallow
NeedsCompilation: no
Packaged: 2020-03-06 10:27:19 UTC; adrien
Author: Adrien Fabre [aut, cre]
Maintainer: Adrien Fabre <fabre.adri1@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-14 15:30:10 UTC

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New package haldensify with initial version 0.0.5
Package: haldensify
Title: Highly Adaptive Lasso Conditional Density Estimation
Version: 0.0.5
Authors@R: c( person("Nima", "Hejazi", email = "nh@nimahejazi.org", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-7127-2789")), person("David", "Benkeser", email = "benkeser@emory.edu", role = "aut", comment = c(ORCID = "0000-0002-1019-8343")), person("Mark", "van der Laan", email = "laan@berkeley.edu", role = c("aut", "ths"), comment = c(ORCID = "0000-0003-1432-5511")) )
Maintainer: Nima Hejazi <nh@nimahejazi.org>
Description: Conditional density estimation is a longstanding and challenging problem in statistical theory, and numerous proposals exist for optimally estimating such complex functions. Algorithms for nonparametric estimation of conditional densities based on a pooled hazard regression formulation and semiparametric estimation via conditional hazards modeling are implemented based on the highly adaptive lasso, a nonparametric regression function for efficient estimation with fast convergence under mild assumptions. The pooled hazards formulation implemented was first described by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>.
Depends: R (>= 3.2.0)
Imports: stats, ggplot2, data.table, future.apply, assertthat, hal9001 (>= 0.2.5), origami (>= 1.0.0), Rdpack
Suggests: testthat, knitr, rmarkdown, future, dplyr
License: MIT + file LICENSE
URL: https://github.com/nhejazi/haldensify
BugReports: https://github.com/nhejazi/haldensify/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2020-03-06 06:00:01 UTC; nsh
Author: Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>), David Benkeser [aut] (<https://orcid.org/0000-0002-1019-8343>), Mark van der Laan [aut, ths] (<https://orcid.org/0000-0003-1432-5511>)
Repository: CRAN
Date/Publication: 2020-03-14 15:20:05 UTC

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Package GenEst updated to version 1.4.2 with previous version 1.4.1 dated 2020-03-06

Title: Generalized Mortality Estimator
Description: Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.
Author: Daniel Dalthorp [aut, cre], Juniper Simonis [aut], Lisa Madsen [aut], Manuela Huso [aut], Paul Rabie [aut], Jeffrey Mintz [aut], Robert Wolpert [aut], Jared Studyvin [aut], Franzi Korner-Nievergelt [aut]
Maintainer: Daniel Dalthorp <ddalthorp@usgs.gov>

Diff between GenEst versions 1.4.1 dated 2020-03-06 and 1.4.2 dated 2020-03-14

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New package ForecastTB with initial version 1.0.1
Package: ForecastTB
Type: Package
Title: Test Bench for the Comparison of Forecast Methods
Version: 1.0.1
Authors@R: c(person(given = "Neeraj Dhanraj", family = "Bokde", role = c("aut", "cre"), email = "neerajdhanraj@gmail.com", comment = c(ORCID = "0000-0002-3493-9302")), person(given = "Gorm Bruun", family = "Andersen", role = c("aut"), email = "gba@eng.au.dk"))
Maintainer: Neeraj Dhanraj Bokde <neerajdhanraj@gmail.com>
Description: Provides a test bench for the comparison of forecasting methods in uni-variate time series. Forecasting methods are compared using different error metrics. Proposed forecasting methods and alternative error metrics can be used. Detailed discussion is provided in the vignette.
License: CC0
Imports: PSF, decomposedPSF, ggplot2, gridExtra, imputeTestbench, methods, reshape2, forecast, circlize, RColorBrewer, stats, graphics, utils
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Suggests: knitr, testthat (>= 2.1.0)
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-03-06 13:13:25 UTC; au644200
Author: Neeraj Dhanraj Bokde [aut, cre] (<https://orcid.org/0000-0002-3493-9302>), Gorm Bruun Andersen [aut]
Repository: CRAN
Date/Publication: 2020-03-14 15:40:05 UTC

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New package BoostMLR with initial version 1.0.1
Package: BoostMLR
Type: Package
Title: Boosting for Multivariate Longitudinal Response
Version: 1.0.1
Date: 2020-03-05
Author: Amol Pande, Hemant Ishwaran
Maintainer: Amol Pande <amoljpande@gmail.com>
Description: Jointly models the multivariate longitudinal response and multiple covariates and time using gradient boosting approach.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 0.12.18), stats, splines, nlme
Suggests: mlbench
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2020-03-06 13:34:46 UTC; pandea2
Repository: CRAN
Date/Publication: 2020-03-14 15:50:03 UTC

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Package NHMSAR updated to version 1.16 with previous version 1.15 dated 2019-11-22

Title: Non-Homogeneous Markov Switching Autoregressive Models
Description: Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations. Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
Author: Valerie Monbet
Maintainer: Valerie Monbet <valerie.monbet@gmail.com>

Diff between NHMSAR versions 1.15 dated 2019-11-22 and 1.16 dated 2020-03-14

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Package jsonify updated to version 1.1.1 with previous version 1.0.0 dated 2019-11-03

Title: Convert Between 'R' Objects and Javascript Object Notation (JSON)
Description: Conversions between 'R' objects and Javascript Object Notation (JSON) using the 'rapidjsonr' library <https://CRAN.R-project.org/package=rapidjsonr>.
Author: David Cooley [aut, cre], Chris Muir [ctb]
Maintainer: David Cooley <dcooley@symbolix.com.au>

Diff between jsonify versions 1.0.0 dated 2019-11-03 and 1.1.1 dated 2020-03-14

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Package yamlet updated to version 0.4.6 with previous version 0.4.2 dated 2020-02-29

Title: Versatile Curation of Table Metadata
Description: The 'yamlet' package implements a file-based mechanism for documenting datasets. It reads and writes YAML-formatted metadata and applies it as data item attributes. Data and metadata are stored independently but can be coordinated by using similar file paths with different extensions. The 'yamlet' dialect is valid 'YAML', but some conventions are chosen to improve readability. Defaults and conventions can be over-ridden by the user. See ?yamlet and ?decorate.data.frame. See ?read_yamlet ?write_yamlet, and ?io_csv.
Author: Tim Bergsma
Maintainer: Tim Bergsma <bergsmat@gmail.com>

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Package V8 updated to version 3.0.2 with previous version 3.0.1 dated 2020-01-22

Title: Embedded JavaScript and WebAssembly Engine for R
Description: An R interface to V8: Google's open source JavaScript and WebAssembly engine. This package can be compiled either with V8 version 6 and up, a NodeJS shared library, or the legacy 3.14/3.15 branch of V8.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>)
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between V8 versions 3.0.1 dated 2020-01-22 and 3.0.2 dated 2020-03-14

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Package utile.tools updated to version 0.2.4 with previous version 0.2.3 dated 2019-12-01

Title: Summarize Data for Publication
Description: A set of tools for preparing and summarizing data for publication purposes. Includes functions for tabulating models, means to produce human-readable summary statistics from raw data, macros for calculating duration of time, and simplistic hypothesis testing tools.
Author: Eric Finnesgard [aut, cre]
Maintainer: Eric Finnesgard <finnesgard.eric@mayo.edu>

Diff between utile.tools versions 0.2.3 dated 2019-12-01 and 0.2.4 dated 2020-03-14

 DESCRIPTION          |   10 +++++-----
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 6 files changed, 27 insertions(+), 17 deletions(-)

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Package SQRL updated to version 0.7.1 with previous version 0.7.0 dated 2019-11-07

Title: Enhanced Database Interactivity
Description: Facilitates interaction with ODBC data sources, by remembering and hiding communication settings and supporting parameterised multi-statement SQL with embedded R.
Author: Mike Lee
Maintainer: Mike Lee <random.deviate@gmail.com>

Diff between SQRL versions 0.7.0 dated 2019-11-07 and 0.7.1 dated 2020-03-14

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Package RMySQL updated to version 0.10.20 with previous version 0.10.19 dated 2020-02-03

Title: Database Interface and 'MySQL' Driver for R
Description: Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code ported from S-PLUS. A modern 'MySQL' client based on 'Rcpp' is available from the 'RMariaDB' package.
Author: Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), David James [aut], Saikat DebRoy [aut], Hadley Wickham [aut], Jeffrey Horner [aut], RStudio [cph]
Maintainer: Jeroen Ooms <jeroen@berkeley.edu>

Diff between RMySQL versions 0.10.19 dated 2020-02-03 and 0.10.20 dated 2020-03-14

 DESCRIPTION   |    6 +++---
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Package mixIndependR updated to version 0.2.1 with previous version 0.2.0 dated 2020-02-11

Title: Genetics and Independence Testing of Mixed Genetic Panels
Description: Developed to deal with multi-locus genotype data, this package is especially designed for those panel which include different type of markers. Basic genetic parameters like allele frequency, genotype frequency, heterozygosity and Hardy-Weinberg test of mixed genetic data can be obtained. In addition, a new test for mutual independence which is compatible for mixed genetic data is developed in this package.
Author: Bing Song
Maintainer: Bing Song <bs0316@my.unthsc.edu>

Diff between mixIndependR versions 0.2.0 dated 2020-02-11 and 0.2.1 dated 2020-03-14

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Package dann updated to version 0.2.0 with previous version 0.1.0 dated 2019-12-10

Title: Discriminant Adaptive Nearest Neighbor Classification
Description: Discriminant Adaptive Nearest Neighbor Classification is a variation of k nearest neighbors where the neighborhood is elongated along class boundaries. This package implements dann and sub_dann from Hastie (1995) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.
Author: Greg McMahan
Maintainer: Greg McMahan <gmcmacran@gmail.com>

Diff between dann versions 0.1.0 dated 2019-12-10 and 0.2.0 dated 2020-03-14

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Package IOHexperimenter (with last version 0.1.0) was removed from CRAN

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

2020-02-28 0.1.0

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Package fdaPDE (with last version 1.0-6) was removed from CRAN

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

2020-02-28 1.0-6

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Package upwaver (with last version 1.2.1) was removed from CRAN

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

2020-01-14 1.2.1
2019-04-15 1.2.0
2018-04-24 1.1.0
2018-01-22 1.0.1
2018-01-16 1.0.0

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