Tue, 12 May 2020

Package ptstem updated to version 0.0.7 with previous version 0.0.6 dated 2020-05-11

Title: Stemming Algorithms for the Portuguese Language
Description: Wraps a collection of stemming algorithms for the Portuguese Language.
Author: Daniel Falbel [aut, cre]
Maintainer: Daniel Falbel <dfalbel@gmail.com>

Diff between ptstem versions 0.0.6 dated 2020-05-11 and 0.0.7 dated 2020-05-12

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Package nimbleEcology updated to version 0.2.2 with previous version 0.2.1 dated 2020-04-07

Title: Distributions for Ecological Models in 'nimble'
Description: Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models. (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).
Author: Benjamin R. Goldstein [aut, cre], Daniel Turek [aut], Lauren Ponisio [aut], Perry de Valpine [aut]
Maintainer: Benjamin R. Goldstein <ben.goldstein@berkeley.edu>

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Package mfGARCH updated to version 0.2.0 with previous version 0.1.9 dated 2019-12-04

Title: Mixed-Frequency GARCH Models
Description: Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.
Author: Onno Kleen [aut, cre] (<https://orcid.org/0000-0003-4731-4640>)
Maintainer: Onno Kleen <r@onnokleen.de>

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Package escalation updated to version 0.1.3 with previous version 0.1.2 dated 2020-04-14

Title: Modular Approach to Dose Finding Clinical Trials
Description: Methods for working with dose-finding clinical trials. We start by providing a common interface to various dose-finding methodologies like the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. We then add optional embellishments to provide extra desirable behaviour, like avoiding skipping doses, stopping after n patients have been treated at the recommended dose, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these embellishments using the pipe operator from 'magrittr', it is simple to tailor the behaviour of dose-finding designs so that they do what you want. Furthermore, using this flexible interface for creating dose-finding designs, it is simple to run simulations or calculate dose-pathways for future cohorts of patients.
Author: Kristian Brock [aut, cre] (<https://orcid.org/0000-0002-3921-0166>)
Maintainer: Kristian Brock <kristian.brock@gmail.com>

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Package vroom updated to version 1.2.1 with previous version 1.2.0 dated 2020-01-13

Title: Read and Write Rectangular Text Data Quickly
Description: The goal of 'vroom' is to read and write data (like 'csv', 'tsv' and 'fwf') quickly. When reading it uses a quick initial indexing step, then reads the values lazily , so only the data you actually use needs to be read. The writer formats the data in parallel and writes to disk asynchronously from formatting.
Author: Jim Hester [aut, cre] (<https://orcid.org/0000-0002-2739-7082>), Hadley Wickham [aut] (<https://orcid.org/0000-0003-4757-117X>), https://github.com/mandreyel/ [cph] (mio library), R Core Team [ctb] (localtime.c code adapted from R), Jukka Jylänki [cph] (grisu3 implementation), Mikkel Jørgensen [cph] (grisu3 implementation), RStudio [cph, fnd]
Maintainer: Jim Hester <james.f.hester@gmail.com>

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Package polyRAD updated to version 1.2 with previous version 1.1 dated 2019-06-04

Title: Genotype Calling with Uncertainty from Sequencing Data in Polyploids and Diploids
Description: Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. 'polyRAD' is described by Clark et al. (2019) <doi:10.1534/g3.118.200913>. A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020) <doi:10.1101/2020.01.11.902890>.
Author: Lindsay V. Clark [aut, cre] (<https://orcid.org/0000-0002-3881-9252>), U.S. National Science Foundation [fnd]
Maintainer: Lindsay V. Clark <lvclark@illinois.edu>

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Package nosoi updated to version 1.0.3 with previous version 1.0.2 dated 2020-04-03

Title: A Forward Agent-Based Transmission Chain Simulator
Description: The aim of 'nosoi' (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. bioRxiv 2020.03.03.973107). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. 'nosoi' is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.
Author: Sebastian Lequime [aut, cre] (<https://orcid.org/0000-0002-3140-0651>), Paul Bastide [aut] (<https://orcid.org/0000-0002-8084-9893>), Simon Dellicour [aut] (<https://orcid.org/0000-0001-9558-1052>), Philippe Lemey [aut] (<https://orcid.org/0000-0003-2826-5353>), Guy Baele [aut] (<https://orcid.org/0000-0002-1915-7732>)
Maintainer: Sebastian Lequime <sebastian.lequime@gmail.com>

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Package visvow updated to version 0.9.0 with previous version 0.8.0 dated 2019-09-23

Title: Visible Vowels: Visualization of Vowel Variation
Description: Visualizes vowel variation in f0, F1, F2, F3 and duration.
Author: Wilbert Heeringa, Hans Van de Velde
Maintainer: Wilbert Heeringa <wjheeringa@gmail.com>

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Package MEDseq updated to version 1.1.1 with previous version 1.1.0 dated 2020-03-31

Title: Mixtures of Exponential-Distance Models with Covariates
Description: Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2019) <arXiv:1908.07963>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics. Sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>), Raffaella Piccarreta [ctb], Isobel Claire Gormley [ctb]
Maintainer: Keefe Murphy <keefe.murphy@ucd.ie>

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Package rtemps updated to version 0.6.0 with previous version 0.5.0 dated 2020-05-02

Title: R Templates for Reproducible Data Analyses
Description: A collection of R Markdown templates for nicely structured, reproducible data analyses in R. The templates have embedded examples on how to write citations, footnotes, equations and use colored message/info boxes, how to cross-reference different parts/sections in the report, provide a nice table of contents (toc) with a References section and proper R session information as well as examples using DT tables and ggplot2 graphs. The bookdown Lite template theme supports code folding.
Author: John Zobolas [aut, cph, cre] (<https://orcid.org/0000-0002-3609-8674>)
Maintainer: John Zobolas <bblodfon@gmail.com>

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Package EIAdata updated to version 0.1.1 with previous version 0.0.5 dated 2019-08-23

Title: R Wrapper for the Energy Information Administration (EIA) API
Description: An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API <https://www.eia.gov/opendata/>.
Author: Matthew Brigida and others (see GitHub)
Maintainer: Matthew Brigida <mdbrigida@gmail.com>

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Package spatstat updated to version 1.64-1 with previous version 1.63-3 dated 2020-03-01

Title: Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Description: Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Author: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>, Rolf Turner <r.turner@auckland.ac.nz> and Ege Rubak <rubak@math.aau.dk>, with substantial contributions of code by Kasper Klitgaard Berthelsen; Ottmar Cronie; Tilman Davies; Yongtao Guan; Ute Hahn; Abdollah Jalilian; Marie-Colette van Lieshout; Greg McSwiggan; Tuomas Rajala; Suman Rakshit; Dominic Schuhmacher; Rasmus Waagepetersen; and Hangsheng Wang. Additional contributions by M. Adepeju; C. Anderson; Q.W. Ang; R. Arellano; J. Astrom; R. Aue; M. Austenfeld; S. Azaele; M. Baddeley; C. Beale; M. Bell; R. Bernhardt; T. Bendtsen; A. Bevan; B. Biggerstaff; A. Bilgrau; L. Bischof; C. Biscio; R. Bivand; J.M. Blanco Moreno; F. Bonneu; J. Brown; J. Burgos; S. Byers; Y.M. Chang; J.B. Chen; I. Chernayavsky; Y.C. Chin; B. Christensen; L. Cobo Sanchez; J.-F. Coeurjolly; K. Colyvas; H. Commenges; R. Constantine; R. Corria Ainslie; R. Cotton; M. de la Cruz; P. Dalgaard; M. D'Antuono; S. Das; P.J. Diggle; P. Donnelly; I. Dryden; S. Eglen; A. El-Gabbas; B. Fandohan; O. Flores; E.D. Ford; P. Forbes; S. Frank; J. Franklin; N. Funwi-Gabga; O. Garcia; A. Gault; J. Geldmann; M. Genton; S. Ghalandarayeshi; J. Gilbey; J. Goldstick; P. Grabarnik; C. Graf; U. Hahn; A. Hardegen; M.B. Hansen; M. Hazelton; J. Heikkinen; M. Hering; M. Herrmann; M. Hesselbarth; P. Hewson; H. Heydarian; K. Hingee; K. Hornik; P. Hunziker; J. Hywood; R. Ihaka; C. Icos; A. Jammalamadaka; R. John-Chandran; D. Johnson; M. Khanmohammadi; R. Klaver; P. Kovesi; L. Kozmian-Ledward; M. Kuhn; J. Laake; R.A. Lamb; F. Lavancier; T. Lawrence; T. Lazauskas; J. Lee; G.P. Leser; A. Li; H.T. Li; G. Limitsios; A. Lister; N. Luambua; B. Madin; M. Maechler; J. Marcus; K. Marchikanti; R. Mark; J. Mateu; P. McCullagh; U. Mehlig; F. Mestre; S. Meyer; X.C. Mi; L. De Middeleer; R.K. Milne; E. Miranda; J. Moller; A. Mollie; I. Moncada; M. Moradi; V. Morera Pujol; E. Mudrak; G.M. Nair; N. Najari; N. Nava; L.S. Nielsen; F. Nunes; J.R. Nyengaard; J. Oehlschlaegel; T. Onkelinx; S. O'Riordan; E. Parilov; J. Picka; N. Picard; T. Pollington; M. Porter; S. Protsiv; A. Raftery; S. Rakshit; B. Ramage; P. Ramon; X. Raynaud; N. Read; M. Reiter; I. Renner; T.O. Richardson; B.D. Ripley; E. Rosenbaum; B. Rowlingson; J. Rudokas; T. Rudolph; J. Rudge; C. Ryan; F. Safavimanesh; A. Sarkka; C. Schank; K. Schladitz; S. Schutte; B.T. Scott; O. Semboli; F. Semecurbe; V. Shcherbakov; G.C. Shen; P. Shi; H.-J. Ship; T.L. Silva; I.-M. Sintorn; Y. Song; M. Spiess; M. Stevenson; K. Stucki; J. Sulavik; M. Sumner; P. Surovy; B. Taylor; T. Thorarinsdottir; L. Torres; B. Turlach; T. Tvedebrink; K. Ummer; M. Uppala; A. van Burgel; T. Verbeke; M. Vihtakari; A. Villers; F. Vinatier; M. Vogtland; S. Voss; S. Wagner; H. Wang; H. Wendrock; J. Wild; C. Witthoft; S. Wong; M. Woringer; L. Yates; M.E. Zamboni and A. Zeileis.
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>

Diff between spatstat versions 1.63-3 dated 2020-03-01 and 1.64-1 dated 2020-05-12

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Package mdmb updated to version 1.4-12 with previous version 1.3-18 dated 2019-04-16

Title: Model Based Treatment of Missing Data
Description: Contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2019a, 2019b, <doi:10.1037/met0000233>; <doi:10.1080/00273171.2019.1640104>). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.
Author: Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]
Maintainer: Alexander Robitzsch <robitzsch@leibniz-ipn.de>

Diff between mdmb versions 1.3-18 dated 2019-04-16 and 1.4-12 dated 2020-05-12

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Package ipaddress updated to version 0.3.0 with previous version 0.2.0 dated 2020-03-25

Title: Tidy IP Addresses
Description: Classes and functions for working with IP (Internet Protocol) addresses and networks, inspired by the Python 'ipaddress' module. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces. It is specifically designed to work well with the 'tidyverse'.
Author: David Hall [aut, cre] (<https://orcid.org/0000-0002-2193-0480>)
Maintainer: David Hall <david.hall.physics@gmail.com>

Diff between ipaddress versions 0.2.0 dated 2020-03-25 and 0.3.0 dated 2020-05-12

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Package unitizer updated to version 1.4.10 with previous version 1.4.9 dated 2020-03-02

Title: Interactive R Unit Tests
Description: Simplifies regression tests by comparing objects produced by test code with earlier versions of those same objects. If objects are unchanged the tests pass, otherwise execution stops with error details. If in interactive mode, tests can be reviewed through the provided interactive environment.
Author: Brodie Gaslam [aut, cre]
Maintainer: Brodie Gaslam <brodie.gaslam@yahoo.com>

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Package survParamSim updated to version 0.1.1 with previous version 0.1.0 dated 2020-01-13

Title: Parametric Survival Simulation with Parameter Uncertainty
Description: Perform survival simulation with parametric survival model generated from 'survreg' function in 'survival' package. In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to capture uncertainty in model parameters. Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.
Author: Kenta Yoshida [aut, cre] (<https://orcid.org/0000-0003-4967-3831>), Laurent Claret [aut]
Maintainer: Kenta Yoshida <6.kurabupasu@gmail.com>

Diff between survParamSim versions 0.1.0 dated 2020-01-13 and 0.1.1 dated 2020-05-12

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Package OCNet updated to version 0.3.0 with previous version 0.2.0 dated 2020-02-19

Title: Optimal Channel Networks
Description: Generate and analyze Optimal Channel Networks (OCNs): oriented spanning trees reproducing all scaling features characteristic of real, natural river networks. As such, they can be used in a variety of numerical experiments in the fields of hydrology, ecology and epidemiology. See Carraro et al. (2020) <doi:10.1101/2020.02.17.948851> for a presentation of the package; Rinaldo et al. (2014) <doi:10.1073/pnas.1322700111> for a theoretical overview on the OCN concept; Furrer and Sain (2010) <doi:10.18637/jss.v036.i10> for the construct used.
Author: Luca Carraro [aut, cre], Florian Altermatt [ctb], Emanuel A. Fronhofer [ctb], Reinhard Furrer [ctb], Isabelle Gounand [ctb], Andrea Rinaldo [ctb], Enrico Bertuzzo [aut]
Maintainer: Luca Carraro <luca.carraro@hotmail.it>

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Package MoEClust updated to version 1.3.1 with previous version 1.3.0 dated 2020-03-30

Title: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
Description: Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2019) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>)
Maintainer: Keefe Murphy <keefe.murphy@ucd.ie>

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Package IMIFA updated to version 2.1.3 with previous version 2.1.2 dated 2020-03-30

Title: Infinite Mixtures of Infinite Factor Analysers and Related Models
Description: Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2019) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Cinzia Viroli [ctb], Isobel Claire Gormley [ctb]
Maintainer: Keefe Murphy <keefe.murphy@ucd.ie>

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Package exuber updated to version 0.4.1 with previous version 0.4.0 dated 2020-05-04

Title: Econometric Analysis of Explosive Time Series
Description: Testing for and dating periods of explosive dynamics (exuberance) in time series using the univariate and panel recursive unit root tests proposed by Phillips et al. (2015) <doi:10.1111/iere.12132> and Pavlidis et al. (2016) <doi:10.1007/s11146-015-9531-2>. The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes.
Author: Kostas Vasilopoulos [cre, aut], Efthymios Pavlidis [aut], Simon Spavound [aut], Enrique Martínez-García [aut]
Maintainer: Kostas Vasilopoulos <k.vasilopoulo@gmail.com>

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Package caschrono updated to version 2.2 with previous version 2.1 dated 2019-01-28

Title: S<e9>ries Temporelles Avec R
Description: Functions, data sets and exercises solutions for the book 'S<e9>ries Temporelles Avec R' (Yves Aragon, edp sciences, 2016). For all chapters, a vignette is available with some additional material and exercises solutions.
Author: Yves Aragon
Maintainer: Thibault Laurent <Thibault.Laurent@univ-tlse1.fr>

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Package dscore updated to version 1.3.0 with previous version 1.2.0 dated 2020-04-02

Title: D-Score for Child Development
Description: The D-score is a quantitative measure of child development. The D-score follows the Rasch model. See Jacobusse, van Buuren and Verkerk (2006) <doi:10.1002/sim.2351>. The user can convert milestone scores from 19 assessment instruments into the D-score and the DAZ (D-score adjusted for age). Several tools assist in mapping milestone names into the 9-position Global Scale of Early Development (GSED) convention. Supports calculation of the D-score using 'dutch' <doi:10.1177/0962280212473300>, 'gcdg' <doi:10.1136/bmjgh-2019-001724> and 'gsed' conversion keys. The user can calculate DAZ using 'dutch' and 'gcdg' age-conditional references.
Author: Stef van Buuren [cre, aut], Iris Eekhout [aut], Arjan Huizing [aut]
Maintainer: Stef van Buuren <stef.vanbuuren@tno.nl>

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New package MRReg with initial version 0.1.1
Package: MRReg
Title: MDL Multiresolution Linear Regression Framework
Version: 0.1.1
Authors@R: person(given = "Chainarong", family = "Amornbunchornvej", role = c("aut", "cre"), email = "grandca@gmail.com", comment = c(ORCID = "0000-0003-3131-0370"))
Maintainer: Chainarong Amornbunchornvej <grandca@gmail.com>
Description: We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q). We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2019) <arXiv:1907.05234>.
License: MIT + file LICENSE
URL: https://github.com/DarkEyes/MRReg
BugReports: https://github.com/DarkEyes/MRReg/issues
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0), caret
Imports: igraph, ggplot2 (>= 3.0)
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-05-11 07:14:50 UTC; sai
Author: Chainarong Amornbunchornvej [aut, cre] (<https://orcid.org/0000-0003-3131-0370>)
Repository: CRAN
Date/Publication: 2020-05-12 14:30:02 UTC

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Package stringfish (with last version 0.1) was removed from CRAN

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

2020-05-11 0.1

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Package bayesCT (with last version 0.99.2) was removed from CRAN

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

2020-02-09 0.99.2

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New package rKolada with initial version 0.1.2
Package: rKolada
Type: Package
Title: Access Data from the 'Kolada' Database in R
Version: 0.1.2
Authors@R: person("Love", "Hansson", email = "love.hansson@gmail.com", role = c("aut", "cre"))
Maintainer: Love Hansson <love.hansson@gmail.com>
Description: Methods for downloading and processing data and metadata from 'Kolada', the official Swedish regions and municipalities database.
License: AGPL-3
Encoding: UTF-8
LazyData: true
Suggests: testthat, knitr, rmarkdown, scales
RoxygenNote: 7.1.0
Imports: tibble, dplyr, glue, stringr, httr, jsonlite, magrittr, tidyr, purrr, stringi, ggplot2, rlang
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-05-10 19:45:20 UTC; love
Author: Love Hansson [aut, cre]
Repository: CRAN
Date/Publication: 2020-05-12 13:50:02 UTC

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Package OUwie updated to version 2.1 with previous version 1.57 dated 2019-12-06

Title: Analysis of Evolutionary Rates in an OU Framework
Description: Estimates rates for continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al (2012) <doi:10.1111/j.1558-5646.2012.01619.x>.
Author: Jeremy M. Beaulieu <jbeaulieu@nimbios.org>, Brian O'Meara <bomeara@utk.edu>
Maintainer: Jeremy Beaulieu <jbeaulieu@nimbios.org>

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New package match2C with initial version 0.1.0
Package: match2C
Type: Package
Title: Match One Sample using Two Criteria
Version: 0.1.0
Author: Bo Zhang
Maintainer: Bo Zhang <bozhan@wharton.upenn.edu>
Description: Multivariate matching in observational studies typically has two goals: 1. to construct treated and control groups that have similar distribution of observed covariates and 2. to produce matched pairs or sets that are homogeneous in a few priority variables. This packages implements a network-based method built around a tripartite graph that can simultaneously achieve both goals. A detailed 'RMarkdown' tutorial can be found at <https://github.com/bzhangupenn/match2C/tree/master/tutorial>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: mvnfast, stats, rcbalance, Rcpp (>= 1.0.3), utils
Suggests: optmatch
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2020-05-10 19:24:48 UTC; zhangb22
Repository: CRAN
Date/Publication: 2020-05-12 13:40:03 UTC

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New package airt with initial version 0.1.0
Package: airt
Type: Package
Title: Evaluation of Algorithm Collections Using Item Response Theory
Version: 0.1.0
Authors@R: person("Sevvandi", "Kandanaarachchi", email = "sevvandik@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0337-0395"))
Maintainer: Sevvandi Kandanaarachchi <sevvandik@gmail.com>
Description: An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: R (>= 3.4.0)
Imports: pracma, mirt, tidyr
Suggests: knitr, rmarkdown, ggplot2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-05-11 04:31:33 UTC; Sevvandi
Author: Sevvandi Kandanaarachchi [aut, cre] (<https://orcid.org/0000-0002-0337-0395>)
Repository: CRAN
Date/Publication: 2020-05-12 14:00:02 UTC

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Package ncmeta updated to version 0.2.5 with previous version 0.2.0 dated 2019-10-22

Title: Straightforward 'NetCDF' Metadata
Description: Extract metadata from 'NetCDF' data sources, these can be files, file handles or servers. This package leverages and extends the lower level functions of the 'RNetCDF' package providing a consistent set of functions that all return data frames. We introduce named concepts of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library itself with only the named concepts of 'variables', 'dimensions' and 'attributes'. 'ncmeta' provides a required framework for the in-development 'tidync' project <https://github.com/hypertidy/tidync>.
Author: Michael Sumner [aut, cre], Tomas Remenyi [ctb], Ben Raymond [ctb], David Blodgett [ctb]
Maintainer: Michael Sumner <mdsumner@gmail.com>

Diff between ncmeta versions 0.2.0 dated 2019-10-22 and 0.2.5 dated 2020-05-12

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Package mlt updated to version 1.2-0 with previous version 1.1-2 dated 2020-02-14

Title: Most Likely Transformations
Description: Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
Author: Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)
Maintainer: Torsten Hothorn <Torsten.Hothorn@R-project.org>

Diff between mlt versions 1.1-2 dated 2020-02-14 and 1.2-0 dated 2020-05-12

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Package mkin updated to version 0.9.50.2 with previous version 0.9.50.1 dated 2020-05-11

Title: Kinetic Evaluation of Chemical Degradation Data
Description: Calculation routines based on the FOCUS Kinetics Report (2006, 2014). Includes a function for conveniently defining differential equation models, model solution based on eigenvalues if possible or using numerical solvers. If a C compiler (on windows: 'Rtools') is installed, differential equation models are solved using automatically generated C functions. Please note that no warranty is implied for correctness of results or fitness for a particular purpose.
Author: Johannes Ranke [aut, cre, cph] (<https://orcid.org/0000-0003-4371-6538>), Katrin Lindenberger [ctb], René Lehmann [ctb], Eurofins Regulatory AG [cph]
Maintainer: Johannes Ranke <jranke@uni-bremen.de>

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Package areal updated to version 0.1.6 with previous version 0.1.5 dated 2019-05-21

Title: Areal Weighted Interpolation
Description: A pipeable, transparent implementation of areal weighted interpolation with support for interpolating multiple variables in a single function call. These tools provide a full-featured workflow for validation and estimation that fits into both modern data management (e.g. tidyverse) and spatial data (e.g. sf) frameworks.
Author: Christopher Prener [aut, cre] (<https://orcid.org/0000-0002-4310-9888>), Charlie Revord [aut], Branson Fox [aut] (<https://orcid.org/0000-0002-4361-2811>)
Maintainer: Christopher Prener <chris.prener@slu.edu>

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Package AdhereR updated to version 0.6.1 with previous version 0.6.0 dated 2020-05-07

Title: Adherence to Medications
Description: Computation of adherence to medications from Electronic Health care Data and visualization of individual medication histories and adherence patterns. The package implements a set of S3 classes and functions consistent with current adherence guidelines and definitions. It allows the computation of different measures of adherence (as defined in the literature, but also several original ones), their publication-quality plotting, the estimation of event duration and time to initiation, the interactive exploration of patient medication history and the real-time estimation of adherence given various parameter settings. It scales from very small datasets stored in flat CSV files to very large databases and from single-thread processing on mid-range consumer laptops to parallel processing on large heterogeneous computing clusters. It exposes a standardized interface allowing it to be used from other programming languages and platforms, such as Python.
Author: Dan Dediu [aut, cre], Alexandra Dima [aut], Samuel Allemann [aut]
Maintainer: Dan Dediu <ddediu@gmail.com>

Diff between AdhereR versions 0.6.0 dated 2020-05-07 and 0.6.1 dated 2020-05-12

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Package Segmentor3IsBack (with last version 2.0) was removed from CRAN

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

2016-06-30 2.0
2013-10-15 1.8
2013-08-27 1.6
2013-04-25 1.5
2013-04-04 1.4
2012-07-16 1.2
2012-07-10 1.1

Permanent link
Package AR1seg (with last version 1.0) was removed from CRAN

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

2014-06-05 1.0

Permanent link
Package spdplyr updated to version 0.4.0 with previous version 0.3.0 dated 2019-05-13

Title: Data Manipulation Verbs for the Spatial Classes
Description: Methods for 'dplyr' verbs for 'sp' 'Spatial' classes. The basic verbs that modify data attributes, remove or re-arrange rows are supported and provide complete 'Spatial' analogues of the input data. The group by and summarize work flow returns a non-topological spatial union. There is limited support for joins, with left and inner to copy attributes from another table.
Author: Michael D. Sumner [aut, cre], Joscha Legewie [ctb], Jussi Jousimo [ctb]
Maintainer: Michael D. Sumner <mdsumner@gmail.com>

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Package threeBrain updated to version 0.1.7 with previous version 0.1.5 dated 2020-01-20

Title: 3D Brain Visualization
Description: In neuroscience, 'AFNI/SUMA' is a great tool to visualize 3D brain. However, it takes efforts to interact and share the viewer to others. In addition, 'AFNI/SUMA' doesn't support Windows platform. In the 'EEG/iEEG' field, it's hard to have multiple cortical electrodes mapped to a template brain for group analysis. Therefore this package is written aimed at providing a fast, stable, interactive and easy to share tool based on 'Three.js', a 'WebGL' engine to render 3D objects in the web browser such that we can display brain surfaces on webpage interactively. This package translates R objects to JavaScript objects via 'JSON' format, and provides 'R-Shiny' interface to manipulate geometries interactively. The visualizations can also serve as standalone widgets that can be easily shared across different platforms. Along with 'rave', another package developed by Beauchamp's lab at Baylor College Medicine, this package provides solutions to easily map surface electrodes from multiple subjects to one template 141 brain.
Author: Zhengjia Wang [aut, cre, cph], John Magnotti [aut], Brian Metzger [aut], Elizabeth Nesbitt [res], Michael Beauchamp [aut, dtc, fnd]
Maintainer: Zhengjia Wang <zhengjia.wang@rice.edu>

Diff between threeBrain versions 0.1.5 dated 2020-01-20 and 0.1.7 dated 2020-05-12

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Package SIBER updated to version 2.1.5 with previous version 2.1.4 dated 2019-02-24

Title: Stable Isotope Bayesian Ellipses in R
Description: Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche.
Author: Andrew Jackson and Andrew Parnell
Maintainer: Andrew Jackson <jacksoan@tcd.ie>

Diff between SIBER versions 2.1.4 dated 2019-02-24 and 2.1.5 dated 2020-05-12

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Permanent link

New package shinyreforms with initial version 0.0.1
Package: shinyreforms
Title: Add Forms to your 'Shiny' App
Version: 0.0.1
Author: Piotr Bajger <piotr.bajger@hotmail.com>
Maintainer: Piotr Bajger <piotr.bajger@hotmail.com>
Description: Allows to create modular, reusable 'HTML' forms which can be embedded in your 'shiny' app with minimal effort. Features include conditional code execution on form submission, automatic input validation and input tooltips.
URL: https://github.com/piotrbajger/shinyreforms
Depends: R (>= 3.0)
License: GPL-3
Imports: shiny (>= 1.0.0), htmltools (>= 0.2.6), R6
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
BugReports: https://github.com/piotrbajger/shinyreforms
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-05-09 11:12:05 UTC; piotrbajger
Repository: CRAN
Date/Publication: 2020-05-12 10:10:03 UTC

More information about shinyreforms at CRAN
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New package shinyobjects with initial version 0.1.0
Package: shinyobjects
Title: Access Reactive Data Interactively
Version: 0.1.0
Authors@R: person("Jake", "Riley", email = "rjake@sas.upenn.edu", role = c("aut", "cre"))
Description: Troubleshooting reactive data in 'shiny' can be difficult. These functions will convert reactive data frames into functions and load all assigned objects into your local environment. If you create a dummy input object, as the function will suggest, you will be able to test your server and ui functions interactively.
BugReports: https://github.com/rjake/shinyobjects/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: dplyr, glue, knitr, magrittr, pander, readr, rstudioapi, shiny, stringr, styler, tibble, tidyr
VignetteBuilder: knitr
Suggests: rmarkdown, testthat, mockery, spelling, covr
Language: en-US
NeedsCompilation: no
Packaged: 2020-05-10 03:28:22 UTC; foxtr
Author: Jake Riley [aut, cre]
Maintainer: Jake Riley <rjake@sas.upenn.edu>
Repository: CRAN
Date/Publication: 2020-05-12 10:40:02 UTC

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New package RolWinMulCor with initial version 0.1.0
Package: RolWinMulCor
Type: Package
Title: Subroutines to Estimate Rolling Window Multiple Correlation
Version: 0.1.0
Authors@R: c(person("Josue M.", "Polanco-Martinez", role = c("aut", "cph", "cre"), email = "josue.m.polanco@gmail.com"))
Author: Josue M. Polanco-Martinez [aut, cph, cre]
Maintainer: Josue M. Polanco-Martinez <josue.m.polanco@gmail.com>
Depends: R (>= 3.5.0), stats, gtools, pracma, colorspace
Description: Rolling Window Multiple Correlation ('RolWinMulCor') estimates the rolling (running) window correlation for the bi- and multi-variate cases between regular (sampled on identical time points) time series, with especial emphasis to environmental data (although this can be applied to other kinds of data sets). 'RolWinMulCor' is based on the concept of rolling or running window and is useful to evaluate the evolution of correlation through time and time-scales. 'RolWinMulCor' contains four functions: (1) the first two functions are focus on the bi-variate case and one of them produces a simple plot of correlation coefficients and p-values (<=0.05)for only one window-length (time-scale) and the other function produces a heat map for the statistically significant (p-values <=0.05) correlation coefficients taking into account all the possible window-lengths (which are determined by the number of elements of the time series under analysis) or for a band of window-lengths; (2) the second two functions are designed to analyse the multi-variate case and follow the bi-variate case to display visually the results. The four functions contained in 'RolWinMulCor' are highly flexible since this contains a great number of parameters to control the estimation of correlation and the features of the plot output, e.g. to remove the (linear) trend contained in the time series under analysis, to choose different p-value correction methods (which are used to address the multiple comparison problem) or to personalise the plot output (e.g. this can be displayed in the screen or can be saved as PNG, JPEG, EPS or PDF formats). The 'RolWinMulCor' package also provides examples with synthetic and real environmental time series to exemplify its use. Methods derived from H. Abdi. (2007) <https://personal.utdallas.edu/~herve/Abdi-MCC2007-pretty.pdf>, J. M. Polanco-Martinez (2019) <doi:10.1007/s11071-019-04974-y>, and R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/running-correlations-running-into-problems/>.
License: GPL (>= 2)
Repository: CRAN
Encoding: UTF-8
LazyData: true
Date/Publication: 2020-05-12 10:10:12 UTC
NeedsCompilation: no
Packaged: 2020-05-09 11:12:05 UTC; jomopo

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New package ProcData with initial version 0.2.5
Package: ProcData
Type: Package
Title: Process Data Analysis
Version: 0.2.5
Date: 2020-05-08
Authors@R: c( person("Xueying", "Tang", email = "xueyingtang1989@gmail.com", role = c("aut", "cre")), person("Susu", "Zhang", email = "susu.zhang1992@gmail.com", role = c("aut")), person("Zhi", "Wang", email = "zhiwpku@gmail.com", role=c("aut")), person("Jingchen", "Liu", email = "jcliu@stat.columbia.edu", role=c("aut")), person("Zhiliang", "Ying", email = "zying@stat.columbia.edu", role=c("aut")))
Description: General tools for exploratory process data analysis. Process data refers to the data describing participants' problem solving processes in computer-based assessments. It is often recorded in computer log files. This package provides two action sequence generators and implements two automatic feature extraction methods that compress the information stored in process data, which often has a nonstandard format, into standard numerical vectors. This package also provides recurrent neural network based models that relate response processes with other binary or scale variables of interest. The functions that involve training and evaluating neural networks are wrappers of functions in 'keras'.
BugReports: https://github.com/xytangtang/ProcData/issues
License: GPL (>= 2)
Depends: R (>= 3.5)
Imports: Rcpp (>= 0.12.16), keras (>= 2.2.4)
LinkingTo: Rcpp
SystemRequirements: Python (>= 2.7)
RoxygenNote: 6.1.1
LazyData: true
NeedsCompilation: yes
Packaged: 2020-05-09 03:25:06 UTC; xueyingtang
Author: Xueying Tang [aut, cre], Susu Zhang [aut], Zhi Wang [aut], Jingchen Liu [aut], Zhiliang Ying [aut]
Maintainer: Xueying Tang <xueyingtang1989@gmail.com>
Repository: CRAN
Date/Publication: 2020-05-12 10:10:15 UTC

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Package mrds updated to version 2.2.2 with previous version 2.2.1 dated 2020-01-22

Title: Mark-Recapture Distance Sampling
Description: Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.
Author: Jeff Laake <jeff.laake@noaa.gov>, David Borchers <dlb@st-and.ac.uk>, Len Thomas <len.thomas@st-and.ac.uk>, David Miller <dave@ninepointeightone.net> and Jon Bishop
Maintainer: Laura Marshall <lhm@st-andrews.ac.uk>

Diff between mrds versions 2.2.1 dated 2020-01-22 and 2.2.2 dated 2020-05-12

 DESCRIPTION               |    8 ++--
 MD5                       |   91 +++++++++++++++++++++++++---------------------
 NAMESPACE                 |    6 +++
 NEWS                      |    8 ++++
 R/AIC.R                   |only
 R/add_df_covar_line.R     |only
 R/check.bounds.R          |    2 -
 R/ddf.rem.R               |    2 -
 R/ddf.trial.R             |    2 -
 R/dht.R                   |    8 ++--
 R/histline.R              |    2 -
 R/logLik.R                |only
 R/p_dist_table.R          |only
 R/plot.det.tables.R       |   75 +++++++++++++++++++++++--------------
 R/plot.ds.R               |   67 ++++++++++++++-------------------
 R/plot.io.R               |   18 ++++-----
 R/plot.io.fi.R            |   13 +++---
 R/plot.layout.R           |    4 +-
 R/plot.rem.R              |   66 +++++++++++++++++----------------
 R/plot.rem.fi.R           |   59 +++++++++++++++--------------
 R/plot.trial.R            |   25 ++++++------
 R/plot.trial.fi.R         |   22 +++++------
 R/predict.ds.R            |    2 -
 man/AIC.ds.Rd             |only
 man/add_df_covar_line.Rd  |only
 man/book.tee.data.Rd      |    6 ++-
 man/check.bounds.Rd       |    2 -
 man/ddf.rem.Rd            |    2 -
 man/ddf.trial.Rd          |    2 -
 man/dht.Rd                |    2 -
 man/lfbcvi.Rd             |    6 ++-
 man/lfgcwa.Rd             |    6 ++-
 man/logLik.ds.Rd          |only
 man/p_dist_table.Rd       |only
 man/plot.det.tables.Rd    |   28 +++++++++-----
 man/plot.ds.Rd            |   34 ++++++++---------
 man/plot.io.Rd            |   16 ++++----
 man/plot.io.fi.Rd         |   14 +++----
 man/plot.layout.Rd        |    4 +-
 man/plot.rem.Rd           |   20 +++++-----
 man/plot.rem.fi.Rd        |   20 +++++-----
 man/plot.trial.Rd         |   22 +++++------
 man/plot.trial.fi.Rd      |   20 +++++-----
 man/print.p_dist_table.Rd |only
 man/pronghorn.Rd          |    6 ++-
 man/ptdata.distance.Rd    |    6 ++-
 man/ptdata.dual.Rd        |    6 ++-
 man/ptdata.removal.Rd     |    6 ++-
 man/ptdata.single.Rd      |    6 ++-
 man/stake77.Rd            |    6 ++-
 man/stake78.Rd            |    6 ++-
 51 files changed, 396 insertions(+), 330 deletions(-)

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New package iNZightTS with initial version 1.5.2
Package: iNZightTS
Type: Package
Title: Time Series for 'iNZight'
Version: 1.5.2
Authors@R: c( person("Tom", "Elliott", role = c("aut", "cre"), email = "tom.elliott@auckland.ac.nz", comment = c(ORCID = "0000-0002-7815-6318")), person("Junjie", "Zeng", role = "aut"), person("Simon", "Potter", role = "aut"), person("David", "Banks", role = "aut"), person("Marco", "Kuper", role = "aut"), person("Dongning", "Zhang", role = "ctb") )
Depends: R (>= 3.2)
Imports: methods, grid, gridExtra, grDevices, graphics, stats, utils, ggplot2, tidyr, dplyr, forcats, magrittr, egg, rlang
Suggests: testthat, covr
Description: Provides a collection of functions for working with time series data, including functions for drawing, decomposing, and forecasting. Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) <doi:10.1016/j.ijforecast.2003.09.015>, Winters (1960) <doi:10.1287/mnsc.6.3.324>, Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess".
BugReports: https://github.com/iNZightVIT/iNZightTS/issues
Contact: inzight_support@stat.auckland.ac.nz
URL: https://www.stat.auckland.ac.nz/~wild/iNZight/
LazyData: true
License: GPL-3
Encoding: UTF-8
Language: en-GB
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-05-10 01:31:30 UTC; emperor
Author: Tom Elliott [aut, cre] (<https://orcid.org/0000-0002-7815-6318>), Junjie Zeng [aut], Simon Potter [aut], David Banks [aut], Marco Kuper [aut], Dongning Zhang [ctb]
Maintainer: Tom Elliott <tom.elliott@auckland.ac.nz>
Repository: CRAN
Date/Publication: 2020-05-12 10:20:09 UTC

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New package dmtools with initial version 0.2.4
Package: dmtools
Title: Tools for Validation the Dataset
Version: 0.2.4
Authors@R: person(given = "Konstantin", family = "Ryabov", role = c("aut", "cre"), email = "chachabooms@gmail.com")
Description: For checking the dataset from EDC(Electronic Data Capture) in clinical trials. 'dmtools' can check laboratory, dates, WBCs(White Blood Cells) count and rename the dataset. Laboratory - does the investigator correctly estimate the laboratory analyzes? Dates - do all dates correspond to the protocol's timeline? WBCs count - do absolute equal (all * relative) / 100? If the clinical trial has different lab reference ranges, 'dmtools' also can help.
Depends: R (>= 3.6)
Imports: magrittr (>= 1.5), dplyr (>= 0.8.3), readxl (>= 1.3.1), purrr (>= 0.3.3), lubridate (>= 1.7.4)
License: MIT + file LICENSE
URL: https://github.com/chachabooms/dmtools
BugReports: https://github.com/chachabooms/dmtools/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-05-10 13:05:54 UTC; X7-KR
Author: Konstantin Ryabov [aut, cre]
Maintainer: Konstantin Ryabov <chachabooms@gmail.com>
Repository: CRAN
Date/Publication: 2020-05-12 11:00:06 UTC

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New package covsim with initial version 0.1.0
Package: covsim
Type: Package
Title: VITA and IG Simulation for Given Covariance and Marginals
Version: 0.1.0
Authors@R: c(person("Njål", "Foldnes", email = "njal.foldnes@gmail.com", role = c("aut", "cre")), person("Steffen", "Grønneberg", email = "steffeng@gmail.com", role = c("aut")))
Description: User specifies population covariance matrix. Marginal information may be fully specified, for which the package implements the VITA (VIne-To-Anything) algorithm. Groenneberg and Foldnes (2017) <doi:10.1007/s11336-017-9569-6>. Alternatively, marginal skewness and kurtosis may be specified, for which the package implements the IG (independent generator) algorithm. Foldnes and Olsson (2016) <doi:10.1080/00273171.2015.1133274>.
License: GPL (>= 2)
Depends: R (>= 3.1.0)
Imports: rvinecopulib (>= 0.5.1.1.0), lavaan (>= 0.6-5), nleqslv, PearsonDS, MASS, stats, Rcpp, gsl
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-05-09 21:26:34 UTC; Njaal0
Author: Njål Foldnes [aut, cre], Steffen Grønneberg [aut]
Maintainer: Njål Foldnes <njal.foldnes@gmail.com>
Repository: CRAN
Date/Publication: 2020-05-12 10:10:06 UTC

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New package buildr with initial version 0.0.4
Package: buildr
Type: Package
Title: Comfort Way to Run Build Scripts
Version: 0.0.4
Author: Jan Netik
Maintainer: Jan Netik <netikja@gmail.com>
Description: Working with reproducible reports or any other similar projects often requires to run the script that builds the output file in a specified way. One can become tired from repeatedly switching to the build script and sourcing it. The 'buildr' package does this one simple thing via 'RStudio' addin – user can set up the keyboard shortcut and run the build script with one keystroke anywhere anytime. The second way is to pass buildr() command to console which does the same thing. Both ways source the build.R (case insensitive) file present in the current working directory.
License: GPL-3
Encoding: UTF-8
LazyData: true
URL: https://github.com/netique/buildr
BugReports: https://github.com/netique/buildr/issues
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-05-09 11:52:33 UTC; netik
Repository: CRAN
Date/Publication: 2020-05-12 10:10:09 UTC

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New package xmlr with initial version 0.1.2
Package: xmlr
Version: 0.1.2
Date: 2020-05-06
Title: Read, Write and Work with 'XML' Data
Authors@R: c( person("Per", "Nyfelt", email = "per@alipsa.se", role = c("cre", "aut")), person("Alipsa HB", email = "info@alipsa.se", role = "cph"), person("Steven", "Brandt", role = "ctb") )
Maintainer: Per Nyfelt <per@alipsa.se>
Depends: R (>= 3.1.0)
Encoding: UTF-8
Description: 'XML' package for creating and reading and manipulating 'XML', with an object model based on 'Reference Classes'.
License: MIT + file LICENSE
URL: https://github.com/Alipsa/xmlr
BugReports: https://github.com/Alipsa/xmlr/issues
Imports: methods
Suggests: testthat, knitr, rmarkdown
Collate: 'xmlr.R' 'utils.R' 'AbstractClass.R' 'Content.R' 'Document.R' 'Text.R' 'Element.R' 'Stack.R' 'DomBuilder.R' 'Parser.R' 'xmlImporter.R' 'xmlConverter.R'
RoxygenNote: 7.1.0
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-05-06 18:45:58 UTC; per
Author: Per Nyfelt [cre, aut], Alipsa HB [cph], Steven Brandt [ctb]
Repository: CRAN
Date/Publication: 2020-05-12 09:30:02 UTC

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New package SBdecomp with initial version 1.0
Package: SBdecomp
Type: Package
Title: Estimation of the Proportion of SB Explained by Confounders
Version: 1.0
Date: 2020-05-08
Author: Layla Parast
Maintainer: Layla Parast <parast@rand.org>
Description: Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, In press (doi to be added when published).
License: GPL
Imports: stats, twang, graphics, survey
NeedsCompilation: no
Packaged: 2020-05-08 22:47:39 UTC; parast
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2020-05-12 09:50:10 UTC

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New package RobustBayesianCopas with initial version 1.0
Package: RobustBayesianCopas
Type: Package
Title: Robust Bayesian Copas Selection Model
Version: 1.0
Date: 2020-05-08
Author: Ray Bai
Maintainer: Ray Bai <raybaistat@gmail.com>
Description: Implementation of the robust Bayesian Copas (RBC) selection model for correcting and quantifying publication bias, as introduced in Bai et al. (2020) <arXiv:2005.02930>. This package also implements standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.
License: GPL-3
LazyData: true
Depends: R (>= 3.6.0)
Imports: stats, statip, rjags
NeedsCompilation: yes
Repository: CRAN
Packaged: 2020-05-08 21:11:56 UTC; rayba
Date/Publication: 2020-05-12 09:40:06 UTC

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New package piRF with initial version 0.1.0
Package: piRF
Title: Prediction Intervals for Random Forests
Version: 0.1.0
Date: 2020-04-17
Authors@R: c(person(given = "Chancellor", family = "Johnstone", role = c("cre", "aut", "cph"), email = "chancellor.johnstone@gmail.com"), person(given = "Haozhe", family = "Zhang", role = c("aut", "cph"), email = "haozhe.stat@gmail.com"), person(given = "Martin", family = "Wright", role = c("ctb", "cph"), email = "cran@wrig.de"), person(given = "Gregor", family = "DeCillia", role = c("ctb", "cph")))
Maintainer: Chancellor Johnstone <chancellor.johnstone@gmail.com>
Description: Implements multiple state-of-the-art prediction interval methodologies for random forests. These include: quantile regression intervals, out-of-bag intervals, bag-of-observations intervals, one-step boosted random forest intervals, bias-corrected intervals, high-density intervals, and split-conformal intervals. The implementations include a combination of novel adjustments to the original random forest methodology and novel prediction interval methodologies. All of these methodologies can be utilized using solely this package, rather than a collection of separate packages. Currently, only regression trees are supported. Also capable of handling high dimensional data. Roy, Marie-Helene and Larocque, Denis (2019) <doi:10.1177/0962280219829885>. Ghosal, Indrayudh and Hooker, Giles (2018) <arXiv:1803.08000>. Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) <arXiv:1905.10101>. Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) <doi:10.1080/00031305.2019.1585288>. Meinshausen, Nicolai (2006) <http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>. Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) <arXiv:1905.03222>. Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) <doi:10.13140/2.1.2500.8002>.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 2.10)
Suggests: testthat, devtools, foreach, doParallel, hdrcde, rfinterval, ranger
URL: http://github.com/chancejohnstone/piRF
LazyData: true
RoxygenNote: 7.0.2
Imports: Rdpack
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2020-05-09 00:05:56 UTC; thechanceyman
Author: Chancellor Johnstone [cre, aut, cph], Haozhe Zhang [aut, cph], Martin Wright [ctb, cph], Gregor DeCillia [ctb, cph]
Repository: CRAN
Date/Publication: 2020-05-12 09:50:02 UTC

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New package migrbc with initial version 2.0.8
Package: migrbc
Type: Package
Title: Production Rules Based Classification of Migration
Version: 2.0.8
URL: https://github.com/statisticsnz/migrbc, https://statisticsnz.github.io/migrbc
BugReports: https://github.com/statisticsnz/migrbc/issues
Language: en-US
Authors@R: c( person(given = "Leshi", family = "Chen", email = "leshi.chen@stats.govt.nz", role = c("aut", "cre")), person(given = "Pubudu", family = "Senanayake", email = "pubudu.senanayake@stats.govt.nz", role = c("aut")), person(given = "Del", family = "Robinson", email = "del.robinson@stats.govt.nz", role = c("aut")), person("Statistics New Zealand", role = c("cph")))
Author: Leshi Chen [aut, cre], Pubudu Senanayake [aut], Del Robinson [aut], Statistics New Zealand [cph]
Description: Provides mechanisms for classifying border crossings using a rules-based methodology. The goal of performing this type of classification is to identify any potential long-term migrants. A long-term migration is defined as a border crossing involving a change in residence status. A border crossing counts as a long-term migration to/from a country if it entails a change from non-residence to residence / residence to non-residence. The rules-based classification that used to determine a long-term migration is defined by a threshold duration and a test duration, alternatively named window size. Under a 12/16 rule, for instance, the threshold duration is 12 months and the test duration (window size) is 16 months. With a 9/12 rule, the threshold duration is 9 months and the test duration (window size) is 12 months. For more information about the methodology applied, please visit Stats NZ (2020) <https://www.stats.govt.nz/methods/defining-migrants-using-travel-histories-and-the-1216-month-rule>.
License: MIT + file LICENSE
LinkingTo: Rcpp
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
Depends: R (>= 3.5)
Imports: Rcpp (>= 1.0), lubridate (>= 1.7), stringr (>= 1.4), dplyr (>= 0.8), methods, parallel, futile.logger
Suggests: magrittr, tools, knitr, rmarkdown, testthat
RoxygenNote: 6.1.1
Collate: 'RcppExports.R' 'check_functions.R' 'plot_mig_hist.R' 'pre_process.R' 'resolve_data.R' 'resolve_data_with_error.R' 'run_rbc.R' 'utility_functions.R' 'migrbc.R'
VignetteBuilder: knitr
Packaged: 2020-05-08 04:28:08 UTC; lchen
Maintainer: Leshi Chen <leshi.chen@stats.govt.nz>
Repository: CRAN
Date/Publication: 2020-05-12 09:40:02 UTC

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New package extendedFamily with initial version 0.1.0
Package: extendedFamily
Type: Package
Title: Additional Families for Generalized Linear Models
Version: 0.1.0
Author: Greg McMahan
Maintainer: Greg McMahan <gmcmacran@gmail.com>
Description: Creates family objects identical to stats family but for new links.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: testthat, covr
Imports: stats (>= 3.5.3), assertthat (>= 0.2.1)
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2020-05-09 17:44:08 UTC; gmcma
Repository: CRAN
Date/Publication: 2020-05-12 10:00:02 UTC

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Package dipsaus updated to version 0.0.7 with previous version 0.0.6 dated 2020-04-05

Title: A Dipping Sauce for Data Analysis and Visualizations
Description: Works as an "add-on" to packages like 'shiny', 'future', as well as 'rlang', and provides utility functions. Just like dipping sauce adding flavors to potato chips or pita bread, 'dipsaus' for data analysis and visualizations adds handy functions and enhancements to popular packages. The goal is to provide simple solutions that are frequently asked for online, such as how to synchronize 'shiny' inputs without freezing the app, or how to get memory size on 'Linux' or 'MacOS' system. The enhancements roughly fall into these four categories: 1. 'shiny' input widgets; 2. high-performance computing using 'RcppParallel' and 'future' package; 3. modify R calls and convert among numbers, strings, and other objects. 4. utility functions to get system information such like CPU chip-set, memory limit, etc.
Author: Zhengjia Wang [aut, cre]
Maintainer: Zhengjia Wang <zhengjia.wang@rice.edu>

Diff between dipsaus versions 0.0.6 dated 2020-04-05 and 0.0.7 dated 2020-05-12

 dipsaus-0.0.6/dipsaus/R/queue-s3.R                    |only
 dipsaus-0.0.7/dipsaus/DESCRIPTION                     |   18 -
 dipsaus-0.0.7/dipsaus/MD5                             |   45 ++--
 dipsaus-0.0.7/dipsaus/NAMESPACE                       |    5 
 dipsaus-0.0.7/dipsaus/NEWS.md                         |   20 +
 dipsaus-0.0.7/dipsaus/R/aaa.R                         |    1 
 dipsaus-0.0.7/dipsaus/R/fastmap2.R                    |   35 ++-
 dipsaus-0.0.7/dipsaus/R/language.R                    |  131 +++++++++++
 dipsaus-0.0.7/dipsaus/R/queue-txtq.R                  |  197 +++++++++---------
 dipsaus-0.0.7/dipsaus/R/shiny-compoundInput2.R        |   17 +
 dipsaus-0.0.7/dipsaus/R/strings.R                     |    1 
 dipsaus-0.0.7/dipsaus/R/utils-package.R               |    4 
 dipsaus-0.0.7/dipsaus/build/vignette.rds              |binary
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Package ClusterR updated to version 1.2.2 with previous version 1.2.1 dated 2019-11-29

Title: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering
Description: Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
Author: Lampros Mouselimis [aut, cre], Conrad Sanderson [cph] (Author of the C++ Armadillo library), Ryan Curtin [cph] (Author of the C++ Armadillo library), Siddharth Agrawal [cph] (Author of the C code of the Mini-Batch-Kmeans algorithm (https://github.com/siddharth-agrawal/Mini-Batch-K-Means)), Brendan Frey [cph] (Author of the matlab code of the Affinity propagation algorithm (for commercial use please contact the author of the matlab code)), Delbert Dueck [cph] (Author of the matlab code of the Affinity propagation algorithm)
Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>

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New package bets.covid19 with initial version 1.0.0
Package: bets.covid19
Title: The BETS Model for Early Epidemic Data
Version: 1.0.0
Date: 2020-05-07
Authors@R: c(person("Qingyuan", "Zhao", email = "qyzhao@statslab.cam.ac.uk", role = c("aut", "cre")),person("Nianqiao", "Ju", email = "nju@g.harvard.edu", role = "aut"))
Description: Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.
Depends: R (>= 3.4.0),
Imports: stats, rootSolve, parallel
License: CC BY 4.0
URL: https://github.com/qingyuanzhao/bets.covid19
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-05-09 14:37:21 UTC; qyzhao
Author: Qingyuan Zhao [aut, cre], Nianqiao Ju [aut]
Maintainer: Qingyuan Zhao <qyzhao@statslab.cam.ac.uk>
Repository: CRAN
Date/Publication: 2020-05-12 09:50:06 UTC

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Package padr updated to version 0.5.2 with previous version 0.5.1 dated 2020-04-03

Title: Quickly Get Datetime Data Ready for Analysis
Description: Transforms datetime data into a format ready for analysis. It offers two core functionalities; aggregating data to a higher level interval (thicken) and imputing records where observations were absent (pad).
Author: Edwin Thoen
Maintainer: Edwin Thoen <edwinthoen@gmail.com>

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Package tidync updated to version 0.2.4 with previous version 0.2.3 dated 2019-11-06

Title: A Tidy Approach to 'NetCDF' Data Exploration and Extraction
Description: Tidy tools for 'NetCDF' data sources. Explore the contents of a 'NetCDF' source (file or URL) presented as variables organized by grid with a database-like interface. The hyper_filter() interactive function translates the filter value or index expressions to array-slicing form. No data is read until explicitly requested, as a data frame or list of arrays via hyper_tibble() or hyper_array().
Author: Michael Sumner [aut, cre], Simon Wotherspoon [ctb], Tomas Remenyi [ctb], Ben Raymond [ctb], Jakub Nowosad [ctb], Tim Lucas [ctb], Hadley Wickham [ctb]
Maintainer: Michael Sumner <mdsumner@gmail.com>

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Package tidygraph updated to version 1.2.0 with previous version 1.1.2 dated 2019-02-18

Title: A Tidy API for Graph Manipulation
Description: A graph, while not "tidy" in itself, can be thought of as two tidy data frames describing node and edge data respectively. 'tidygraph' provides an approach to manipulate these two virtual data frames using the API defined in the 'dplyr' package, as well as provides tidy interfaces to a lot of common graph algorithms.
Author: Thomas Lin Pedersen [cre, aut] (<https://orcid.org/0000-0002-5147-4711>)
Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com>

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Package MedSurvey updated to version 1.1.1.2.0 with previous version 1.1.1.1.0 dated 2019-07-23

Title: Mediation Analysis for Complex Surveys
Description: It is a computer tool to conduct mediation analysis for complex surveys using multi-stage sampling. Specifically, the mediation analysis method using balanced repeated replication was proposed by Mai, Ha, and Soulakova (2019) <DOI:10.1080/10705511.2018.1559065>. The development of 'MedSurvey' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of MedSurvey do not necessarily represent the policy of the ALSAC.
Author: Yujiao Mai [aut, cre], Deo Kumar Srivastava [aut], Hui Zhang [aut]
Maintainer: Hui Zhang <hzhang@northwestern.edu>

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Package silicate updated to version 0.6.1 with previous version 0.4.0 dated 2020-04-15

Title: Common Forms for Complex Hierarchical and Relational Data Structures
Description: Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and provide intermediate forms that relate spatial data structures to this mathematical construct.
Author: Michael D. Sumner [aut, cre], John Corbett [ctb] (the original inspiration), Simon Wotherspoon [ctb], Kent Johnson [dtc], Mark Padgham [aut]
Maintainer: Michael D. Sumner <mdsumner@gmail.com>

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Package DHARMa updated to version 0.3.1 with previous version 0.3.0 dated 2020-04-20

Title: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models
Description: The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB' and 'spaMM', generalized additive models ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.
Author: Florian Hartig [aut, cre] (<https://orcid.org/0000-0002-6255-9059>)
Maintainer: Florian Hartig <florian.hartig@biologie.uni-regensburg.de>

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Package rdlocrand updated to version 0.6 with previous version 0.5 dated 2019-08-21

Title: Local Randomization Methods for RD Designs
Description: The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://sites.google.com/site/rdpackages/rdlocrand/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.
Author: Matias D. Cattaneo, Rocio Titiunik, Gonzalo Vazquez-Bare
Maintainer: Gonzalo Vazquez-Bare <gvazquez@econ.ucsb.edu>

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Package packager (with last version 1.3.0) was removed from CRAN

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

2020-05-08 1.3.0
2020-04-25 1.2.1
2020-04-14 1.2.0
2020-03-13 1.1.0
2020-02-21 1.0.0

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Package rasciidoc (with last version 2.1.2) was removed from CRAN

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

2020-05-08 2.1.2
2020-04-14 2.1.1
2020-04-08 2.1.0
2019-12-18 2.0.1
2019-03-03 2.0.0

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Package dLagM updated to version 1.1.3 with previous version 1.1.2 dated 2020-02-29

Title: Time Series Regression Models with Distributed Lag Models
Description: Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.
Author: Haydar Demirhan [aut, cre, cph] (<https://orcid.org/0000-0002-8565-4710>)
Maintainer: Haydar Demirhan <haydar.demirhan@rmit.edu.au>

Diff between dLagM versions 1.1.2 dated 2020-02-29 and 1.1.3 dated 2020-05-12

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Package decisionSupport updated to version 1.105.3 with previous version 1.105.2 dated 2019-10-16

Title: Quantitative Support of Decision Making under Uncertainty
Description: Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.
Author: Eike Luedeling [cre, aut] (University of Bonn), Lutz Goehring [aut] (ICRAF and Lutz Goehring Consulting), Katja Schiffers [aut] (University of Bonn)
Maintainer: Eike Luedeling <eike@eikeluedeling.com>

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Package CB2 updated to version 1.3.2 with previous version 1.3.1 dated 2020-04-15

Title: CRISPR Pooled Screen Analysis using Beta-Binomial Test
Description: Provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.
Author: Hyun-Hwan Jeong [aut, cre]
Maintainer: Hyun-Hwan Jeong <hyun-hwan.jeong@bcm.edu>

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Package cancensus updated to version 0.2.2 with previous version 0.2.1 dated 2020-03-05

Title: Access, Retrieve, and Work with Canadian Census Data and Geography
Description: Integrated, convenient, and uniform access to Canadian Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready tidy data frames and spatial data in multiple formats, as well as convenience functions for working with Census variables, variable hierarchies, and region selection. API keys are freely available with free registration at <https://censusmapper.ca/api>. Census data and boundary geometries are reproduced and distributed on an "as is" basis with the permission of Statistics Canada (Statistics Canada 2001; 2006; 2011; 2016).
Author: Jens von Bergmann [aut] (API creator and maintainer), Dmitry Shkolnik [aut, cre] (Package maintainer, responsible for correspondence), Aaron Jacobs [aut]
Maintainer: Dmitry Shkolnik <shkolnikd@gmail.com>

Diff between cancensus versions 0.2.1 dated 2020-03-05 and 0.2.2 dated 2020-05-12

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