Thu, 13 Jun 2019

Package polspline updated to version 1.1.15 with previous version 1.1.14 dated 2019-02-28

Title: Polynomial Spline Routines
Description: Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
Author: Charles Kooperberg <clk@fredhutch.org>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between polspline versions 1.1.14 dated 2019-02-28 and 1.1.15 dated 2019-06-13

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Package logspline updated to version 2.1.13 with previous version 2.1.12 dated 2019-02-05

Title: Routines for Logspline Density Estimation
Description: Contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e. the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997) <DOI:10.1214/aos/1031594728>.
Author: Charles Kooperberg <clk@fredhutch.org>
Maintainer: Charles Kooperberg <clk@fredhutch.org>

Diff between logspline versions 2.1.12 dated 2019-02-05 and 2.1.13 dated 2019-06-13

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Package ggpage updated to version 0.2.3 with previous version 0.2.2 dated 2018-07-27

Title: Creates Page Layout Visualizations
Description: Facilitates the creation of page layout visualizations in which words are represented as rectangles with sizes relating to the length of the words. Which then is divided in lines and pages for easy overview of up to quite large texts.
Author: Emil Hvitfeldt [aut, cre]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt@gmail.com>

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Package arsenal updated to version 3.2.0 with previous version 3.1.0 dated 2019-05-03

Title: An Arsenal of 'R' Functions for Large-Scale Statistical Summaries
Description: An Arsenal of 'R' functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in 'R' and 'RStudio' and which use formulas and versatile summary statistics for summary tables and models. The primary functions include tableby(), a Table-1-like summary of multiple variable types 'by' the levels of one or more categorical variables; paired(), a Table-1-like summary of multiple variable types paired across two time points; modelsum(), which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates); freqlist(), a powerful frequency table across many categorical variables; comparedf(), a function for comparing data.frames; and write2(), a function to output tables to a document.
Author: Ethan Heinzen [aut, cre], Jason Sinnwell [aut], Elizabeth Atkinson [aut], Tina Gunderson [aut], Gregory Dougherty [aut], Patrick Votruba [ctb], Ryan Lennon [ctb], Andrew Hanson [ctb], Krista Goergen [ctb], Emily Lundt [ctb], Brendan Broderick [ctb], Maddie McCullough [art]
Maintainer: Ethan Heinzen <heinzen.ethan@mayo.edu>

Diff between arsenal versions 3.1.0 dated 2019-05-03 and 3.2.0 dated 2019-06-13

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Package dfConn updated to version 0.2.1 with previous version 0.2.0 dated 2019-05-31

Title: Dynamic Functional Connectivity Analysis
Description: An implementation of multivariate linear process bootstrap (MLPB) method and sliding window technique to assess the dynamic functional connectivity (dFC) estimate by providing its confidence bands, based on Maria Kudela (2017) <doi: 10.1016/j.neuroimage.2017.01.056>. It also integrates features to visualize non-zero coverage for selected a-priori regions of interest estimated by the dynamic functional connectivity model (dFCM) and dynamic functional connectivity (dFC) curves for reward-related a-priori regions of interest where the activation-based analysis reported.
Author: Zikai Lin [aut, cre], Maria Kudela [aut], Jaroslaw Harezlak [aut], Mario Dzemidzic [aut]
Maintainer: Zikai Lin <ziklin@iu.edu>

Diff between dfConn versions 0.2.0 dated 2019-05-31 and 0.2.1 dated 2019-06-13

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Package crochet updated to version 2.2.0 with previous version 2.1.0 dated 2018-08-06

Title: Implementation Helper for '[' and '[<-' of Custom Matrix-Like Types
Description: Functions to help implement the extraction / subsetting / indexing function '[' and replacement function '[<-' of custom matrix-like types (based on S3, S4, etc.), modeled as closely to the base matrix class as possible (with tests to prove it).
Author: Alexander Grueneberg [aut, cre]
Maintainer: Alexander Grueneberg <alexander.grueneberg@googlemail.com>

Diff between crochet versions 2.1.0 dated 2018-08-06 and 2.2.0 dated 2019-06-13

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Package qrnn updated to version 2.0.4 with previous version 2.0.3 dated 2018-06-26

Title: Quantile Regression Neural Network
Description: Fit quantile regression neural network models with optional left censoring, partial monotonicity constraints, generalized additive model constraints, and the ability to fit multiple non-crossing quantile functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Author: Alex J. Cannon [aut, cre] (<https://orcid.org/0000-0002-8025-3790>)
Maintainer: Alex J. Cannon <alex.cannon@canada.ca>

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Package hIRT updated to version 0.2.0 with previous version 0.1.3 dated 2018-09-16

Title: Hierarchical Item Response Theory Models
Description: Implementation of a class of hierarchical item response theory (IRT) models where both the mean and the variance of latent preferences (ability parameters) may depend on observed covariates. The current implementation includes both the two-parameter latent trait model for binary data and the graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM) algorithm. Asymptotic standard errors are derived from the observed information matrix.
Author: Xiang Zhou [aut, cre]
Maintainer: Xiang Zhou <xiang_zhou@fas.harvard.edu>

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Package ggspectra updated to version 0.3.3.1 with previous version 0.3.2 dated 2019-02-20

Title: Extensions to 'ggplot2' for Radiation Spectra
Description: Additional annotations, stats, geoms and scales for plotting "light" spectra with 'ggplot2', together with specializations of ggplot() and autoplot() methods for spectral data and waveband definitions stored in objects of classes defined in package 'photobiology'. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Author: Pedro J. Aphalo [aut, cre] (<https://orcid.org/0000-0003-3385-972X>), Titta K. Kotilainen [ctb] (<https://orcid.org/0000-0002-2822-9734>)
Maintainer: Pedro J. Aphalo <pedro.aphalo@helsinki.fi>

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New package variantspark with initial version 0.1.1
Package: variantspark
Type: Package
Title: A 'Sparklyr' Extension for 'VariantSpark'
Version: 0.1.1
Authors@R: c( person("Samuel", "Macêdo", email = "samuelmacedo@recife.ifpe.edu.br", role = c("aut", "cre")), person("Javier", "Luraschi", email = "javier@rstudio.com", role = "aut") )
Maintainer: Samuel Macêdo <samuelmacedo@recife.ifpe.edu.br>
Description: This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is a framework based on 'scala' and 'spark' to analyze genome datasets, see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples each one containing 80 million features in either unsupervised clustering approaches and supervised applications, like classification and regression. The genome datasets are usually writing in VCF, a specific text file format used in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great tool for genome research, because it is able to read VCF files, run analyses and return the output in a 'spark' data frame.
License: Apache License 2.0 | file LICENSE
LazyData: true
Imports: sparklyr (>= 1.0.1)
RoxygenNote: 6.1.1
Suggests: testthat
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2019-06-11 23:30:12 UTC; dmmad
Author: Samuel Macêdo [aut, cre], Javier Luraschi [aut]
Repository: CRAN
Date/Publication: 2019-06-13 16:20:03 UTC

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New package scModels with initial version 1.0.0
Package: scModels
Title: Fitting Discrete Distribution Models to Count Data
Version: 1.0.0
Maintainer: Lisa Amrhein <amrheinlisa@gmail.com>
Authors@R: c( person("Lisa", "Amrhein", email="amrheinlisa@gmail.com", role=c("aut", "cre")), person("Kumar", "Harsha", email="kumar.harsha@tum.de", role="aut"), person("Christiane", "Fuchs", email="christiane.fuchs@helmholtz-muenchen.de", role="aut"), person("Pavel", "Holoborodko", email="pavel@holoborodko.com", role="ctb", comment="Author and copyright holder of 'mpreal.h'"))
License: GPL-3
Description: Provides functions for fitting discrete distribution models to count data. Included are the Poisson, the negative binomial and, most importantly, a new implementation of the Poisson-beta distribution (density, distribution and quantile functions, and random number generator) together with a needed new implementation of Kummer's function (also: confluent hypergeometric function of the first kind). Three different implementations of the Gillespie algorithm allow data simulation based on the basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for four variants of each of the three aforementioned distributions are also available. The variants include one population and two population mixtures, both with and without zero-inflation. The package depends on the 'MPFR' libraries which need to be installed separately (see description at <https://github.com/fuchslab/scModels>). This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on bioRxiv.
Depends: R (>= 3.1.0)
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, testthat
LinkingTo: Rcpp
Imports: Rcpp
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2019-06-13 14:01:09 UTC; kumarharsha
Author: Lisa Amrhein [aut, cre], Kumar Harsha [aut], Christiane Fuchs [aut], Pavel Holoborodko [ctb] (Author and copyright holder of 'mpreal.h')
Repository: CRAN
Date/Publication: 2019-06-13 16:10:03 UTC

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Package oddsratio updated to version 2.0.0 with previous version 1.0.3 dated 2018-07-25

Title: Odds Ratio Calculation for GAM(M)s & GLM(M)s
Description: Simplified odds ratio calculation of GAM(M)s & GLM(M)s. Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals for further analyses. It helps to avoid false references of predictors and increments by specifying these parameters in a list instead of using 'exp(coef(model))' (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of the multiple 'predict()' calls of the chosen predictor while holding other predictors constant. Also, this package allows odds ratio calculation of percentage steps across the whole predictor distribution range for GAM(M)s. In both cases, confident intervals are returned additionally. Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot.
Author: Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>)
Maintainer: Patrick Schratz <patrick.schratz@gmail.com>

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New package nomnoml with initial version 0.1.0
Package: nomnoml
Type: Package
Title: Sassy 'UML' Diagrams
Version: 0.1.0
Authors@R: c( person("Javier", "Luraschi", email = "javier@rstudio.com", role = c("aut", "cre")), person("Daniel", "Kallin", role = c("ctb"), comment = "nomnoml.js library, http://nomnoml.com") )
Maintainer: Javier Luraschi <javier@rstudio.com>
Description: A tool for drawing sassy 'UML' diagrams based on a simple syntax, see <http://www.nomnoml.com>. Supports styling, R Markdown and exporting diagrams in the PNG format.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.1.2)
Imports: htmlwidgets, png, webshot
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2019-06-12 16:13:35 UTC; javierluraschi
Author: Javier Luraschi [aut, cre], Daniel Kallin [ctb] (nomnoml.js library, http://nomnoml.com)
Repository: CRAN
Date/Publication: 2019-06-13 16:20:07 UTC

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Permanent link

Package outbreaker2 updated to version 1.1.0 with previous version 1.0.1 dated 2018-03-06

Title: Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data
Description: Bayesian reconstruction of disease outbreaks using epidemiological and genetic information. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N. 2014. <doi:10.1371/journal.pcbi.1003457>. Campbell, F, Cori A, Ferguson N, Jombart T. 2019. <doi:10.1371/journal.pcbi.1006930>.
Author: Thibaut Jombart [aut], Finlay Campbell [aut, cre], Rich Fitzjohn [aut], Gerry Tonkin-Hill [ctb], Alexis Robert [ctb]
Maintainer: Finlay Campbell <f.campbell15@imperial.ac.uk>

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Package lvm4net updated to version 0.3 with previous version 0.2.1 dated 2018-10-07

Title: Latent Variable Models for Networks
Description: Latent variable models for network data using fast inferential procedures. For more information please visit: <http://igollini.github.io/lvm4net/>.
Author: Isabella Gollini [aut, cre] (<https://orcid.org/0000-0002-7738-5688>)
Maintainer: Isabella Gollini <igollini.stats@gmail.com>

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Package longitudinalcascade updated to version 0.3.1.1 with previous version 0.2.1.1 dated 2019-04-03

Title: Longitudinal Cascade
Description: Creates a series of set of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests.
Author: Noah Haber [aut, cre]
Maintainer: Noah Haber <noahhaber@gmail.com>

Diff between longitudinalcascade versions 0.2.1.1 dated 2019-04-03 and 0.3.1.1 dated 2019-06-13

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New package HiResTEC with initial version 0.58
Package: HiResTEC
Type: Package
Title: Non-Targeted Fluxomics on High-Resolution Mass-Spectrometry Data
Version: 0.58
Date: 2019-06-13
Authors@R: c(person("Jan", "Lisec", role = c("aut", "cre"), email = "jan.lisec@bam.de"), person("Friederike", "Hoffmann", role = c("aut"), email = "Friederike.Hoffmann@charite.de"))
Maintainer: Jan Lisec <jan.lisec@bam.de>
Description: Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion. The main idea is to use changing intensity ratios of ion pairs from peak list generated with 'xcms' as candidates and evaluate those against base peak chromatograms and spectra information within the raw measurement data automatically. The functionality is described in Hoffmann et al. (2018) <doi:10.1021/acs.analchem.8b00356>.
License: GPL-3
URL: https://pubs.acs.org/doi/10.1021/acs.analchem.8b00356
LazyData: TRUE
Depends: R(>= 2.10.0)
biocViews:
Imports: plyr, openxlsx, InterpretMSSpectrum, Rdisop, beeswarm, Biobase
Suggests: xcms
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-06-13 12:45:04 UTC; jlisec
Author: Jan Lisec [aut, cre], Friederike Hoffmann [aut]
Repository: CRAN
Date/Publication: 2019-06-13 15:40:16 UTC

More information about HiResTEC at CRAN
Permanent link

Package earthtide updated to version 0.0.8 with previous version 0.0.6 dated 2019-05-05

Title: Parallel Implementation of 'ETERNA 3.40' for Prediction and Analysis of Earth Tides
Description: This is a port of 'Fortran ETERNA 3.4' <http://igets.u-strasbg.fr/soft_and_tool.php> by H.G. Wenzel for calculating synthetic Earth tides using the Hartmann and Wenzel (1994) <doi:10.1029/95GL03324> or Kudryavtsev (2004) <doi:10.1007/s00190-003-0361-2> tidal catalogs.
Author: Jonathan Kennel [aut, cre, trl], Beth Parker [ths], Wenzel Hans-Georg [ctb]
Maintainer: Jonathan Kennel <jkennel@uoguelph.ca>

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New package SimJoint with initial version 0.1.1
Package: SimJoint
Type: Package
Title: Simulate Joint Distribution
Version: 0.1.1
Author: Charlie Wusuo Liu
Maintainer: Charlie Wusuo Liu <liuwusuo@gmail.com>
Description: Simulate joint distribution given nonparametric marginals and their covariance structure characterized by a correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas and parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach (<doi:10.1080/03610918208812265>) and the Ruscio-Kaczetow (2008) iteration (<doi:10.1080/00273170802285693>). The package is implemented in C++ and carefully designed toward computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both outperform various CRAN packages to date by a substantial margin. Benchmarks are detailed in function examples.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 1.0.0), RcppParallel,
LinkingTo: Rcpp, RcppParallel, RcppArmadillo
SystemRequirements: GNU make
Suggests: R.rsp
VignetteBuilder: R.rsp
NeedsCompilation: yes
Packaged: 2019-06-12 05:18:03 UTC; Charlie
Repository: CRAN
Date/Publication: 2019-06-13 13:10:14 UTC

More information about SimJoint at CRAN
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Package provParseR updated to version 0.1.2 with previous version 0.1.1 dated 2018-12-27

Title: Pulls Information from Prov.Json Files
Description: R functions to access provenance information collected by 'rdt' or 'rdtLite'. The information is stored inside a 'ProvInfo' object and can be accessed through a collection of functions that will return the requested data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
Author: Orenna Brand [aut], Joseph Wonsil [aut], Emery Boose [aut], Barbara Lerner [cre]
Maintainer: Barbara Lerner <blerner@mtholyoke.edu>

Diff between provParseR versions 0.1.1 dated 2018-12-27 and 0.1.2 dated 2019-06-13

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Package nor1mix updated to version 1.3-0 with previous version 1.2-3 dated 2017-08-30

Title: Normal aka Gaussian (1-d) Mixture Models (S3 Classes and Methods)
Description: Onedimensional Normal (i.e. Gaussian) Mixture Models Classes, for, e.g., density estimation or clustering algorithms research and teaching; providing the widely used Marron-Wand densities. Efficient random number generation and graphics. Fitting to data by efficient ML (Maximum Likelihood) or traditional EM estimation.
Author: Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Friedrich Leisch [ctb] (norMixEM(), <https://orcid.org/0000-0001-7278-1983>), Erik Jørgensen [ctb] (pnorMix(), qnorMix())
Maintainer: Martin Maechler <maechler@stat.math.ethz.ch>

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Package lgr updated to version 0.3.1 with previous version 0.3.0 dated 2019-05-30

Title: A Fully Featured Logging Framework
Description: A flexible, feature-rich yet light-weight logging framework based on 'R6' classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, 'JSON', (rotating) files, memory buffers, and databases, as well as email and push notifications. For a full list of features with examples please refer to the package vignette.
Author: Stefan Fleck [aut, cre] (<https://orcid.org/0000-0003-3344-9851>)
Maintainer: Stefan Fleck <stefan.b.fleck@gmail.com>

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Permanent link

New package archetypal with initial version 1.0.0
Package: archetypal
Version: 1.0.0
Title: Finds the Archetypal Analysis of a Data Frame
Description: Performs archetypal analysis by using Convex Hull approximation under a full control of all algorithmic parameters. It contains functions useful for finding the proper initial approximation, the optimal number of archetypes and function for applying main algorithm. Morup, M., Hansen, LK (2012) <doi:10.1016/j.neucom.2011.06.033>. Hochbaum, DS, Shmoys, DB (1985) <doi:10.1287/moor.10.2.180>. Eddy, WF(1977) <doi:10.1145/355759.355768>. Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) <doi:10.1145/235815.235821>. Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
Authors@R: c( person("Demetris", "Christopoulos", email = "dchristop@econ.uoa.gr", role = c("aut", "cre")), person("David", "Midgley", email = "david.midgley@insead.edu", role = c("ctb")), person("INSEAD Fontainebleau France", role = c("fnd", "cph")) )
Maintainer: Demetris Christopoulos <dchristop@econ.uoa.gr>
Depends: R (>= 3.1.0)
Imports: Matrix, geometry, inflection, doParallel, lpSolve, methods
Suggests: knitr, rmarkdown, plot3D
VignetteBuilder: knitr
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
ByteCompile: true
NeedsCompilation: no
Packaged: 2019-06-12 10:34:23 UTC; demetris_ws
Author: Demetris Christopoulos [aut, cre], David Midgley [ctb], INSEAD Fontainebleau France [fnd, cph]
Repository: CRAN
Date/Publication: 2019-06-13 13:20:07 UTC

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New package JSmediation with initial version 0.1.0
Package: JSmediation
Version: 0.1.0
Title: Mediation Analysis Using Joint Significance
Authors@R: c( person("Cédric", "Batailler", email = "cedric.batailler@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0553-6827")), person("Dominique", "Muller", role = "aut", comment = c(ORCID = "0000-0001-9544-5317")), person("Vincent", "Yzerbyt", role = "aut"), person("Charles", "Judd", role = "aut"), person("Arnold", "Ho", role = "dtc"), person("Nour", "Kteily", role = "dtc"), person("Jacqueline", "Chen", role = "dtc"), person("Simone", "Dohle", role = "dtc"), person("Michael", "Siegrist", role = "dtc") )
Description: A set of helper functions to conduct joint-significance tests for mediation analysis, as recommended by Yzerbyt, Muller, Batailler, & Judd. (2018) <doi:10.1037/pspa0000132>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
ByteCompile: true
RoxygenNote: 6.1.1
Depends: R (>= 2.10)
URL: https://github.com/cedricbatailler/JSmediation
BugReports: https://github.com/cedricbatailler/JSmediation/issues
Imports: rlang (>= 0.1.2), dplyr, magrittr, purrr, glue, broom, tibble, stats, MASS, data.table, knitr
Suggests: rmarkdown, testthat, covr, roxygen2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-06-11 12:41:14 UTC; batailce
Author: Cédric Batailler [aut, cre] (<https://orcid.org/0000-0003-0553-6827>), Dominique Muller [aut] (<https://orcid.org/0000-0001-9544-5317>), Vincent Yzerbyt [aut], Charles Judd [aut], Arnold Ho [dtc], Nour Kteily [dtc], Jacqueline Chen [dtc], Simone Dohle [dtc], Michael Siegrist [dtc]
Maintainer: Cédric Batailler <cedric.batailler@gmail.com>
Repository: CRAN
Date/Publication: 2019-06-13 12:10:03 UTC

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New package CGGP with initial version 1.0.0
Package: CGGP
Type: Package
Title: Composite Grid Gaussian Processes
Version: 1.0.0
Authors@R: c( person("Collin", "Erickson", email = "collinberickson@gmail.com", role = c("aut", "cre")), # Creator is who gets bothered with issues person("Matthew", "Plumlee", role = c("aut")) ) # Maintainer: Who to complain to <yourfault@somewhere.net>
Description: Run computer experiments using the adaptive composite grid algorithm with a Gaussian process model. The algorithm works best when running an experiment that can evaluate thousands of points from a deterministic computer simulation. This package is an implementation of a forthcoming paper by Plumlee, Erickson, Ankenman, et al. For a preprint of the paper, contact the maintainer of this package.
License: GPL-3
Imports: Rcpp (>= 0.12.18)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, covr, ggplot2, reshape2, plyr, MASS, knitr
URL: https://github.com/CollinErickson/CGGP
BugReports: https://github.com/CollinErickson/CGGP/issues
Encoding: UTF-8
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2019-06-11 14:24:22 UTC; cbe117
Author: Collin Erickson [aut, cre], Matthew Plumlee [aut]
Maintainer: Collin Erickson <collinberickson@gmail.com>
Repository: CRAN
Date/Publication: 2019-06-13 12:20:03 UTC

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New package its.analysis with initial version 1.4.0
Package: its.analysis
Type: Package
Title: Running Interrupted Time Series Analysis
Version: 1.4.0
Author: Patrick English
Maintainer: Patrick English <p.english@exeter.ac.uk>
Description: Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.
Imports: plyr, car, stats, graphics, grDevices, forecast, boot, ggplot2
License: MIT + file LICENCE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1.9000
NeedsCompilation: no
Packaged: 2019-06-10 09:43:20 UTC; patrickenglish
Repository: CRAN
Date/Publication: 2019-06-13 11:40:03 UTC

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Package ICRanks updated to version 3.0 with previous version 2.0 dated 2018-12-13

Title: Simultaneous Confidence Intervals for Ranks
Description: Algorithms to construct simultaneous confidence intervals for the ranks of means mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques.
Author: Diaa Al Mohamad and Erik W. van Zwet and Jelle J. Goeman
Maintainer: Diaa Al Mohamad <diaa.almohamad@gmail.com>

Diff between ICRanks versions 2.0 dated 2018-12-13 and 3.0 dated 2019-06-13

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Package recorder updated to version 0.8.2 with previous version 0.8.1 dated 2019-05-21

Title: Toolkit to Validate New Data for a Predictive Model
Description: A lightweight toolkit to validate new observations when computing their predictions with a predictive model. The validation process consists of two steps: (1) record relevant statistics and meta data of the variables in the original training data for the predictive model and (2) use these data to run a set of basic validation tests on the new set of observations.
Author: Lars Kjeldgaard [aut, cre]
Maintainer: Lars Kjeldgaard <lars_kjeldgaard@hotmail.com>

Diff between recorder versions 0.8.1 dated 2019-05-21 and 0.8.2 dated 2019-06-13

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Package pvaluefunctions updated to version 1.2.0 with previous version 1.1.0 dated 2019-05-22

Title: Creates and Plots P-Value Functions, S-Value Functions, Confidence Distributions and Confidence Densities
Description: Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; and Greenland S. (2019) <doi:10.1080/00031305.2018.1529625>.
Author: Denis Infanger [aut, cre] (<https://orcid.org/0000-0001-9028-7110>)
Maintainer: Denis Infanger <denis.infanger@unibas.ch>

Diff between pvaluefunctions versions 1.1.0 dated 2019-05-22 and 1.2.0 dated 2019-06-13

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Package R.utils updated to version 2.9.0 with previous version 2.8.0 dated 2019-02-14

Title: Various Programming Utilities
Description: Utility functions useful when programming and developing R packages.
Author: Henrik Bengtsson [aut, cre, cph]
Maintainer: Henrik Bengtsson <henrikb@braju.com>

Diff between R.utils versions 2.8.0 dated 2019-02-14 and 2.9.0 dated 2019-06-13

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Package crmPack updated to version 1.0.0 with previous version 0.2.9 dated 2018-12-21

Title: Object-Oriented Implementation of CRM Designs
Description: Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules.
Author: Daniel Sabanes Bove [aut], Wai Yin Yeung [aut], Giuseppe Palermo [aut, cre], Thomas Jaki [aut]
Maintainer: Giuseppe Palermo <giuseppe.palermo@roche.com>

Diff between crmPack versions 0.2.9 dated 2018-12-21 and 1.0.0 dated 2019-06-13

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Package apcf updated to version 0.1.3 with previous version 0.1.2 dated 2019-01-17

Title: Adapted Pair Correlation Function
Description: The adapted pair correlation function transfers the concept of the pair correlation function from point patterns to patterns of objects of finite size and irregular shape (e.g. lakes within a country). This is a reimplementation of the method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050> using the libraries 'GEOS' and 'GDAL' directly instead of through 'PostGIS'.
Author: Robert Nuske [aut, cre] (<https://orcid.org/0000-0001-9773-2061>)
Maintainer: Robert Nuske <robert.nuske@mailbox.org>

Diff between apcf versions 0.1.2 dated 2019-01-17 and 0.1.3 dated 2019-06-13

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