Tue, 15 Sep 2020

Package DJL updated to version 3.6 with previous version 3.5 dated 2020-09-12

Title: Distance Measure Based Judgment and Learning
Description: Implements various decision support tools related to the Econometrics & Technometrics. Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, etc.
Author: Dong-Joon Lim, Ph.D. <technometrics.org>
Maintainer: Dong-Joon Lim <tgno3.com@gmail.com>

Diff between DJL versions 3.5 dated 2020-09-12 and 3.6 dated 2020-09-15

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Package webglobe updated to version 1.0.3 with previous version 1.0.2 dated 2017-06-02

Title: 3D Interactive Globes
Description: Displays geospatial data on an interactive 3D globe in the web browser.
Author: Richard Barnes [aut, cre]
Maintainer: Richard Barnes <rijard.barnes@gmail.com>

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Package surveydata updated to version 0.2.6 with previous version 0.2.5 dated 2020-04-21

Title: Tools to Work with Survey Data
Description: Data obtained from surveys contains information not only about the survey responses, but also the survey metadata, e.g. the original survey questions and the answer options. The 'surveydata' package makes it easy to keep track of this metadata, and to easily extract columns with specific questions.
Author: Andrie de Vries [aut, cre, cph], Evan Odell [ctb]
Maintainer: Andrie de Vries <apdevries@gmail.com>

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Package statespacer updated to version 0.3.0 with previous version 0.2.1 dated 2020-07-15

Title: State Space Modelling in 'R'
Description: A tool that makes estimating models in state space form a breeze. See "Time Series Analysis by State Space Methods" by Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details about the algorithms implemented.
Author: Dylan Beijers [aut, cre]
Maintainer: Dylan Beijers <dylanbeijers@gmail.com>

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Package ifaTools updated to version 0.22 with previous version 0.21 dated 2020-02-04

Title: Toolkit for Item Factor Analysis with 'OpenMx'
Description: Tools, tutorials, and demos of Item Factor Analysis using 'OpenMx'. This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
Author: Joshua N. Pritikin [cre, aut]
Maintainer: Joshua N. Pritikin <jpritikin@pobox.com>

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Package highfrequency updated to version 0.7.0 with previous version 0.6.5 dated 2020-04-15

Title: Tools for Highfrequency Data Analysis
Description: Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity.
Author: Kris Boudt [aut, cre] (<https://orcid.org/0000-0002-1000-5142>), Jonathan Cornelissen [aut], Scott Payseur [aut], Giang Nguyen [ctb], Onno Kleen [aut] (<https://orcid.org/0000-0003-4731-4640>), Emil Sjoerup [aut]
Maintainer: Kris Boudt <kris.boudt@ugent.be>

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Package DPWeibull updated to version 1.7 with previous version 1.6 dated 2020-09-13

Title: Dirichlet Process Weibull Mixture Model for Survival Data
Description: Use Dirichlet process Weibull mixture model and dependent Dirichlet process Weibull mixture model for survival data with and without competing risks. Dirichlet process Weibull mixture model is used for data without covariates and dependent Dirichlet process model is used for regression data. The package is designed to handle exact/right-censored/ interval-censored observations without competing risks and exact/right-censored observations for data with competing risks. Inside each cluster of Dirichlet process, we assume a multiplicative effect of covariates as in Cox model and Fine and Gray model. For wrapper of the DPdensity function from the R package DPpackage (already archived by CRAN) that uses the Low Information Omnibus prior, please check (<https://github.com/mjmartens/DPdensity-wrapper-with-LIO-prior>).
Author: Yushu Shi
Maintainer: Yushu Shi <shiyushu2006@gmail.com>

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Package backports updated to version 1.1.10 with previous version 1.1.9 dated 2020-08-24

Title: Reimplementations of Functions Introduced Since R-3.0.0
Description: Functions introduced or changed since R v3.0.0 are re-implemented in this package. The backports are conditionally exported in order to let R resolve the function name to either the implemented backport, or the respective base version, if available. Package developers can make use of new functions or arguments by selectively importing specific backports to support older installations.
Author: Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), R Core Team [aut]
Maintainer: Michel Lang <michellang@gmail.com>

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Package Rxnat updated to version 1.0.14 with previous version 1.0.12 dated 2020-07-28

Title: Queries and Extracts Images from Extensible Neuroimaging Archive Toolkit Public/Private Datasets
Description: Allows communication with Extensible Neuroimaging Archive Toolkit <https://www.xnat.org>. 'Rxnat' is using the 'XNAT' REST API to perform data queries and download images.
Author: Adi Gherman [aut, cre]
Maintainer: Adi Gherman <adig@jhu.edu>

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Package workflows updated to version 0.2.0 with previous version 0.1.3 dated 2020-08-10

Title: Modeling Workflows
Description: Managing both a 'parsnip' model and a preprocessor, such as a model formula or recipe from 'recipes', can often be challenging. The goal of 'workflows' is to streamline this process by bundling the model alongside the preprocessor, all within the same object.
Author: Davis Vaughan [aut, cre], RStudio [cph]
Maintainer: Davis Vaughan <davis@rstudio.com>

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Package miniCRAN updated to version 0.2.14 with previous version 0.2.13 dated 2020-07-13

Title: Create a Mini Version of CRAN Containing Only Selected Packages
Description: Makes it possible to create an internally consistent repository consisting of selected packages from CRAN-like repositories. The user specifies a set of desired packages, and 'miniCRAN' recursively reads the dependency tree for these packages, then downloads only this subset. The user can then install packages from this repository directly, rather than from CRAN. This is useful in production settings, e.g. server behind a firewall, or remote locations with slow (or zero) Internet access.
Author: Andrie de Vries [aut, cre, cph], Alex Chubaty [ctb], Microsoft Corporation [cph]
Maintainer: Andrie de Vries <apdevries@gmail.com>

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Package gtfs2gps updated to version 1.3-0 with previous version 1.2-1 dated 2020-06-12

Title: Converting Transport Data from GTFS Format to GPS-Like Records
Description: Convert general transit feed specification (GTFS) data to global positioning system (GPS) records in 'data.table' format. It also has some functions to subset GTFS data in time and space and to convert both representations to simple feature format.
Author: Rafael H. M. Pereira [aut] (<https://orcid.org/0000-0003-2125-7465>), Pedro R. Andrade [aut, cre] (<https://orcid.org/0000-0001-8675-4046>), Joao Bazzo [aut] (<https://orcid.org/0000-0003-4536-5006>), Marcin Stepniak [ctb], Ipea - Institue for Applied Economic Research [cph, fnd]
Maintainer: Pedro R. Andrade <pedro.andrade@inpe.br>

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Package tidyseurat updated to version 0.1.8 with previous version 0.1.6 dated 2020-09-11

Title: Brings Seurat to the Tidyverse
Description: It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.
Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>

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Package RPANDA updated to version 1.9 with previous version 1.8 dated 2020-03-06

Title: Phylogenetic ANalyses of DiversificAtion
Description: Implements macroevolutionary analyses on phylogenetic trees. See Morlon et al. (2010) <DOI:10.1371/journal.pbio.1000493>, Morlon et al. (2011) <DOI:10.1073/pnas.1102543108>, Condamine et al. (2013) <DOI:10.1111/ele.12062>, Morlon et al. (2014) <DOI:10.1111/ele.12251>, Manceau et al. (2015) <DOI:10.1111/ele.12415>, Lewitus & Morlon (2016) <DOI:10.1093/sysbio/syv116>, Drury et al. (2016) <DOI:10.1093/sysbio/syw020>, Manceau et al. (2016) <DOI:10.1093/sysbio/syw115>, Morlon et al. (2016) <DOI:10.1111/2041-210X.12526>, Clavel & Morlon (2017) <DOI:10.1073/pnas.1606868114>, Drury et al. (2017) <DOI:10.1093/sysbio/syx079>, Lewitus & Morlon (2017) <DOI:10.1093/sysbio/syx095>, Drury et al. (2018) <DOI:10.1371/journal.pbio.2003563>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, Maliet et al. (2019) <DOI:10.1038/s41559-019-0908-0>, Billaud et al. (2019) <DOI:10.1093/sysbio/syz057>, Lewitus et al. (2019) <DOI:10.1093/sysbio/syz061>, Aristide & Morlon (2019) <DOI:10.1111/ele.13385>, and Maliet et al. (2020) <DOI:10.1111/ele.13592>.
Author: Hélène Morlon [aut, cre, cph], Eric Lewitus [aut, cph], Fabien Condamine [aut, cph], Marc Manceau [aut, cph], Julien Clavel [aut, cph], Jonathan Drury [aut, cph], Olivier Billaud [aut, cph], Odile Maliet [aut, cph], Leandro Aristide [aut, cph]
Maintainer: Hélène Morlon <helene.morlon@bio.ens.psl.eu>

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Package RCarb updated to version 0.1.4 with previous version 0.1.3 dated 2019-06-03

Title: Dose Rate Modelling of Carbonate-Rich Samples
Description: Translation of the 'MATLAB' program 'Carb' (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.
Author: Sebastian Kreutzer [aut, trl, cre, dtc] (<https://orcid.org/0000-0002-0734-2199>), Roger P. Nathan [aut, cph], Barbara Mauz [aut, cph] (<https://orcid.org/0000-0003-1504-333X>)
Maintainer: Sebastian Kreutzer <sebastian.kreutzer@aber.ac.uk>

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Package PSweight updated to version 1.1.0 with previous version 0.1.1 dated 2020-07-19

Title: Propensity Score Weighting for Causal Inference with Observational Studies and Randomized Trials
Description: Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.
Author: Tianhui Zhou [aut, cre], Guangyu Tong [aut], Fan Li [aut], Laine Thomas [aut], Fan Li [aut]
Maintainer: Tianhui Zhou <tianhui.zhou@duke.edu>

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Package pcFactorStan updated to version 1.5.2 with previous version 1.5.1 dated 2020-04-25

Title: Stan Models for the Paired Comparison Factor Model
Description: Provides convenience functions and pre-programmed Stan models related to the paired comparison factor model. Its purpose is to make fitting paired comparison data using Stan easy. This package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.
Author: Joshua N. Pritikin [aut, cre] (<https://orcid.org/0000-0002-9862-5484>), Daniel C. Furr [ctb], Trustees of Columbia University [cph]
Maintainer: Joshua N. Pritikin <jpritikin@pobox.com>

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Package ParBayesianOptimization updated to version 1.2.1 with previous version 1.1.0 dated 2020-02-24

Title: Parallel Bayesian Optimization of Hyperparameters
Description: Fast, flexible framework for implementing Bayesian optimization of model hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>. The package allows the user to run scoring function in parallel, save intermediary results, and tweak other aspects of the process to fully utilize the computing resources available to the user.
Author: Samuel Wilson [aut, cre]
Maintainer: Samuel Wilson <samwilson303@gmail.com>

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Package iglu updated to version 2.0.1 with previous version 1.0.2 dated 2020-07-01

Title: Interpreting Glucose Data from Continuous Glucose Monitors
Description: Implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series and lasagna plots.
Author: Steve Broll [aut], Jacek Urbanek [aut], David Buchanan [aut], Elizabeth Chun [aut], John Muschelli [aut], John Schwenck [aut], Mary Martin [aut], Pratik Patel [aut], Marielle Hicban [aut], Nhan Nguyen [aut], Irina Gaynanova [aut, cre]
Maintainer: Irina Gaynanova <irinag@stat.tamu.edu>

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Package GridOnClusters updated to version 0.0.8 with previous version 0.0.7 dated 2020-04-06

Title: Cluster-Preserving Multivariate Joint Grid Discretization
Description: Discretize multivariate continuous data using a grid that captures the joint distribution via preserving clusters in the original data (Wang et al. 2020). Joint grid discretization is applicable as a data transformation step to prepare data for model-free inference of association, function, or causality.
Author: Jiandong Wang [aut], Sajal Kumar [aut] (<https://orcid.org/0000-0003-0930-1582>), Joe Song [aut, cre] (<https://orcid.org/0000-0002-6883-6547>)
Maintainer: Joe Song <joemsong@cs.nmsu.edu>

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Package briskaR updated to version 1.0.0 with previous version 0.1.2 dated 2018-06-07

Title: Biological Risk Assessment
Description: A spatio-temporal exposure-hazard model for assessing biological risk and impact. The model is based on stochastic geometry for describing the landscape and the exposed individuals, a dispersal kernel for the dissemination of contaminants, a set of tools to handle spatio-temporal dataframe and ecotoxicological equations. Walker E, Leclerc M, Rey JF, Beaudouin R, Soubeyrand S, and Messean A, (2017), A Spatio-Temporal Exposure-Hazard Model for Assessing Biological Risk and Impact, Risk Analysis, <doi:10.1111/risa.12941>. Leclerc M, Walker E, Messean A, Soubeyrand S (2018), Spatial exposure-hazard and landscape models for assessing the impact of GM crops on non-target organisms, Science of the Total Environment, 624, 470-479.
Author: Virgile Baudrot [aut], Emily Walker [aut], Jean-Francois Rey [aut, cre], Melen Leclerc [aut], Samuel Soubeyrand [ctb], Marc Bourotte [ctb]
Maintainer: Jean-Francois Rey <jean-francois.rey@inrae.fr>

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Package tmap updated to version 3.2 with previous version 3.1 dated 2020-07-09

Title: Thematic Maps
Description: Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
Author: Martijn Tennekes [aut, cre], Jakub Nowosad [ctb], Joel Gombin [ctb], Sebastian Jeworutzki [ctb], Kent Russell [ctb], Richard Zijdeman [ctb], John Clouse [ctb], Robin Lovelace [ctb], Jannes Muenchow [ctb]
Maintainer: Martijn Tennekes <mtennekes@gmail.com>

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Package nhdplusTools updated to version 0.3.15 with previous version 0.3.14 dated 2020-08-03

Title: NHDPlus Tools
Description: Tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'nhdplusTools' are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>.
Author: David Blodgett [aut, cre], Mike Johnson [ctb]
Maintainer: David Blodgett <dblodgett@usgs.gov>

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Package crunch updated to version 1.27.5 with previous version 1.27.0 dated 2020-07-22

Title: Crunch.io Data Tools
Description: The Crunch.io service <https://crunch.io/> provides a cloud-based data store and analytic engine, as well as an intuitive web interface. Using this package, analysts can interact with and manipulate Crunch datasets from within R. Importantly, this allows technical researchers to collaborate naturally with team members, managers, and clients who prefer a point-and-click interface.
Author: Greg Freedman Ellis [aut, cre], Jonathan Keane [aut], Mike Malecki [aut], Neal Richardson [aut], Gordon Shotwell [aut]
Maintainer: Greg Freedman Ellis <greg@crunch.io>

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More information about crunch at CRAN
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Package jfa updated to version 0.3.0 with previous version 0.2.0 dated 2020-08-05

Title: Bayesian and Classical Audit Sampling
Description: Implements the audit sampling workflow as discussed in Derks et al. (2019) <doi:10.31234/osf.io/9f6ub>. The package makes it easy for an auditor to plan an audit sample, sample from the population, and evaluating that sample using various confidence bounds according to the International Standards on Auditing. Furthermore, the package implements Bayesian equivalents of these methods.
Author: Koen Derks [aut, cre]
Maintainer: Koen Derks <k.derks@nyenrode.nl>

Diff between jfa versions 0.2.0 dated 2020-08-05 and 0.3.0 dated 2020-09-15

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Package mokken updated to version 3.0.3 with previous version 3.0.2 dated 2020-04-30

Title: Conducts Mokken Scale Analysis
Description: Contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions.
Author: L. Andries van der Ark [aut, cre], Letty Koopman [aut], J. Hendrik Straat [ctb], Don van den Bergh [ctb]
Maintainer: L. Andries van der Ark <L.A.vanderArk@uva.nl>

Diff between mokken versions 3.0.2 dated 2020-04-30 and 3.0.3 dated 2020-09-15

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New package testtwice with initial version 1.0.3
Package: testtwice
Type: Package
Title: Testing One Hypothesis Twice in Observational Studies
Version: 1.0.3
Author: Paul R. Rosenbaum
Maintainer: Paul R. Rosenbaum <rosenbaum@wharton.upenn.edu>
Description: Tests one hypothesis with several test statistics, correcting for multiple testing. The central function in the package is testtwice(). In a sensitivity analysis, the method has the largest design sensitivity of its component tests. The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: stats, mvtnorm
NeedsCompilation: no
Packaged: 2020-09-08 00:35:42 UTC; Paul Rosenbaum
Repository: CRAN
Date/Publication: 2020-09-15 10:00:02 UTC

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Package spbabel updated to version 0.5.5 with previous version 0.5.1 dated 2020-04-07

Title: Convert Spatial Data Using Tidy Tables
Description: Tools to convert from specific formats to more general forms of spatial data. Using tables to store the actual entities present in spatial data provides flexibility, and the functions here deliberately minimize the level of interpretation applied, leaving that for specific applications. Includes support for simple features, round-trip for 'Spatial' classes and long-form tables, analogous to 'ggplot2::fortify'. There is also a more 'normal form' representation that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.
Author: Michael D. Sumner [aut, cre]
Maintainer: Michael D. Sumner <mdsumner@gmail.com>

Diff between spbabel versions 0.5.1 dated 2020-04-07 and 0.5.5 dated 2020-09-15

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New package rsbp with initial version 0.1.0
Package: rsbp
Type: Package
Title: Part Information from the Registry of Standard Biological Parts
Version: 0.1.0
Authors@R: person("Santone", "Adam", email = "adam.santone+rsbp@gmail.com", role = c("aut", "cre"))
Description: Provides an R interface to the Registry of Standard Biological Parts API maintained by the iGEM Foundation: <https://igem.org/Main_Page>. Facilitates retrieval of the part number, authorship, date of entry, url, short description, type, and sequence following the guidelines set forth at <http://parts.igem.org/Registry_API/Guidelines>. All Registry content falls under Creative Commons Attribution-ShareAlike: <https://creativecommons.org/licenses/by-sa/4.0/>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
LazyData: true
Depends: R (>= 3.6.0)
Imports: magrittr (>= 1.5), dplyr (>= 0.4.0), xml2 (>= 1.3.2), reshape2 (>= 1.4.4), lubridate (>= 1.7.9), tidyr (>= 1.1.1), purrr (>= 0.3.4), tibble (>= 3.0.3)
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-07 17:09:19 UTC; adams
Author: Santone Adam [aut, cre]
Maintainer: Santone Adam <adam.santone+rsbp@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-15 09:40:02 UTC

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New package robomit with initial version 1.0.3
Package: robomit
Version: 1.0.3
Date: 2020-08-25
Title: Robustness Checks for Omitted Variable Bias
Authors@R: c( person(given = "Sergei", family = "Schaub", role = c("aut", "cre"), email = "seschaub@ethz.ch", comment = c(ORCID = "0000-0001-8477-3737")), person("ETH Zurich", role = c("cph")) )
Maintainer: Sergei Schaub <seschaub@ethz.ch>
Description: Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). 'robomit' replicates (and extends) function available in Stata (closed source) for use in R (open source). See: Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: plm, dplyr, ggplot2, broom, tidyr, tibble, stats,
Suggests: testthat
NeedsCompilation: no
Packaged: 2020-08-31 13:01:05 UTC; schaubse
Author: Sergei Schaub [aut, cre] (<https://orcid.org/0000-0001-8477-3737>), ETH Zurich [cph]
Repository: CRAN
Date/Publication: 2020-09-15 09:30:03 UTC

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New package incase with initial version 0.1.0
Type: Package
Package: incase
Title: Pipe-Friendly Vector Replacement with Case Statements
Version: 0.1.0
Authors@R: person(given = "Alexander", family = "Rossell Hayes", role = c("aut", "cre", "cph"), email = "alexander@rossellhayes.com", comment = c(ORCID = "0000-0001-9412-0457"))
Description: Offers a pipe-friendly alternative to the 'dplyr' functions case_when() and if_else(). These functions accept a vector as an optional first argument, allowing conditional statements to be built using the 'magrittr' dot operator. The functions also coerce all possible outputs to the same type, meaning you no longer have to worry about using specific typed variants of NA or explicitly declaring integer outputs.
License: MIT + file LICENSE
URL: https://incase.rossellhayes.com, https://github.com/rossellhayes/incase
BugReports: https://github.com/rossellhayes/incase/issues
Imports: magrittr, plu, rlang
Suggests: cli, covr, crayon, dplyr, testthat
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-08 01:46:48 UTC; Alex
Author: Alexander Rossell Hayes [aut, cre, cph] (<https://orcid.org/0000-0001-9412-0457>)
Maintainer: Alexander Rossell Hayes <alexander@rossellhayes.com>
Repository: CRAN
Date/Publication: 2020-09-15 10:00:06 UTC

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New package image.textlinedetector with initial version 0.1.0
Package: image.textlinedetector
Type: Package
Title: Segment Images in Text Lines and Words
Version: 0.1.0
Maintainer: Jan Wijffels <jan.wijffels@vub.be>
Authors@R: c( person('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = 'jan.wijffels@vub.be', comment = "R wrapper"), person('Vrije Universiteit Brussel - DIGI: Brussels Platform for Digital Humanities', role = 'cph', comment = "R wrapper"), person("Jeroen", "Ooms", role = c("ctb", "cph"), comment = "More details in LICENSE.note file"), person("Arthur Flôr", role = c("ctb", "cph"), comment = "More details in LICENSE.note file"), person("Saverio Meucci", role = c("ctb", "cph"), comment = "More details in LICENSE.note file"), person("Yeara Kozlov", role = c("ctb", "cph"), comment = "More details in LICENSE.note file"), person("Tino Weinkauf", role = c("ctb", "cph"), comment = "More details in LICENSE.note file"))
Description: Find text lines in scanned images and segment the lines into words. Includes implementations of the paper 'Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents' by Surinta O. et al (2014) <doi:10.1109/ICFHR.2014.37> available at <https://github.com/smeucci/LineSegm>, an implementation of 'A Statistical approach to line segmentation in handwritten documents' by Arivazhagan M. et al (2007) <doi:10.1117/12.704538>, and a wrapper for an image segmentation technique to detect words in text lines as described in the paper 'Scale Space Technique for Word Segmentation in Handwritten Documents' by Manmatha R. and Srimal N. (1999) paper at <doi:10.1007/3-540-48236-9_3>, wrapper for code available at <https://github.com/arthurflor23/text-segmentation>.
License: MIT + file LICENSE
URL: https://github.com/DIGI-VUB/image.textlinedetector
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 0.12.9), magick
Suggests: opencv
LinkingTo: Rcpp
SystemRequirements: C++11
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2020-09-07 13:43:09 UTC; Jan
Author: Jan Wijffels [aut, cre, cph] (R wrapper), Vrije Universiteit Brussel - DIGI: Brussels Platform for Digital Humanities [cph] (R wrapper), Jeroen Ooms [ctb, cph] (More details in LICENSE.note file), Arthur Flôr [ctb, cph] (More details in LICENSE.note file), Saverio Meucci [ctb, cph] (More details in LICENSE.note file), Yeara Kozlov [ctb, cph] (More details in LICENSE.note file), Tino Weinkauf [ctb, cph] (More details in LICENSE.note file)
Repository: CRAN
Date/Publication: 2020-09-15 09:20:02 UTC

More information about image.textlinedetector at CRAN
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New package html2R with initial version 0.1.0
Package: html2R
Type: Package
Title: Convert 'HTML' to 'R' with a 'Shiny' App
Version: 0.1.0
Authors@R: c( person("Stéphane", "Laurent", email = "laurent_step@outlook.fr", role = c("aut","cre")), person("Chris", "Andrejewski", role = c("ctb","cph"), comment = "'Himalaya' library"), person("Boniface", "Pereira", role = c("ctb","cph"), comment = "'jquery-confirm' library"), person("Mario", "Heiderich", role = c("ctb","cph"), comment = "'DOMPurify' library") )
Description: Provides a 'Shiny' app allowing to convert 'HTML' code to 'R' code (e.g. '<span>Hello</span>' to 'tags$span("Hello")'), for usage in a 'Shiny' UI.
URL: https://github.com/stla/html2R
BugReports: https://github.com/stla/html2R/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: glue, shiny, shinyAce, shinythemes, shinyjqui, stats
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-08 01:04:33 UTC; stla
Author: Stéphane Laurent [aut, cre], Chris Andrejewski [ctb, cph] ('Himalaya' library), Boniface Pereira [ctb, cph] ('jquery-confirm' library), Mario Heiderich [ctb, cph] ('DOMPurify' library)
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>
Repository: CRAN
Date/Publication: 2020-09-15 09:50:02 UTC

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New package FuncNN with initial version 1.0
Package: FuncNN
Title: Functional Neural Networks
Version: 1.0
Authors@R: c( person("Richard", "Groenewald", role = "ctb"), person("Barinder", "Thind", email = "barinder.thi@gmail.com", role = c("aut", "cre", "cph")), person("Jiguo", "Cao", role = "aut"), person("Sidi Wu", role = "ctb") )
Description: A collection of functions which fit functional neural network models. In other words, this package will allow users to build deep learning models that have either functional or scalar responses paired with functional and scalar covariates. We implement the theoretical discussion found in Thind, Multani and Cao (2020) <arXiv:2006.09590> through the help of a main fitting and prediction function as well as a number of helper functions to assist with cross-validation, tuning, and the display of estimated functional weights.
Imports: keras, tensorflow, fda.usc, fda, ggplot2, ggpubr, caret, pbapply, reshape2, flux, doParallel, foreach, Matrix
URL: https://arxiv.org/abs/2006.09590, https://github.com/b-thi/FuncNN
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2020-09-07 23:52:53 UTC; Richard
Author: Richard Groenewald [ctb], Barinder Thind [aut, cre, cph], Jiguo Cao [aut], Sidi Wu [ctb]
Maintainer: Barinder Thind <barinder.thi@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-15 09:40:15 UTC

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New package ec50estimator with initial version 0.1.0
Package: ec50estimator
Type: Package
Title: An Automated Way to Estimate EC50 for Stratified Datasets
Version: 0.1.0
Date: 2020-09-07
Authors@R: c( person(given = "Kaique dos S.", family = "Alves", role = c("aut", "cre"), email = "kaiquedsalves@gmail.com", comment = c(ORCID = "0000-0001-9187-0252") ))
Maintainer: Kaique dos S. Alves <kaiquedsalves@gmail.com>
Description: An implementation for estimating Effective control to 50% of growth inhibition (EC50) for multi isolates and stratified datasets. It implements functions from the drc package in a way that is displayed a tidy data.frame as output. Info about the drc package is available in Ritz C, Baty F, Streibig JC, Gerhard D (2015) <doi:10.1371/journal.pone.0146021>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: dplyr, tidyr, tibble, magrittr, drc
RoxygenNote: 7.0.2
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, ggplot2, ggridges, cowplot
URL: https://github.com/AlvesKS/ec50estimator
BugReports: https://github.com/AlvesKS/ec50estimator/issues
NeedsCompilation: no
Packaged: 2020-09-07 19:08:46 UTC; kai-q
Author: Kaique dos S. Alves [aut, cre] (<https://orcid.org/0000-0001-9187-0252>)
Repository: CRAN
Date/Publication: 2020-09-15 09:40:06 UTC

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New package crimeutils with initial version 0.1.0
Package: crimeutils
Title: A Comprehensive Set of Functions to Clean, Analyze, and Present Crime Data
Version: 0.1.0
Authors@R: person("Jacob", "Kaplan", email = "jkkaplan6@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0601-0387"))
Description: A collection of functions that make it easier to understand crime (or other) data, and assist others in understanding it. The package helps you read data from various sources, clean it, fix column names, and graph the data.
Depends: R (>= 2.10)
Imports: dplyr, ggplot2, gridExtra, scales, magrittr, gt, grDevices, tidyr, stats, data.table, lubridate, janitor
License: MIT + file LICENSE
LazyData: TRUE
URL: https://github.com/jacobkap/crimeutils
BugReports: https://github.com/jacobkap/crimeutils/issues
RoxygenNote: 7.1.1
Suggests: spelling, testthat (>= 2.1.0), covr
Language: en-US
NeedsCompilation: no
Packaged: 2020-09-07 18:27:41 UTC; user
Author: Jacob Kaplan [aut, cre] (<https://orcid.org/0000-0002-0601-0387>)
Maintainer: Jacob Kaplan <jkkaplan6@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-15 09:40:11 UTC

More information about crimeutils at CRAN
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Package AnaCoDa updated to version 0.1.4.4 with previous version 0.1.3.0 dated 2019-05-11

Title: Analysis of Codon Data under Stationarity using a Bayesian Framework
Description: Is a collection of models to analyze genome scale codon data using a Bayesian framework. Provides visualization routines and checkpointing for model fittings. Currently published models to analyze gene data for selection on codon usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In addition 'AnaCoDa' contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data.
Author: Authors@R
Maintainer: Cedric Landerer <cedric.landerer@gmail.com>

Diff between AnaCoDa versions 0.1.3.0 dated 2019-05-11 and 0.1.4.4 dated 2020-09-15

 AnaCoDa-0.1.3.0/AnaCoDa/man/calculate_marginal_log_likelihood.Rd                                               |only
 AnaCoDa-0.1.4.4/AnaCoDa/DESCRIPTION                                                                            |   24 
 AnaCoDa-0.1.4.4/AnaCoDa/MD5                                                                                    |  191 +
 AnaCoDa-0.1.4.4/AnaCoDa/NAMESPACE                                                                              |    5 
 AnaCoDa-0.1.4.4/AnaCoDa/R/RcppExports.R                                                                        |only
 AnaCoDa-0.1.4.4/AnaCoDa/R/genomeObject.R                                                                       |   32 
 AnaCoDa-0.1.4.4/AnaCoDa/R/mcmcObject.R                                                                         |   10 
 AnaCoDa-0.1.4.4/AnaCoDa/R/modelObject.R                                                                        |   16 
 AnaCoDa-0.1.4.4/AnaCoDa/R/parameterObject.R                                                                    |  713 +++---
 AnaCoDa-0.1.4.4/AnaCoDa/R/plotModelObject.R                                                                    |  102 
 AnaCoDa-0.1.4.4/AnaCoDa/R/plotParameterObject.R                                                                |   38 
 AnaCoDa-0.1.4.4/AnaCoDa/R/plotTraceObject.R                                                                    |  269 +-
 AnaCoDa-0.1.4.4/AnaCoDa/R/traceObject.R                                                                        |   26 
 AnaCoDa-0.1.4.4/AnaCoDa/build/vignette.rds                                                                     |binary
 AnaCoDa-0.1.4.4/AnaCoDa/inst/doc/anacoda.R                                                                     |  115 -
 AnaCoDa-0.1.4.4/AnaCoDa/inst/doc/anacoda.Rmd                                                                   |   81 
 AnaCoDa-0.1.4.4/AnaCoDa/inst/doc/anacoda.html                                                                  |   74 
 AnaCoDa-0.1.4.4/AnaCoDa/inst/extdata/expression.csv                                                            |    2 
 AnaCoDa-0.1.4.4/AnaCoDa/man/AAToCodon.Rd                                                                       |    2 
 AnaCoDa-0.1.4.4/AnaCoDa/man/acfCSP.Rd                                                                          |   12 
 AnaCoDa-0.1.4.4/AnaCoDa/man/acfMCMC.Rd                                                                         |    3 
 AnaCoDa-0.1.4.4/AnaCoDa/man/addObservedSynthesisRateSet.Rd                                                     |    7 
 AnaCoDa-0.1.4.4/AnaCoDa/man/calculateMarginalLogLikelihood.Rd                                                  |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/convergence.test.Rd                                                                |   12 
 AnaCoDa-0.1.4.4/AnaCoDa/man/fixDEta.Rd                                                                         |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/fixDM.Rd                                                                           |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/fixSphi.Rd                                                                         |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getAdaptiveWidth.Rd                                                                |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getCSPEstimates.Rd                                                                 |   14 
 AnaCoDa-0.1.4.4/AnaCoDa/man/getCodonCounts.Rd                                                                  |    2 
 AnaCoDa-0.1.4.4/AnaCoDa/man/getCodonSpecificPosteriorMeanForCodon.Rd                                           |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getCodonSpecificPosteriorVarianceForCodon.Rd                                       |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getCodonSpecificQuantilesForCodon.Rd                                               |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getEstimatedMixtureAssignmentForGene.Rd                                            |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getEstimatedMixtureAssignmentProbabilitiesForGene.Rd                               |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getExpressionEstimates.Rd                                                          |   11 
 AnaCoDa-0.1.4.4/AnaCoDa/man/getGroupList.Rd                                                                    |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getLogLikelihoodTrace.Rd                                                           |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getLogPosteriorMean.Rd                                                             |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getLogPosteriorTrace.Rd                                                            |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getNoiseOffsetPosteriorMean.Rd                                                     |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getNoiseOffsetVariance.Rd                                                          |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getSamples.Rd                                                                      |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getStdDevSynthesisRatePosteriorMean.Rd                                             |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getStdDevSynthesisRateVariance.Rd                                                  |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getStepsToAdapt.Rd                                                                 |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getSynthesisRate.Rd                                                                |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getSynthesisRatePosteriorMeanForGene.Rd                                            |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getSynthesisRatePosteriorVarianceForGene.Rd                                        |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getThinning.Rd                                                                     |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/getTraceObject.Rd                                                                  |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/initMutationCategories.Rd                                                          |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/initSelectionCategories.Rd                                                         |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/initializeCovarianceMatrices.Rd                                                    |    9 
 AnaCoDa-0.1.4.4/AnaCoDa/man/initializeGenomeObject.Rd                                                          |   30 
 AnaCoDa-0.1.4.4/AnaCoDa/man/initializeMCMCObject.Rd                                                            |   18 
 AnaCoDa-0.1.4.4/AnaCoDa/man/initializeModelObject.Rd                                                           |   17 
 AnaCoDa-0.1.4.4/AnaCoDa/man/initializeParameterObject.Rd                                                       |   48 
 AnaCoDa-0.1.4.4/AnaCoDa/man/initializeSynthesisRateByGenome.Rd                                                 |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/initializeSynthesisRateByList.Rd                                                   |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/initializeSynthesisRateByRandom.Rd                                                 |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/plot.Rcpp_FONSEModel.Rd                                                            |   13 
 AnaCoDa-0.1.4.4/AnaCoDa/man/plot.Rcpp_FONSEParameter.Rd                                                        |   10 
 AnaCoDa-0.1.4.4/AnaCoDa/man/plot.Rcpp_MCMCAlgorithm.Rd                                                         |    3 
 AnaCoDa-0.1.4.4/AnaCoDa/man/plot.Rcpp_ROCModel.Rd                                                              |    5 
 AnaCoDa-0.1.4.4/AnaCoDa/man/plot.Rcpp_ROCParameter.Rd                                                          |   10 
 AnaCoDa-0.1.4.4/AnaCoDa/man/plot.Rcpp_Trace.Rd                                                                 |   15 
 AnaCoDa-0.1.4.4/AnaCoDa/man/plotAcceptanceRatios.Rd                                                            |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/plotCodonSpecificParameters.Rd                                                     |   14 
 AnaCoDa-0.1.4.4/AnaCoDa/man/readPhiValue.Rd                                                                    |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/setAdaptiveWidth.Rd                                                                |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/setGroupList.Rd                                                                    |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/setRestartFileSettings.Rd                                                          |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/setSamples.Rd                                                                      |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/setStepsToAdapt.Rd                                                                 |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/setThinning.Rd                                                                     |only
 AnaCoDa-0.1.4.4/AnaCoDa/man/simulateGenome.Rd                                                                  |only
 AnaCoDa-0.1.4.4/AnaCoDa/src/CovarianceMatrix.cpp                                                               |   60 
 AnaCoDa-0.1.4.4/AnaCoDa/src/FONSEModel.cpp                                                                     |  396 ++-
 AnaCoDa-0.1.4.4/AnaCoDa/src/FONSEParameter.cpp                                                                 |  393 ++-
 AnaCoDa-0.1.4.4/AnaCoDa/src/Gene.cpp                                                                           |    7 
 AnaCoDa-0.1.4.4/AnaCoDa/src/Genome.cpp                                                                         |   52 
 AnaCoDa-0.1.4.4/AnaCoDa/src/MCMCAlgorithm.cpp                                                                  |  429 +++
 AnaCoDa-0.1.4.4/AnaCoDa/src/Model.cpp                                                                          |   95 
 AnaCoDa-0.1.4.4/AnaCoDa/src/PAModel.cpp                                                                        |  398 ++-
 AnaCoDa-0.1.4.4/AnaCoDa/src/PANSEModel.cpp                                                                     | 1111 +++++++---
 AnaCoDa-0.1.4.4/AnaCoDa/src/PANSEParameter.cpp                                                                 |  640 +++++
 AnaCoDa-0.1.4.4/AnaCoDa/src/PAParameter.cpp                                                                    |   40 
 AnaCoDa-0.1.4.4/AnaCoDa/src/Parameter.cpp                                                                      |  692 ++++--
 AnaCoDa-0.1.4.4/AnaCoDa/src/RCPP_Model.cpp                                                                     |   13 
 AnaCoDa-0.1.4.4/AnaCoDa/src/RCPP_Parameter.cpp                                                                 |  233 +-
 AnaCoDa-0.1.4.4/AnaCoDa/src/RCPP_Trace.cpp                                                                     |   12 
 AnaCoDa-0.1.4.4/AnaCoDa/src/ROCModel.cpp                                                                       |  219 +
 AnaCoDa-0.1.4.4/AnaCoDa/src/ROCParameter.cpp                                                                   |  746 ++++--
 AnaCoDa-0.1.4.4/AnaCoDa/src/SequenceSummary.cpp                                                                |   41 
 AnaCoDa-0.1.4.4/AnaCoDa/src/Testing.cpp                                                                        |   12 
 AnaCoDa-0.1.4.4/AnaCoDa/src/Trace.cpp                                                                          |  255 +-
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/CovarianceMatrix.h                                                         |    3 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/FONSE/FONSEModel.h                                                         |   43 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/FONSE/FONSEParameter.h                                                     |   39 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/Gene.h                                                                     |    4 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/Genome.h                                                                   |    4 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/MCMCAlgorithm.h                                                            |    2 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/PA/PAModel.h                                                               |   28 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/PA/PAParameter.h                                                           |    3 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/PANSE/PANSEModel.h                                                         |   79 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/PANSE/PANSEParameter.h                                                     |   66 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/ROC/ROCModel.h                                                             |   26 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/ROC/ROCParameter.h                                                         |   86 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/SequenceSummary.h                                                          |    9 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/base/Model.h                                                               |   24 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/base/Parameter.h                                                           |   60 
 AnaCoDa-0.1.4.4/AnaCoDa/src/include/base/Trace.h                                                               |   72 
 AnaCoDa-0.1.4.4/AnaCoDa/src/main.cpp                                                                           |   99 
 AnaCoDa-0.1.4.4/AnaCoDa/tests/testthat/UnitTestingData/testMCMCROCFiles/simulatedAllUniqueR_phi_withPhiSet.csv |    2 
 AnaCoDa-0.1.4.4/AnaCoDa/tests/testthat/testMCMCROC.R                                                           |   98 
 AnaCoDa-0.1.4.4/AnaCoDa/vignettes/anacoda.Rmd                                                                  |   81 
 117 files changed, 6511 insertions(+), 2114 deletions(-)

More information about AnaCoDa at CRAN
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New package rater with initial version 1.0.0
Package: rater
Title: Fit Statistical Models of Repeated Categorical Rating Data
Version: 1.0.0
Authors@R: c(person("Jeffrey", "Pullin", role = c("aut", "cre"), email = "jeffrey.pullin@gmail.com", comment = c(ORCID = "0000-0003-3651-5471")), person("Damjan", "Vukcevic", role = c("aut"), comment = c(ORCID = "0000-0001-7780-9586")) )
Description: Fit statistical models based on the Dawid-Skene model - Dawid and Skene (1979) <doi:10.2307/2346806> - to repeated categorical rating data. Full Bayesian inference for these models is supported through the Stan modelling language. 'rater' also allows the user to extract and plot key parameters of these models.
License: GPL-2
URL: https://jeffreypullin.github.io/rater/, https://github.com/jeffreypullin/rater
BugReports: https://github.com/jeffreypullin/rater/issues
Encoding: UTF-8
LazyData: true
Biarch: true
Depends: R (>= 3.4.0)
Imports: methods, Rcpp (>= 0.12.0), rstan (>= 2.19.2), rstantools (>= 2.0.0), RcppParallel (>= 5.0.1), ggplot2 (>= 2.2.1), rlang (> 0.2.0)
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), rstan (>= 2.18.1), StanHeaders (>= 2.18.0), RcppParallel (>= 5.0.1)
RoxygenNote: 7.1.1
Suggests: testthat, knitr, rmarkdown, covr
VignetteBuilder: knitr
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2020-09-07 09:28:15 UTC; jeffreypullin
Author: Jeffrey Pullin [aut, cre] (<https://orcid.org/0000-0003-3651-5471>), Damjan Vukcevic [aut] (<https://orcid.org/0000-0001-7780-9586>)
Maintainer: Jeffrey Pullin <jeffrey.pullin@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-15 08:40:03 UTC

More information about rater at CRAN
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New package pdfboxr with initial version 2.0.19
Package: pdfboxr
Type: Package
Title: Interface to 'PDFBox'
Version: 2.0.19
Authors@R: c(person("Florian", "Schwendinger", role = c("aut", "cre"), email = "FlorianSchwendinger@gmx.at"), person("Benjamin", "Schwendinger", role = c("aut")), person("Apache", "Software Foundation", role = c("ctb", "cph"), comment = "Apache PDFBox Java library"))
Description: Interface to the 'Apache' 'PDFBox' library <https://pdfbox.apache.org/>. 'PDFBox' is an open source 'Java' tool for working with 'PDF' documents. Currently 'pdfboxr' only implements functions to extract data from 'PDF'-files.
Imports: checkmate, rJava (>= 0.9-6)
SystemRequirements: Java (>= 8)
License: GPL-3
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-07 11:16:24 UTC; f
Author: Florian Schwendinger [aut, cre], Benjamin Schwendinger [aut], Apache Software Foundation [ctb, cph] (Apache PDFBox Java library)
Maintainer: Florian Schwendinger <FlorianSchwendinger@gmx.at>
Repository: CRAN
Date/Publication: 2020-09-15 08:40:08 UTC

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New package onlineforecast with initial version 0.9.3
Package: onlineforecast
Type: Package
Title: Forecast Modelling for Online Applications
Version: 0.9.3
Description: A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the paper "Short-term heat load forecasting for single family houses" <doi:10.1016/j.enbuild.2013.04.022>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Authors@R: c( person("Peder", "Bacher", email="pbac@dtu.dk", role = "cre"), person("Hjorleifur G", "Bergsteinsson", email="hgbe@dtu.dk", role = "aut"))
Depends: R (>= 3.0.0)
Imports: Rcpp (>= 0.12.18), R6 (>= 2.2.2), splines (>= 3.1.1), pbs, digest
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, R.rsp, testthat (>= 2.1.0), data.table, plotly
VignetteBuilder: knitr
RoxygenNote: 7.1.1
URL: https://onlineforecasting.org
BugReports: https://lab.compute.dtu.dk/packages/onlineforecast/-/issues
NeedsCompilation: yes
Packaged: 2020-09-07 11:14:57 UTC; pbac
Author: Peder Bacher [cre], Hjorleifur G Bergsteinsson [aut]
Maintainer: Peder Bacher <pbac@dtu.dk>
Repository: CRAN
Date/Publication: 2020-09-15 08:50:02 UTC

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New package eatATA with initial version 0.7.0
Package: eatATA
Type: Package
Title: Create Constraints for Small Test Assembly Problems
Version: 0.7.0
Authors@R: c( person("Benjamin", "Becker", email = "b.becker@iqb.hu-berlin.de", role = c("aut", "cre")), person("Dries", "Debeer", email = "dries.debeer@kuleuven.be", role = c("aut")))
Description: Provides simple functions to create constraints for small test assembly problems (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, only 'Gurobi' is supported as an optimizer.
License: GPL
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: Matrix
RoxygenNote: 7.1.0
Suggests: testthat (>= 2.1.0), covr, knitr, rmarkdown, readxl
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-09-07 07:49:34 UTC; benjb
Author: Benjamin Becker [aut, cre], Dries Debeer [aut]
Maintainer: Benjamin Becker <b.becker@iqb.hu-berlin.de>
Repository: CRAN
Date/Publication: 2020-09-15 08:30:03 UTC

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Package bnlearn updated to version 4.6 with previous version 4.5 dated 2019-08-05

Title: Bayesian Network Structure Learning, Parameter Learning and Inference
Description: Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.
Author: Marco Scutari [aut, cre], Robert Ness [ctb]
Maintainer: Marco Scutari <marco.scutari@gmail.com>

Diff between bnlearn versions 4.5 dated 2019-08-05 and 4.6 dated 2020-09-15

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 118 files changed, 2659 insertions(+), 1603 deletions(-)

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

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

2020-05-18 1.1

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Package eNetXplorer (with last version 1.1.2) was removed from CRAN

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

2020-08-29 1.1.2
2020-06-14 1.1.1
2019-09-20 1.1.0
2019-04-17 1.0.2
2018-06-10 1.0.1
2018-04-25 1.0

Permanent link
Package fy updated to version 0.3.0 with previous version 0.2.0 dated 2019-10-21

Title: Utilities for Financial Years
Description: In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June of the following calendar year. As such, many databases need to represent and validate financial years efficiently. While the use of integer years with a convention that they represent the year ending is common, it may lead to ambiguity with calendar years. On the other hand, string representations may be too inefficient and do not easily admit arithmetic operations. This package tries to make validation of financial years quicker while retaining clarity.
Author: Hugh Parsonage [aut, cre]
Maintainer: Hugh Parsonage <hugh.parsonage@gmail.com>

Diff between fy versions 0.2.0 dated 2019-10-21 and 0.3.0 dated 2020-09-15

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 MD5                           |   18 +++++++++---------
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 R/fy_year.R                   |   35 ++++++++++++++++++++++++++---------
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 R/validate_fys_permitted.R    |   10 +++++++++-
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 man/validate_fys_permitted.Rd |   12 +++++++++---
 tests/testthat/test-is_fy.R   |   19 ++++++++++++-------
 tests/testthat/test-yr2fy.R   |    7 +++++++
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Package simukde updated to version 1.2.0 with previous version 1.1.0 dated 2018-10-07

Title: Simulation with Kernel Density Estimation
Description: Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) <doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.
Author: MAKHGAL Ganbold [aut, cre], BAYARBAATAR Amgalan [aut]
Maintainer: MAKHGAL Ganbold <makhgal@seas.num.edu.mn>

Diff between simukde versions 1.1.0 dated 2018-10-07 and 1.2.0 dated 2020-09-15

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Package sigr updated to version 1.1.0 with previous version 1.0.8 dated 2020-08-11

Title: Succinct and Correct Statistical Summaries for Reports
Description: Succinctly and correctly format statistical summaries of various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). The main purpose is unified reporting of experimental results, working around issue such as the difficulty of extracting model summary facts (such as with 'lm'/'glm'). This package also includes empirical tests, such as bootstrap estimates.
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

Diff between sigr versions 1.0.8 dated 2020-08-11 and 1.1.0 dated 2020-09-15

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Package languageserver updated to version 0.3.7 with previous version 0.3.6 dated 2020-05-25

Title: Language Server Protocol
Description: An implementation of the Language Server Protocol for R. The Language Server protocol is used by an editor client to integrate features like auto completion. See <https://microsoft.github.io/language-server-protocol/> for details.
Author: Randy Lai [aut, cre], Kun Ren [ctb]
Maintainer: Randy Lai <randy.cs.lai@gmail.com>

Diff between languageserver versions 0.3.6 dated 2020-05-25 and 0.3.7 dated 2020-09-15

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Package eBsc (with last version 4.11) was removed from CRAN

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

2020-09-01 4.11

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Package mpMap2 (with last version 1.0.4) was removed from CRAN

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

2020-09-11 1.0.4
2020-04-23 1.0.3
2020-04-07 1.0.2

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