Fri, 12 Apr 2019

Package subscore updated to version 3.1 with previous version 3.0 dated 2019-03-03

Title: Computing Subscores in Classical Test Theory and Item Response Theory
Description: Functions for computing subscores for a test using different methods in both classical test theory (CTT) and item response theory (IRT). This package enables three sets of subscoring methods within the framework of CTT and IRT: Wainer's augmentation method, Haberman's three subscoring methods, and Yen's objective performance index (OPI). The package also includes the function to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to examine whether test subscores are of added value.
Author: Shenghai Dai [aut, cre], Xiaolin Wang [aut], Dubravka Svetina [aut]
Maintainer: Shenghai Dai <s.dai@wsu.edu>

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Package precrec updated to version 0.10.1 with previous version 0.10 dated 2019-03-05

Title: Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves
Description: Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics) curves.
Author: Takaya Saito [aut, cre], Marc Rehmsmeier [aut]
Maintainer: Takaya Saito <takaya.saito@outlook.com>

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Package rpf updated to version 0.62 with previous version 0.60 dated 2019-03-08

Title: Response Probability Functions
Description: The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions are made available with R_RegisterCCallable().
Author: Joshua Pritikin [cre, aut], Jonathan Weeks [ctb], Li Cai [ctb], Carrie Houts [ctb], Phil Chalmers [ctb], Michael D. Hunter [ctb], Carl F. Falk [ctb]
Maintainer: Joshua Pritikin <jpritikin@pobox.com>

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Package NAM updated to version 1.7.2 with previous version 1.7.1 dated 2019-03-11

Title: Nested Association Mapping
Description: Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
Author: Alencar Xavier, William Muir, Katy Rainey, Shizhong Xu.
Maintainer: Alencar Xavier <alenxav@gmail.com>

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Package kiwisR updated to version 0.1.5 with previous version 0.1.4 dated 2019-03-10

Title: A Wrapper for Querying KISTERS 'WISKI' Databases via the 'KiWIS' API
Description: A wrapper for querying 'WISKI' databases via the 'KiWIS' 'REST' API. 'WISKI' is an 'SQL' relational database used for the collection and storage of water data developed by KISTERS and 'KiWIS' is a 'REST' service that provides access to 'WISKI' databases via HTTP requests (<https://water.kisters.de/en/technology-trends/kisters-and-open-data/>). Contains a list of default databases (called 'hubs') and also allows users to provide their own 'KiWIS' URL. Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles.
Author: Ryan Whaley [aut, cre], Sam Albers [ctb]
Maintainer: Ryan Whaley <rdgwhaley@gmail.com>

Diff between kiwisR versions 0.1.4 dated 2019-03-10 and 0.1.5 dated 2019-04-12

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Package GenEst updated to version 1.2.4 with previous version 1.2.3 dated 2019-03-08

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

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Package textfeatures updated to version 0.3.2 with previous version 0.3.0 dated 2018-11-29

Title: Extracts Features from Text
Description: A tool for extracting some generic features (e.g., number of words, line breaks, characters per word, URLs, lower case, upper case, commas, periods, exclamation points, etc.) from strings of text.
Author: Michael Wayne Kearney [aut, cre] (<https://orcid.org/0000-0002-0730-4694>)
Maintainer: Michael Wayne Kearney <kearneymw@missouri.edu>

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Package scriptexec updated to version 0.3.1 with previous version 0.3.0 dated 2018-10-30

Title: Execute Native Scripts
Description: Run complex native scripts with a single command, similar to system commands.
Author: Sagie Gur-Ari [aut, cre]
Maintainer: Sagie Gur-Ari <sagiegurari@gmail.com>

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Package fungible updated to version 1.81 with previous version 1.80 dated 2019-03-06

Title: Psychometric Functions from the Waller Lab
Description: Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications: Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>. Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>. Waller, N. G. (2016). Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568, <DOI:10.1080/00273171.2016.1178566>. Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>. Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.
Author: Niels Waller [aut, cre], Jeff Jones [ctb], Casey Giordano [ctb]
Maintainer: Niels Waller <nwaller@umn.edu>

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Package FastGaSP updated to version 0.5.1 with previous version 0.5.0 dated 2019-01-08

Title: Fast and Exact Computation of Gaussian Stochastic Process
Description: Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It allows for the cases with or without a noise. See the reference: Mengyang Gu and Yanxun Xu (2017) <arXiv:1711.11501>.
Author: Mengyang Gu [aut, cre]
Maintainer: Mengyang Gu <mgu6@jhu.edu>

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Package coxed updated to version 0.2.4 with previous version 0.2.0 dated 2018-08-23

Title: Duration-Based Quantities of Interest for the Cox Proportional Hazards Model
Description: Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model.
Author: Kropko, Jonathan [aut, cre], Harden, Jeffrey J. [aut]
Maintainer: "Kropko, Jonathan" <jkropko@virginia.edu>

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 man/coxed.gam.tvc.Rd                                                                           |    5 
 man/generate.lm.Rd                                                                             |    4 
 man/sim.survdata.Rd                                                                            |    8 
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Package rpart updated to version 4.1-15 with previous version 4.1-13 dated 2018-02-23

Title: Recursive Partitioning and Regression Trees
Description: Recursive partitioning for classification, regression and survival trees. An implementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Author: Terry Therneau [aut], Beth Atkinson [aut, cre], Brian Ripley [trl] (producer of the initial R port, maintainer 1999-2017)
Maintainer: Beth Atkinson <atkinson@mayo.edu>

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Package npsf updated to version 0.5.1 with previous version 0.4.2 dated 2019-01-02

Title: Nonparametric and Stochastic Efficiency and Productivity Analysis
Description: Provides a variety of tools for nonparametric and parametric efficiency measurement.
Author: Oleg Badunenko [aut, cre], Yaryna Kolomiytseva [aut], Pavlo Mozharovskyi [aut]
Maintainer: Oleg Badunenko <oleg.badunenko@port.ac.uk>

Diff between npsf versions 0.4.2 dated 2019-01-02 and 0.5.1 dated 2019-04-12

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Package netgwas updated to version 1.10 with previous version 1.9 dated 2019-03-26

Title: Network-Based Genome Wide Association Studies
Description: A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. We use conditional independence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <arXiv:1710.01063>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.
Author: Pariya Behrouzi <https://orcid.org/0000-0001-6762-5433> and Ernst C. Wit
Maintainer: Pariya Behrouzi <pariya.behrouzi@gmail.com>

Diff between netgwas versions 1.9 dated 2019-03-26 and 1.10 dated 2019-04-12

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Package psychmeta updated to version 2.3.3 with previous version 2.3.2 dated 2019-02-26

Title: Psychometric Meta-Analysis Toolkit
Description: Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
Author: Jeffrey A. Dahlke [aut, cre], Brenton M. Wiernik [aut], Michael T. Brannick [ctb] (Testing), Jack Kostal [ctb] (Code for reshape_mat2dat function), Sean Potter [ctb] (Testing; Code for cumulative and leave1out plots), John Sakaluk [ctb] (Code for funnel and forest plots), Yuejia (Mandy) Teng [ctb] (Testing)
Maintainer: Jeffrey A. Dahlke <dahlk068@umn.edu>

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Package SeqFeatR updated to version 0.3.1 with previous version 0.3 dated 2018-11-30

Title: A Tool to Associate FASTA Sequences and Features
Description: Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.
Author: Bettina Budeus
Maintainer: Bettina Budeus <bettina.budeus@stud.uni-due.de>

Diff between SeqFeatR versions 0.3 dated 2018-11-30 and 0.3.1 dated 2019-04-12

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Package sctransform updated to version 0.2.0 with previous version 0.1.0 dated 2018-11-18

Title: Variance Stabilizing Transformations for Single Cell UMI Data
Description: A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija 2019 <doi:10.1101/576827> for more details.
Author: Christoph Hafemeister [aut, cre] (<https://orcid.org/0000-0001-6365-8254>)
Maintainer: Christoph Hafemeister <chafemeister@nygenome.org>

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Package SARP.moodle updated to version 0.3.10 with previous version 0.3.9 dated 2019-03-28

Title: XML Output Functions for Easy Creation of Moodle Questions
Description: Provides a set of basic functions for creating Moodle XML output files suited for importing questions in Moodle (a learning management system, see <https://moodle.org/> for more information).
Author: Emmanuel Curis [aut, cre]
Maintainer: Emmanuel Curis <emmanuel.curis@parisdescartes.fr>

Diff between SARP.moodle versions 0.3.9 dated 2019-03-28 and 0.3.10 dated 2019-04-12

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Package prioritizr updated to version 4.0.4 with previous version 4.0.3 dated 2019-04-08

Title: Systematic Conservation Prioritization in R
Description: Conservation prioritization using integer programming techniques. To solve large-scale problems, users should install the 'gurobi' optimizer (available from <http://www.gurobi.com/>).
Author: Jeffrey O Hanson [aut], Richard Schuster [aut, cre], Nina Morrell [aut], Matthew Strimas-Mackey [aut], Matthew E Watts [aut], Peter Arcese [aut], Joeseph Bennett [aut], Hugh P Possingham [aut]
Maintainer: Richard Schuster <richard.schuster@glel.carleton.ca>

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Package konfound updated to version 0.1.2 with previous version 0.1.1 dated 2019-01-21

Title: Quantify the Robustness of Causal Inferences
Description: Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al. (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with null hypothesis cases to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively.
Author: Joshua M Rosenberg [aut, cre], Ran Xu [ctb], Kenneth A Frank [ctb]
Maintainer: Joshua M Rosenberg <jmichaelrosenberg@gmail.com>

Diff between konfound versions 0.1.1 dated 2019-01-21 and 0.1.2 dated 2019-04-12

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Package visualR updated to version 2.0.0 with previous version 1.0.0 dated 2019-03-08

Title: Generates a 3D Visualization of Volatility Skew and Stock Option Parameters Plotted Over Time
Description: Generates a 3D graph which plots the volatility skew of a stock or a selected stock option parameter over time. The default setting plots the net parameter position of a double vertical spread over time.
Author: John T. Buynak
Maintainer: John T. Buynak <jbuynak94@gmail.com>

Diff between visualR versions 1.0.0 dated 2019-03-08 and 2.0.0 dated 2019-04-12

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Package rpart.plot updated to version 3.0.7 with previous version 3.0.6 dated 2018-11-24

Title: Plot 'rpart' Models: An Enhanced Version of 'plot.rpart'
Description: Plot 'rpart' models. Extends plot.rpart() and text.rpart() in the 'rpart' package.
Author: Stephen Milborrow
Maintainer: Stephen Milborrow <milbo@sonic.net>

Diff between rpart.plot versions 3.0.6 dated 2018-11-24 and 3.0.7 dated 2019-04-12

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New package qualtRics with initial version 3.1.0
Package: qualtRics
Type: Package
Title: Download 'Qualtrics' Survey Data
Version: 3.1.0
Authors@R: c(person("Jasper", "Ginn", role = c("aut"), email = "jasperginn@gmail.com"), person("Samuel", "Kaminsky", role = c("ctb"), email = "samuel.e.kaminsky@gmail.com"), person("Eric", "Knudsen", role = c("ctb"), email = "ericknud1@gmail.com"), person("Julia", "Silge", role = c("aut", "cre"), email = "julia.silge@gmail.com", comment = c(ORCID = "0000-0002-3671-836X")), person("Phoebe", "Wong", role = c("ctb"), email = "phoebe.wong@berkeley.edu"))
Description: Provides functions to access survey results directly into R using the 'Qualtrics' API. 'Qualtrics' <https://www.qualtrics.com/about/> is an online survey and data collection software platform. See <https://api.qualtrics.com/> for more information about the 'Qualtrics' API. This package is community-maintained and is not officially supported by 'Qualtrics'.
URL: https://ropensci.github.io/qualtRics/, https://github.com/ropensci/qualtRics
BugReports: https://github.com/ropensci/qualtRics/issues
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: TRUE
RoxygenNote: 6.1.1
Imports: httr, stringr, readr, jsonlite, assertthat, sjlabelled, yaml, dplyr, rlang
Suggests: knitr, rmarkdown, testthat, httptest
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-04-11 18:51:08 UTC; juliasilge
Author: Jasper Ginn [aut], Samuel Kaminsky [ctb], Eric Knudsen [ctb], Julia Silge [aut, cre] (<https://orcid.org/0000-0002-3671-836X>), Phoebe Wong [ctb]
Maintainer: Julia Silge <julia.silge@gmail.com>
Repository: CRAN
Date/Publication: 2019-04-12 10:13:49 UTC

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Package MixGHD updated to version 2.3.2 with previous version 2.3.1 dated 2019-03-18

Title: Model Based Clustering, Classification and Discriminant Analysis Using the Mixture of Generalized Hyperbolic Distributions
Description: Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD'(Tortora et al. (2016) <arXiv:1403.2332v7>), mixture of multiple scaled generalized hyperbolic distributions. The 'cMSGHD' (Tortora et al. (2016) <arXiv:1403.2332v7>) is a 'MSGHD' with convex contour plots. The 'MCGHD' (Tortora et al. (2016) <arXiv:1403.2332v7>), mixture of coalesced generalized hyperbolic distributions is a new more flexible model.
Author: Cristina Tortora [aut, cre, cph], Aisha ElSherbiny [com], Ryan P. Browne [aut, cph], Brian C. Franczak [aut, cph], and Paul D. McNicholas [aut, cph], and Donald D. Amos [ctb].
Maintainer: Cristina Tortora <grikris1@gmail.com>

Diff between MixGHD versions 2.3.1 dated 2019-03-18 and 2.3.2 dated 2019-04-12

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Package hdme updated to version 0.2.3 with previous version 0.2.2 dated 2019-03-07

Title: High-Dimensional Regression with Measurement Error
Description: Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
Author: Oystein Sorensen
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>

Diff between hdme versions 0.2.2 dated 2019-03-07 and 0.2.3 dated 2019-04-12

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Package GSE updated to version 4.2 with previous version 4.1 dated 2016-12-30

Title: Robust Estimation in the Presence of Cellwise and Casewise Contamination and Missing Data
Description: Robust Estimation of Multivariate Location and Scatter in the Presence of Cellwise and Casewise Contamination and Missing Data.
Author: Andy Leung, Mike Danilov, Victor Yohai, Ruben Zamar
Maintainer: Claudio Agostinelli <claudio.agostinelli@unitn.it>

Diff between GSE versions 4.1 dated 2016-12-30 and 4.2 dated 2019-04-12

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Package getCRUCLdata updated to version 0.3.0 with previous version 0.2.5 dated 2018-09-12

Title: Use and Explore 'CRU' 'CL' v. 2.0 Climatology Elements
Description: Provides functions that automate downloading and importing University of East Anglia Climate Research Unit ('CRU') 'CL' v. 2.0 climatology data, facilitates the calculation of minimum temperature and maximum temperature and formats the data into a tidy data frame as a 'tibble' or a list of 'raster' 'stack' objects for use. 'CRU' 'CL' v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arcminutes (0.1666667 degree) resolution. For more information see the description of the data provided by the University of East Anglia Climate Research Unit, <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.
Author: Adam Sparks [aut, cre] (<https://orcid.org/0000-0002-0061-8359>)
Maintainer: Adam Sparks <adamhsparks@gmail.com>

Diff between getCRUCLdata versions 0.2.5 dated 2018-09-12 and 0.3.0 dated 2019-04-12

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Package FPDclustering updated to version 1.3 with previous version 1.2 dated 2017-08-23

Title: PD-Clustering and Factor PD-Clustering
Description: Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different size. Factor PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional datasets.
Author: Cristina Tortora [aut, cre, cph], Noe Vidales [aut], and Paul D. McNicholas [fnd]
Maintainer: Cristina Tortora <grikris1@gmail.com>

Diff between FPDclustering versions 1.2 dated 2017-08-23 and 1.3 dated 2019-04-12

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New package frost with initial version 0.0.4
Package: frost
Type: Package
Title: Prediction of Minimum Temperature for Frost Forecasting in Agriculture
Version: 0.0.4
Authors@R: c(person(given = "Ana Laura", family = "Diedrichs", role = c("cre", "aut"), comment = c(ORCID = "0000-0001-9973-4554"), email = "ana.diedrichs@frm.utn.edu.ar" ), person(given = "Facundo", family = "Bromberg", role = c("ths") ), person(given = "Diego", family = "Dujovne", role = c("ths"), comment = c(ORCID = "0000-0003-4207-633X") ))
Description: A compilation of empirical methods used by farmers and agronomic engineers to predict the minimum temperature to detect a frost night. These functions use variables such as environmental temperature, relative humidity, and dew point. See <http://sedici.unlp.edu.ar/handle/10915/72102> <http://www.fao.org/docrep/008/y7223e/y7223e0b.htm#bm11.8> for details.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr,rmarkdown,ggplot2, testthat
Imports: methods
VignetteBuilder: knitr
URL: https://github.com/anadiedrichs/frost
BugReports: https://github.com/anadiedrichs/frost/issues
Date: 2019-04-08
NeedsCompilation: no
Packaged: 2019-04-11 22:25:53 UTC; anadiedrichs
Author: Ana Laura Diedrichs [cre, aut] (<https://orcid.org/0000-0001-9973-4554>), Facundo Bromberg [ths], Diego Dujovne [ths] (<https://orcid.org/0000-0003-4207-633X>)
Maintainer: Ana Laura Diedrichs <ana.diedrichs@frm.utn.edu.ar>
Repository: CRAN
Date/Publication: 2019-04-12 10:23:17 UTC

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Package earth updated to version 5.1.1 with previous version 5.1.0 dated 2019-04-03

Title: Multivariate Adaptive Regression Splines
Description: Build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines" <doi:10.1214/aos/1176347963>. (The term "MARS" is trademarked and thus not used in the name of the package.)
Author: Stephen Milborrow. Derived from mda:mars by Trevor Hastie and Rob Tibshirani. Uses Alan Miller's Fortran utilities with Thomas Lumley's leaps wrapper.
Maintainer: Stephen Milborrow <milbo@sonic.net>

Diff between earth versions 5.1.0 dated 2019-04-03 and 5.1.1 dated 2019-04-12

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 MD5                                   |   18 +++---
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New package HS with initial version 1.0
Package: HS
Type: Package
Title: Homogenous Segmentation for Spatial Lines Data
Date: 2019-04-07
Version: 1.0
Authors@R: person("Yongze Song", email = "yongze.song@postgrad.curtin.edu.au", comment = c(ORCID = "0000-0003-3420-9622"), role = c("aut", "cre"))
Maintainer: Yongze Song <yongze.song@postgrad.curtin.edu.au>
Description: Methods of homogenous segmentation for spatial lines data, such as pavement performance indicators and traffic volumes. A moving coefficient of variation method is available for homogenous segmentation.
Imports: zoo
Depends: R (>= 3.4.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2019-04-09 02:55:05 UTC; zack
Author: Yongze Song [aut, cre] (<https://orcid.org/0000-0003-3420-9622>)
Repository: CRAN
Date/Publication: 2019-04-12 09:02:42 UTC

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New package trackeRapp with initial version 1.0
Package: trackeRapp
Title: Interface for the Analysis of Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices
Version: 1.0
Authors@R: c(person(given = "Ioannis", family = "Kosmidis", role = c("aut", "cre"), email = "ioannis.kosmidis@warwick.ac.uk", comment = c(ORCID = "0000-0003-1556-0302")), person(given = "Robin", family = "Hornak", role = c("aut"), email = "robinhornak@gmail.com"))
Description: Provides an integrated user interface and workflow for the analysis of running, cycling and swimming data from GPS-enabled tracking devices through the 'trackeR' <https://CRAN.R-project.org/package=trackeR> R package.
Depends: R (>= 3.5.0), trackeR (>= 1.5.0)
Imports: colorspace, zoo, foreach, mgcv, plotly, DT, changepoint, shiny, shinyjs, shinydashboard, shinyWidgets, sf, mapdeck, V8
License: GPL-3
URL: https://github.com/trackerproject/trackeRapp
BugReports: https://github.com/trackerproject/trackeRapp/issues
RoxygenNote: 6.1.1
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2019-04-10 23:15:24 UTC; yiannis
Author: Ioannis Kosmidis [aut, cre] (<https://orcid.org/0000-0003-1556-0302>), Robin Hornak [aut]
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>
Repository: CRAN
Date/Publication: 2019-04-12 08:02:41 UTC

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New package rsparse with initial version 0.3.3
Package: rsparse
Type: Package
Title: Statistical Learning on Sparse Matrices
Version: 0.3.3
Authors@R: c( person("Dmitriy", "Selivanov", role=c("aut", "cre", "cph"), email="selivanov.dmitriy@gmail.com", comment = c(ORCID = "0000-0001-5413-1506")), person("Drew", "Schmidt", role="ctb", comment="configure script for BLAS, LAPACK detection"), person("Wei-Chen", "Chen", role="ctb", comment="configure script and work on linking to float package") )
Maintainer: Dmitriy Selivanov <selivanov.dmitriy@gmail.com>
Description: Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>) 2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, <arXiv:1410.2596>) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, <https://www.aclweb.org/anthology/D14-1162>) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
ByteCompile: true
Depends: R (>= 3.1.0), methods
Imports: Matrix (>= 1.2), Rcpp (>= 0.11), mlapi (>= 0.1.0), data.table (>= 1.10.0), float (>= 0.2-2), RhpcBLASctl, lgr (>= 0.2)
LinkingTo: Rcpp, RcppArmadillo (>= 0.9.100.5.0)
Suggests: testthat, covr
RoxygenNote: 6.1.0
NeedsCompilation: yes
Packaged: 2019-04-11 09:41:34 UTC; dselivanov
Author: Dmitriy Selivanov [aut, cre, cph] (<https://orcid.org/0000-0001-5413-1506>), Drew Schmidt [ctb] (configure script for BLAS, LAPACK detection), Wei-Chen Chen [ctb] (configure script and work on linking to float package)
Repository: CRAN
Date/Publication: 2019-04-12 08:42:39 UTC

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New package RSCAT with initial version 1.0.0
Package: RSCAT
Title: Shadow-Test Approach to Computerized Adaptive Testing
Version: 1.0.0
Authors@R: c( person("Bingnan", "Jiang", email = "bnjiangece@gmail.com", role = c("aut", "cre")), person("ACT, Inc.", role = "cph") )
Author: Bingnan Jiang [aut, cre], ACT, Inc. [cph]
Maintainer: Bingnan Jiang <bnjiangece@gmail.com>
BugReports: https://github.com/act-org/RSCAT/issues
Description: As an advanced approach to computerized adaptive testing (CAT), shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically assembles entire shadow tests as a part of selecting items throughout the testing process. Selecting items from shadow tests guarantees the compliance of all content constraints defined by the blueprint. 'RSCAT' is an R package for the shadow-test approach to CAT. The objective of 'RSCAT' is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation; 2) Contributing to the academic and scientific community for CAT research.
Depends: R (>= 3.4.0), rJava, shiny, shinycssloaders, shinyjs
License: CC BY-NC 4.0
Encoding: UTF-8
RoxygenNote: 6.1.1
Imports: Metrics, ggplot2, gridExtra, grid, methods, stats, utils
Collate: 'EAPConfig.R' 'SimResult.R' 'configClasses.R' 'launchApp.R' 'runSim.R' 'scoreMethodConfig.R' 'shinyAppServer.R' 'shinyAppUI.R' 'utilFunctions.R' 'zzz.R'
Suggests: testthat
NeedsCompilation: no
Packaged: 2019-04-11 03:23:40 UTC; Bingnan
Repository: CRAN
Date/Publication: 2019-04-12 08:32:42 UTC

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New package R.SamBada with initial version 0.1.0
Package: R.SamBada
Type: Package
Title: Processing Pipeline for 'SamBada' from Pre- To Post-Processing
Version: 0.1.0
Date: 2019-03-18
Author: Solange Duruz, Sylvie Stucki, Oliver Selmoni, Elia Vajana
Maintainer: Solange Duruz <solange.duruz@alumni.epfl.ch>
Description: Processing pipeline for 'SamBada' from pre- to post-processing. 'SamBada' is a landscape genomic software designed to run univariate or multivariate logistic regression between the presence of a genotype and one or several environmental variables. See Stucki (2017) <doi:10.1111/1755-0998.12629> and <https://github.com/Sylvie/sambada>. The package provides functions that can be classified into four categories: 1) Install 'SamBada' 2) Preprocessing (prepare genomic file into standards compatible with 'SamBada' and apply quality-control; retrieve environmental conditions at sampling location; prepare environmental file including removal of correlated variables and computation of population structure) 3) Processing (run 'SamBada' on multiple cores using 'Supervision') 4) Post-processing (calculate p-values and q-values, produce interactive Manhattan plots and query 'Ensembl' database, produce maps).
License: GPL (>= 2)
Imports: SNPRelate, gdsfmt
LinkingTo:
RoxygenNote: 6.1.0
Suggests: Rcpp, utils, data.table, shiny, plotly, httr, biomaRt, ggplot2, sp, packcircles, raster, mapplots, spdep, rgdal, gdalUtils, rworldmap, doParallel, foreach, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-04-11 11:35:30 UTC; Solange
Repository: CRAN
Date/Publication: 2019-04-12 08:52:40 UTC

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New package CloneSeeker with initial version 1.0.4
Package: CloneSeeker
Version: 1.0.4
Date: 2019-04-11
Title: Seeking and Finding Clones in Copy Number and Sequencing Data
Author: Kevin R. Coombes, Mark Zucker
Maintainer: Kevin R. Coombes <krc@silicovore.com>
Description: Defines the classes and functions used to simulate and to analyze data sets describing copy number variants and, optionally, sequencing mutations in order to detect clonal subsets. See Zucker et al. (2019) <doi:10.1093/bioinformatics/btz057>.
Depends: R (>= 3.0)
Imports: methods, graphics, combinat, gtools, quantmod, cluster, sirt
License: Apache License (== 2.0)
URL: http://oompa.r-forge.r-project.org/
NeedsCompilation: no
Packaged: 2019-04-11 11:09:47 UTC; Kevin
Repository: CRAN
Date/Publication: 2019-04-12 08:52:44 UTC

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New package Irescale with initial version 0.1.4
Package: Irescale
Type: Package
Title: Calculate and Scale Moran's I
Version: 0.1.4
Author: Ivan Fuentes, Thomas DeWitt
Maintainer: Ivan Fuentes <jivfur@tamu.edu>
Description: Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. Researchers calculate Moran's I to express the spatial autocorrelation of their data. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2009) <arXiv:1606.03658>.
License: GPL (>= 2)
URL: https://github.tamu.edu/jivfur/Irescale
Encoding: UTF-8
LazyData: true
Imports: testthat, rgeos
Depends: graphics, Rdpack, sp, e1071
RoxygenNote: 6.1.1
RdMacros: Rdpack
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-04-10 19:48:11 UTC; jivfur
Repository: CRAN
Date/Publication: 2019-04-12 07:53:53 UTC

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Package shiny updated to version 1.3.1 with previous version 1.3.0 dated 2019-04-07

Title: Web Application Framework for R
Description: Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.
Author: Winston Chang [aut, cre], Joe Cheng [aut], JJ Allaire [aut], Yihui Xie [aut], Jonathan McPherson [aut], RStudio [cph], jQuery Foundation [cph] (jQuery library and jQuery UI library), jQuery contributors [ctb, cph] (jQuery library; authors listed in inst/www/shared/jquery-AUTHORS.txt), jQuery UI contributors [ctb, cph] (jQuery UI library; authors listed in inst/www/shared/jqueryui/AUTHORS.txt), Mark Otto [ctb] (Bootstrap library), Jacob Thornton [ctb] (Bootstrap library), Bootstrap contributors [ctb] (Bootstrap library), Twitter, Inc [cph] (Bootstrap library), Alexander Farkas [ctb, cph] (html5shiv library), Scott Jehl [ctb, cph] (Respond.js library), Stefan Petre [ctb, cph] (Bootstrap-datepicker library), Andrew Rowls [ctb, cph] (Bootstrap-datepicker library), Dave Gandy [ctb, cph] (Font-Awesome font), Brian Reavis [ctb, cph] (selectize.js library), Kristopher Michael Kowal [ctb, cph] (es5-shim library), es5-shim contributors [ctb, cph] (es5-shim library), Denis Ineshin [ctb, cph] (ion.rangeSlider library), Sami Samhuri [ctb, cph] (Javascript strftime library), SpryMedia Limited [ctb, cph] (DataTables library), John Fraser [ctb, cph] (showdown.js library), John Gruber [ctb, cph] (showdown.js library), Ivan Sagalaev [ctb, cph] (highlight.js library), R Core Team [ctb, cph] (tar implementation from R)
Maintainer: Winston Chang <winston@rstudio.com>

Diff between shiny versions 1.3.0 dated 2019-04-07 and 1.3.1 dated 2019-04-12

 DESCRIPTION                  |    6 +++---
 MD5                          |   10 +++++-----
 NEWS.md                      |   20 +++++++++++++++++++-
 R/graph.R                    |    7 ++++++-
 inst/www/shared/shiny.js     |    2 +-
 inst/www/shared/shiny.min.js |    4 ++--
 6 files changed, 36 insertions(+), 13 deletions(-)

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Package reticulate updated to version 1.12 with previous version 1.11.1 dated 2019-03-06

Title: Interface to 'Python'
Description: Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7.
Author: Kevin Ushey [aut, cre], JJ Allaire [aut], RStudio [cph, fnd], Yuan Tang [aut, cph] (<https://orcid.org/0000-0001-5243-233X>), Dirk Eddelbuettel [ctb, cph], Bryan Lewis [ctb, cph], Marcus Geelnard [ctb, cph] (TinyThread library, http://tinythreadpp.bitsnbites.eu/)
Maintainer: Kevin Ushey <kevin@rstudio.com>

Diff between reticulate versions 1.11.1 dated 2019-03-06 and 1.12 dated 2019-04-12

 DESCRIPTION                                          |   16 +-
 MD5                                                  |   54 ++++----
 NEWS.md                                              |   19 ++
 R/conda.R                                            |  122 ++++++++++++++++---
 R/config.R                                           |   60 ++++++++-
 R/conversion.R                                       |   16 +-
 R/install.R                                          |    7 -
 R/package.R                                          |   54 ++------
 R/use_python.R                                       |   51 ++++---
 R/utils.R                                            |   37 +++++
 R/virtualenv.R                                       |   74 ++++++++---
 build/vignette.rds                                   |binary
 inst/python/rpytools/__pycache__                     |only
 inst/python/rpytools/output.pyc                      |binary
 inst/python/rpytools/test.pyc                        |binary
 inst/python/rpytools/thread.pyc                      |binary
 man/conda-tools.Rd                                   |    6 
 man/py_install.Rd                                    |    4 
 man/use_python.Rd                                    |    4 
 man/virtualenv-tools.Rd                              |   13 +-
 tests/testthat/resources/eng-reticulate-example.Rmd  |    1 
 tests/testthat/resources/eng-reticulate-example.html |   19 +-
 tests/testthat/test-help-handlers.R                  |    9 -
 tests/testthat/test-python-datetime.R                |   17 +-
 tests/testthat/test-python-knitr-engine.R            |    1 
 tests/testthat/test-python-pandas.R                  |    9 +
 26 files changed, 416 insertions(+), 177 deletions(-)

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Package postal (with last version 0.1.1) was removed from CRAN

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

2019-02-02 0.1.1
2018-07-27 0.1.0

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