Sat, 01 Aug 2020

Package ribd updated to version 1.2.0 with previous version 1.1.0 dated 2020-06-19

Title: Pedigree-based Relatedness Coefficients
Description: Recursive algorithms for computing various relatedness coefficients, including pairwise kinship, kappa and identity coefficients. Both autosomal and X-linked coefficients are computed. Founders are allowed to be inbred, enabling construction of any given kappa coefficients (Vigeland (2020) <doi:10.1007/s00285-020-01505-x>). In addition to the standard pairwise coefficients, ribd also computes a range of lesser-known coefficients, including generalised kinship coefficients (Karigl (1981) <doi:10.1111/j.1469-1809.1981.tb00341.x>; Weeks and Lange (1988) <https:www.ncbi.nlm.nih.gov/pmc/articles/PMC1715269>), two-locus coefficients (Thompson (1988) <doi:10.1093/imammb/5.4.261>) and multi-person coefficients. This package is part of the ped suite, a collection of packages for pedigree analysis in R.
Author: Magnus Dehli Vigeland [aut, cre] (<https://orcid.org/0000-0002-9134-4962>)
Maintainer: Magnus Dehli Vigeland <m.d.vigeland@medisin.uio.no>

Diff between ribd versions 1.1.0 dated 2020-06-19 and 1.2.0 dated 2020-08-01

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Package rdwd updated to version 1.4.0 with previous version 1.3.1 dated 2020-02-18

Title: Select and Download Climate Data from 'DWD' (German Weather Service)
Description: Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see <https://www.dwd.de/EN/climate_environment/cdc/cdc.html> for more information). Choose observational time series from meteorological stations with 'selectDWD()'. Find raster data from radar and interpolation according to <https://bookdown.org/brry/rdwd/raster-data.html>. Download (multiple) data sets with progress bars and no re-downloads through 'dataDWD()'. Read both tabular observational data and binary gridded datasets with 'readDWD()'.
Author: Berry Boessenkool
Maintainer: Berry Boessenkool <berry-b@gmx.de>

Diff between rdwd versions 1.3.1 dated 2020-02-18 and 1.4.0 dated 2020-08-01

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New package ldsep with initial version 1.0.0
Package: ldsep
Title: Linkage Disequilibrium Shrinkage Estimation for Polyploids
Version: 1.0.0
Authors@R: person(given = "David", family = "Gerard", role = c("aut", "cre"), email = "gerard.1787@gmail.com", comment = c(ORCID = "0000-0001-9450-5023"))
Description: Estimate gametic or composite pairwise linkage disequilibrium (LD) in polyploids, using either genotypes or genotype likelihoods. Support is provided to estimate the popular measures of LD: the LD coefficient D, the standardized LD coefficient D', and the Pearson correlation coefficient r. All estimates are returned with corresponding standard errors. These estimates and standard errors can then be used for shrinkage estimation. The main functions are ldest(), mldest(), sldest(), plot.lddf(), format_lddf(), and ldshrink().
License: GPL-3
BugReports: https://github.com/dcgerard/ldsep/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, foreach, doParallel, ashr, corrplot, lpSolve
Suggests: testthat, covr, knitr, rmarkdown, updog (>= 2.0.2)
Depends: R (>= 2.10)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-07-26 15:44:25 UTC; dgerard
Author: David Gerard [aut, cre] (<https://orcid.org/0000-0001-9450-5023>)
Maintainer: David Gerard <gerard.1787@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-02 01:20:03 UTC

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Package GMDHreg updated to version 0.2.1 with previous version 0.2.0 dated 2019-07-26

Title: Regression using GMDH Algorithms
Description: Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko. Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons. An introduction of GMDH algorithms: Farlow, S.J. (1981): "The GMDH algorithm of Ivakhnenko", The American Statistician, 35(4), pp. 210-215. <doi:10.2307/2683292> Ivakhnenko A.G. (1968): "The Group Method of Data Handling - A Rival of the Method of Stochastic Approximation", Soviet Automatic Control, 13(3), pp. 43-55.
Author: Manuel Villacorta Tilve
Maintainer: Manuel Villacorta Tilve <mvt.oviedo@gmail.com>

Diff between GMDHreg versions 0.2.0 dated 2019-07-26 and 0.2.1 dated 2020-08-01

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New package iemisc with initial version 0.9.9
Package: iemisc
Title: Irucka Embry's Miscellaneous Functions
Version: 0.9.9
Author: Irucka Embry [aut, cre], Felix Andrews [aut, ctb] (zoo code), Hans Werner Borchers [aut, ctb] (pracma code), Samit Basu [aut, ctb] (FreeMat code), David Bateman [aut, ctb] (GNU Octave code), Rik Wehbring [aut, ctb] (GNU Octave code)
Maintainer: Irucka Embry <iembry@ecoccs.com>
Depends: R (>= 3.0.0)
Imports: zoo, pracma, iemiscdata, import, gsubfn (>= 0.7), fpCompare, units, CHNOSZ, testit, stringi, rgdal, sp, stats, data.table (>= 1.10.2)
Suggests: install.load, ie2misc, Rcpp (>= 0.11.5), NISTunits, optiRum, rivr, knitr, rmarkdown, ramify
Description: A collection of Irucka Embry's miscellaneous functions (Engineering Economics, Civil & Environmental/Water Resources Engineering, Geometry, Statistics, GNU Octave length functions, Trigonometric functions in degrees, etc.).
URL: https://gitlab.com/iembry/iemisc
BugReports: https://gitlab.com/iembry/iemisc/issues
License: GPL (>= 3) | file LICENSE
Language: en-US
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
RoxygenNote: 7.1.1
Packaged: 2020-07-31 03:05:08 UTC; xbyri
Repository: CRAN
Date/Publication: 2020-08-02 01:30:02 UTC

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Package fabricerin updated to version 0.1.1 with previous version 0.1.0 dated 2020-08-01

Title: Create Easily Canvas in 'shiny' and 'RMarkdown' Documents
Description: Allows the user to implement easily canvas elements within a 'shiny' app or an 'RMarkdown' document. The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes. The package relies on the 'fabricjs' 'JavaScript' library. See <http://fabricjs.com/>.
Author: Mohamed El Fodil Ihaddaden [aut, cre], fabricjs contributors [ctb, cph] (fabricjs JavaScript library), jQuery contributors [ctb, cph] (jQuery JavaScript library), FileSaver.js contributors [ctb, cph] (FileSaver JavaScript library)
Maintainer: Mohamed El Fodil Ihaddaden <ihaddaden.fodeil@gmail.com>

Diff between fabricerin versions 0.1.0 dated 2020-08-01 and 0.1.1 dated 2020-08-01

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Package ctrdata updated to version 1.3.1 with previous version 1.3.0 dated 2020-07-27

Title: Retrieve and Analyze Clinical Trials in Public Registers
Description: Provides functions for querying, retrieving and analysing protocol- and results-related information on clinical trials from two public registers, the European Union Clinical Trials Register (EUCTR, <https://www.clinicaltrialsregister.eu/>) and ClinicalTrials.gov (CTGOV, <https://clinicaltrials.gov/>). The information is transformed and then stored in a database (nodbi). Functions are provided for accessing and analysing the locally stored information on the clinical trials, as well as for identifying duplicate records. The package is motivated by the need for aggregating and trend-analysing the design, conduct and outcomes across clinical trials.
Author: Ralf Herold [aut, cre] (<https://orcid.org/0000-0002-8148-6748>)
Maintainer: Ralf Herold <ralf.herold@mailbox.org>

Diff between ctrdata versions 1.3.0 dated 2020-07-27 and 1.3.1 dated 2020-08-01

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Package CropScapeR updated to version 1.1.1 with previous version 1.1.0 dated 2020-08-01

Title: Access Cropland Data Layer Data via the 'CropScape' Web Service
Description: Interface to easily access Cropland Data Layer (CDL) data for any area of interest via the 'CropScape' <https://nassgeodata.gmu.edu/CropScape/> web service.
Author: Bowen Chen [aut, cre] (<https://orcid.org/0000-0003-0370-2756>), Benjamin Gramig [ctb], Taro Mieno [ctb]
Maintainer: Bowen Chen <bwchen0719@gmail.com>

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New package coxed with initial version 0.3.3
Package: coxed
Type: Package
Title: Duration-Based Quantities of Interest for the Cox Proportional Hazards Model
Version: 0.3.3
Depends: R (>= 3.5.0), rms, survival, mgcv
Authors@R: c( person("Kropko,", "Jonathan", email = "jkropko@virginia.edu", role = c("aut", "cre")), person("Harden,", "Jeffrey J.", email = "jeff.harden@nd.edu", role = c("aut")))
Description: Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model as described in Kropko and Harden (2017) <doi:10.1017/S000712341700045X> and Harden and Kropko (2018) <doi:10.1017/psrm.2018.19>.
License: GPL-2
Encoding: UTF-8
URL: https://github.com/jkropko/coxed
LazyData: true
Imports: PermAlgo, dplyr, tidyr, ggplot2, gridExtra, mediation, utils
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown, bindrcpp
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-08-01 16:31:01 UTC; jk8sd
Author: Kropko, Jonathan [aut, cre], Harden, Jeffrey J. [aut]
Maintainer: "Kropko, Jonathan" <jkropko@virginia.edu>
Repository: CRAN
Date/Publication: 2020-08-02 01:20:07 UTC

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Package rdlocrand updated to version 0.7 with previous version 0.6 dated 2020-05-12

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

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Package gausscov updated to version 0.0.4 with previous version 0.0.3 dated 2020-02-13

Title: The Gaussian Covariate Method for Variable Selection
Description: Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets. Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", 2020, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633>.
Author: Laurie Davies [aut, cre]
Maintainer: Laurie Davies <laurie.davies@uni-due.de>

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More information about gausscov at CRAN
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Package precautionary updated to version 0.1-2 with previous version 0.1-1 dated 2020-07-20

Title: Safety Diagnostics for Dose-Escalation Trial Designs
Description: Enhances various R packages that support the design and simulation of phase 1 dose-escalation trials, adding diagnostics to examine the safety characteristics of these designs in light of expected inter-individual variation in pharmacokinetics and pharmacodynamics. See Norris (2020), "Retrospective analysis of a fatal dose-finding trial" <arXiv:2004.12755>.
Author: David C. Norris [aut, cre, cph] (<https://orcid.org/0000-0001-9593-6343>)
Maintainer: David C. Norris <david@precisionmethods.guru>

Diff between precautionary versions 0.1-1 dated 2020-07-20 and 0.1-2 dated 2020-08-01

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Permanent link

New package tboot with initial version 0.2.0
Package: tboot
Title: Tilted Bootstrap
Version: 0.2.0
Authors@R: c( person( family = "Morris", given = "Nathan", email = "morris_nathan@lilly.com", role = c("aut", "cre")), person( family = "Landau", given = c("William", "Michael"), email = "will.landau@lilly.com", role = c("ctb")), person( family = "Eli Lilly and Company", role = "cph"))
Description: Creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.
License: GPL-3
Depends: R (>= 3.4.0)
Imports: stats, quadprog, kernlab
Suggests: knitr, rmarkdown, testthat, MASS, ggplot2
VignetteBuilder: knitr
URL: https://github.com/njm18/tboot
BugReports: https://github.com/njm18/tboot/issues
RoxygenNote: 7.0.2
NeedsCompilation: no
Packaged: 2020-07-27 17:43:23 UTC; c243080
Author: Nathan Morris [aut, cre], William Michael Landau [ctb], Eli Lilly and Company [cph]
Maintainer: Nathan Morris <morris_nathan@lilly.com>
Repository: CRAN
Date/Publication: 2020-08-01 11:10:02 UTC

More information about tboot at CRAN
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New package image.CannyEdges with initial version 0.1.0
Package: image.CannyEdges
Type: Package
Title: Implementation of the Canny Edge Detector for Images
Version: 0.1.0
Authors@R: c( person("Jan", "Wijffels", role = c("aut", "cre", "cph"), email = "jwijffels@bnosac.be"), person("BNOSAC", role = "cph"), person("Vincent Maioli", role = c("ctb", "cph"), email = "maioli@crans.org"), person("IPOL Image Processing On Line", role = "cph"))
Maintainer: Jan Wijffels <jwijffels@bnosac.be>
Description: An implementation of the Canny Edge Detector for detecting edges in images. The package provides an interface to the algorithm available at <https://github.com/Neseb/canny>.
License: GPL-3
URL: https://github.com/bnosac/image
Encoding: UTF-8
Imports: Rcpp (>= 0.12.9)
LinkingTo: Rcpp
Suggests: pixmap, magick
RoxygenNote: 7.1.0
SystemRequirements: libpng, fftw3
NeedsCompilation: yes
Packaged: 2020-07-27 13:20:52 UTC; Jan
Author: Jan Wijffels [aut, cre, cph], BNOSAC [cph], Vincent Maioli [ctb, cph], IPOL Image Processing On Line [cph]
Repository: CRAN
Date/Publication: 2020-08-01 11:00:02 UTC

More information about image.CannyEdges at CRAN
Permanent link

New package bspm with initial version 0.3.3
Package: bspm
Type: Package
Title: Bridge to System Package Manager
Version: 0.3.3
Authors@R: c( person("Iñaki", "Ucar", email="iucar@fedoraproject.org", role=c("aut", "cph", "cre"), comment=c(ORCID="0000-0001-6403-5550")))
Description: Enables binary package installations on Linux distributions. Provides functions to manage packages via the distribution's package manager. Also provides transparent integration with R's install.packages() and a fallback mechanism. When installed as a system package, interacts with the system's package manager without requiring administrative privileges via an integrated D-Bus service; otherwise, uses sudo. Currently, the following backends are supported: DNF, APT.
License: MIT + file LICENSE
Encoding: UTF-8
OS_type: unix
SystemRequirements: systemd, python3-dbus, python3-(gobject|gi), python3-(dnf|apt)
Suggests: tinytest
URL: https://github.com/Enchufa2/bspm
BugReports: https://github.com/Enchufa2/bspm/issues
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-07-27 16:24:59 UTC; iucar
Author: Iñaki Ucar [aut, cph, cre] (<https://orcid.org/0000-0001-6403-5550>)
Maintainer: Iñaki Ucar <iucar@fedoraproject.org>
Repository: CRAN
Date/Publication: 2020-08-01 11:10:05 UTC

More information about bspm at CRAN
Permanent link

New package base.rms with initial version 1.0
Package: base.rms
Title: Convert Regression Between Base Function and 'rms' Package
Version: 1.0
Authors@R: c( person("Jing","Zhang",role = c("aut", "cre"),email = "zj391120@163.com"), person("Zhi", "Jin", role="aut") )
Description: We perform linear, logistic, and cox regression using the base functions lm(), glm(), and coxph() in the R software and the 'survival' package. Likewise, we can use ols(), lrm() and cph() from the 'rms' package for the same functionality. Each of these two sets of commands has a different focus. In many cases, we need to use both sets of commands in the same situation, e.g. we need to filter the full subset model using AIC, and we need to build a visualization graph for the final model. 'base.rms' package can help you to switch between the two sets of commands easily.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: rms, survival, do, splines, stats
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-07-27 13:36:47 UTC; asus
Author: Jing Zhang [aut, cre], Zhi Jin [aut]
Maintainer: Jing Zhang <zj391120@163.com>
Repository: CRAN
Date/Publication: 2020-08-01 11:10:08 UTC

More information about base.rms at CRAN
Permanent link

Package actel updated to version 1.1.0 with previous version 1.0.0 dated 2020-06-13

Title: Acoustic Telemetry Data Analysis
Description: Designed for studies where fish tagged with acoustic tags are expected to move through receiver arrays. This package combines the advantages of automatic sorting and checking of fish movements with the possibility for user intervention on tags that deviate from expected behaviour. The three analysis functions (explore(), migration() and residency()) allow the users to analyse their data in a systematic way, making it easy to compare results from different studies. CJS calculations are based on Perry et al. (2012) <https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>.
Author: Hugo Flávio [aut, cre] (<https://orcid.org/0000-0002-5174-1197>)
Maintainer: Hugo Flávio <hdmfla@aqua.dtu.dk>

Diff between actel versions 1.0.0 dated 2020-06-13 and 1.1.0 dated 2020-08-01

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New package PortfolioAnalysis with initial version 1.0.6
Package: PortfolioAnalysis
Title: Portfolio Optimization Methods
Version: 1.0.6
Authors@R: person(given = "Anurag", family = "Agrawal", role = c("aut", "cre"), email = "agrawalanurag1999@gmail.com", comment = c(ORCID = "0000-0003-2272-8273"))
Description: Collection of functions to optimize portfolio weights using quadratic programming. This package includes different functions to compute portfolio weights based on different constraints and methods. For more information see Markowitz, H.M. (1952), <doi:10.2307/2975974>. "Analysis of Investments & Management of Portfolios" [2012, ISBN:978-8131518748].
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: PerformanceAnalytics, stringr, stringi, plotly, ggplot2, purrr, rvest, quantmod, rMorningStar, quadprog, dplyr, xts, lubridate, readr, tidyr, xml2
NeedsCompilation: no
Packaged: 2020-07-26 19:43:43 UTC; Kanhaiiya Agrawal
Author: Anurag Agrawal [aut, cre] (<https://orcid.org/0000-0003-2272-8273>)
Maintainer: Anurag Agrawal <agrawalanurag1999@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-01 10:30:02 UTC

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Package DiallelAnalysisR updated to version 0.3.0 with previous version 0.1.1 dated 2016-04-08

Title: Diallel Analysis with R
Description: Performs Diallel Analysis with R using Griffing's and Hayman's approaches. Four different Methods (1: Method-I (Parents + F1's + reciprocals); 2: Method-II (Parents and one set of F1's); 3: Method-III (One set of F1's and reciprocals); 4: Method-IV (One set of F1's only)) and two Models (1: Fixed Effects Model; 2: Random Effects Model) can be applied using Griffing's approach.
Author: Muhammad Yaseen [aut, cre, cph] (<https://orcid.org/0000-0002-5923-1714>), Kent M. Eskridge [ctb], Pedro A. M. Barbosa [ctb]
Maintainer: Muhammad Yaseen <myaseen208@gmail.com>

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New package waveformlidar with initial version 1.2.0
Package: waveformlidar
Type: Package
Title: Waveform LiDAR Data Processing and Analysis
Version: 1.2.0
Date: 2020-07-25
Authors@R: person("Tan", "Zhou", email = "tankchow12@gmail.com", role = c("aut", "cre"))
Depends: R (>= 4.0.0)
Maintainer: Tan Zhou <tankchow12@gmail.com>
Imports: data.table, caTools, minpack.lm, flux, raster, rgdal, sp, rgeos, sqldf, splitstackshape, reshape2
Suggests: knitr, rPeaks
Additional_repositories: https://tankwin08.github.io/drat
Description: A wealth of Full Waveform (FW) Light Detection and Ranging (LiDAR) data are available to the public from different sources, which is poised to boost the extensive application of FW LiDAR data. However, we lack a handy and open source tool that can be used by ecological and remote sensing communities for processing and analyzing FW LiDAR data. To this end, we introduce 'waveformlidar', an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, adaptive Gaussian and Weibull decompositions, and deconvolution approaches (Gold and Richard-Lucy (RL)) with customized settings. In addition, we also develop some functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed through converting waveforms into points to form the Hyper Point cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as 'LAStools' and 'FUSION'. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data, but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. References: Zhou et al. (2017a) <doi:10.1016/j.isprsjprs.2017.04.021>; Zhou et al. (2017b) <doi:10.1016/j.rse.2017.08.012>; Zhou et al. (2018a) <doi:10.3390/rs10010039>;Zhou et al. (2018b) <doi:10.3390/rs10121949>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
URL: https://github.com/tankwin08/waveformlidar,
BugReports: https://github.com/tankwin08/waveformlidar/issues
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2020-07-25 22:07:03 UTC; tank
Author: Tan Zhou [aut, cre]
Repository: CRAN
Date/Publication: 2020-08-01 09:20:03 UTC

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New package rocTree with initial version 1.1.1
Package: rocTree
Title: Receiver Operating Characteristic (ROC)-Guided Classification and Survival Tree
Version: 1.1.1
Authors@R: c(person("Yifei", "Sun", email = "ys3072@cumc.columbia.edu", role = "aut"), person("Mei-Cheng", "Wang", email = "mcwang@jhu.edu", role = "aut"), person("Sy Han", "Chiou", email = "schiou@utdallas.edu", role = c("aut", "cre")))
Description: Receiver Operating Characteristic (ROC)-guided survival trees and ensemble algorithms are implemented, providing a unified framework for tree-structured analysis with censored survival outcomes. A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) <arXiv:1809.05627>.
Depends: R (>= 3.5.0)
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
LinkingTo: Rcpp, RcppArmadillo
Imports: DiagrammeR (>= 1.0.0), data.tree (>= 0.7.5), graphics, stats, survival (>= 2.38), ggplot2, MASS, flexsurv, Rcpp
URL: http://github.com/stc04003/rocTree
BugReports: http://github.com/stc04003/rocTree/issues
NeedsCompilation: yes
Packaged: 2020-07-26 03:44:23 UTC; schiou
Author: Yifei Sun [aut], Mei-Cheng Wang [aut], Sy Han Chiou [aut, cre]
Maintainer: Sy Han Chiou <schiou@utdallas.edu>
Repository: CRAN
Date/Publication: 2020-08-01 09:40:02 UTC

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New package mhazard with initial version 0.1.2
Package: mhazard
Title: Multivariate Survival Function Estimation and Regression
Version: 0.1.2
Authors@R: c(person(given = "Eric", family = "Bair", role = c("aut", "cre"), email = "eric.bair@sciome.com"), person(given = "Shanshan", family = "Zhao", role = "aut"))
Description: Estimates the survival function and Cox regression parameters for the multivariate survival setting where there are multiple (right-censored) outcome variables. The Volterra, Dabrowska, and Prentice-Cai estimates of the bivariate survival function may be computed as well as the Dabrowska estimate of the trivariate survival function. Bivariate Cox regression estimates can also be computed. Functions are also provided to compute (bootstrap) confidence intervals and plot the estimates of the bivariate survival function. For details, see "The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, boot, plot3D, survival, rootSolve
NeedsCompilation: yes
Packaged: 2020-07-26 00:53:40 UTC; ebair
Author: Eric Bair [aut, cre], Shanshan Zhao [aut]
Maintainer: Eric Bair <eric.bair@sciome.com>
Repository: CRAN
Date/Publication: 2020-08-01 09:40:06 UTC

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New package fabricerin with initial version 0.1.0
Package: fabricerin
Title: Create Easily Canvas in 'shiny' and 'RMarkdown' Documents
Version: 0.1.0
Authors@R: c( person("Mohamed El Fodil", "Ihaddaden", email = "ihaddaden.fodeil@gmail.com", role = c("aut", "cre")), person("fabricjs contributors", role = c("ctb", "cph"), comment = "fabricjs JavaScript library"))
Description: Allows the user to implement easily canvas elements within a 'shiny' app or an 'RMarkdown' document. The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes. The package relies on the 'fabricjs' 'JavaScript' library. See <http://fabricjs.com/>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: htmltools, glue
NeedsCompilation: no
Packaged: 2020-07-26 00:53:08 UTC; Administrateur
Author: Mohamed El Fodil Ihaddaden [aut, cre], fabricjs contributors [ctb, cph] (fabricjs JavaScript library)
Maintainer: Mohamed El Fodil Ihaddaden <ihaddaden.fodeil@gmail.com>
Repository: CRAN
Date/Publication: 2020-08-01 09:40:09 UTC

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Package dmbc updated to version 0.4.0 with previous version 0.3.0 dated 2020-06-12

Title: Model Based Clustering of Binary Dissimilarity Measurements
Description: Functions for fitting a Bayesian model for grouping binary dissimilarity matrices in homogeneous clusters. Currently, it includes methods only for binary data.
Author: Sergio Venturini [aut, cre], Raffaella Piccarreta [ctb]
Maintainer: Sergio Venturini <sergio.venturini@unito.it>

Diff between dmbc versions 0.3.0 dated 2020-06-12 and 0.4.0 dated 2020-08-01

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Package NMF updated to version 0.23.0 with previous version 0.22.0 dated 2020-02-12

Title: Algorithms and Framework for Nonnegative Matrix Factorization (NMF)
Description: Provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines.
Author: Renaud Gaujoux, Cathal Seoighe
Maintainer: Nicolas Sauwen <nicolas.sauwen@openanalytics.eu>

Diff between NMF versions 0.22.0 dated 2020-02-12 and 0.23.0 dated 2020-08-01

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Package ggnewscale updated to version 0.4.2 with previous version 0.4.1 dated 2020-02-13

Title: Multiple Fill and Colour Scales in 'ggplot2'
Description: Use multiple fill and colour scales in 'ggplot2'.
Author: Elio Campitelli [cre, aut] (<https://orcid.org/0000-0002-7742-9230>)
Maintainer: Elio Campitelli <elio.campitelli@cima.fcen.uba.ar>

Diff between ggnewscale versions 0.4.1 dated 2020-02-13 and 0.4.2 dated 2020-08-01

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Package geouy updated to version 0.2.2 with previous version 0.2.1 dated 2020-06-03

Title: Geographic Information of Uruguay
Description: The toolbox have functions to load and process geographic information for Uruguay. And extra-function to get address coordinates and orthophotos through the uruguayan 'IDE' API <https://www.gub.uy/infraestructura-datos-espaciales/tramites-y-servicios/servicios/servicio-direcciones-geograficas>.
Author: Richard Detomasi [aut, cre] (<https://orcid.org/0000-0002-6725-0261>), Ministerio de Desarrollo Social, Uruguay (MIDES) [dtc], Infraestrutura de Datos Espaciales, Uruguay (IDE) [dtc], Servicio Geográfico Militar, Uruguay (SGM) [dtc], Ministerio de Transporte y Obras Públicas, Uruguay (MTOP) [dtc], Instituto Nacional de Estadística, Uruguay (INE) [dtc], Instituto Geográfico Militar, Uruguay (IGM) [dtc], Ministerio de Vivienda, Ordenamiento Territorial y Medio Ambiente, Uruguay (MVOTMA) [dtc]
Maintainer: Richard Detomasi <richard.detomasi@gmail.com>

Diff between geouy versions 0.2.1 dated 2020-06-03 and 0.2.2 dated 2020-08-01

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Package DALEX updated to version 1.3.1.1 with previous version 1.3.0 dated 2020-07-02

Title: moDel Agnostic Language for Exploration and eXplanation
Description: Unverified black box model is the path to the failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack of direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) <arXiv:1806.08915>.
Author: Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>), Szymon Maksymiuk [aut], Hubert Baniecki [aut] (<https://orcid.org/0000-0001-6661-5364>)
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>

Diff between DALEX versions 1.3.0 dated 2020-07-02 and 1.3.1.1 dated 2020-08-01

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Package coxed (with last version 0.3.2) was removed from CRAN

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

2020-01-10 0.3.2

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Package survxai (with last version 0.2.0) was removed from CRAN

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

2018-08-24 0.2.0

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Package splithalfr updated to version 2.0.12 with previous version 1.0.10 dated 2019-06-07

Title: Extensible Bootstrapped Split-Half Reliabilities
Description: Estimates split-half reliabilities for scoring algorithms of reaction time (RT) tasks and questionnaires. The 'splithalfr' supports user-provided scoring algorithms. It provides four splitting techniques, split stratification, a number of reliability coefficients, and the option to sub-sample data.
Author: Thomas Pronk [aut, cre]
Maintainer: Thomas Pronk <pronkthomas@gmail.com>

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More information about splithalfr at CRAN
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Package CropScapeR updated to version 1.1.0 with previous version 1.0.1 dated 2020-07-05

Title: Access Cropland Data Layer Data via the 'CropScape' Web Service
Description: Interface to easily access Cropland Data Layer (CDL) data for any area of interest via the 'CropScape' <https://nassgeodata.gmu.edu/CropScape/> web service.
Author: Bowen Chen [aut, cre] (<https://orcid.org/0000-0003-0370-2756>), Benjamin Gramig [ctb], Taro Mieno [ctb]
Maintainer: Bowen Chen <bwchen0719@gmail.com>

Diff between CropScapeR versions 1.0.1 dated 2020-07-05 and 1.1.0 dated 2020-08-01

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More information about CropScapeR at CRAN
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