Fri, 22 Jan 2021

Package TestDesign updated to version 1.2.0 with previous version 1.1.3 dated 2020-10-01

Title: Optimal Test Design Approach to Fixed and Adaptive Test Construction
Description: Use the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and van der Linden (2018) <doi:10.1201/9781315117430> in constructing fixed and adaptive tests. Supports the following mixed-integer programming (MIP) solver packages: 'lpsymphony', 'Rsymphony', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads/>. See vignette for installing 'Rsymphony' package on Mac systems.
Author: Seung W. Choi [aut, cre] (<https://orcid.org/0000-0003-4777-5420>), Sangdon Lim [aut] (<https://orcid.org/0000-0002-2988-014X>)
Maintainer: Seung W. Choi <schoi@austin.utexas.edu>

Diff between TestDesign versions 1.1.3 dated 2020-10-01 and 1.2.0 dated 2021-01-22

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More information about TestDesign at CRAN
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Package StrathE2E2 updated to version 3.3.0 with previous version 3.2.0 dated 2020-09-02

Title: End-to-End Marine Food Web Model
Description: A dynamic model of the big-picture, whole ecosystem effects of hydrodynamics, temperature, nutrients, and fishing on continental shelf marine food webs. The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) <doi:10.1111/2041-210X.13510> StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp.
Author: Michael Heath [aut], Ian Thurlbeck [ctb]
Maintainer: Michael Heath <m.heath@strath.ac.uk>

Diff between StrathE2E2 versions 3.2.0 dated 2020-09-02 and 3.3.0 dated 2021-01-22

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 StrathE2E2-3.3.0/StrathE2E2/DESCRIPTION                                                                            |   20 
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 StrathE2E2-3.3.0/StrathE2E2/NAMESPACE                                                                              |    1 
 StrathE2E2-3.3.0/StrathE2E2/NEWS.md                                                                                |   21 
 StrathE2E2-3.3.0/StrathE2E2/R/StrathE2E2.R                                                                         |   21 
 StrathE2E2-3.3.0/StrathE2E2/R/derive_annual_results_inshore.R                                                      |    2 
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 StrathE2E2-3.3.0/StrathE2E2/R/derive_model_target_results.R                                                        |    6 
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 StrathE2E2-3.3.0/StrathE2E2/R/e2e_run_ycurve.R                                                                     |    2 
 StrathE2E2-3.3.0/StrathE2E2/R/generate_df_yield_curve_data.R                                                       |    2 
 StrathE2E2-3.3.0/StrathE2E2/R/generate_pf_yield_curve_data.R                                                       |    2 
 StrathE2E2-3.3.0/StrathE2E2/R/internal.R                                                                           |    2 
 StrathE2E2-3.3.0/StrathE2E2/R/perturb_parameters.R                                                                 |    2 
 StrathE2E2-3.3.0/StrathE2E2/R/perturb_parameters_all.R                                                             |    6 
 StrathE2E2-3.3.0/StrathE2E2/R/plot_act_hr_optimization_diagnostics.R                                               |    2 
 StrathE2E2-3.3.0/StrathE2E2/R/plot_eco_hr_act_eco_optimization_diagnostics.R                                       |    2 
 StrathE2E2-3.3.0/StrathE2E2/R/plot_inshore_vs_offshore_anavmass.R                                                  |    6 
 StrathE2E2-3.3.0/StrathE2E2/R/plot_sensitivity_results.R                                                           |   23 
 StrathE2E2-3.3.0/StrathE2E2/R/process_sensitivity_analysis_results.R                                               |   20 
 StrathE2E2-3.3.0/StrathE2E2/README.md                                                                              |    8 
 StrathE2E2-3.3.0/StrathE2E2/inst/CITATION                                                                          |    8 
 StrathE2E2-3.3.0/StrathE2E2/inst/doc/StrathE2E2_CheatSheet.pdf                                                     |binary
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 StrathE2E2-3.3.0/StrathE2E2/man/StrathE2E2-package.Rd                                                              |   21 
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 StrathE2E2-3.3.0/StrathE2E2/man/e2e_plot_opt_diagnostics.Rd                                                        |    2 
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 StrathE2E2-3.3.0/StrathE2E2/man/e2e_run_ycurve.Rd                                                                  |    2 
 StrathE2E2-3.3.0/StrathE2E2/src/StrathE2E2_ecology_model_function.c                                                |  566 +++++++---
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 StrathE2E2-3.3.0/StrathE2E2/tests/testthat/test-for-steady-state-in-testbed-model.R                                |    2 
 StrathE2E2-3.3.0/StrathE2E2/tests/testthat/test-top-predator-fluxes.R                                              |  120 +-
 StrathE2E2-3.3.0/StrathE2E2/vignettes/CheatSheet.pdf                                                               |binary
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Package RZooRoH updated to version 0.3.0 with previous version 0.2.4 dated 2021-01-20

Title: Partitioning of Individual Autozygosity into Multiple Homozygous-by-Descent Classes
Description: Functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017) <doi:10.1111/mec.14324>.
Author: Tom Druet, Naveen Kumar Kadri, Amandine Bertrand and Mathieu Gautier
Maintainer: Tom Druet <tom.druet@uliege.be>

Diff between RZooRoH versions 0.2.4 dated 2021-01-20 and 0.3.0 dated 2021-01-22

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Package PhysicalActivity updated to version 0.2-4 with previous version 0.2-2 dated 2018-05-18

Title: Process Accelerometer Data for Physical Activity Measurement
Description: It provides a function "wearingMarking" for classification of monitor wear and nonwear time intervals in accelerometer data collected to assess physical activity. The package also contains functions for making plot for accelerometer data and obtaining the summary of various information including daily monitor wear time and the mean monitor wear time during valid days. "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail; "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete days and removing low activity days; "markPAI" can categorize physical activity intensity level based on user-defined cut-points of accelerometer counts. It also supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions.
Author: Leena Choi [aut, cre] (<https://orcid.org/0000-0002-2544-7090>), Cole Beck [aut] (<https://orcid.org/0000-0002-6849-6255>), Zhouwen Liu [aut], Ryan Moore [aut], Charles E. Matthews [aut], Maciej S. Buchowski [aut]
Maintainer: Leena Choi <leena.choi@Vanderbilt.Edu>

Diff between PhysicalActivity versions 0.2-2 dated 2018-05-18 and 0.2-4 dated 2021-01-22

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Package htmltools updated to version 0.5.1.1 with previous version 0.5.1 dated 2021-01-12

Title: Tools for HTML
Description: Tools for HTML generation and output.
Author: Joe Cheng [aut], Carson Sievert [aut, cre] (<https://orcid.org/0000-0002-4958-2844>), Winston Chang [aut], Yihui Xie [aut], Jeff Allen [aut], RStudio [cph]
Maintainer: Carson Sievert <carson@rstudio.com>

Diff between htmltools versions 0.5.1 dated 2021-01-12 and 0.5.1.1 dated 2021-01-22

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Package HBV.IANIGLA updated to version 0.2.1 with previous version 0.2.0 dated 2021-01-21

Title: Modular Hydrological Model
Description: The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.
Author: Ezequiel Toum <etoum@mendoza-conicet.gob.ar>
Maintainer: Ezequiel Toum <etoum@mendoza-conicet.gob.ar>

Diff between HBV.IANIGLA versions 0.2.0 dated 2021-01-21 and 0.2.1 dated 2021-01-22

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Package eList updated to version 0.2.0 with previous version 0.0.1.0 dated 2021-01-11

Title: List Comprehension and Tools
Description: Create list comprehensions (and other types of comprehension) similar to those in 'python', 'haskell', and other languages. List comprehension in 'R' converts a regular for() loop into a vectorized lapply() function. Support for looping with multiple variables, parallelization, and across non-standard objects included. Package also contains a variety of functions to help with list comprehension.
Author: Chris Mann <cmann3@unl.edu>
Maintainer: Chris Mann <cmann3@unl.edu>

Diff between eList versions 0.0.1.0 dated 2021-01-11 and 0.2.0 dated 2021-01-22

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Package BAT updated to version 2.4.2 with previous version 2.4.1 dated 2020-12-17

Title: Biodiversity Assessment Tools
Description: Includes algorithms to assess alpha and beta diversity in all their dimensions (taxonomic, phylogenetic and functional). It allows performing a number of analyses based on species identities/abundances, phylogenetic/functional trees, convex-hulls or kernel density n-dimensional hypervolumes depicting species relationships.
Author: Pedro Cardoso, Stefano Mammola, Francois Rigal, Jose Carlos Carvalho
Maintainer: Pedro Cardoso <pedro.cardoso@helsinki.fi>

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Package MiRKAT updated to version 1.1.2 with previous version 1.1.1 dated 2020-08-27

Title: Microbiome Regression-Based Analysis Tests
Description: Test for overall association between microbiome composition data and phenotypes via phylogenetic kernels. The phenotype can be univariate continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). The package can also use robust and quantile regression (Fu et al. (2020+), in preparation). In each case, the microbiome community effect is modeled nonparametrically through a kernel function, which can incorporate phylogenetic tree information.
Author: Anna Plantinga [aut, cre], Nehemiah Wilson [aut, ctb], Haotian Zheng [aut, ctb], Xiang Zhan [aut, ctb], Michael Wu [aut], Ni Zhao [aut, ctb], Jun Chen [aut]
Maintainer: Anna Plantinga <amp9@williams.edu>

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Package statsr updated to version 0.3.0 with previous version 0.2.0 dated 2020-05-05

Title: Companion Software for the Coursera Statistics with R Specialization
Description: Data and functions to support Bayesian and frequentist inference and decision making for the Coursera Specialization "Statistics with R". See <https://github.com/StatsWithR/statsr> for more information.
Author: Colin Rundel [aut], Mine Cetinkaya-Rundel [aut], Merlise Clyde [aut, cre], David Banks [aut]
Maintainer: Merlise Clyde <clyde@duke.edu>

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Package discoveR updated to version 2.1.6 with previous version 1.2.9 dated 2020-08-03

Title: Exploratory Data Analysis System
Description: Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.
Author: Oldemar Rodriguez [aut, cre], Diego Jiménez [ctb]
Maintainer: Oldemar Rodriguez <oldemar.rodriguez@ucr.ac.cr>

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Package scTenifoldKnk updated to version 1.0.1 with previous version 1.0.0 dated 2020-11-23

Title: In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks
Description: A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
Author: Daniel Osorio [aut, cre] (<https://orcid.org/0000-0003-4424-8422>), Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Qian Xu [aut, ctb], Andrew Hillhouse [aut, ctb], Jingshu Chen [aut, ctb], Laurie Davidson [aut, ctb], Yanan Tian [aut, ctb], Robert Chapkin [aut, ctb], Jianhua Huang [aut, ctb], James Cai [aut, ctb, ths] (<https://orcid.org/0000-0002-8081-6725>)
Maintainer: Daniel Osorio <dcosorioh@tamu.edu>

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Package KnockoffScreen updated to version 0.2.0 with previous version 0.1.0 dated 2020-08-11

Title: Whole-Genome Sequencing Data Analysis via Knockoff Statistics
Description: Functions for identification of putative causal loci in whole-genome sequencing data. The functions allow genome-wide association scan. It also includes an efficient knockoff generator for genetic data.
Author: Zihuai He <zihuai@stanford.edu>
Maintainer: Zihuai He <zihuai@stanford.edu>

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Package candisc updated to version 0.8-5 with previous version 0.8-3 dated 2020-04-22

Title: Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
Description: Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative.
Author: Michael Friendly [aut, cre], John Fox [aut]
Maintainer: Michael Friendly <friendly@yorku.ca>

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Package asymmetry updated to version 2.0.3 with previous version 2.0.2 dated 2019-07-10

Title: Multidimensional Scaling of Asymmetric Data
Description: Multidimensional scaling models and methods for the visualization for asymmetric data <doi:10.1111/j.2044-8317.1996.tb01078.x>. An asymmetric matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example is a student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides the slide-vector model <doi:10.1007/BF02294474>, a scaling model with unique dimensions and the asymscal model for asymmetric multidimensional scaling. Furthermore, a heat map for skew-symmetric data, and the decomposition of asymmetry are provided for the analysis of asymmetric tables.
Author: Berrie Zielman
Maintainer: Berrie Zielman <berrie.zielman@gmail.com>

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Package riverplot updated to version 0.10 with previous version 0.6 dated 2017-02-17

Title: Sankey or Ribbon Plots
Description: Sankey plots are a type of diagram that is convenient to illustrate how flow of information, resources etc. separates and joins, much like observing how rivers split and merge. For example, they can be used to compare different clusterings.
Author: January Weiner <january.weiner@gmail.com>
Maintainer: January Weiner <january.weiner@gmail.com>

Diff between riverplot versions 0.6 dated 2017-02-17 and 0.10 dated 2021-01-22

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Package PRIMME updated to version 3.1-4 with previous version 3.1-3 dated 2020-10-25

Title: Eigenvalues and Singular Values and Vectors from Large Matrices
Description: R interface to 'PRIMME' <http://www.cs.wm.edu/~andreas/software/>, a C library for computing a few eigenvalues and their corresponding eigenvectors of a real symmetric or complex Hermitian matrix, or generalized Hermitian eigenproblem. It can also compute singular values and vectors of a square or rectangular matrix. 'PRIMME' finds largest, smallest, or interior singular/eigenvalues and can use preconditioning to accelerate convergence. General description of the methods are provided in the papers Stathopoulos (2010, <doi:10.1145/1731022.1731031>) and Wu (2017, <doi:10.1137/16M1082214>). See 'citation("PRIMME")' for details.
Author: Eloy Romero [aut, cre], Andreas Stathopoulos [aut], Lingfei Wu [aut], College of William & Mary [cph]
Maintainer: Eloy Romero <eloy@cs.wm.edu>

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New package circglmbayes with initial version 1.3.0
Package: circglmbayes
Type: Package
Date: 2021-01-22
Title: Bayesian Analysis of a Circular GLM
Version: 1.3.0
Authors@R: person("Kees", "Mulder", email = "keestimmulder@gmail.com", role = c("aut", "cre"))
Maintainer: Kees Mulder <keestimmulder@gmail.com>
Description: Perform a Bayesian analysis of a circular outcome General Linear Model (GLM), which allows regressing a circular outcome on linear and categorical predictors. Posterior samples are obtained by means of an MCMC algorithm written in 'C++' through 'Rcpp'. Estimation and credible intervals are provided, as well as hypothesis testing through Bayes Factors. See Mulder and Klugkist (2017) <doi:10.1016/j.jmp.2017.07.001>.
License: GPL-3
Encoding: UTF-8
ByteCompile: true
URL: https://github.com/keesmulder/circglmbayes
BugReports: https://github.com/keesmulder/circglmbayes/issues
LazyData: true
Depends: R (>= 2.10)
LinkingTo: Rcpp, BH, RcppArmadillo
Imports: Rcpp, stats, graphics, shiny, grDevices, ggplot2, reshape2, coda
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-01-22 12:26:03 UTC; keest
Author: Kees Mulder [aut, cre]
Repository: CRAN
Date/Publication: 2021-01-22 13:10:02 UTC

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Package brglm2 updated to version 0.7.1 with previous version 0.7.0 dated 2020-10-19

Title: Bias Reduction in Generalized Linear Models
Description: Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression). 'brglm2' also provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in binomial response generalized linear models.
Author: Ioannis Kosmidis [aut, cre] (<https://orcid.org/0000-0003-1556-0302>), Kjell Konis [ctb], Euloge Clovis Kenne Pagui [ctb], Nicola Sartori [ctb]
Maintainer: Ioannis Kosmidis <ioannis.kosmidis@warwick.ac.uk>

Diff between brglm2 versions 0.7.0 dated 2020-10-19 and 0.7.1 dated 2021-01-22

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Package rminizinc updated to version 0.0.4 with previous version 0.0.3 dated 2021-01-05

Title: R Interface to Use 'MiniZinc'
Description: Constraint optimization, or constraint programming, is the name given to identifying feasible solutions out of a very large set of candidates, where the problem can be modeled in terms of arbitrary constraints. 'MiniZinc' is a free and open-source constraint modeling language. Constraint satisfaction and discrete optimization problems can be formulated in a high-level modeling language. Models are compiled into an intermediate representation that is understood by a wide range of solvers. 'MiniZinc' itself provides several solvers, for instance 'GeCode'. R users can use the package to solve constraint programming problems without using 'MiniZinc' directly, modify existing 'MiniZinc' models and also create their own models.
Author: Akshit Achara, Lars Kotthoff, Hans W. Borchers, Guido Tack
Maintainer: Akshit Achara <acharaakshit@gmail.com>

Diff between rminizinc versions 0.0.3 dated 2021-01-05 and 0.0.4 dated 2021-01-22

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New package heemod with initial version 0.14.2
Package: heemod
Title: Markov Models for Health Economic Evaluations
Version: 0.14.2
Authors@R: c( person("Kevin", "Zarca", email = "kevin.zarca@gmail.com", role = c("aut", "cre")), person("Antoine", "Filipovic-Pierucci", role = "aut"), person("Matthew", "Wiener", role = "ctb"), person("Zdenek", "Kabat", role = "ctb"), person("Vojtech", "Filipec", role = "ctb"), person("Jordan", "Amdahl", role=c("ctb")), person("Yonatan", "Carranza Alarcon", role=c("ctb")), person("Vince", "Daniels", role=c("ctb")) )
Description: An implementation of the modelling and reporting features described in reference textbook and guidelines (Briggs, Andrew, et al. Decision Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011; Siebert, U. et al. State-Transition Modeling. Medical Decision Making 32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, heterogeneity analysis, time dependency on state-time and model-time (semi-Markov and non-homogeneous Markov models), etc.
Depends: R (>= 3.3.0)
Imports: dplyr (>= 0.7.2), ggplot2 (>= 2.2.0), lazyeval (>= 0.2.0), memoise (>= 1.1.0), mvnfast (>= 0.2.2), plyr (>= 1.8.0), pryr (>= 0.1.2), tibble (>= 1.3.0), rlang (>= 0.3), purrr (>= 0.3)
License: GPL (>= 3)
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 7.1.1
Suggests: BCEA, diagram, flexsurv, knitr, logitnorm, lpSolve, mgcv, optimx, parallel, readxl, rgho, rmarkdown, stringr, survival, testthat, triangle, magrittr, cli
BugReports: https://github.com/pierucci/heemod/issues
NeedsCompilation: no
Packaged: 2021-01-22 11:59:21 UTC; kevin
Author: Kevin Zarca [aut, cre], Antoine Filipovic-Pierucci [aut], Matthew Wiener [ctb], Zdenek Kabat [ctb], Vojtech Filipec [ctb], Jordan Amdahl [ctb], Yonatan Carranza Alarcon [ctb], Vince Daniels [ctb]
Maintainer: Kevin Zarca <kevin.zarca@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-22 13:00:02 UTC

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New package ECTTDNN with initial version 0.1.0
Package: ECTTDNN
Type: Package
Title: Cointegration Based Timedelay Neural Network Model
Version: 0.1.0
Authors@R: c(person("Pankaj", "Das", role = c("aut","cre"),email="pankaj.das2@icar.gov.in"),person("Achal", "Lama", role = "aut",email="achal.lama@icar.gov.in"), person("Girish Kumar", "Jha", role = "aut",email="grish.stat@gmail.com"))
Author: Pankaj Das [aut, cre], Achal Lama [aut], Girish Kumar Jha [aut]
Maintainer: Pankaj Das <pankaj.das2@icar.gov.in>
Depends: R (>= 3.3.0),urca,forecast,vars
Description: This cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.
Encoding: UTF-8
LazyData: true
License: GPL-3
NeedsCompilation: no
Packaged: 2021-01-21 12:08:07 UTC; USER
Repository: CRAN
Date/Publication: 2021-01-22 11:20:02 UTC

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Package airGR updated to version 1.6.9.27 with previous version 1.4.3.65 dated 2020-02-28

Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
Description: Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A), a snow accumulation and melt model (CemaNeige) and the associated functions for their calibration and evaluation. Use help(airGR) for package description and references.
Author: Laurent Coron [aut, trl] (<https://orcid.org/0000-0002-1503-6204>), Olivier Delaigue [aut, cre] (<https://orcid.org/0000-0002-7668-8468>), Guillaume Thirel [aut] (<https://orcid.org/0000-0002-1444-1830>), David Dorchies [aut] (<https://orcid.org/0000-0002-6595-7984>), Charles Perrin [aut, ths] (<https://orcid.org/0000-0001-8552-1881>), Claude Michel [aut, ths], Vazken Andréassian [ctb, ths] (<https://orcid.org/0000-0001-7124-9303>), François Bourgin [ctb] (<https://orcid.org/0000-0002-2820-7260>), Pierre Brigode [ctb] (<https://orcid.org/0000-0001-8257-0741>), Nicolas Le Moine [ctb], Thibaut Mathevet [ctb] (<https://orcid.org/0000-0002-4142-4454>), Safouane Mouelhi [ctb], Ludovic Oudin [ctb] (<https://orcid.org/0000-0002-3712-0933>), Raji Pushpalatha [ctb], Audrey Valéry [ctb]
Maintainer: Olivier Delaigue <airGR@inrae.fr>

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New package spatstat.core with initial version 1.65-0
Package: spatstat.core
Version: 1.65-0
Date: 2021-01-07
Title: Core Functionality of the 'spatstat' Package
Authors@R: c(person("Adrian", "Baddeley", role = c("aut", "cre"), email = "Adrian.Baddeley@curtin.edu.au"), person("Rolf", "Turner", role = "aut", email="r.turner@auckland.ac.nz"), person("Ege", "Rubak", role = "aut", email = "rubak@math.aau.dk"), person("Kasper", "Klitgaard Berthelsen", role = "ctb"), person("Ottmar", "Cronie", role = "ctb"), person("Tilman", "Davies", role = "ctb"), person("Yongtao", "Guan", role = "ctb"), person("Ute", "Hahn", role = "ctb"), person("Abdollah", "Jalilian", role = "ctb"), person("Marie-Colette", "van Lieshout", role = "ctb"), person("Greg", "McSwiggan", role = "ctb"), person("Tuomas", "Rajala", role = "ctb"), person("Suman", "Rakshit", role = "ctb"), person("Dominic", "Schuhmacher", role = "ctb"), person("Rasmus", "Plenge Waagepetersen", role = "ctb"), person("Hangsheng", "Wang", role = "ctb"))
Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au>
Depends: R (>= 3.5.0), spatstat.data (>= 1.6-1), spatstat.geom, stats, graphics, grDevices, utils, methods, nlme, rpart
Imports: spatstat.utils (>= 1.18-0), spatstat.sparse, mgcv, Matrix, abind, tensor, goftest (>= 1.2-2)
Suggests: sm, maptools (>= 0.9-9), gsl, locfit, spatial, RandomFields (>= 3.1.24.1), RandomFieldsUtils(>= 0.3.3.1), fftwtools (>= 0.9-8), spatstat.linnet
Description: This is a subset of the original 'spatstat' package, containing all of the user-level code from 'spatstat', except for the code for linear networks.
License: GPL (>= 2)
URL: http://spatstat.org/
LazyData: true
NeedsCompilation: yes
ByteCompile: true
BugReports: https://github.com/spatstat/spatstat.core/issues
Packaged: 2021-01-09 00:33:34 UTC; adrian
Author: Adrian Baddeley [aut, cre], Rolf Turner [aut], Ege Rubak [aut], Kasper Klitgaard Berthelsen [ctb], Ottmar Cronie [ctb], Tilman Davies [ctb], Yongtao Guan [ctb], Ute Hahn [ctb], Abdollah Jalilian [ctb], Marie-Colette van Lieshout [ctb], Greg McSwiggan [ctb], Tuomas Rajala [ctb], Suman Rakshit [ctb], Dominic Schuhmacher [ctb], Rasmus Plenge Waagepetersen [ctb], Hangsheng Wang [ctb]
Repository: CRAN
Date/Publication: 2021-01-22 10:50:02 UTC

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New package sdmApp with initial version 0.0.1
Package: sdmApp
Title: A User-Friendly Application for Species Distribution Modeling
Version: 0.0.1
Authors@R: c(person(given = "Aboubacar", family = "HEMA", role = c("aut", "cre"), email = "aboubacarhema94@gmail.com"), person(given = "Babacar", family = "NDAO", role = "ctb", email = "babacar.ndao@cse.sn"), person(given = "Louise", family = "LEROUX", role = "aut", email = "louise.leroux@cirad.fr"), person(given = "Abdoul Aziz", family = "DIOUF", role = "ctb", email = "aziz.diouf@cse.sn"))
Author: Aboubacar HEMA [aut, cre], Babacar NDAO [ctb], Louise LEROUX [aut], Abdoul Aziz DIOUF [ctb]
Maintainer: Aboubacar HEMA <aboubacarhema94@gmail.com>
Description: A 'shiny' application that allows non-expert 'R' users to easily model species distribution. It offers a reproducible work flow for species distribution modeling into a single and user friendly environment. 'sdmApp' takes 'raster' data (in format supported by the 'raster package') and species occurrence data (several format supported) as input argument. It provides an interactive graphical user interface (GUI).
License: GPL-3
URL: https://github.com/Abson-dev/sdmApp
BugReports: https://github.com/Abson-dev/sdmApp/issues
Depends: R (>= 3.5.0)
Imports: raster (>= 2.6.7), sp (>= 1.2.0), biomod2 (>= 3.4.6), blockCV (>= 2.1.1), CENFA (>= 1.1.0), dismo (>= 1.0.12), DT, kernlab (>= 0.9-29), randomForest (>= 4.6.10), readxl (>= 1.3.1), rhandsontable (>= 0.3.7), sf, shiny (>= 0.12.2), shinyBS (>= 0.61), shinyFiles (>= 0.7.0), SSDM (>= 0.2.8), ggcorrplot (>= 0.1.3), ggplot2 (>= 3.1.1), ggpubr (>= 0.4.0), haven (>= 2.3.1), tidyverse (>= 1.3.0), data.table, rgeos (>= 0.3-8), rJava (>= 0.9-13)
Suggests: covr, grDevices, knitr, rmarkdown, stats, testthat, utils, rgdal (>= 1.5-8), automap (>= 1.0-14), graphics, future.apply
VignetteBuilder: knitr, rmarkdown
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 7.1.1
SystemRequirements: Java (>= 8)
NeedsCompilation: no
Packaged: 2021-01-21 10:05:28 UTC; DELLDRAMOMO
Repository: CRAN
Date/Publication: 2021-01-22 10:40:03 UTC

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Package mdatools updated to version 0.11.3 with previous version 0.11.2 dated 2020-10-22

Title: Multivariate Data Analysis for Chemometrics
Description: Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi: 10.1016/j.chemolab.2020.103937>.
Author: Sergey Kucheryavskiy (<https://orcid.org/0000-0002-3145-7244>)
Maintainer: Sergey Kucheryavskiy <svkucheryavski@gmail.com>

Diff between mdatools versions 0.11.2 dated 2020-10-22 and 0.11.3 dated 2021-01-22

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New package healthyR.ts with initial version 0.1.0
Package: healthyR.ts
Title: The Time Series Modeling Companion to 'healthyR'
Version: 0.1.0
Authors@R: c( person("Steven","Sanderson", email = "spsanderson@gmail.com", role = c("aut","cre")), person("Steven Sanderson", role = "cph"))
Description: Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
URL: https://github.com/spsanderson/healthyR.ts
BugReports: https://github.com/spsanderson/healthyR.ts/issues
Suggests: knitr, rmarkdown, roxygen2, scales
VignetteBuilder: knitr
Imports: magrittr, rlang (>= 0.1.2), tibble, timetk, modeltime, modeltime.ensemble, modeltime.resample, dplyr, purrr, ggplot2, tidyquant, healthyR.data, lubridate, stringr, plotly
NeedsCompilation: no
Packaged: 2021-01-21 03:30:57 UTC; Steve
Author: Steven Sanderson [aut, cre], Steven Sanderson [cph]
Maintainer: Steven Sanderson <spsanderson@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-22 10:40:06 UTC

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New package GFisher with initial version 0.1.0
Package: GFisher
Type: Package
Title: Generalized Fisher's Combination Tests Under Dependence
Version: 0.1.0
Author: Hong Zhang
Maintainer: Hong Zhang <hzhang@wpi.edu>
Description: Accurate and computationally efficient p-value calculation methods for a general family of Fisher type statistics (GFisher). The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new p-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution approximation. The technical details can be found in Hong Zhang and Zheyang Wu (2020) <arXiv:2003.01286>.
License: GPL-2
Imports: stats, Matrix, mvtnorm
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.0
NeedsCompilation: no
Packaged: 2021-01-20 22:12:49 UTC; consi
Repository: CRAN
Date/Publication: 2021-01-22 10:40:12 UTC

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New package cinaRgenesets with initial version 0.1.1
Package: cinaRgenesets
Type: Package
Title: Ready-to-Use Curated Gene Sets for 'cinaR'
Version: 0.1.1
Authors@R: c(person(given = "Onur", family = "Karakaslar", role = c("aut", "cre"), email = "eonurkara@gmail.com"), person(given = "Duygu", family = "Ucar", role = "aut", comment = c(ORCID = "0000-0002-9772-3066")) )
Description: Immune related gene sets provided along with the 'cinaR' package.
License: GPL-3
Encoding: UTF-8
LazyData: true
URL: https://github.com/eonurk/cinaR-genesets
BugReports: https://github.com/eonurk/cinaR-genesets/issues/
biocViews:
Depends: R (>= 3.5.0)
Imports:
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-20 18:04:39 UTC; karako
Author: Onur Karakaslar [aut, cre], Duygu Ucar [aut] (<https://orcid.org/0000-0002-9772-3066>)
Maintainer: Onur Karakaslar <eonurkara@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-22 10:40:09 UTC

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New package bayesforecast with initial version 0.0.1
Package: bayesforecast
Title: Bayesian Time Series Modeling with Stan
Version: 0.0.1
Authors@R: c(person("Asael","Alonzo Matamoros", role = c("aut", "cre"),email = "asael.alonzo@gmail.com"), person("Cristian","Cruz Torres", role = 'aut', email = "cristian.cruz@unah.edu.hn"), person("Rob", "Hyndman", email="Rob.Hyndman@monash.edu", role = "ctb"), person("Mitchell", "O'Hara-Wild", role = "ctb") )
Description: Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Biarch: true
Depends: R (>= 4.0.0)
Imports: bayesplot (>= 1.5.0), methods, gridExtra, ggplot2, forecast, loo (>= 2.2.0), Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>= 2.0.0), RcppParallel (>= 5.0.1), bridgesampling (>= 0.3-0), MASS, StanHeaders, astsa,lubridate, prophet,zoo
Suggests: knitr, rmarkdown,ggfortify
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), RcppEigen (>= 0.3.3.3.0), rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
Collate: 'autoplot.R' 'auto_sarima.R' 'bayes_factor.R' 'bayesforecast-package.R' 'Birth.R' 'Fit.R' 'forecast.R' 'garch.R' 'get_params.R' 'ipc.R' 'log_lik.R' 'Misc.R' 'model.R' 'naive.R' 'posterior_intervals.R' 'posterior_predict.R' 'predictive_error.R' 'print.R' 'prior.R' 'report.R' 'Sarima.R' 'summary.R' 'ssm.R' 'stanmodels.R' 'SVM.R' 'varstan.R' 'stan_models.R'
NeedsCompilation: yes
Packaged: 2021-01-20 20:24:19 UTC; asael
Author: Asael Alonzo Matamoros [aut, cre], Cristian Cruz Torres [aut], Rob Hyndman [ctb], Mitchell O'Hara-Wild [ctb]
Maintainer: Asael Alonzo Matamoros <asael.alonzo@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-22 10:20:02 UTC

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New package MixTwice with initial version 1.1
Package: MixTwice
Type: Package
Title: MixTwice--a Package for Large-Scale Hypothesis Testing
Version: 1.1
Imports: alabama, ashr, fdrtool
Date: 2021-01-14
Author: Zihao Zheng and Michael A.Newton
Maintainer: Zihao Zheng <zihao.zheng@wisc.edu>
Description: Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2020) <arXiv:2011.07420>.
License: GPL-2
NeedsCompilation: no
Packaged: 2021-01-20 15:34:05 UTC; appli
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2021-01-22 09:40:05 UTC

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New package margins with initial version 0.3.26
Package: margins
Type: Package
Title: Marginal Effects for Model Objects
Description: An R port of Stata's 'margins' command, which can be used to calculate marginal (or partial) effects from model objects.
License: MIT + file LICENSE
Version: 0.3.26
Date: 2021-01-10
Authors@R: c(person("Thomas J.", "Leeper", role = c("aut", "cre"), email = "thosjleeper@gmail.com", comment = c(ORCID = "0000-0003-4097-6326")), person("Jeffrey", "Arnold", role = c("ctb"), email = "jeffrey.arnold@gmail.com"), person("Vincent", "Arel-Bundock", role = c("ctb")), person("Jacob A.", "Long", role = c("ctb"), email = "long.1377@osu.edu", comment = c(ORCID = "0000-0002-1582-6214")) )
URL: https://github.com/leeper/margins
BugReports: https://github.com/leeper/margins/issues
Imports: utils, stats, prediction (>= 0.3.6), data.table, graphics, grDevices, MASS
Suggests: methods, knitr, rmarkdown, testthat, ggplot2, gapminder, sandwich, stargazer, lme4
Enhances: AER, betareg, nnet, ordinal, survey
ByteCompile: true
VignetteBuilder: knitr
RoxygenNote: 7.1.0
NeedsCompilation: no
Packaged: 2021-01-21 22:48:36 UTC; THOMAS
Author: Thomas J. Leeper [aut, cre] (<https://orcid.org/0000-0003-4097-6326>), Jeffrey Arnold [ctb], Vincent Arel-Bundock [ctb], Jacob A. Long [ctb] (<https://orcid.org/0000-0002-1582-6214>)
Maintainer: Thomas J. Leeper <thosjleeper@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-22 09:20:02 UTC

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Package islasso updated to version 1.2.2 with previous version 1.2.0 dated 2021-01-21

Title: The Induced Smoothed Lasso
Description: An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper: Cilluffo, G., Sottile, G., La Grutta, S. and Muggeo, V. (2019) The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression. <doi:10.1177/0962280219842890>, and discussed in a tutorial: Sottile, G., Cilluffo, G., and Muggeo, V. (2019) The R package islasso: estimation and hypothesis testing in lasso regression. <doi:10.13140/RG.2.2.16360.11521>.
Author: Gianluca Sottile [aut, cre], Giovanna Cilluffo [aut, ctb], Vito MR Muggeo [aut, cre]
Maintainer: Gianluca Sottile <gianluca.sottile@unipa.it>

Diff between islasso versions 1.2.0 dated 2021-01-21 and 1.2.2 dated 2021-01-22

 DESCRIPTION            |    6 +++---
 MD5                    |   12 ++++++------
 NEWS                   |   12 +++++++++++-
 man/islasso-package.Rd |    2 +-
 src/auxiliary.f90      |   28 ++++++++++++++++++++--------
 src/islasso2.f90       |    7 +------
 src/islasso_glm.f90    |    6 ------
 7 files changed, 42 insertions(+), 31 deletions(-)

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New package deepMOU with initial version 0.1.0
Package: deepMOU
Title: Clustering of Short Texts by Mixture of Unigrams and Its Deep Extensions
Version: 0.1.0
Authors@R: c( person("Martin", "D'Ippolito", email = "martinmy69@gmail.com", role = c("aut","cre")), person("Anderlucci", "Laura", email = "laura.anderlucci@unibo.it", role = "aut"), person("Cinzia", "Viroli", email = "cinzia.viroli@unibo.it", role = "aut"))
Description: Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). <doi:10.1023/A:1007692713085>), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) <doi:10.1007/s11634-020-00399-3>) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) <arXiv:1902.06615v2>). There are also functions for graphical representation of clusters obtained.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: skmeans, extraDistr, dplyr, Rfast, entropy, ggplot2, RColorBrewer, stats, graphics, MASS
NeedsCompilation: no
Packaged: 2021-01-20 15:19:45 UTC; dippolitom
Author: Martin D'Ippolito [aut, cre], Anderlucci Laura [aut], Cinzia Viroli [aut]
Maintainer: Martin D'Ippolito <martinmy69@gmail.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2021-01-22 09:40:02 UTC

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New package cmprskcoxmsm with initial version 0.1.0
Package: cmprskcoxmsm
Type: Package
Title: Use IPW to Estimate Treatment Effect under Competing Risks
Version: 0.1.0
Author: Yiran Zhang
Maintainer: Yiran Zhang <yiz038@health.ucsd.edu>
Description: Uses inverse probability weighting methods to estimate treatment effect under marginal structure model for the cause-specific hazard of competing risk events. Estimates also the cumulative incidence function (i.e. risk) of the potential outcomes, and provides inference on risk difference and risk ratio. Reference: Kalbfleisch & Prentice (2002)<doi:10.1002/9781118032985>; Hernan et al (2001)<doi:10.1198/016214501753168154>.
License: GPL (>= 2)
Imports: ggplot2, survival, stats, twang, graphics, sandwich
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-01-20 18:48:13 UTC; yiran
Repository: CRAN
Date/Publication: 2021-01-22 09:50:02 UTC

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New package Biostatistics with initial version 1.0.0
Package: Biostatistics
Type: Package
Title: Statistics Tutorials for Biologists
Version: 1.0.0
Author: Rob Knell
Maintainer: Rob Knell <r.knell@qmul.ac.uk>
Description: Tutorials for statistics, aimed at biological scientists. Subjects range from basic descriptive statistics through to complex linear modelling. The tutorials include text, videos, interactive coding exercises and multiple choice quizzes. The package also includes 19 datasets which are used in the tutorials.
Encoding: UTF-8
LazyData: true
Imports: learnr
Suggests: ggplot2, car, plotrix, knitr, rmarkdown
License: GPL-3
VignetteBuilder: knitr
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-20 17:37:47 UTC; ugbt794
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2021-01-22 09:40:08 UTC

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New package autoharp with initial version 0.0.4
Package: autoharp
Title: Semi-Automatic Grading of R and Rmd Scripts
Version: 0.0.4
Authors@R: c( person("Vik", "Gopal", email="vik.gopal@nus.edu.sg", role=c("aut", "cre")), person("Samuel", "Seah", email="samuelseah@u.nus.edu", role="aut"), person("Viknesh", "Jeya Kumar", email="viknesh@u.nus.edu", role="aut"), person("Gabriel", "Ang", email="gabrielang@u.nus.edu", role="aut"), person("Ruofan", "Liu", email="kelseyliu1998@gmail.com", role="ctb"), person("National University of Singapore", role="cph"))
Description: A customisable set of tools for assessing and grading R or R-markdown scripts from students. It allows for checking correctness of code output, runtime statistics and static code analysis. The latter feature is made possible by representing R expressions using a tree structure.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: magrittr, dplyr, stringr, rlang, tidyr, tibble, knitr, rmarkdown, pryr, shiny, lintr, methods, igraph, testthat
Collate: 'treeharp.R' 'th_getter-length.R' 'as.matrix.R' 'check_correctness.R' 'check_rmd.R' 'check_runtime.R' 'count_lints.R' 'env_size.R' 'examplify_to_r.R' 'forestharp.R' 'forestharp_helpers.R' 'generate_thumbnails.R' 'join_treeharp.R' 'lang_2_tree.R' 'lang_2_tree_helpers.R' 'log_summary.R' 'lum_local_match.R' 'nlp_related.R' 'populate_soln_env.R' 'render_one.R' 'reset_path.R' 'run_tuner.R' 'to_BFS.R' 'tree_kernel.R' 'tree_routines.R' 'utils-pipe.R' 'utils.R' 'write_html.R' 'zzz.R'
Suggests: readxl, xml2, rvest, formatR
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-01-20 12:23:52 UTC; viknesh
Author: Vik Gopal [aut, cre], Samuel Seah [aut], Viknesh Jeya Kumar [aut], Gabriel Ang [aut], Ruofan Liu [ctb], National University of Singapore [cph]
Maintainer: Vik Gopal <vik.gopal@nus.edu.sg>
Repository: CRAN
Date/Publication: 2021-01-22 09:20:06 UTC

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Package terra updated to version 1.0-10 with previous version 1.0-7 dated 2021-01-04

Title: Spatial Data Analysis
Description: Methods for spatial data analysis, especially raster data. Methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/terra/> to get started. 'terra' is very similar to the 'raster' package; but 'terra' is simpler, better, and faster.
Author: Robert J. Hijmans [cre, aut] (<https://orcid.org/0000-0001-5872-2872>), Roger Bivand [ctb] (<https://orcid.org/0000-0003-2392-6140>), Karl Forner [ctb], Jeroen Ooms [ctb] (<https://orcid.org/0000-0002-4035-0289>), Edzer Pebesma [ctb] (<https://orcid.org/0000-0001-8049-7069>)
Maintainer: Robert J. Hijmans <r.hijmans@gmail.com>

Diff between terra versions 1.0-7 dated 2021-01-04 and 1.0-10 dated 2021-01-22

 terra-1.0-10/terra/DESCRIPTION                  |    8 
 terra-1.0-10/terra/MD5                          |   81 ++---
 terra-1.0-10/terra/NAMESPACE                    |    2 
 terra-1.0-10/terra/R/Agenerics.R                |    3 
 terra-1.0-10/terra/R/coerce.R                   |   40 ++
 terra-1.0-10/terra/R/generics.R                 |   24 -
 terra-1.0-10/terra/R/geom.R                     |    8 
 terra-1.0-10/terra/R/merge.R                    |    8 
 terra-1.0-10/terra/R/options.R                  |    4 
 terra-1.0-10/terra/R/plot.R                     |   24 +
 terra-1.0-10/terra/R/rast.R                     |    9 
 terra-1.0-10/terra/R/rasterize.R                |    7 
 terra-1.0-10/terra/R/sample.R                   |    4 
 terra-1.0-10/terra/R/show.R                     |    2 
 terra-1.0-10/terra/R/spatvec.R                  |    2 
 terra-1.0-10/terra/R/vect.R                     |    6 
 terra-1.0-10/terra/inst/tinytest/test_extract.R |   18 -
 terra-1.0-10/terra/inst/tinytest/test_window.R  |   58 ++--
 terra-1.0-10/terra/man/click.Rd                 |    6 
 terra-1.0-10/terra/man/geometry.Rd              |    2 
 terra-1.0-10/terra/man/merge.Rd                 |    6 
 terra-1.0-10/terra/man/mosaic.Rd                |only
 terra-1.0-10/terra/man/rast.Rd                  |    6 
 terra-1.0-10/terra/man/terra-package.Rd         |   49 +--
 terra-1.0-10/terra/man/terrain.Rd               |only
 terra-1.0-10/terra/man/union.Rd                 |   15 -
 terra-1.0-10/terra/man/vect.Rd                  |    3 
 terra-1.0-10/terra/src/RcppModule.cpp           |    7 
 terra-1.0-10/terra/src/distRaster.cpp           |  110 ++++++-
 terra-1.0-10/terra/src/extract.cpp              |    6 
 terra-1.0-10/terra/src/math_utils.cpp           |   26 +
 terra-1.0-10/terra/src/raster_methods.cpp       |  122 +++++++-
 terra-1.0-10/terra/src/rasterize.cpp            |   10 
 terra-1.0-10/terra/src/read_ogr.cpp             |   14 -
 terra-1.0-10/terra/src/sample.cpp               |   35 ++
 terra-1.0-10/terra/src/spatBase.h               |    3 
 terra-1.0-10/terra/src/spatOptions.cpp          |    3 
 terra-1.0-10/terra/src/spatRaster.h             |    3 
 terra-1.0-10/terra/src/spatRasterMultiple.h     |   29 +-
 terra-1.0-10/terra/src/terrain.cpp              |  332 +++++++++++++++++++++---
 terra-1.0-10/terra/src/write.cpp                |   40 +-
 terra-1.0-7/terra/man/slope.Rd                  |only
 terra-1.0-7/terra/src/buffer.cpp                |only
 terra-1.0-7/terra/src/mosaic.cpp                |only
 44 files changed, 868 insertions(+), 267 deletions(-)

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Package SubtypeDrug updated to version 0.1.4 with previous version 0.1.3 dated 2020-11-24

Title: Prioritization of Candidate Cancer Subtype Specific Drugs
Description: A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.
Author: Xudong Han, Junwei Han, Chonghui Liu
Maintainer: Junwei Han <hanjunwei1981@163.com>

Diff between SubtypeDrug versions 0.1.3 dated 2020-11-24 and 0.1.4 dated 2021-01-22

 DESCRIPTION              |   10 
 MD5                      |   12 -
 R/plotDScoreHeatmap.R    |    4 
 R/plotGlobalGraph.R      |    2 
 build/vignette.rds       |binary
 inst/doc/vignette.html   |  484 +++++++++++++++--------------------------------
 man/plotDScoreHeatmap.Rd |    2 
 7 files changed, 177 insertions(+), 337 deletions(-)

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New package SPlit with initial version 1.0
Package: SPlit
Type: Package
Title: Split a Dataset for Training and Testing
Version: 1.0
Date: 2021-01-11
Authors@R: c(person("Akhil", "Vakayil", role = c("aut", "cre"), email = "akhilv@gatech.edu"), person("Roshan", "Joseph", role = c("aut", "ths")), person("Simon", "Mak", role = "aut"))
Description: Procedure to optimally split a dataset for training and testing. 'SPlit' is based on the method of support points, which is independent of modeling methods. Please see Joseph and Vakayil (2020) <arXiv:2012.10945> for details. This work is supported by U.S. National Science Foundation grant CBET-1921873.
LazyData: TRUE
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.4)
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.1.0
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2021-01-19 20:13:32 UTC; akhil
Author: Akhil Vakayil [aut, cre], Roshan Joseph [aut, ths], Simon Mak [aut]
Maintainer: Akhil Vakayil <akhilv@gatech.edu>
Repository: CRAN
Date/Publication: 2021-01-22 08:30:05 UTC

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New package SAMtool with initial version 1.0.0
Package: SAMtool
Type: Package
Title: Stock Assessment Methods Toolkit
Version: 1.0.0
Authors@R: c(person("Quang", "Huynh", email = "quang@bluematterscience.com", role = c("aut", "cre")), person("Tom", "Carruthers", email = "tom@bluematterscience.com", role = "aut"), person("Adrian", "Hordyk", email = "adrian@bluematterscience.com", role = "aut"))
Date: 2021-01-19
Maintainer: Quang Huynh <quang@bluematterscience.com>
Description: Simulation tools for closed-loop simulation are provided for the 'MSEtool' operating model to inform data-rich fisheries. 'SAMtool' provides a conditioning model, assessment models of varying complexity with standardized reporting, model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation.
License: GPL-3
Depends: R (>= 3.5.0), MSEtool (>= 3.0.0)
Imports: TMB, corpcor, dplyr, gplots, grDevices, graphics, methods, snowfall, stats, utils, rmarkdown
LinkingTo: TMB, RcppEigen
Suggests: knitr, testthat, shiny, mvtnorm, reshape2
LazyData: yes
LazyLoad: yes
RoxygenNote: 7.1.1
Encoding: UTF-8
VignetteBuilder: knitr
URL: https://github.com/Blue-Matter/SAMtool
BugReports: https://github.com/Blue-Matter/SAMtool/issues
NeedsCompilation: yes
Packaged: 2021-01-19 23:41:00 UTC; qhuynh
Author: Quang Huynh [aut, cre], Tom Carruthers [aut], Adrian Hordyk [aut]
Repository: CRAN
Date/Publication: 2021-01-22 08:50:06 UTC

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New package rtables with initial version 0.3.6
Package: rtables
Title: Reporting Tables
Date: 2020-12-21
Version: 0.3.6
Authors@R: c( person("Gabriel", "Becker", email = "gabembecker@gmail.com", role = c("aut", "cre")), person("Adrian", "Waddell", email = "adrian.waddell@roche.com", role = "aut"), person("Daniel", "Sabanés Bové", email = "daniel.sabanes_bove@roche.com", role = "ctb"), person("Maximilian", "Mordig", email = "maximilian_oliver.mordig@roche.com", role = "ctb") )
Description: Reporting tables often have structure that goes beyond simple rectangular data. The 'rtables' package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.
Depends: methods, magrittr, R (>= 2.10)
Imports: stats, htmltools
Suggests: dplyr, tibble, tidyr, testthat, xml2, knitr, rmarkdown
License: Apache License 2.0 | file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
VignetteBuilder: knitr
URL: https://github.com/roche/rtables, https://roche.github.io/rtables/
BugReports: https://github.com/roche/rtables/issues
Collate: '00tabletrees.R' 'Viewer.R' 'argument_conventions.R' 'as_html.R' 'utils.R' 'colby_constructors.R' 'compare_rtables.R' 'deprecated.R' 'format_rcell.R' 'indent.R' 'make_subset_expr.R' 'prune.R' 'rtable.R' 'simple_analysis.R' 'split_funs.R' 'summary.R' 'tree_accessors.R' 'tt_afun_utils.R' 'tt_compare_tables.R' 'tt_compatibility.R' 'tt_deprecated.R' 'tt_dotabulation.R' 'tt_paginate.R' 'tt_pos_and_access.R' 'tt_showmethods.R' 'tt_sort.R' 'tt_test_afuns.R' 'tt_toString.R' 'labels.R' 'zzz_constants.R'
NeedsCompilation: no
Packaged: 2021-01-19 20:33:45 UTC; gabrielbecker
Author: Gabriel Becker [aut, cre], Adrian Waddell [aut], Daniel Sabanés Bové [ctb], Maximilian Mordig [ctb]
Maintainer: Gabriel Becker <gabembecker@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-22 08:40:02 UTC

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Package Numero updated to version 1.7.4 with previous version 1.7.3 dated 2021-01-11

Title: Statistical Framework to Define Subgroups in Complex Datasets
Description: High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Author: Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], Ville-Petteri Makinen [aut, cre]
Maintainer: Ville-Petteri Makinen <vpmakine@gmail.com>

Diff between Numero versions 1.7.3 dated 2021-01-11 and 1.7.4 dated 2021-01-22

 DESCRIPTION             |    8 -
 MD5                     |   20 +--
 R/nroPostprocess.R      |    5 
 R/nroPreprocess.R       |    6 +
 R/numero.subgroup.R     |    2 
 inst/doc/intro.R        |   48 ++++-----
 inst/doc/intro.html     |  251 ++++++++++++++++++++++++++++++------------------
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 inst/extcode/examples.R |    4 
 man/nroPreprocess.Rd    |    9 +
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 11 files changed, 270 insertions(+), 179 deletions(-)

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New package mifa with initial version 0.2.0
Package: mifa
Title: Multiple Imputation for Exploratory Factor Analysis
Version: 0.2.0
Authors@R: c( person("Vahid", "Nassiri", role = "aut"), person("Anikó", "Lovik", role = "aut"), person("Geert ", "Molenberghs", role = "aut"), person("Geert ", "Verbeke", role = "aut"), person("Tobias", "Busch", email = "teebusch@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0002-8390-7892")) )
URL: https://github.com/teebusch/mifa
BugReports: https://github.com/teebusch/mifa/issues
Imports: stats, mice, dplyr, checkmate
Suggests: psych, testthat, knitr, rmarkdown, ggplot2, tidyr, covr
Description: Impute the covariance matrix of incomplete data so that factor analysis can be performed. Imputations are made using multiple imputation by Multivariate Imputation with Chained Equations (MICE) and combined with Rubin's rules. Parametric Fieller confidence intervals and nonparametric bootstrap confidence intervals can be obtained for the variance explained by different numbers of principal components. The method is described in Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-19 21:44:47 UTC; teebu
Author: Vahid Nassiri [aut], Anikó Lovik [aut], Geert Molenberghs [aut], Geert Verbeke [aut], Tobias Busch [aut, cre] (<https://orcid.org/0000-0002-8390-7892>)
Maintainer: Tobias Busch <teebusch@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-22 08:40:08 UTC

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New package ggx with initial version 0.1.1
Package: ggx
Type: Package
Title: A Natural Language Interface to 'ggplot2'
Version: 0.1.1
Authors@R: person("Andreas M.", "Brandmaier", email = "andy@brandmaier.de", role = c("aut", "cre"))
Description: The 'ggplot2' package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid 'ggplot2' commands.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: sets, ggplot2
RoxygenNote: 7.1.0
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, markdown
NeedsCompilation: no
Packaged: 2021-01-19 17:02:42 UTC; brandmaier
Author: Andreas M. Brandmaier [aut, cre]
Maintainer: Andreas M. Brandmaier <andy@brandmaier.de>
Repository: CRAN
Date/Publication: 2021-01-22 08:30:02 UTC

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New package ggshadow with initial version 0.0.2
Package: ggshadow
Title: Shadow and Glow Geoms for 'ggplot2'
Version: 0.0.2
Authors@R: person("Marc", "Menem", email = "marc.menem@m4x.org", role = c("aut", "cre"))
Description: A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a neon glow around lines to get a steampunk style.
Depends: R (>= 3.4.0)
Imports: ggplot2 (>= 3.3.0), grid, scales, rlang, glue
Suggests: rmarkdown, knitr
VignetteBuilder: knitr
License: GPL-2
Encoding: UTF-8
LazyData: true
URL: https://github.com/marcmenem/ggshadow/
BugReports: https://github.com/marcmenem/ggshadow/issues
RoxygenNote: 7.1.0
Collate: 'geom-glowpath.r' 'geom-glowpoint.r' 'geom-shadowpath.r' 'geom-shadowpoint.r' 'internal-doc.r' 'scale-shadow.r'
NeedsCompilation: no
Packaged: 2021-01-19 20:58:13 UTC; marc
Author: Marc Menem [aut, cre]
Maintainer: Marc Menem <marc.menem@m4x.org>
Repository: CRAN
Date/Publication: 2021-01-22 08:50:03 UTC

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New package FMM with initial version 0.1.1
Package: FMM
Type: Package
Title: Rhythmic Patterns Modeling by FMM Models
Version: 0.1.1
Author: Adrian Lamela, Itziar Fernandez, Yolanda Larriba, Alejandro Rodriguez, Cristina Rueda
Maintainer: Itziar Fernandez <itziar.fernandez@uva.es>
Description: Provides a collection of functions to fit and explore single, multi-component and restricted Frequency Modulated Moebius (FMM) models. 'FMM' is a nonlinear parametric regression model capable of fitting non-sinusoidal shapes in rhythmic patterns. Details about the mathematical formulation of 'FMM' models can be found in Rueda et al. (2019) <doi:10.1038/s41598-019-54569-1>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5)
Imports: methods, foreach, iterators, parallel, doParallel
Suggests: ggplot2, RColorBrewer, testthat
NeedsCompilation: no
RoxygenNote: 7.1.1
Packaged: 2021-01-19 22:31:32 UTC; Buhardilla
Repository: CRAN
Date/Publication: 2021-01-22 09:00:12 UTC

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New package aweSOM with initial version 1.0
Package: aweSOM
Title: Interactive Self-Organizing Maps
Version: 1.0
Date: 2021-01-19
Authors@R: c(person("Julien", "Boelaert", role= c("aut", "cre"), email="julien.boelaert@univ-lille.fr"), person("Etienne", "Ollion", role= "aut"), person("Jan", "Sodoge", role= "aut"), person("Mohamed", "Megdoud", role= "ctb"), person("Otmane", "Naji", role= "ctb"), person("Arnaud", "Lemba Kote", role= "ctb"), person("Theo", "Renoud", role= "ctb"), person("Samuel", "Hym", role= "ctb"))
Description: Self-organizing maps (also known as SOM, see Kohonen (2001) <doi:10.1007/978-3-642-56927-2>) are a method for dimensionality reduction and clustering of continuous data. This package introduces interactive (html) graphics for easier analysis of SOM results. It also features an interactive interface, for push-button training and visualization of SOM, as well as tools to evaluate the quality of SOM.
License: GPL (>= 2)
Depends: R (>= 3.1.0)
Imports: kohonen, shiny, htmlwidgets, rmarkdown, htmltools, rclipboard, RColorBrewer, viridis, data.table, DT, kernlab, stats, cluster, e1071, haven, foreign, readxl
Suggests: knitr
VignetteBuilder: knitr
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-19 15:42:54 UTC; bart
Author: Julien Boelaert [aut, cre], Etienne Ollion [aut], Jan Sodoge [aut], Mohamed Megdoud [ctb], Otmane Naji [ctb], Arnaud Lemba Kote [ctb], Theo Renoud [ctb], Samuel Hym [ctb]
Maintainer: Julien Boelaert <julien.boelaert@univ-lille.fr>
Repository: CRAN
Date/Publication: 2021-01-22 08:10:10 UTC

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Package alfred updated to version 0.1.11 with previous version 0.1.10 dated 2021-01-07

Title: Downloading Time Series from ALFRED Database for Various Vintages
Description: Provides direct access to the ALFRED (<https://alfred.stlouisfed.org>) and FRED (<https://fred.stlouisfed.org>) databases. Its functions return tidy data frames for different releases of the specified time series. Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.
Author: Onno Kleen [aut, cre] (<https://orcid.org/0000-0003-4731-4640>)
Maintainer: Onno Kleen <r@onnokleen.de>

Diff between alfred versions 0.1.10 dated 2021-01-07 and 0.1.11 dated 2021-01-22

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 tests/testthat/test_download.R |    2 +-
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 10 files changed, 20 insertions(+), 21 deletions(-)

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Package sisireg updated to version 0.8.0 with previous version 0.7.0 dated 2021-01-14

Title: Sign-Simplicity-Regression-Solver
Description: Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 9798682394203, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 9798593470270, "Adäquates Maschinelles Lernen").
Author: Lars Metzner [aut, cre]
Maintainer: Lars Metzner <lars.metzner@ppi.de>

Diff between sisireg versions 0.7.0 dated 2021-01-14 and 0.8.0 dated 2021-01-22

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New package cropDemand with initial version 1.0.0
Package: cropDemand
Type: Package
Title: Spatial Crop Water Demand for Brazil
Version: 1.0.0
Authors@R: c(person(given = "Roberto", family = "Filgueiras", role = c("aut","cre"), email = "betofilgueiras@gmail.com", comment = c(ORCID = "0000-0002-0186-8907")), person(given = "Luan P.", family = "Venancio", role = ("aut"), comment = c(ORCID = "0000-0002-5544-8588")), person(given = "Catariny C.", family = "Aleman", role = ("aut"), comment = c(ORCID = "0000-0002-3894-3077")), person(given = "Fernando F.", family = "da Cunha", role = ("aut"), comment = c(ORCID = "0000-0002-1671-1021")))
Description: Estimation of crop water demand can be processed via this package. As example, the data from 'TerraClimate' dataset (<http://www.climatologylab.org/terraclimate.html>) calibrated with automatic weather stations of National Meteorological Institute of Brazil is available in a coarse spatial resolution to do the crop water demand. However, the user have also the option to download the variables directly from 'TerraClimate' repository with the download.terraclimate function and access the original 'TerraClimate' products. If the user believes that is necessary calibrate the variables, there is another function to do it. Lastly, the estimation of the crop water demand present in this package can be run for all the Brazilian territory with 'TerraClimate' dataset.
License: CC BY 4.0
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.2.0),
Imports: dplyr(>= 0.3.0.1), ggplot2(>= 3.3.2), extrafont, raster, rgdal, tidyr, ncdf4
BugReports: https://github.com/FilgueirasR/cropDemand/issues
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-19 16:07:36 UTC; betof
Author: Roberto Filgueiras [aut, cre] (<https://orcid.org/0000-0002-0186-8907>), Luan P. Venancio [aut] (<https://orcid.org/0000-0002-5544-8588>), Catariny C. Aleman [aut] (<https://orcid.org/0000-0002-3894-3077>), Fernando F. da Cunha [aut] (<https://orcid.org/0000-0002-1671-1021>)
Maintainer: Roberto Filgueiras <betofilgueiras@gmail.com>
Repository: CRAN
Date/Publication: 2021-01-22 08:00:02 UTC

More information about cropDemand at CRAN
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Package r2dii.analysis updated to version 0.1.5 with previous version 0.1.4 dated 2021-01-05

Title: Tools to Calculate Climate Targets for Financial Portfolios
Description: These tools help you to assess if a financial portfolio aligns with climate goals. They summarize key metrics attributed to the portfolio (e.g. production, emission factors), and calculate targets based on climate scenarios. They implement in R the last step of the free software 'PACTA' (Paris Agreement Capital Transition Assessment; <https://2degrees-investing.org/>). Financial institutions use 'PACTA' to study how their capital allocation impacts the climate.
Author: Jackson Hoffart [aut, cre] (<https://orcid.org/0000-0002-8600-5042>), Mauro Lepore [aut, ctr] (<https://orcid.org/0000-0002-1986-7988>), Klaus Hogedorn [aut], 2 Degrees Investing Initiative [cph, fnd]
Maintainer: Jackson Hoffart <jackson.hoffart@gmail.com>

Diff between r2dii.analysis versions 0.1.4 dated 2021-01-05 and 0.1.5 dated 2021-01-22

 DESCRIPTION                               |    6 -
 MD5                                       |    8 -
 NEWS.md                                   |    8 +
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More information about r2dii.analysis at CRAN
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Package WDI updated to version 2.7.2 with previous version 2.7.1 dated 2020-07-05

Title: World Development Indicators and Other World Bank Data
Description: Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.
Author: Vincent Arel-Bundock [aut, cre] (<https://orcid.org/0000-0003-2042-7063>), Etienne Bacher [ctb]
Maintainer: Vincent Arel-Bundock <vincent.arel-bundock@umontreal.ca>

Diff between WDI versions 2.7.1 dated 2020-07-05 and 2.7.2 dated 2021-01-22

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More information about WDI at CRAN
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Package sbtools updated to version 1.1.13 with previous version 1.1.12 dated 2020-08-30

Title: USGS ScienceBase Tools
Description: Tools for interacting with U.S. Geological Survey ScienceBase <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and collaborative data management platform. Functions included for querying ScienceBase, and creating and fetching datasets.
Author: David Blodgett [cre], Luke Winslow [aut], Scott Chamberlain [ctb], Alison Appling [ctb], Jordan Read [ctb]
Maintainer: David Blodgett <dblodgett@usgs.gov>

Diff between sbtools versions 1.1.12 dated 2020-08-30 and 1.1.13 dated 2021-01-22

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Package RSmallTelescopes updated to version 1.0.3 with previous version 1.0.2 dated 2020-09-23

Title: Empirical Small Telescopes Analysis
Description: We provide functions to perform an empirical small telescopes analysis. This package contains 2 functions, SimulatePower() and EstimatePower(). Users only need to call SimulatePower() to conduct the analysis. For more information on small telescopes analysis see Uri Simonsohn (2015) <doi:10.1177/0956797614567341>.
Author: John Ruscio [aut, cre], Samantha Costigan [ctb]
Maintainer: John Ruscio <ruscio@tcnj.edu>

Diff between RSmallTelescopes versions 1.0.2 dated 2020-09-23 and 1.0.3 dated 2021-01-22

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Package quantreg updated to version 5.83 with previous version 5.82 dated 2021-01-10

Title: Quantile Regression
Description: Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also now included.
Author: Roger Koenker [cre, aut], Stephen Portnoy [ctb] (Contributions to Censored QR code), Pin Tian Ng [ctb] (Contributions to Sparse QR code), Blaise Melly [ctb] (Contributions to preprocessing code), Achim Zeileis [ctb] (Contributions to dynrq code essentially identical to his dynlm code), Philip Grosjean [ctb] (Contributions to nlrq code), Cleve Moler [ctb] (author of several linpack routines), Yousef Saad [ctb] (author of sparskit2), Victor Chernozhukov [ctb] (contributions to extreme value inference code), Ivan Fernandez-Val [ctb] (contributions to extreme value inference code), Brian D Ripley [trl, ctb] (Initial (2001) R port from S (to my everlasting shame -- how could I have been so slow to adopt R!) and for numerous other suggestions and useful advice)
Maintainer: Roger Koenker <rkoenker@illinois.edu>

Diff between quantreg versions 5.82 dated 2021-01-10 and 5.83 dated 2021-01-22

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Package hal9001 updated to version 0.2.7 with previous version 0.2.6 dated 2020-06-27

Title: The Scalable Highly Adaptive Lasso
Description: A scalable implementation of the highly adaptive lasso algorithm, including routines for constructing sparse matrices of basis functions of the observed data, as well as a custom implementation of Lasso regression tailored to enhance efficiency when the matrix of predictors is composed exclusively of indicator functions. For ease of use and increased flexibility, the Lasso fitting routines invoke code from the 'glmnet' package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) <doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>.
Author: Jeremy Coyle [aut, cre] (<https://orcid.org/0000-0002-9874-6649>), Nima Hejazi [aut] (<https://orcid.org/0000-0002-7127-2789>), David Benkeser [ctb] (<https://orcid.org/0000-0002-1019-8343>), Oleg Sofrygin [ctb], Rachael Phillips [ctb] (<https://orcid.org/0000-0002-8474-591X>), Weixin Cai [ctb] (<https://orcid.org/0000-0003-2680-3066>), Mark van der Laan [aut, cph, ths] (<https://orcid.org/0000-0003-1432-5511>)
Maintainer: Jeremy Coyle <jeremyrcoyle@gmail.com>

Diff between hal9001 versions 0.2.6 dated 2020-06-27 and 0.2.7 dated 2021-01-22

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Package GNRS updated to version 0.1.1 with previous version 0.1.0 dated 2020-04-28

Title: Access the 'Geographic Name Resolution Service'
Description: Provides tools for interacting with the 'geographic name resolution service' ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated functionality. The 'GNRS' is a batch application for resolving & standardizing political division names against standard name in the geonames database <http://www.geonames.org/>. The 'GNRS' resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the 'GNRS' attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The 'GNRS' works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the 'GNRS' does not attempt to match state or county.
Author: Brad Boyle [aut], Brian Maitner [aut, cre]
Maintainer: Brian Maitner <bmaitner@gmail.com>

Diff between GNRS versions 0.1.0 dated 2020-04-28 and 0.1.1 dated 2021-01-22

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Package BioInstaller (with last version 0.3.7) was removed from CRAN

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

2018-11-20 0.3.7
2018-08-22 0.3.6
2018-01-24 0.3.3
2018-01-08 0.3.2.1
2018-01-04 0.3.2
2017-11-26 0.3.1
2017-10-31 0.2.2
2017-09-19 0.2.1
2017-07-25 0.2.0
2017-06-23 0.1.2
2017-05-25 0.1.1
2017-04-25 0.1.0
2017-04-02 0.0.3
2017-04-01 0.0.2
2017-03-16 0.0.1

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