Tue, 12 Jan 2021

Package VGAMdata updated to version 1.1-5 with previous version 1.1-4 dated 2020-10-20

Title: Data Supporting the 'VGAM' Package
Description: Mainly data sets to accompany the VGAM package and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7>. These are used to illustrate vector generalized linear and additive models (VGLMs/VGAMs), and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column Interaction Models, and constrained and unconstrained ordination models in ecology). This package now contains some old VGAM family functions which have been replaced by newer ones (often because they are now special cases).
Author: Thomas Yee [aut, cre, cph], James Gray [dtc]
Maintainer: Thomas Yee <t.yee@auckland.ac.nz>

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Package supernova updated to version 2.3.0 with previous version 2.2.3 dated 2020-11-04

Title: Judd, McClelland, & Ryan Formatting for ANOVA Output
Description: Produces ANOVA tables in the format used by Judd, McClelland, and Ryan (2017, ISBN: 978-1138819832) in their introductory textbook, Data Analysis. This includes proportional reduction in error and formatting to improve ease the transition between the book and R.
Author: Adam Blake [aut] (<https://orcid.org/0000-0001-7881-8652>), Jeff Chrabaszcz [aut], Ji Son [aut] (<https://orcid.org/0000-0002-4258-4791>), Jim Stigler [cre, aut] (<https://orcid.org/0000-0001-6107-7827>)
Maintainer: Jim Stigler <jstigler@gmail.com>

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Package archdata updated to version 1.2-1 with previous version 1.2 dated 2018-02-19

Title: Example Datasets from Archaeological Research
Description: The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology.
Author: David L. Carlson [aut, cre], Georg Roth [ctb]
Maintainer: David L. Carlson <dcarlson@tamu.edu>

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Package precautionary updated to version 0.2-1 with previous version 0.2-0 dated 2020-12-15

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 (2020b), "Retrospective analysis of a fatal dose-finding trial" <arXiv:2004.12755> and (2020c) "What Were They Thinking? Pharmacologic priors implicit in a choice of 3+3 dose-escalation design" <arXiv:2012.05301>.
Author: David C. Norris [aut, cre, cph] (<https://orcid.org/0000-0001-9593-6343>), Markus Triska [ctb]
Maintainer: David C. Norris <david@precisionmethods.guru>

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Package cpp11 updated to version 0.2.5 with previous version 0.2.4 dated 2020-11-05

Title: A C++11 Interface for R's C Interface
Description: Provides a header only, C++11 interface to R's C interface. Compared to other approaches 'cpp11' strives to be safe against long jumps from the C API as well as C++ exceptions, conform to normal R function semantics and supports interaction with 'ALTREP' vectors.
Author: Jim Hester [aut, cre] (<https://orcid.org/0000-0002-2739-7082>), Romain François [ctb], Benjamin Kietzman [ctb], RStudio [cph, fnd]
Maintainer: Jim Hester <jim.hester@rstudio.com>

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Package blocksdesign updated to version 4.7 with previous version 4.6 dated 2020-11-14

Title: Nested and Crossed Block Designs for Factorial and Unstructured Treatment Sets
Description: Constructs treatment and block designs for linear treatment models with crossed or nested block factors. The treatment design can be any feasible linear model and the block design can be any feasible combination of crossed or nested block factors. The block design is a sum of one or more block factors and the block design is optimized sequentially with the levels of each successive block factor optimized conditional on all previously optimized block factors. D-optimality is used throughout except for square or rectangular lattice block designs which are constructed algebraically using mutually orthogonal Latin squares. Crossed block designs with interaction effects are optimized using a weighting scheme which allows for differential weighting of first and second-order block effects. Outputs include a table showing the allocation of treatments to blocks and tables showing the achieved D-efficiency factors for each block and treatment design. Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020). <doi:10.1007/s13253-020-00416-0>.
Author: R. N. Edmondson.
Maintainer: Rodney Edmondson <rodney.edmondson@gmail.com>

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Package SeerMapperWest updated to version 1.2.5 with previous version 1.2.4 dated 2020-06-23

Title: Supplemental U.S. 2000 Census Tract Boundaries for 13 Western States without Seer Registries for 'SeerMapper'
Description: Provides supplemental 2000 census tract boundaries for the 13 states without Seer Registries that are west of the Mississippi river for use with the 'SeerMapper' package. The data contained in this package is derived from U.S. 2000 Census data and is in the public domain. Updated to Alber Equal Area projection.
Author: Jim Pearson <jbpearson353@gmail.com> with data provided by U.S. Census Bureau
Maintainer: Joe Zou <zouj@imsweb.com>

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Package SeerMapperRegs updated to version 1.2.5 with previous version 1.2.4 dated 2020-06-23

Title: Supplemental U.S. 2000 Census Tract Boundaries for 19 States with Seer Registries for 'SeerMapper'
Description: Provides supplemental 2000 census tract boundaries for the 19 states containing Seer Registries for use with the 'SeerMapper' package. The data contained in this package is derived from U.S. Census data and is in the public domain.
Author: Jim Pearson <jbpearson353@gmail.com> with data provided by U.S. Census Bureau
Maintainer: Joe Zou <zouj@imsweb.com>

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Package SeerMapperEast updated to version 1.2.5 with previous version 1.2.4 dated 2020-06-23

Title: Supplemental U.S. 2000 Census Tract Boundaries for 20 States, District and Territory without Seer Registries that are East of the Mississippi River for Use with 'SeerMapper' Package
Description: Provides supplemental 2000 census tract boundaries for the 20 states, district and territory without Seer Registries that are east of the Mississippi river for use with the 'SeerMapper' package. The data contained in this package is derived from U.S. Census data and is in the public domain.
Author: Jim Pearson <jbpearson353@gmail.com> with data provided by U.S. Census Bureau
Maintainer: Joe Zou <zouj@imsweb.com>

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Package SeerMapper2010West updated to version 1.2.5 with previous version 1.2.4 dated 2020-06-23

Title: Supplemental U.S. 2010 Census Tract Boundaries for 13 Western States without Seer Registries for 'SeerMapper'
Description: Provides supplemental 2010 census tract boundaries for the 13 states without Seer Registries that are west of the Mississippi river for use with the 'SeerMapper' package. The data contained in this package is derived from U.S. 2010 Census data and is in public domain.
Author: Jim Pearson <jbpearson353@gmail.com> with data provided by U.S. Census Bureau
Maintainer: Joe Zou <zouj@imsweb.com>

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Package SeerMapper2010Regs updated to version 1.2.5 with previous version 1.2.4 dated 2020-06-23

Title: Supplemental U.S. 2010 Census Tract Boundaries for 19 States with Seer Registries for 'SeerMapper'
Description: Provides supplemental 2010 census tract boundaries of the 19 states containing Seer Registries for use with the 'SeerMapper' package. The data contained in this package is derived from U.S. 2010 Census data and is in public domain.
Author: Jim Pearson <jbpearson353@gmail.com> with data provided by U.S. Census Bureau
Maintainer: Joe Zou <zouj@imsweb.com>

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Package SeerMapper2010East updated to version 1.2.5 with previous version 1.2.4 dated 2020-06-23

Title: Supplemental U.S. 2010 Census Tract Boundaries for 20 Eastern States (including DC and PR) without Registries for 'SeerMapper'
Description: Provides supplemental 2010 census tract boundary package for 20 states without Seer Registries that are east of the Mississippi river for use with the 'SeerMapper' package. The data contained in this package is derived from U.S. Census data and is in public domain.
Author: Jim Pearson <jbpearson353@gmail.com> with data provided by U.S. Census Bureau
Maintainer: Joe Zou <zouj@imsweb.com>

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Package SeerMapper updated to version 1.2.5 with previous version 1.2.4 dated 2020-06-23

Title: A Quick Way to Map U.S. Rates and Data of U.S. States, Counties, Census Tracts, or Seer Registries using 2000 and 2010 U.S. Census Boundaries
Description: Provides an easy way to map seer registry area rate data on a U.S. map. The U.S. data may be mapped at the state or state/county. U.S. Seer registry data may be mapped at the Seer registry area, Seer Registry area/county or Seer Registry area/county/census tract level. The function uses a calculated default categorization breakpoint list for 5 categories, when not breakpoint list is provided or the number of categories is specified by the user. A user provided break point list is limited to containing 5 values. The number of calculated categories may be from 3 to 11. The user provide the p-value for each area and request hatching over any areas with a p-value > 0.05. Other types of comparisons can be specified. If states or state/counties are used, the area identifier is the U.S. FIPS codes for states and counties, 2 digits and 5 digits respectfully. If the data is for U.S. Seer Registry areas, the Seer Registry area identifier used to link the data to the geographical area is a Seer Registry area abbreviation. See documentation for the list of acceptable Seer Registry area names and abbreviations. The state boundaries are overlaid all rate maps. The package contains the boundary data for state, county, Seer Registry areas, and census tracts for counties within a Seer registry area. The package support boundary data from the 2000 and 2010 U.S. Census. The SeerMapper package version contains the U.S. Census 2000 and 2010 boundary data for the regional, state, Seer Registry, and county levels. Six supplement packages contain the census tract boundary data. Copyrighted 2014, 2015, 2016, 2017, 2018 and 2019 by Pearson and Pickle.
Author: Jim Pearson <jbpearson353@gmail.com>
Maintainer: Joe Zou <zouj@imsweb.com>

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Package webdriver updated to version 1.0.6 with previous version 1.0.5 dated 2018-04-10

Title: 'WebDriver' Client for 'PhantomJS'
Description: A client for the 'WebDriver' 'API'. It allows driving a (probably headless) web browser, and can be used to test web applications, including 'Shiny' apps. In theory it works with any 'WebDriver' implementation, but it was only tested with 'PhantomJS'.
Author: Ariya Hidayat, Gábor Csárdi, Gabor Torok, Ivan De Marino, Robert Gieseke, Steven Masala, Winston Chang, Zack Weinberg
Maintainer: Gábor Csárdi <csardi.gabor@gmail.com>

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Package tkRplotR updated to version 0.1.4 with previous version 0.1.3 dated 2020-06-20

Title: Display Resizable Plots
Description: Display a plot in a Tk canvas.
Author: Filipe Campelo
Maintainer: Filipe Campelo <fcampelo@ci.uc.pt>

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Package GeneralizedUmatrix updated to version 1.2.2 with previous version 1.2.1 dated 2020-10-25

Title: Credible Visualization for Two-Dimensional Projections of Data
Description: Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.
Author: Michael Thrun [aut, cre, cph] (<https://orcid.org/0000-0001-9542-5543>), Felix Pape [ctb, ctr], Tim Schreier [ctb, ctr], Luis Winckelman [ctb, ctr], Alfred Ultsch [ths]
Maintainer: Michael Thrun <m.thrun@gmx.net>

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Package DatabionicSwarm updated to version 1.1.5 with previous version 1.1.3 dated 2020-02-03

Title: Swarm Intelligence for Self-Organized Clustering
Description: Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, <DOI:10.1016/j.artint.2020.103237>. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>. A comparison to 26 common clustering algorithms on 15 datasets is presented on the website.
Author: Michael Thrun [aut, cre, cph] (<https://orcid.org/0000-0001-9542-5543>)
Maintainer: Michael Thrun <m.thrun@gmx.net>

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Package icosa updated to version 0.10.1 with previous version 0.10.0 dated 2020-02-17

Title: Global Triangular and Penta-Hexagonal Grids Based on Tessellated Icosahedra
Description: Employs triangular tessellation to refine icosahedra defined in 3d space. The procedures can be set to provide a grid with a custom resolution. Both the primary triangular and their inverted penta- hexagonal grids are available for implementation. Additional functions are provided to position points (latitude-longitude data) on the grids, to allow 2D and 3D plotting, use raster data and shapefiles.
Author: Adam T. Kocsis [aut, cre]
Maintainer: Adam T. Kocsis <adam.t.kocsis@gmail.com>

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Package gensphere updated to version 1.3 with previous version 1.1 dated 2017-09-16

Title: Generalized Spherical Distributions
Description: Define and compute with generalized spherical distributions - multivariate probability laws that are specified by a star shaped contour (directional behavior) and a radial component. The methods are described in Nolan (2016) <doi:10.1186/s40488-016-0053-0>.
Author: John P Nolan
Maintainer: John P Nolan <jpnolan@american.edu>

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Package DataVisualizations updated to version 1.2.2 with previous version 1.2.1 dated 2020-10-28

Title: Visualizations of High-Dimensional Data
Description: Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
Author: Michael Thrun [aut, cre, cph] (<https://orcid.org/0000-0001-9542-5543>), Felix Pape [aut, rev], Onno Hansen-Goos [ctr, ctb], Hamza Tayyab [ctr, ctb], Dirk Eddelbuettel [ctr], Craig Varrichio [ctr], Alfred Ultsch [dtc, ctb, ctr]
Maintainer: Michael Thrun <m.thrun@gmx.net>

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Package xpose updated to version 0.4.12 with previous version 0.4.11 dated 2020-07-22

Title: Diagnostics for Pharmacometric Models
Description: Diagnostics for non-linear mixed-effects (population) models from 'NONMEM' <https://www.iconplc.com/innovation/nonmem/>. 'xpose' facilitates data import, creation of numerical run summary and provide 'ggplot2'-based graphics for data exploration and model diagnostics.
Author: Benjamin Guiastrennec [aut, cre, cph], Andrew C. Hooker [aut, cph], Sebastian Ueckert [aut, cph], Mike K. Smith [ctb], Mats O. Karlsson [aut, cph]
Maintainer: Benjamin Guiastrennec <guiastrennec@gmail.com>

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Package crplyr updated to version 0.3.7 with previous version 0.3.6 dated 2020-07-21

Title: A 'dplyr' Interface for Crunch
Description: In order to facilitate analysis of datasets hosted on the Crunch data platform <https://crunch.io/>, the 'crplyr' package implements 'dplyr' methods on top of the Crunch backend. The usual methods 'select', 'filter', 'group_by', 'summarize', and 'collect' are implemented in such a way as to perform as much computation on the server and pull as little data locally as possible.
Author: Greg Freedman Ellis [aut, cre], Jonathan Keane [aut], Neal Richardson [aut], Mike Malecki [aut], Gordon Shotwell [aut]
Maintainer: Greg Freedman Ellis <greg@crunch.io>

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Package spm12r updated to version 2.8.2 with previous version 2.8.1 dated 2019-01-09

Title: Wrapper Functions for 'SPM' (Statistical Parametric Mapping) Version 12 from the 'Wellcome' Trust Centre for 'Neuroimaging'
Description: Installs 'SPM12' to the R library directory and has associated functions for 'fMRI' and general imaging utilities, called through 'MATLAB'.
Author: John Muschelli [cre, aut]
Maintainer: John Muschelli <muschellij2@gmail.com>

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Package R2SWF updated to version 0.9-5 with previous version 0.9-4 dated 2020-07-30

Title: Convert R Graphics to Flash Animations
Description: Using the 'Ming' library <http://www.libming.org/> to create Flash animations. Users can either use the 'SWF' device swf() to generate 'SWF' file directly through plotting functions like plot() and lines(), or convert images of other formats ('SVG', 'PNG', 'JPEG') into 'SWF'.
Author: Yixuan Qiu, Yihui Xie, Cameron Bracken and authors of included software. See file AUTHORS for details.
Maintainer: Yixuan Qiu <yixuan.qiu@cos.name>

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Package htmltools updated to version 0.5.1 with previous version 0.5.0 dated 2020-06-16

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>

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Package collapse updated to version 1.5.1 with previous version 1.5.0 dated 2021-01-04

Title: Advanced and Fast Data Transformation
Description: A C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, flexible and parsimonious to code with, class-agnostic and programmer friendly. It is well integrated with base R, 'dplyr' / (grouped) 'tibble', 'data.table' and 'plm' (panel-series and data frames), and non- destructively handles other matrix or data frame based classes (such as 'ts', 'xts' / 'zoo', 'timeSeries', 'tsibble', 'sf' data frames etc.) --- Key Features: --- (1) Advanced statistical programming: A full set of fast statistical functions supporting grouped and weighted computations on vectors, matrices and data frames. Fast and programmable grouping, ordering, unique values / rows, factor generation and interactions. Fast and flexible functions for data manipulation and data object conversions. (2) Advanced aggregation: Fast and easy multi-data-type, multi-function, weighted, parallelized and fully customized data aggregation. (3) Advanced transformations: Fast row / column arithmetic, (grouped) replacing and sweeping out of statistics, (grouped, weighted) scaling / standardizing, between (averaging) and (quasi-)within (centering / demeaning) transformations, higher-dimensional centering (i.e. multiple fixed effects transformations), linear prediction / partialling-out, linear model fitting and testing. (4) Advanced time-computations: Fast (sequences of) lags / leads, and (lagged / leaded, iterated, quasi-, log-) differences and (compounded) growth rates on (unordered, irregular) time series and panel data. Multivariate auto-, partial- and cross-correlation functions for panel data. Panel data to (ts-)array conversions. (5) List processing: (Recursive) list search / identification, splitting, extraction / subsetting, data-apply, and generalized recursive row-binding / unlisting in 2D. (6) Advanced data exploration: Fast (grouped, weighted, panel-decomposed) summary statistics for complex multilevel / panel data.
Author: Sebastian Krantz [aut, cre], Matt Dowle [ctb], Arun Srinivasan [ctb], Laurent Berge [ctb], Dirk Eddelbuettel [ctb], Josh Pasek [ctb], Kevin Tappe [ctb], R Core Team and contributors worldwide [ctb], Martyn Plummer [cph], 1999-2016 The R Core Team [cph]
Maintainer: Sebastian Krantz <sebastian.krantz@graduateinstitute.ch>

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Package afex updated to version 0.28-1 with previous version 0.28-0 dated 2020-09-20

Title: Analysis of Factorial Experiments
Description: Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).
Author: Henrik Singmann [aut, cre] (<https://orcid.org/0000-0002-4842-3657>), Ben Bolker [aut], Jake Westfall [aut], Frederik Aust [aut] (<https://orcid.org/0000-0003-4900-788X>), Mattan S. Ben-Shachar [aut], Søren Højsgaard [ctb], John Fox [ctb], Michael A. Lawrence [ctb], Ulf Mertens [ctb], Jonathon Love [ctb], Russell Lenth [ctb], Rune Haubo Bojesen Christensen [ctb]
Maintainer: Henrik Singmann <singmann@gmail.com>

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Package tbl2xts updated to version 1.0.4 with previous version 1.0.2 dated 2020-08-04

Title: Convert Tibbles or Data Frames to Xts Easily
Description: Facilitate the movement between data frames to 'xts'. Particularly useful when moving from 'tidyverse' to the widely used 'xts' package, which is the input format of choice to various other packages. It also allows the user to use a 'spread_by' argument for a character column 'xts' conversion.
Author: Nico Katzke [aut, cre]
Maintainer: Nico Katzke <nfkatzke@gmail.com>

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Package optiSel updated to version 2.0.5 with previous version 2.0.3 dated 2020-03-12

Title: Optimum Contribution Selection and Population Genetics
Description: A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees.
Author: Robin Wellmann
Maintainer: Robin Wellmann <r.wellmann@uni-hohenheim.de>

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Package insurancerating updated to version 0.6.4 with previous version 0.6.3 dated 2020-10-28

Title: Analytic Insurance Rating Techniques
Description: Methods for insurance rating. It helps actuaries to implement GLMs within all relevant steps needed to construct a risk premium from raw data. It provides a data driven strategy for the construction of insurance tariff classes. This strategy is based on the work by Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>. It also provides recipes on how to easily perform one-way, or univariate, analyses on an insurance portfolio. In addition it adds functionality to include reference categories in the levels of the coefficients in the output of a generalized linear regression analysis.
Author: Martin Haringa [aut, cre]
Maintainer: Martin Haringa <mtharinga@gmail.com>

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Package fmri updated to version 1.9.6 with previous version 1.9.3 dated 2020-02-25

Title: Analysis of fMRI Experiments
Description: Contains R-functions to perform an fMRI analysis as described in Polzehl and Tabelow (2019) <DOI:10.1007/978-3-030-29184-6>, Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>, Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>, Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Author: Karsten Tabelow [aut, cre], Joerg Polzehl [aut], Brandon Whitcher [ctb], Dames Sibylle [ctb]
Maintainer: Karsten Tabelow <tabelow@wias-berlin.de>

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Package eha updated to version 2.8.5 with previous version 2.8.4 dated 2020-12-15

Title: Event History Analysis
Description: Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2012) <doi:10.1201/9781315373942>.
Author: Göran Broström [aut, cre], Jianming Jin [ctb]
Maintainer: Göran Broström <goran.brostrom@umu.se>

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Package causaleffect updated to version 1.3.12 with previous version 1.3.10 dated 2019-09-26

Title: Deriving Expressions of Joint Interventional Distributions and Transport Formulas in Causal Models
Description: Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf> and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.
Author: Santtu Tikka [aut, cre] (<https://orcid.org/0000-0003-4039-4342>)
Maintainer: Santtu Tikka <santtuth@gmail.com>

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Package UniprotR updated to version 2.0.2 with previous version 2.0.1 dated 2020-12-15

Title: Retrieving Information of Proteins from Uniprot
Description: Connect to Uniprot <https://www.uniprot.org/> to retrieve information about proteins using their accession number such information could be name or taxonomy information, For detailed information kindly read the publication <https://www.sciencedirect.com/science/article/pii/S1874391919303859>.
Author: Mohamed Soudy [aut, cre], Ali Mostafa [aut]
Maintainer: Mohamed Soudy <MohmedSoudy2009@gmail.com>

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Package antaresViz updated to version 0.15.4 with previous version 0.15.3 dated 2020-11-13

Title: Antares Visualizations
Description: Visualize results generated by Antares, a powerful open source software developed by RTE to simulate and study electric power systems (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions that create interactive charts to help 'Antares' users visually explore the results of their simulations.
Author: Veronique Bachelier [aut, cre], Jalal-Edine Zawam [aut], Francois Guillem [aut], Benoit Thieurmel [aut], Titouan Robert [aut], Paul Plessiez [aut], Baptiste Seguinot [ctb], RTE [cph]
Maintainer: Veronique Bachelier <veronique.bachelier@rte-france.com>

Diff between antaresViz versions 0.15.3 dated 2020-11-13 and 0.15.4 dated 2021-01-12

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Package GLMcat (with last version 0.1.0) was removed from CRAN

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

2020-12-18 0.1.0

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Package gwsem updated to version 2.0.9 with previous version 2.0.7 dated 2020-08-30

Title: Genome-Wide Structural Equation Modeling
Description: Melds genome-wide association tests with structural equation modeling (SEM) using 'OpenMx'. This package contains low-level C/C++ code to rapidly read genetic data encoded in U.K. Biobank or 'plink' formats. Prebuilt modeling options include one and two factor models. Alternately, analyses may utilize arbitrary, user-provided SEMs. See Verhulst, Maes, & Neale (2017) <doi:10.1007/s10519-017-9842-6> for details. An updated manuscript is in preparation.
Author: Joshua N. Pritikin [aut, cre], Bradley Verhulst [cph], Gavin Band [cph], Yann Collet [cph], Facebook, Inc. [cph], Yuta Mori [cph], Shaun Purcell [cph], Christopher Chang [cph], Wojciech Mula [cph], Kim Walisch [cph]
Maintainer: Joshua N. Pritikin <jpritikin@pobox.com>

Diff between gwsem versions 2.0.7 dated 2020-08-30 and 2.0.9 dated 2021-01-12

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Package BuyseTest updated to version 2.2.3 with previous version 2.2.1 dated 2021-01-05

Title: Generalized Pairwise Comparisons
Description: Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better than a random observation drawn from the other group (Mann-Whitney parameter). The net benefit and win ratio statistics, i.e. the difference and ratio between the probabilities relative to the intervention and control groups, can then also be estimated. Confidence intervals and p-values are obtained using permutations, a non-parametric bootstrap, or the asymptotic theory. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O'Brien test), and can handle right-censoring and competing-risks.
Author: Brice Ozenne [aut, cre] (<https://orcid.org/0000-0001-9694-2956>), Julien Peron [ctb], Eva Cantagallo [aut]
Maintainer: Brice Ozenne <brice.mh.ozenne@gmail.com>

Diff between BuyseTest versions 2.2.1 dated 2021-01-05 and 2.2.3 dated 2021-01-12

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Package weibulltools updated to version 2.0.0 with previous version 1.0.1 dated 2019-01-29

Title: Statistical Methods for Life Data Analysis
Description: Provides statistical methods and visualizations that are often used in reliability engineering. Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, <ISBN:978-0-9653062-3-2>), Johnson (Johnson, 1964, <ISBN:978-0444403223>), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, <DOI:10.1080/08982112.2010.503447>) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, <ISBN:9780471673279>) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. Besides the well-known Weibull analysis, the package also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics. (Verband der Automobilindustrie e.V. (VDA), 2016, <ISSN:0943-9412>). Plots are created statically ('ggplot2') or interactively ('plotly') and can be customized with functions of the respective visualization package. The graphical technique of probability plotting as well as the addition of regression lines and confidence bounds to existing plots are supported.
Author: Tim-Gunnar Hensel [aut, cre], David Barkemeyer [aut]
Maintainer: Tim-Gunnar Hensel <tim-gunnar.hensel@tu-berlin.de>

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Package tranSurv updated to version 1.2.2 with previous version 1.2.1 dated 2020-07-28

Title: Transformation Model Based Estimation of Survival and Regression Under Dependent Truncation and Independent Censoring
Description: A latent, quasi-independent truncation time is assumed to be linked with the observed dependent truncation time, the event time, and an unknown transformation parameter via a structural transformation model. The transformation parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) <doi:10.1198/016214504000001538> or the regression coefficient estimates (Jones and Crowley, 1992) <doi:10.2307/2336782>. The marginal distribution for the truncation time and the event time are completely left unspecified. The methodology is applied to survival curve estimation and regression analysis.
Author: Sy Han (Steven) Chiou [aut, cre], Jing Qian [aut]
Maintainer: Sy Han (Steven) Chiou <schiou@utdallas.edu>

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Package scholar updated to version 0.2.0 with previous version 0.1.7 dated 2018-07-03

Title: Analyse Citation Data from Google Scholar
Description: Provides functions to extract citation data from Google Scholar. Convenience functions are also provided for comparing multiple scholars and predicting future h-index values.
Author: Guangchuang Yu [aut, cre] (<https://orcid.org/0000-0002-6485-8781>), James Keirstead [aut], Gregory Jefferis [ctb], Gordon Getzinger [ctb], Jorge Cimentada [ctb], Max Czapanskiy [ctb], Dominique Makowski [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>

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Package saqgetr updated to version 0.2.21 with previous version 0.2.1 dated 2020-11-19

Title: Import Air Quality Monitoring Data in a Fast and Easy Way
Description: A collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. Air quality data are sourced from open and publicly accessible repositories and can be found in these locations: <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> and <https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm>. The web server space has been provided by Ricardo Energy & Environment.
Author: Stuart K. Grange [cre, aut] (<https://orcid.org/0000-0003-4093-3596>)
Maintainer: Stuart K. Grange <stuart.grange@york.ac.uk>

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Package rbmn updated to version 0.9-4 with previous version 0.9-3 dated 2020-02-13

Title: Handling Linear Gaussian Bayesian Networks
Description: Creation, manipulation, simulation of linear Gaussian Bayesian networks from text files and more...
Author: Jean-Baptiste Denis [aut, cre], Marco Scutari [ctb]
Maintainer: Marco Scutari <marco.scutari@gmail.com>

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Package rAmCharts4 updated to version 1.3.0 with previous version 1.2.0 dated 2020-11-27

Title: Interface to the JavaScript Library 'amCharts 4'
Description: Creates JavaScript charts. The charts can be included in 'Shiny' apps and R markdown documents, or viewed from the R console and 'RStudio' viewer. Based on the JavaScript library 'amCharts 4' and the R packages 'htmlwidgets' and 'reactR'. Currently available types of chart are: vertical and horizontal bar chart, radial bar chart, stacked bar chart, vertical and horizontal Dumbbell chart, line chart, scatter chart, range area chart, gauge chart, boxplot chart, and pie chart.
Author: Stéphane Laurent [aut, cre], Antanas Marcelionis [ctb, cph] ('amCharts' library (https://www.amcharts.com/)), Terence Eden [ctb, cph] ('SuperTinyIcons' library (https://github.com/edent/SuperTinyIcons/)), Tom Alexander [ctb, cph] ('regression-js' library (https://github.com/Tom-Alexander/regression-js))
Maintainer: Stéphane Laurent <laurent_step@outlook.fr>

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Package PRSim updated to version 1.3-1 with previous version 1.2-2 dated 2020-05-28

Title: Stochastic Simulation of Streamflow Time Series using Phase Randomization
Description: Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in <https://hess.copernicus.org/articles/23/3175/2019/>. A detailed description and evaluation of the wavelet-based multi-site approach can be found in <https://hess.copernicus.org/articles/24/3967/2020/>.
Author: Manuela Brunner [aut, cre] (<https://orcid.org/0000-0001-8824-877X>), Reinhard Furrer [aut] (<https://orcid.org/0000-0002-6319-2332>)
Maintainer: Manuela Brunner <manuela.i.brunner@gmail.com>

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Package precisely updated to version 0.1.1 with previous version 0.1.0 dated 2020-06-02

Title: Estimate Sample Size Based on Precision Rather than Power
Description: Estimate sample size based on precision rather than power. 'precisely' is a study planning tool to calculate sample size based on precision. Power calculations are focused on whether or not an estimate will be statistically significant; calculations of precision are based on the same principles as power calculation but turn the focus to the width of the confidence interval. 'precisely' is based on the work of Rothman and Greenland (2018) <doi: 10.1097/EDE.0000000000000876>.
Author: Malcolm Barrett [aut, cre] (<https://orcid.org/0000-0003-0299-5825>)
Maintainer: Malcolm Barrett <malcolmbarrett@gmail.com>

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Package periscope updated to version 0.5.4 with previous version 0.5.3 dated 2021-01-06

Title: Enterprise Streamlined 'Shiny' Application Framework
Description: An enterprise-targeted scalable and UI-standardized 'shiny' framework including a variety of developer convenience functions with the goal of both streamlining robust application development while assisting with creating a consistent user experience regardless of application or developer.
Author: Constance Brett [aut, cre], Isaac Neuhaus [aut] (canvasXpress JavaScript Library Maintainer), Ger Inberg [ctb], Bristol-Meyers Squibb (BMS) [cph]
Maintainer: Constance Brett <connie@aggregate-genius.com>

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Package microsynth updated to version 2.0.17 with previous version 2.0.13 dated 2019-04-10

Title: Synthetic Control Methods with Micro- And Meso-Level Data
Description: A generalization of the 'Synth' package that is designed for data at a more granular level (e.g., micro-level). Provides functions to construct weights (including propensity score-type weights) and run analyses for synthetic control methods with micro- and meso-level data; see Robbins, Saunders, and Kilmer (2017) <doi:10.1080/01621459.2016.1213634> and Robbins and Davenport (2021) <doi:10.18637/jss.v097.i02>.
Author: Michael Robbins [aut, cre], Steven Davenport [aut]
Maintainer: Michael Robbins <mrobbins@rand.org>

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Package ftrCOOL updated to version 1.1.1 with previous version 1.1.0 dated 2020-12-16

Title: Feature Extraction from Biological Sequences
Description: Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from Nucleotide and Peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: 'iLearn' 'Z. Chen et al.' (2019) <DOI:10.1093/bib/bbz041>. 'iFeature' 'Z. Chen et al.' (2018) <DOI:10.1093/bioinformatics/bty140>. <https://CRAN.R-project.org/package=rDNAse>. 'PseKRAAC' 'Y. Zuo et al.' 'PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition' (2017) <DOI:10.1093/bioinformatics/btw564>. 'iDNA6mA-PseKNC' 'P. Feng et al.' 'iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC' (2019) <DOI:10.1016/j.ygeno.2018.01.005>. 'I. Dubchak et al.' 'Prediction of protein folding class using global description of amino acid sequence' (1995) <DOI:10.1073/pnas.92.19.8700>. 'W. Chen et al.' 'Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome' (2015) <DOI:10.1038/srep13859>.
Author: Sare Amerifar
Maintainer: Sare Amerifar <sare.ameri.01@gmail.com>

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More information about ftrCOOL at CRAN
Permanent link

Package fitzRoy updated to version 0.3.3 with previous version 0.3.2 dated 2020-05-23

Title: Easily Scrape and Process AFL Data
Description: An easy package for scraping and processing Australia Rules Football (AFL) data. 'fitzRoy' provides a range of functions for accessing publicly available data from 'AFL Tables' <https://afltables.com/afl/afl_index.html>, 'Footy Wire' <https://www.footywire.com> and 'The Squiggle' <https://squiggle.com.au>. Further functions allow for easy processing, cleaning and transformation of this data into formats that can be used for analysis.
Author: James Day [cre, aut], Robert Nguyen [aut], Matthew Erbs [ctb], Oscar Lane [aut], Jason Zivkovic [ctb]
Maintainer: James Day <jamesthomasday@gmail.com>

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More information about fitzRoy at CRAN
Permanent link

Package epiR updated to version 2.0.19 with previous version 2.0.17 dated 2020-12-04

Title: Tools for the Analysis of Epidemiological Data
Description: Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.
Author: Mark Stevenson <mark.stevenson1@unimelb.edu.au> and Evan Sergeant <evansergeant@gmail.com> with contributions from Telmo Nunes, Cord Heuer, Jonathon Marshall, Javier Sanchez, Ron Thornton, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Kazuki Yoshida, Geoff Jones, Sarah Pirikahu, Simon Firestone, Ryan Kyle, Johann Popp, Mathew Jay and Charles Reynard.
Maintainer: Mark Stevenson <mark.stevenson1@unimelb.edu.au>

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More information about epiR at CRAN
Permanent link

Package crosstalk updated to version 1.1.1 with previous version 1.1.0.1 dated 2020-03-13

Title: Inter-Widget Interactivity for HTML Widgets
Description: Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files). Currently supports linked brushing and filtering.
Author: Joe Cheng [aut], Carson Sievert [aut, cre] (<https://orcid.org/0000-0002-4958-2844>), RStudio [cph], jQuery Foundation [cph] (jQuery library and jQuery UI library), jQuery contributors [ctb, cph] (jQuery library; authors listed in inst/www/shared/jquery-AUTHORS.txt), Mark Otto [ctb] (Bootstrap library), Jacob Thornton [ctb] (Bootstrap library), Bootstrap contributors [ctb] (Bootstrap library), Twitter, Inc [cph] (Bootstrap library), Brian Reavis [ctb, cph] (selectize.js library), Kristopher Michael Kowal [ctb, cph] (es5-shim library), es5-shim contributors [ctb, cph] (es5-shim library), Denis Ineshin [ctb, cph] (ion.rangeSlider library), Sami Samhuri [ctb, cph] (Javascript strftime library)
Maintainer: Carson Sievert <carson@rstudio.com>

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More information about crosstalk at CRAN
Permanent link

Package agridat updated to version 1.18 with previous version 1.17 dated 2020-08-03

Title: Agricultural Datasets
Description: Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.
Author: Kevin Wright [aut, cre] (<https://orcid.org/0000-0002-0617-8673>)
Maintainer: Kevin Wright <kw.stat@gmail.com>

Diff between agridat versions 1.17 dated 2020-08-03 and 1.18 dated 2021-01-12

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Package ymlthis updated to version 0.1.3 with previous version 0.1.2 dated 2020-02-03

Title: Write 'YAML' for 'R Markdown', 'bookdown', 'blogdown', and More
Description: Write 'YAML' front matter for R Markdown and related documents. yml_*() functions write 'YAML' and use_*() functions let you write the resulting 'YAML' to your clipboard or to .yml files related to your project.
Author: Malcolm Barrett [aut, cre] (<https://orcid.org/0000-0003-0299-5825>), Richard Iannone [aut] (<https://orcid.org/0000-0003-3925-190X>), RStudio [cph, fnd]
Maintainer: Malcolm Barrett <malcolmbarrett@gmail.com>

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Package vegawidget updated to version 0.3.2 with previous version 0.3.1 dated 2020-01-22

Title: 'Htmlwidget' for 'Vega' and 'Vega-Lite'
Description: 'Vega' and 'Vega-Lite' parse text in 'JSON' notation to render chart-specifications into 'HTML'. This package is used to facilitate the rendering. It also provides a means to interact with signals, events, and datasets in a 'Vega' chart using 'JavaScript' or 'Shiny'.
Author: Ian Lyttle [aut, cre] (<https://orcid.org/0000-0001-9962-4849>), Vega/Vega-Lite Developers [aut], Alicia Schep [ctb] (<https://orcid.org/0000-0002-3915-0618>), Stuart Lee [ctb], Kanit Wongsuphasawat [ctb] (Vega/Vega-Lite library), Dominik Moritz [ctb] (Vega/Vega-Lite library), Arvind Satyanarayan [ctb] (Vega/Vega-Lite library), Jeffrey Heer [ctb] (Vega/Vega-Lite library), Mike Bostock [ctb] (D3 library), David Frank [ctb] (node-fetch library), Hadley Wickham [ctb] (s3_register)
Maintainer: Ian Lyttle <ian.lyttle@se.com>

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Package portfolioBacktest updated to version 0.2.3 with previous version 0.2.2 dated 2020-08-03

Title: Automated Backtesting of Portfolios over Multiple Datasets
Description: Automated backtesting of multiple portfolios over multiple datasets of stock prices in a rolling-window fashion. Intended for researchers and practitioners to backtest a set of different portfolios, as well as by a course instructor to assess the students in their portfolio design in a fully automated and convenient manner, with results conveniently formatted in tables and plots. Each portfolio design is easily defined as a function that takes as input a window of the stock prices and outputs the portfolio weights. Multiple portfolios can be easily specified as a list of functions or as files in a folder. Multiple datasets can be conveniently extracted randomly from different markets, different time periods, and different subsets of the stock universe. The results can be later assessed and ranked with tables based on a number of performance criteria (e.g., expected return, volatility, Sharpe ratio, drawdown, turnover rate, return on investment, computational time, etc.), as well as plotted in a number of ways with nice barplots and boxplots.
Author: Daniel P. Palomar [cre, aut], Rui Zhou [aut]
Maintainer: Daniel P. Palomar <daniel.p.palomar@gmail.com>

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Package jmcm updated to version 0.2.4 with previous version 0.2.3 dated 2021-01-06

Title: Joint Mean-Covariance Models using 'Armadillo' and S4
Description: Fit joint mean-covariance models for longitudinal data. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Armadillo' C++ library for numerical linear algebra and 'RcppArmadillo' glue.
Author: Jianxin Pan [aut, cre], Yi Pan [aut]
Maintainer: Jianxin Pan <Jianxin.Pan@manchester.ac.uk>

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Package geomorph updated to version 3.3.2 with previous version 3.3.1 dated 2020-06-12

Title: Geometric Morphometric Analyses of 2D/3D Landmark Data
Description: Read, manipulate, and digitize landmark data, generate shape variables via Procrustes analysis for points, curves and surfaces, perform shape analyses, and provide graphical depictions of shapes and patterns of shape variation.
Author: Dean Adams [aut, cre], Michael Collyer [aut], Antigoni Kaliontzopoulou [aut], Erica Baken [aut]
Maintainer: Dean Adams <dcadams@iastate.edu>

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Package neonUtilities updated to version 1.3.9 with previous version 1.3.8 dated 2020-11-11

Title: Utilities for Working with NEON Data
Description: NEON data packages can be accessed through the NEON Data Portal <https://data.neonscience.org/home> or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.
Author: Claire Lunch [aut, cre, ctb], Christine Laney [aut, ctb], Nathan Mietkiewicz [aut, ctb], Eric Sokol [aut, ctb], Kaelin Cawley [aut, ctb], NEON (National Ecological Observatory Network) [aut]
Maintainer: Claire Lunch <clunch@battelleecology.org>

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Package Eagle updated to version 2.4.3 with previous version 2.4.2 dated 2020-12-06

Title: Multiple Locus Association Mapping on a Genome-Wide Scale
Description: An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred and outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.
Author: Andrew George [aut, cre], Joshua Bowden [ctb], Ryan Stephenson [ctb], Hyun Kang [ctb], Noah Zaitlen [ctb], Claire Wade [ctb], Andrew Kirby [ctb], David Heckerman [ctb], Mark Daly [ctb], Eleazar Eskin [ctb]
Maintainer: Andrew George <andrew.george@csiro.au>

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2020-03-23 1.1.0
2020-01-21 1.0.0
2019-04-02 0.2.1
2019-03-29 0.2.0

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2018-06-30 0.9
2016-10-18 0.8
2016-09-16 0.7
2016-03-12 0.6
2014-09-16 0.5
2014-06-13 0.4
2014-02-20 0.3
2014-02-17 0.2
2013-05-07 0.1

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2020-12-18 0.1.0

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2020-01-28 2.0.0
2014-04-03 1.1.1
2014-02-25 1.1.0
2012-07-16 1.0.5
2011-06-03 1.0.4
2011-06-01 1.0.3
2010-12-15 1.0.1
2010-05-03 1.0.0

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2020-09-10 1.5.3
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2018-11-21 1.2.3
2018-09-10 1.2.2
2018-06-13 1.2.0
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2017-09-25 1.1.0
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Package ggdag updated to version 0.2.3 with previous version 0.2.2 dated 2020-02-13

Title: Analyze and Create Elegant Directed Acyclic Graphs
Description: Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses the 'DAGitty' web tool (<http://dagitty.net>) for creating and analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.
Author: Malcolm Barrett [aut, cre] (<https://orcid.org/0000-0003-0299-5825>)
Maintainer: Malcolm Barrett <malcolmbarrett@gmail.com>

Diff between ggdag versions 0.2.2 dated 2020-02-13 and 0.2.3 dated 2021-01-12

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 NEWS.md                                                                  |    6 
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 R/geom_dag.R                                                             |    1 
 R/utils.R                                                                |   13 
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 man/as_tbl_graph.Rd                                                      |    2 
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 man/geom_dag_text.Rd                                                     |   13 
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Package BET updated to version 0.3.0 with previous version 0.2.0 dated 2020-11-18

Title: Binary Expansion Testing
Description: Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921> and Zhigen Zhao, Michael Baiocchi, Kai Zhang. SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables.
Author: Wan Zhang [aut, cre], Zhigen Zhao [aut], Michael Baiocchi [aut], Kai Zhang [aut]
Maintainer: Wan Zhang <wanz63@live.unc.edu>

Diff between BET versions 0.2.0 dated 2020-11-18 and 0.3.0 dated 2021-01-12

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