Sat, 16 Nov 2024

Package sparsenet updated to version 1.7 with previous version 1.6 dated 2024-02-05

Title: Fit Sparse Linear Regression Models via Nonconvex Optimization
Description: Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Author: Trevor Hastie [aut, cre], Rahul Mazumder [aut], Jerome Friedman [aut]
Maintainer: Trevor Hastie <hastie@stanford.edu>

Diff between sparsenet versions 1.6 dated 2024-02-05 and 1.7 dated 2024-11-16

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Package Epi updated to version 2.57 with previous version 2.56 dated 2024-10-01

Title: Statistical Analysis in Epidemiology
Description: Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.
Author: Bendix Carstensen [aut, cre], Martyn Plummer [aut], Esa Laara [ctb], Michael Hills [ctb]
Maintainer: Bendix Carstensen <b@bxc.dk>

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Package DFA.CANCOR updated to version 0.3.6 with previous version 0.2.8 dated 2023-06-29

Title: Linear Discriminant Function and Canonical Correlation Analysis
Description: Produces SPSS- and SAS-like output for linear discriminant function analysis and canonical correlation analysis. The methods are described in Manly & Alberto (2017, ISBN:9781498728966), Rencher (2002, ISBN:0-471-41889-7), and Tabachnik & Fidell (2019, ISBN:9780134790541).
Author: Brian P. O'Connor [aut, cre]
Maintainer: Brian P. O'Connor <brian.oconnor@ubc.ca>

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Package geostan updated to version 0.8.0 with previous version 0.7.0 dated 2024-09-18

Title: Bayesian Spatial Analysis
Description: For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
Author: Connor Donegan [aut, cre] , Mitzi Morris [ctb], Amy Tims [ctb]
Maintainer: Connor Donegan <connor.donegan@gmail.com>

Diff between geostan versions 0.7.0 dated 2024-09-18 and 0.8.0 dated 2024-11-16

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Package cchs updated to version 0.4.5 with previous version 0.4.4 dated 2024-01-31

Title: Cox Model for Case-Cohort Data with Stratified Subcohort-Selection
Description: Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.
Author: E. Jones [aut, cre]
Maintainer: E. Jones <edmundjones79@gmail.com>

Diff between cchs versions 0.4.4 dated 2024-01-31 and 0.4.5 dated 2024-11-16

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Package rgl updated to version 1.3.14 with previous version 1.3.12 dated 2024-10-28

Title: 3D Visualization Using OpenGL
Description: Provides medium to high level functions for 3D interactive graphics, including functions modelled on base graphics (plot3d(), etc.) as well as functions for constructing representations of geometric objects (cube3d(), etc.). Output may be on screen using OpenGL, or to various standard 3D file formats including WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
Author: Duncan Murdoch [aut, cre], Daniel Adler [aut], Oleg Nenadic [ctb], Simon Urbanek [ctb], Ming Chen [ctb], Albrecht Gebhardt [ctb], Ben Bolker [ctb], Gabor Csardi [ctb], Adam Strzelecki [ctb], Alexander Senger [ctb], The R Core Team [ctb, cph], Dirk Ed [...truncated...]
Maintainer: Duncan Murdoch <murdoch.duncan@gmail.com>

Diff between rgl versions 1.3.12 dated 2024-10-28 and 1.3.14 dated 2024-11-16

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Package HGNChelper updated to version 0.8.15 with previous version 0.8.14 dated 2024-05-18

Title: Identify and Correct Invalid HGNC Human Gene Symbols and MGI Mouse Gene Symbols
Description: Contains functions for identifying and correcting HGNC human gene symbols and MGI mouse gene symbols which have been converted to date format by Excel, withdrawn, or aliased. Also contains functions for reversibly converting between HGNC symbols and valid R names.
Author: Sehyun Oh [aut], Ayush Aggarwal [aut], Markus Riester [aut], Levi Waldron [aut, cre]
Maintainer: Levi Waldron <lwaldron.research@gmail.com>

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Package fontawesome updated to version 0.5.3 with previous version 0.5.2 dated 2023-08-19

Title: Easily Work with 'Font Awesome' Icons
Description: Easily and flexibly insert 'Font Awesome' icons into 'R Markdown' documents and 'Shiny' apps. These icons can be inserted into HTML content through inline 'SVG' tags or 'i' tags. There is also a utility function for exporting 'Font Awesome' icons as 'PNG' images for those situations where raster graphics are needed.
Author: Richard Iannone [aut, cre] , Christophe Dervieux [ctb] , Winston Chang [ctb], Dave Gandy [ctb, cph] , Posit Software, PBC [cph, fnd]
Maintainer: Richard Iannone <rich@posit.co>

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Package MortCast updated to version 2.8-0 with previous version 2.7-0 dated 2022-03-31

Title: Estimation and Projection of Age-Specific Mortality Rates
Description: Age-specific mortality rates are estimated and projected using the Kannisto, Lee-Carter and related methods as described in Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.
Author: Hana Sevcikova [cre, aut], Nan Li [aut], Patrick Gerland [aut]
Maintainer: Hana Sevcikova <hanas@uw.edu>

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New package dfr with initial version 0.1.1
Package: dfr
Title: Dual Feature Reduction for SGL
Version: 0.1.1
Date: 2024-11-16
Maintainer: Fabio Feser <ff120@ic.ac.uk>
Description: Implementation of the Dual Feature Reduction (DFR) approach for the Sparse Group Lasso (SGL) and the Adaptive Sparse Group Lasso (aSGL) (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.17094>). The DFR approach is a feature reduction approach that applies strong screening to reduce the feature space before optimisation, leading to speed-up improvements for fitting SGL (Simon et al. (2013) <doi:10.1080/10618600.2012.681250>) and aSGL (Mendez-Civieta et al. (2020) <doi:10.1007/s11634-020-00413-8> and Poignard (2020) <doi:10.1007/s10463-018-0692-7>) models. DFR is implemented using the Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) algorithm, with linear and logistic SGL models supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported.
Imports: sgs, caret, MASS, methods, stats, grDevices, graphics, Matrix
Suggests: SGL, gglasso, glmnet, testthat
License: GPL (>= 3)
Encoding: UTF-8
URL: https://github.com/ff1201/dfr
BugReports: https://github.com/ff1201/dfr/issues
NeedsCompilation: no
Packaged: 2024-11-16 10:32:08 UTC; ff120
Author: Fabio Feser [aut, cre]
Repository: CRAN
Date/Publication: 2024-11-16 16:20:01 UTC

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Package pargasite updated to version 2.1.1 with previous version 2.1.0 dated 2024-04-23

Title: Pollution-Associated Risk Geospatial Analysis Site
Description: Offers tools to estimate and visualize levels of major pollutants (CO, NO2, SO2, Ozone, PM2.5 and PM10) across the conterminous United States for user-defined time ranges. Provides functions to retrieve pollutant data from the U.S. Environmental Protection Agency’s 'Air Quality System' (AQS) API service <https://aqs.epa.gov/aqsweb/documents/data_api.html> for interactive visualization through a 'shiny' application, allowing users to explore pollutant levels for a given location over time relative to the National Ambient Air Quality Standards (NAAQS).
Author: Jaehyun Joo [aut, cre], Rebecca Greenblatt [aut], Avantika Diwadkar [aut], Nisha Narayanan [aut], Blanca Himes [aut]
Maintainer: Jaehyun Joo <jaehyunjoo@outlook.com>

Diff between pargasite versions 2.1.0 dated 2024-04-23 and 2.1.1 dated 2024-11-16

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New package sgs with initial version 0.3.1
Package: sgs
Title: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control
Version: 0.3.1
Date: 2024-11-16
Maintainer: Fabio Feser <ff120@ic.ac.uk>
Description: Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and group-based OSCAR models (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.
Imports: Matrix, MASS, caret, grDevices, graphics, methods, stats, SLOPE, Rlab, Rcpp (>= 1.0.10)
LinkingTo: Rcpp, RcppArmadillo
Suggests: SGL, gglasso, glmnet, testthat, knitr, grpSLOPE, rmarkdown
License: GPL (>= 3)
Encoding: UTF-8
URL: https://github.com/ff1201/sgs
BugReports: https://github.com/ff1201/sgs/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-11-16 10:33:20 UTC; ff120
Author: Fabio Feser [aut, cre]
Repository: CRAN
Date/Publication: 2024-11-16 14:20:02 UTC

More information about sgs at CRAN
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Package gdverse updated to version 1.3 with previous version 1.2 dated 2024-11-07

Title: Analysis of Spatial Stratified Heterogeneity
Description: Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, while also taking into account local spatial dependencies, spatial interpretability, complex spatial interactions, and robust spatial stratification. Additionally, it supports the spatial stratified heterogeneity family established in academic literature.
Author: Wenbo Lv [aut, cre, cph] , Yangyang Lei [aut] , Fangmei Liu [aut] , Jianwu Yan [aut] , Yongze Song [aut] , Wufan Zhao [aut]
Maintainer: Wenbo Lv <lyu.geosocial@gmail.com>

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Package forsearch updated to version 6.3.0 with previous version 6.2.0 dated 2024-07-13

Title: Diagnostic Analysis Using Forward Search Procedure for Various Models
Description: Identifies potential data outliers and their impact on estimates and analyses. Tool for evaluation of study credibility. Uses the forward search approach of Atkinson and Riani, "Robust Diagnostic Regression Analysis", 2000,<ISBN: o-387-95017-6> to prepare descriptive statistics of a dataset that is to be analyzed by functions lm {stats}, glm {stats}, nls {stats}, lme {nlme}, or coxph {survival}, or their equivalent in another language. Includes graphics functions to display the descriptive statistics.
Author: William Fairweather [aut, cre]
Maintainer: William Fairweather <wrf343@flowervalleyconsulting.com>

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 forsearch-6.3.0/forsearch/R/showme.R                                                              |    4 
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Package hgwrr updated to version 0.6-1 with previous version 0.6-0 dated 2024-11-07

Title: Hierarchical and Geographically Weighted Regression
Description: This model divides coefficients into three types, i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022)<doi:10.1177/23998083211063885>. If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness.
Author: Yigong Hu [aut, cre], Richard Harris [aut], Richard Timmerman [aut]
Maintainer: Yigong Hu <yigong.hu@bristol.ac.uk>

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