Tue, 12 Nov 2024

Package shinylive updated to version 0.3.0 with previous version 0.2.0 dated 2024-07-19

Title: Run 'shiny' Applications in the Browser
Description: Exporting 'shiny' applications with 'shinylive' allows you to run them entirely in a web browser, without the need for a separate R server. The traditional way of deploying 'shiny' applications involves in a separate server and client: the server runs R and 'shiny', and clients connect via the web browser. When an application is deployed with 'shinylive', R and 'shiny' run in the web browser (via 'webR'): the browser is effectively both the client and server for the application. This allows for your 'shiny' application exported by 'shinylive' to be hosted by a static web server.
Author: Barret Schloerke [aut, cre] , Winston Chang [aut] , George Stagg [aut], Garrick Aden-Buie [aut] , Posit Software, PBC [cph, fnd]
Maintainer: Barret Schloerke <barret@posit.co>

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Package philentropy updated to version 0.9.0 with previous version 0.8.0 dated 2023-12-02

Title: Similarity and Distance Quantification Between Probability Functions
Description: Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.
Author: Hajk-Georg Drost [aut, cre] , Jakub Nowosad [ctb]
Maintainer: Hajk-Georg Drost <hajk-georg.drost@tuebingen.mpg.de>

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Package OmopSketch updated to version 0.1.2 with previous version 0.1.1 dated 2024-10-15

Title: Characterise Tables of an OMOP Common Data Model Instance
Description: Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends.
Author: Marta Alcalde-Herraiz [aut, cre] , Kim Lopez-Guell [aut] , Elin Rowlands [aut] , Edward Burn [aut] , Marti Catala [aut]
Maintainer: Marta Alcalde-Herraiz <marta.alcaldeherraiz@ndorms.ox.ac.uk>

Diff between OmopSketch versions 0.1.1 dated 2024-10-15 and 0.1.2 dated 2024-11-12

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Package chngpt updated to version 2024.11-15 with previous version 2023.11-29 dated 2023-12-05

Title: Estimation and Hypothesis Testing for Threshold Regression
Description: Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) <DOI:10.1186/s12859-017-1863-x> and the package vignette.
Author: Youyi Fong [cre], Qianqian Chen [aut], Shuangcheng Hua [aut], Hyunju Son [aut], Adam Elder [aut], Tao Yang [aut], Zonglin He [aut], Simone Giannerini [aut]
Maintainer: Youyi Fong <youyifong@gmail.com>

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Package MBC updated to version 0.10-7 with previous version 0.10-6 dated 2023-05-02

Title: Multivariate Bias Correction of Climate Model Outputs
Description: Calibrate and apply multivariate bias correction algorithms for climate model simulations of multiple climate variables. Three methods described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented — (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr), and (iii) MBC N-dimensional PDF transform (MBCn) — as is the Rank Resampling for Distributions and Dependences (R2D2) method.
Author: Alex J. Cannon [aut, cre]
Maintainer: Alex J. Cannon <alex.cannon@canada.ca>

Diff between MBC versions 0.10-6 dated 2023-05-02 and 0.10-7 dated 2024-11-12

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Package miscFuncs updated to version 1.5-10 with previous version 1.5-9 dated 2024-10-18

Title: Miscellaneous Useful Functions Including LaTeX Tables, Kalman Filtering, QQplots with Simulation-Based Confidence Intervals, Linear Regression Diagnostics and Development Tools
Description: Implementing various things including functions for LaTeX tables, the Kalman filter, QQ-plots with simulation-based confidence intervals, linear regression diagnostics, web scraping, development tools, relative risk and odds rati, GARCH(1,1) Forecasting.
Author: Benjamin M. Taylor [aut, cre]
Maintainer: Benjamin M. Taylor <benjamin.taylor.software@gmail.com>

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Package fntl updated to version 0.1.2 with previous version 0.1.1 dated 2024-09-07

Title: Numerical Tools for 'Rcpp' and Lambda Functions
Description: Provides a 'C++' API for routinely used numerical tools such as integration, root-finding, and optimization, where function arguments are given as lambdas. This facilitates 'Rcpp' programming, enabling the development of 'R'-like code in 'C++' where functions can be defined on the fly and use variables in the surrounding environment.
Author: Andrew M. Raim [aut, cre]
Maintainer: Andrew M. Raim <andrew.raim@gmail.com>

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Package DiceView updated to version 3.1-0 with previous version 2.2-0 dated 2024-01-15

Title: Methods for Visualization of Computer Experiments Design and Surrogate
Description: View 2D/3D sections, contour plots, mesh of excursion sets for computer experiments designs, surrogates or test functions.
Author: Yann Richet [aut, cre] , Yves Deville [aut], Clement Chevalier [ctb]
Maintainer: Yann Richet <yann.richet@irsn.fr>

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Package adea updated to version 1.5.2 with previous version 1.5.1 dated 2023-11-24

Title: Alternate DEA Package
Description: The meaning of adea is "alternate DEA". This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. <doi:10.1590/0101-7438.2018.038.01.0031>. A full functional on-line and interactive version is available at <https://knuth.uca.es/shiny/DEA/>.
Author: Fernando Fernandez-Palacin [aut] , Manuel Munoz-Marquez [aut, cre]
Maintainer: Manuel Munoz-Marquez <manuel.munoz@uca.es>

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Package GitStats updated to version 2.1.2 with previous version 2.1.1 dated 2024-10-25

Title: Standardized Git Repository Data
Description: Obtain standardized data from multiple 'Git' services, including 'GitHub' and 'GitLab'. Designed to be 'Git' service-agnostic, this package assists teams with activities spread across various 'Git' platforms by providing a unified way to access repository data.
Author: Maciej Banas [aut, cre], Kamil Koziej [aut], Karolina Marcinkowska [aut], Matt Secrest [aut]
Maintainer: Maciej Banas <banasmaciek@gmail.com>

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Package bgms updated to version 0.1.4.1 with previous version 0.1.4 dated 2024-10-20

Title: Bayesian Analysis of Networks of Binary and/or Ordinal Variables
Description: Bayesian variable selection methods for analyzing the structure of a Markov Random Field model for a network of binary and/or ordinal variables. Details of the implemented methods can be found in: Marsman, van den Bergh, and Haslbeck (in press) <doi:10.31234/osf.io/ukwrf>.
Author: Maarten Marsman [aut, cre] , Karoline Huth [ctb] , Nikola Sekulovski [ctb] , Don van den Bergh [ctb]
Maintainer: Maarten Marsman <m.marsman@uva.nl>

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Package beeca updated to version 0.2.0 with previous version 0.1.3 dated 2024-06-18

Title: Binary Endpoint Estimation with Covariate Adjustment
Description: Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.
Author: Alex Przybylski [cre, aut], Mark Baillie [aut] , Craig Wang [aut] , Dominic Magirr [aut]
Maintainer: Alex Przybylski <alexander.przybylski@novartis.com>

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New package Virusparies with initial version 1.0.0
Package: Virusparies
Title: Visualize and Process Output from 'VirusHunterGatherer'
Version: 1.0.0
Maintainer: Ruff Sergej <serijnh@gmail.com>
Description: A collection of tools for downstream analysis of 'VirusHunterGatherer' output. Processing of hittables and plotting of results, enabling better interpretation, is made easier with the provided functions.
License: GPL (>= 3)
URL: https://github.com/SergejRuff/Virusparies, https://github.com/lauberlab/VirusHunterGatherer
BugReports: https://github.com/SergejRuff/Virusparies/issues
Encoding: UTF-8
Imports: chromote, cowplot, dplyr, ggplot2, gt, parallel, readr, rlang, scales, stats, stringr, tidyr, tools, utils
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2024-11-10 16:37:26 UTC; sergej
Author: Ruff Sergej [aut, cre] , Lauber Chris [ctb] , Chong Li Chuin [ctb]
Repository: CRAN
Date/Publication: 2024-11-12 14:10:06 UTC

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New package textAnnotatoR with initial version 0.1.4
Package: textAnnotatoR
Title: Interactive Text Annotation Tool with 'shiny' GUI
Version: 0.1.4
Date: 2024-10-25
Description: A comprehensive text annotation tool built with 'shiny'. Provides an interactive graphical user interface for coding text documents, managing code hierarchies, creating memos, and analyzing coding patterns. Features include code co-occurrence analysis, visualization of coding patterns, comparison of multiple coding sets, and export capabilities. Supports collaborative qualitative research through standardized annotation formats and analysis tools.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 3.5.0)
Imports: shiny (>= 1.7.0), shinydashboard, shinyjs, shinyFiles, data.tree, jsonlite, DT, readtext, magrittr, tools, utils, stats, grDevices, graphics
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, covr, mockery
URL: https://github.com/chaoliu-cl/textAnnotatoR, http://liu-chao.site/textAnnotatoR/
BugReports: https://github.com/chaoliu-cl/textAnnotatoR/issues
Language: en-US
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-11-11 19:23:25 UTC; chaoliu
Author: Chao Liu [aut, cre, cph]
Maintainer: Chao Liu <chaoliu@cedarville.edu>
Repository: CRAN
Date/Publication: 2024-11-12 14:40:01 UTC

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New package rOCEAN with initial version 1.0
Package: rOCEAN
Title: Two-Way Feature Set Testing for Multi-Omics
Version: 1.0
Maintainer: Mitra Ebrahimpoor <mitra.ebrahimpoor@gmail.com>
Description: For any two way feature-set from a pair of pre-processed omics data, 3 different true discovery proportions (TDP), namely pairwise-TDP, column-TDP and row-TDP are calculated. Due to embedded closed testing procedure, the choice of feature-sets can be changed infinite times and even after seeing the data without any change in type I error rate. For more details refer to Ebrahimpoor et al., (2024) <doi:10.48550/arXiv.2410.19523>.
License: GPL (>= 2)
Date: 2024-10-31
Encoding: UTF-8
Imports: ff
NeedsCompilation: no
Packaged: 2024-11-10 22:01:22 UTC; mitra
Author: Mitra Ebrahimpoor [aut, cre]
Repository: CRAN
Date/Publication: 2024-11-12 14:10:02 UTC

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Package modisfast updated to version 1.0.0 with previous version 0.9.1 dated 2024-07-14

Title: Fast and Efficient Access to MODIS Earth Observation Data
Description: Programmatic interface to several NASA Earth Observation 'OPeNDAP' servers (Open-source Project for a Network Data Access Protocol) (<https://www.opendap.org/>). Allows for easy downloads of MODIS subsets, as well as other Earth Observation datacubes, in a time-saving and efficient way : by sampling it at the very downloading phase (spatially, temporally and dimensionally).
Author: Paul Taconet [aut, cre, cph] , Nicolas Moiroux [fnd] , French National Research Institute for Sustainable Development, IRD [fnd]
Maintainer: Paul Taconet <paul.taconet@gmail.com>

Diff between modisfast versions 0.9.1 dated 2024-07-14 and 1.0.0 dated 2024-11-12

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New package fastml with initial version 0.1.0
Package: fastml
Title: Fast Machine Learning Model Training and Evaluation
Version: 0.1.0
Description: Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing comprehensive data preprocessing and support for a wide range of algorithms with hyperparameter tuning. It offers performance metrics and visualization tools to facilitate efficient and effective machine learning workflows.
License: GPL (>= 2)
Encoding: UTF-8
Imports: caret, pROC, reshape2, ggplot2, foreach, doParallel
Suggests: knitr, rmarkdown, randomForest, kernlab, gbm, nnet, rpart, glmnet, xgboost, pls, C50, mboost, arm, ipred, caTools, e1071, MASS
NeedsCompilation: no
Packaged: 2024-11-11 13:28:27 UTC; selcukkorkmaz
Author: Selcuk Korkmaz [aut, cre] , Dincer Goksuluk [aut]
Maintainer: Selcuk Korkmaz <selcukorkmaz@gmail.com>
Repository: CRAN
Date/Publication: 2024-11-12 14:30:02 UTC

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New package bplsr with initial version 1.0.0
Package: bplsr
Title: Bayesian partial least squares regression
Version: 1.0.0
Maintainer: Szymon Urbas <szymon.urbas@mu.ie>
Description: Fits the Bayesian partial least squares regression model introduced in Urbas et al. (2024) <doi:10.1214/24-AOAS1947>. Suitable for univariate and multivariate regression with high-dimensional data.
License: GPL (>= 3)
Encoding: UTF-8
Language: eng
Depends: R (>= 2.10)
LazyData: true
Imports: coda, progress, statmod, stats, utils
NeedsCompilation: no
Packaged: 2024-11-11 20:55:25 UTC; surbas
Author: Szymon Urbas [aut, cre], Pierre Lovera [ctb], Robert Daly [ctb], Alan O'Riordan [ctb], Donagh Berry [ctb], Isobel Claire Gormley [ctb]
Repository: CRAN
Date/Publication: 2024-11-12 14:50:02 UTC

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New package BayesGP with initial version 0.1.3
Package: BayesGP
Title: Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models
Version: 0.1.3
Description: Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
LinkingTo: TMB (>= 1.9.7), RcppEigen
Imports: TMB (>= 1.9.7), numDeriv, rstan, sfsmisc, Matrix (>= 1.6.3), aghq (>= 0.4.1), fda, tmbstan, LaplacesDemon, methods
Depends: R (>= 3.6.0)
Suggests: rmarkdown, knitr, survival, testthat (>= 3.0.0)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-11-10 22:31:24 UTC; ziangzhang
Author: Ziang Zhang [aut, cre], Yongwei Lin [aut], Alex Stringer [aut], Patrick Brown [aut]
Maintainer: Ziang Zhang <ziangzhang@uchicago.edu>
Repository: CRAN
Date/Publication: 2024-11-12 14:20:03 UTC

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New package SFM with initial version 0.1.0
Package: SFM
Title: A Package for Analyzing Skew Factor Models
Version: 0.1.0
Description: Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC) method to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix. Additionally, it includes robust regression methods such as adaptive Huber regression.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: MASS, SOPC, matrixcalc, sn, stats
NeedsCompilation: no
Language: en-US
Author: Guangbao Guo [aut, cre], Yu Jin [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>
Suggests: testthat (>= 3.0.0), ggplot2, reshape2
Packaged: 2024-11-08 06:55:06 UTC; AIERXUAN
Repository: CRAN
Date/Publication: 2024-11-12 13:10:02 UTC

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Package LaMa updated to version 2.0.0 with previous version 1.0.0 dated 2024-06-04

Title: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models
Description: A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.
Author: Jan-Ole Koslik [aut, cre]
Maintainer: Jan-Ole Koslik <jan-ole.koslik@uni-bielefeld.de>

Diff between LaMa versions 1.0.0 dated 2024-06-04 and 2.0.0 dated 2024-11-12

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 LaMa-2.0.0/LaMa/DESCRIPTION                                                                           |   34 
 LaMa-2.0.0/LaMa/MD5                                                                                   |  235 ++-
 LaMa-2.0.0/LaMa/NAMESPACE                                                                             |   40 
 LaMa-2.0.0/LaMa/R/RcppExports.R                                                                       |    4 
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 LaMa-2.0.0/LaMa/data                                                                                  |only
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 LaMa-2.0.0/LaMa/inst/doc/HSMMs.Rmd                                                                    |  127 --
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 LaMa-2.0.0/LaMa/man/stateprobs_g.Rd                                                                   |   37 
 LaMa-2.0.0/LaMa/man/stateprobs_p.Rd                                                                   |   39 
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 LaMa-2.0.0/LaMa/man/tpm_cont.Rd                                                                       |   42 
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Package GeneNMF updated to version 0.6.2 with previous version 0.6.0 dated 2024-07-22

Title: Non-Negative Matrix Factorization for Single-Cell Omics
Description: A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures.
Author: Massimo Andreatta [aut, cre] , Santiago Carmona [aut]
Maintainer: Massimo Andreatta <massimo.andreatta@unil.ch>

Diff between GeneNMF versions 0.6.0 dated 2024-07-22 and 0.6.2 dated 2024-11-12

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Package cppdoubles updated to version 0.3.0 with previous version 0.2.0 dated 2024-02-21

Title: Fast Relative Comparisons of Floating Point Numbers in 'C++'
Description: Compare double-precision floating point vectors using relative differences. All equality operations are calculated using 'cpp11'.
Author: Nick Christofides [aut, cre]
Maintainer: Nick Christofides <nick.christofides.r@gmail.com>

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New package CIMPLE with initial version 0.1.0
Package: CIMPLE
Title: Analysis of Longitudinal Electronic Health Record (EHR) Data with Possibly Informative Observational Time
Version: 0.1.0
Description: Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) <doi:10.48550/arXiv.2410.13113>.
License: GPL (>= 3)
Depends: R (>= 2.10)
Imports: dplyr, JMbayes2, lme4, mice, nleqslv, nlme, statmod, stats, survival, utils
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2024-11-09 05:45:29 UTC; hb636
Author: Jiacong Du [aut] , Howard Baik [cre]
Maintainer: Howard Baik <howard.baik@yale.edu>
Repository: CRAN
Date/Publication: 2024-11-12 13:20:10 UTC

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Package explore updated to version 1.3.3 with previous version 1.3.2 dated 2024-09-02

Title: Simplifies Exploratory Data Analysis
Description: Interactive data exploration with one line of code, automated reporting or use an easy to remember set of tidy functions for low code exploratory data analysis.
Author: Roland Krasser [aut, cre]
Maintainer: Roland Krasser <roland.krasser@gmail.com>

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Package maplegend updated to version 0.2.0 with previous version 0.1.0 dated 2023-10-18

Title: Legends for Maps
Description: Create legends for maps and other graphics. Thematic maps need to be accompanied by legible legends to be fully comprehensible. This package offers a wide range of legends useful for cartography, some of which may also be useful for other types of graphics.
Author: Timothee Giraud [cre, aut]
Maintainer: Timothee Giraud <timothee.giraud@cnrs.fr>

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Package FishResp updated to version 1.1.2 with previous version 1.1.1 dated 2022-09-18

Title: Analytical Tool for Aquatic Respirometry
Description: Calculates metabolic rate of fish and other aquatic organisms measured using an intermittent-flow respirometry approach. The tool is used to run a set of graphical QC tests of raw respirometry data, correct it for background respiration and chamber effect, filter and extract target values of absolute and mass-specific metabolic rate. Experimental design should include background respiration tests and measuring of one or more metabolic rate traits. The R package is ideally integrated with the pump controller 'PumpResp' and the DO meter 'SensResp' (open-source hardware by FishResp). Raw respirometry data can be also imported from 'AquaResp' (free software), 'AutoResp' ('LoligoSystems'), 'OxyView' ('PreSens'), 'Pyro Oxygen Logger' ('PyroScience') and 'Q-box Aqua' ('QubitSystems'). More information about the R package 'FishResp'is available in the publication by Morozov et al. (2019) <doi:10.1093/conphys/coz003>.
Author: Sergey Morozov [aut, cre], Scott McCairns [aut], Juha Merila [ctb]
Maintainer: Sergey Morozov <sergey.morozov@helsinki.fi>

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Package RGraphSpace updated to version 1.0.7 with previous version 1.0.6 dated 2024-08-18

Title: A Lightweight Interface Between 'ggplot2' and 'igraph' Objects
Description: Interface to integrate 'igraph' and 'ggplot2' graphics within spatial maps. 'RGraphSpace' implements new geometric objects using 'ggplot2' prototypes, customized for representing large 'igraph' objects in a normalized coordinate system. By scaling shapes and graph elements, 'RGraphSpace' can provide a framework for layered visualizations.
Author: Victor Apolonio [ctb], Vinicius Chagas [ctb], Mauro Castro [aut, cre]
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>

Diff between RGraphSpace versions 1.0.6 dated 2024-08-18 and 1.0.7 dated 2024-11-12

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Package geeVerse updated to version 0.2.2 with previous version 0.2.1 dated 2024-07-29

Title: A Comprehensive Analysis of High Dimensional Longitudinal Data
Description: To provide a comprehensive analysis of high dimensional longitudinal data,this package provides analysis for any combination of 1) simultaneous variable selection and estimation, 2) mean regression or quantile regression for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced or imbalanced data, 5) moderate, high or even ultra-high dimensional data, via computationally efficient implementations of penalized generalized estimating equations.
Author: Tianhai Zu [aut, cre], Brittany Green [aut, ctb], Yan Yu [aut, ctb]
Maintainer: Tianhai Zu <zuti@mail.uc.edu>

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New package forestGYM with initial version 1.0.0
Package: forestGYM
Title: Forest Growth and Yield Model Based on Clutter Model
Version: 1.0.0
Depends: R (>= 3.5.0)
Imports: gtools(>= 3.8.5),stats(>= 4.3.1)
Description: The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021)<doi:10.1007/s10342-021-01380-1>. Guera O, Silva J, Ferreira R, et al(2019)<doi:10.1590/2179-8087.038117>.
License: GPL-2
LazyData: TRUE
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-11-12 01:39:36 UTC; 78438
Author: Zongzheng Chai [aut, cre]
Maintainer: Zongzheng Chai <chaizz@126.com>
Repository: CRAN
Date/Publication: 2024-11-12 10:20:05 UTC

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Package condor updated to version 3.0.0 with previous version 2.1.0 dated 2023-08-27

Title: Interact with 'Condor' from R via SSH
Description: Interact with 'Condor' from R via SSH connection. Files are first uploaded from user machine to submitter machine, and the job is then submitted from the submitter machine to 'Condor'. Functions are provided to submit, list, and download 'Condor' jobs from R. 'Condor' is an open source high-throughput computing software framework for distributed parallelization of computationally intensive tasks.
Author: Arni Magnusson [aut, cre], Nan Yao [aut], Jemery Day [ctb], Thomas Teears [ctb]
Maintainer: Arni Magnusson <thisisarni@gmail.com>

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Package bundle updated to version 0.1.2 with previous version 0.1.1 dated 2023-09-09

Title: Serialize Model Objects with a Consistent Interface
Description: Typically, models in 'R' exist in memory and can be saved via regular 'R' serialization. However, some models store information in locations that cannot be saved using 'R' serialization alone. The goal of 'bundle' is to provide a common interface to capture this information, situate it within a portable object, and restore it for use in new settings.
Author: Julia Silge [aut, cre] , Simon Couch [aut], Qiushi Yan [aut], Max Kuhn [aut], Posit Software, PBC [cph, fnd]
Maintainer: Julia Silge <julia.silge@posit.co>

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Package Bolstad updated to version 0.2.42 with previous version 0.2-41 dated 2020-10-05

Title: Functions for Elementary Bayesian Inference
Description: A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.
Author: James Curran [aut, cre]
Maintainer: James Curran <j.curran@auckland.ac.nz>

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Package REffectivePred updated to version 1.0.1 with previous version 1.0.0 dated 2024-02-02

Title: Pandemic Prediction Model in an SIRS Framework
Description: A suite of methods to fit and predict case count data using a compartmental SIRS (Susceptible – Infectious – Recovered – Susceptible) model, based on an assumed specification of the effective reproduction number. The significance of this approach is that it relates epidemic progression to the average number of contacts of infected individuals, which decays as a function of the total susceptible fraction remaining in the population. The main functions are pred.curve(), which computes the epidemic curve for a set of parameters, and estimate.mle(), which finds the best fitting curve to observed data. The easiest way to pass arguments to the functions is via a config file, which contains input settings required for prediction, and the package offers two methods, navigate_to_config() which points the user to the configuration file, and re_predict() for starting the fit-predict process. The main model was published in Razvan G. Romanescu et al. <doi:10.1016/j.epidem.2023.100708>.
Author: Razvan Romanescu [aut, cre], Songdi Hu [aut], Md Ashiqul Haque [aut], Olivier Tremblay-Savard [aut]
Maintainer: Razvan Romanescu <razvan_romanescu@hotmail.com>

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Package PEPBVS updated to version 2.1 with previous version 2.0 dated 2024-10-30

Title: Bayesian Variable Selection using Power-Expected-Posterior Prior
Description: Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior distributions either the power-expected-posterior (PEP) or the intrinsic (a special case of the former) (Fouskakis and Ntzoufras (2022) <doi: 10.1214/21-BA1288>, Fouskakis and Ntzoufras (2020) <doi: 10.3390/econometrics8020017>). The prior distribution on model space is the uniform over all models or the uniform on model dimension (a special case of the beta-binomial prior). The selection is performed by either implementing a full enumeration and evaluation of all possible models or using the Markov Chain Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi: 10.2307/1403615>). Complementary functions for hypothesis testing, estimation and predictions under Bayesian model averaging, as well as, plotting and printing the results are also provided. The results can be compared to the ones obtained under other well-known priors on [...truncated...]
Author: Konstantina Charmpi [aut, cre], Dimitris Fouskakis [aut], Ioannis Ntzoufras [aut]
Maintainer: Konstantina Charmpi <xarmpi.kon@gmail.com>

Diff between PEPBVS versions 2.0 dated 2024-10-30 and 2.1 dated 2024-11-12

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Package LearnNonparam updated to version 1.2.4 with previous version 1.2.3 dated 2024-10-15

Title: 'R6'-Based Flexible Framework for Permutation Tests
Description: Implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with 'Rcpp' for efficiency and 'R6' for flexible, object-oriented design, the package provides a unified framework for performing or creating custom permutation tests.
Author: Yan Du [aut, cre]
Maintainer: Yan Du <isduyan@outlook.com>

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Package Ecfun updated to version 0.3-6 with previous version 0.3-2 dated 2022-10-09

Title: Functions for 'Ecdat'
Description: Functions and vignettes to update data sets in 'Ecdat' and to create, manipulate, plot, and analyze those and similar data sets.
Author: Spencer Graves [aut, cre]
Maintainer: Spencer Graves <spencer.graves@effectivedefense.org>

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Package bootcluster updated to version 0.4.1 with previous version 0.3.2 dated 2022-01-29

Title: Bootstrapping Estimates of Clustering Stability
Description: Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>. Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) <doi:10.1002/sam.11593>. Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method.
Author: Han Yu [aut], Mingmei Tian [aut], Tianmou Liu [aut, cre]
Maintainer: Tianmou Liu <tianmouliu@outlook.com>

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Package netseer updated to version 0.1.1 with previous version 0.1.0 dated 2024-02-21

Title: Graph Prediction from a Graph Time Series
Description: Predicting the structure of a graph including new nodes and edges using a time series of graphs. Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2024) <doi:10.48550/arXiv.2401.04280>.
Author: Sevvandi Kandanaarachchi [aut, cre]
Maintainer: Sevvandi Kandanaarachchi <sevvandik@gmail.com>

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Package greta updated to version 0.5.0 with previous version 0.4.5 dated 2024-03-11

Title: Simple and Scalable Statistical Modelling in R
Description: Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.
Author: Nick Golding [aut] , Nicholas Tierney [aut, cre] , Simon Dirmeier [ctb], Adam Fleischhacker [ctb], Shirin Glander [ctb], Martin Ingram [ctb], Lee Hazel [ctb], Lionel Hertzog [ctb], Tiphaine Martin [ctb], Matt Mulvahill [ctb], Michael Quinn [ctb], Dav [...truncated...]
Maintainer: Nicholas Tierney <nicholas.tierney@gmail.com>

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