Thu, 28 Nov 2024

Package DPTM updated to version 1.6.0 with previous version 1.5.0 dated 2024-08-17

Title: Dynamic Panel Multiple Threshold Model with Fixed Effects
Description: Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) <doi:10.1080/07474938.2019.1624401>, and dynamic panel linear model suggested by Hsiao et al. (2002) <doi:10.1016/S0304-4076(01)00143-9>, where maximum likelihood type estimators are used. Multiple threshold estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed.
Author: Bai Hujie [aut, cre, cph]
Maintainer: Bai Hujie <hujiebai@163.com>

Diff between DPTM versions 1.5.0 dated 2024-08-17 and 1.6.0 dated 2024-11-28

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Package verification updated to version 1.44 with previous version 1.42 dated 2015-07-14

Title: Weather Forecast Verification Utilities
Description: Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.
Author: Eric Gilleland [aut, cre] , Matt Pocernich [ctb], Sabrina Wahl [ctb], Ronald Frenette [ctb]
Maintainer: Eric Gilleland <eric.gilleland@colostate.edu>

Diff between verification versions 1.42 dated 2015-07-14 and 1.44 dated 2024-11-28

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Package smoothie updated to version 1.0-4 with previous version 1.0-3 dated 2021-05-31

Title: Two-Dimensional Field Smoothing
Description: Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).
Author: Eric Gilleland [aut, cre]
Maintainer: Eric Gilleland <eric.gilleland@colostate.edu>

Diff between smoothie versions 1.0-3 dated 2021-05-31 and 1.0-4 dated 2024-11-28

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New package rsofun with initial version 5.0.0
Package: rsofun
Title: The P-Model and BiomeE Modelling Framework
Version: 5.0.0
Description: Implements the Simulating Optimal FUNctioning framework for site-scale simulations of ecosystem processes, including model calibration. It contains 'Fortran 90' modules for the P-model (Stocker et al. (2020) <doi:10.5194/gmd-13-1545-2020>), SPLASH (Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>) and BiomeE (Weng et al. (2015) <doi:10.5194/bg-12-2655-2015>).
URL: https://github.com/geco-bern/rsofun
BugReports: https://github.com/geco-bern/rsofun/issues
License: GPL-3
Depends: R (>= 4.1.0)
Imports: dplyr, purrr, tidyr, magrittr, GenSA, BayesianTools, multidplyr, stats, utils
LazyData: true
LazyDataCompression: xz
ByteCompile: true
NeedsCompilation: yes
Suggests: covr, rcmdcheck, testthat, rmarkdown, ggplot2, knitr, sensitivity
VignetteBuilder: knitr
Encoding: UTF-8
Packaged: 2024-11-28 13:06:16 UTC; mmarcadella
Author: Benjamin Stocker [aut, cre] , Koen Hufkens [aut] , Josefa Aran Paredes [aut] , Laura Marques [ctb] , Mayeul Marcadella [ctb] , Ensheng Weng [ctb] , Fabian Bernhard [aut] , Geocomputation and Earth Observation, University of Bern [cph, fnd]
Maintainer: Benjamin Stocker <benjamin.stocker@gmail.com>
Repository: CRAN
Date/Publication: 2024-11-28 23:10:02 UTC

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Package monolix2rx updated to version 0.0.4 with previous version 0.0.3 dated 2024-10-24

Title: Converts 'Monolix' Models to 'rxode2'
Description: 'Monolix' is a tool for running mixed effects model using 'saem'. This tool allows you to convert 'Monolix' models to 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the form compatible with 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). If available, the 'rxode2' model will read in the 'Monolix' data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not required, you c [...truncated...]
Author: Matthew Fidler [aut, cre] , Justin Wilkins [ctb]
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>

Diff between monolix2rx versions 0.0.3 dated 2024-10-24 and 0.0.4 dated 2024-11-28

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Package isocountry updated to version 0.3.0 with previous version 0.2.0 dated 2024-04-09

Title: ISO 3166-1 Country Codes
Description: ISO 3166-1 country codes and ISO 4217 currency codes provided by the International Organization for Standardization.
Author: Maximilian Muecke [aut, cre]
Maintainer: Maximilian Muecke <muecke.maximilian@gmail.com>

Diff between isocountry versions 0.2.0 dated 2024-04-09 and 0.3.0 dated 2024-11-28

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Package ifo updated to version 0.2.0 with previous version 0.1.0 dated 2024-06-06

Title: Client for the Ifo Institute Time Series
Description: Download ifo business survey data and more time series from ifo institute <https://www.ifo.de/en/ifo-time-series>.
Author: Maximilian Muecke [aut, cre]
Maintainer: Maximilian Muecke <muecke.maximilian@gmail.com>

Diff between ifo versions 0.1.0 dated 2024-06-06 and 0.2.0 dated 2024-11-28

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Package drape updated to version 0.0.2 with previous version 0.0.1 dated 2023-09-18

Title: Doubly Robust Average Partial Effects
Description: Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) <doi:10.48550/arXiv.2308.09207>.
Author: Harvey Klyne [aut, cre, cph]
Maintainer: Harvey Klyne <hck33@cantab.ac.uk>

Diff between drape versions 0.0.1 dated 2023-09-18 and 0.0.2 dated 2024-11-28

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Package distillery updated to version 1.2-2 with previous version 1.2-1 dated 2021-05-19

Title: Method Functions for Confidence Intervals and to Distill Information from an Object
Description: Some very simple method functions for confidence interval calculation, bootstrap resampling aimed at atmospheric science applications, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx. To reference this package and for a tutorial on the bootstrap functions, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0069.1> and Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.
Author: Eric Gilleland [aut, cre]
Maintainer: Eric Gilleland <eric.gilleland@colostate.edu>

Diff between distillery versions 1.2-1 dated 2021-05-19 and 1.2-2 dated 2024-11-28

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Package eudract updated to version 1.0.2 with previous version 1.0.1 dated 2024-08-23

Title: Creates Safety Results Summary in XML to Upload to EudraCT, or ClinicalTrials.gov
Description: The remit of the European Clinical Trials Data Base (EudraCT <https://eudract.ema.europa.eu/> ), or ClinicalTrials.gov <https://clinicaltrials.gov/>, is to provide open access to summaries of all registered clinical trial results; thus aiming to prevent non-reporting of negative results and provide open-access to results to inform future research. The amount of information required and the format of the results, however, imposes a large extra workload at the end of studies on clinical trial units. In particular, the adverse-event-reporting component requires entering: each unique combination of treatment group and safety event; for every such event above, a further 4 pieces of information (body system, number of occurrences, number of subjects, number exposed) for non-serious events, plus an extra three pieces of data for serious adverse events (numbers of causally related events, deaths, causally related deaths). This package prepares the required statistics needed by Eudr [...truncated...]
Author: Simon Bond [cre], Beatrice Pantaleo [aut]
Maintainer: Simon Bond <simon.bond7@nhs.net>

Diff between eudract versions 1.0.1 dated 2024-08-23 and 1.0.2 dated 2024-11-28

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Package PatientProfiles updated to version 1.2.2 with previous version 1.2.1 dated 2024-10-25

Title: Identify Characteristics of Patients in the OMOP Common Data Model
Description: Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.
Author: Marti Catala [aut, cre] , Yuchen Guo [aut] , Mike Du [aut] , Kim Lopez-Guell [aut] , Edward Burn [aut] , Nuria Mercade-Besora [aut] , Xintong Li [ctb] , Xihang Chen [ctb]
Maintainer: Marti Catala <marti.catalasabate@ndorms.ox.ac.uk>

Diff between PatientProfiles versions 1.2.1 dated 2024-10-25 and 1.2.2 dated 2024-11-28

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Package DQAgui updated to version 0.2.5 with previous version 0.2.4 dated 2024-06-06

Title: Graphical User Interface for Data Quality Assessment
Description: A graphical user interface (GUI) to the functions implemented in the R package 'DQAstats'. Publication: Mang et al. (2021) <doi:10.1186/s12911-022-01961-z>.
Author: Lorenz A. Kapsner [cre, aut] , Jonathan M. Mang [aut] , Helene Koester [ctb], MIRACUM - Medical Informatics in Research and Care in University Medicine [fnd], Universitaetsklinikum Erlangen [cph]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner@gmail.com>

Diff between DQAgui versions 0.2.4 dated 2024-06-06 and 0.2.5 dated 2024-11-28

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Package rintcal updated to version 1.1.0 with previous version 1.0.0 dated 2024-09-23

Title: Radiocarbon Calibration Curves
Description: The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98), other curves (e.g., NOTCal04 [van der Plicht et al. 2004], Arnold & Libby 1951) and postbomb curves. Also provided are functions to copy the curves into memory, and to read, query and plot the data underlying the IntCal20 curves.
Author: Maarten Blaauw [aut, cre]
Maintainer: Maarten Blaauw <maarten.blaauw@qub.ac.uk>

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Package ReporterScore updated to version 0.1.9 with previous version 0.1.8 dated 2024-08-26

Title: Generalized Reporter Score-Based Enrichment Analysis for Omics Data
Description: Inspired by the classic 'RSA', we developed the improved 'Generalized Reporter Score-based Analysis (GRSA)' method, implemented in the R package 'ReporterScore', along with comprehensive visualization methods and pathway databases. 'GRSA' is a threshold-free method that works well with all types of biomedical features, such as genes, chemical compounds, and microbial species. Importantly, the 'GRSA' supports multi-group and longitudinal experimental designs, because of the included multi-group-compatible statistical methods.
Author: Chen Peng [aut, cre]
Maintainer: Chen Peng <pengchen2001@zju.edu.cn>

Diff between ReporterScore versions 0.1.8 dated 2024-08-26 and 0.1.9 dated 2024-11-28

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Package sgs updated to version 0.3.2 with previous version 0.3.1 dated 2024-11-16

Title: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control
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.
Author: Fabio Feser [aut, cre]
Maintainer: Fabio Feser <ff120@ic.ac.uk>

Diff between sgs versions 0.3.1 dated 2024-11-16 and 0.3.2 dated 2024-11-28

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Package prim updated to version 1.0.22 with previous version 1.0.21 dated 2023-01-06

Title: Patient Rule Induction Method (PRIM)
Description: Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.
Author: Tarn Duong [aut, cre]
Maintainer: Tarn Duong <tarn.duong@gmail.com>

Diff between prim versions 1.0.21 dated 2023-01-06 and 1.0.22 dated 2024-11-28

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Package grpsel updated to version 1.3.2 with previous version 1.3.1 dated 2022-09-07

Title: Group Subset Selection
Description: Provides tools for sparse regression modelling with grouped predictors using the group subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver near optimal estimates. The group subset penalty can be combined with a group lasso or ridge penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping groups.
Author: Ryan Thompson [aut, cre]
Maintainer: Ryan Thompson <ryan.thompson-1@uts.edu.au>

Diff between grpsel versions 1.3.1 dated 2022-09-07 and 1.3.2 dated 2024-11-28

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Package crossnma updated to version 1.3.0 with previous version 1.2.0 dated 2023-09-18

Title: Cross-Design & Cross-Format Network Meta-Analysis and Regression
Description: Network meta-analysis and meta-regression (allows including up to three covariates) for individual participant data, aggregate data, and mixtures of both formats using the three-level hierarchical model. Each format can come from randomized controlled trials or non-randomized studies or mixtures of both. Estimates are generated in a Bayesian framework using JAGS. The implemented models are described by Hamza et al. 2023 <DOI:10.1002/jrsm.1619>.
Author: Tasnim Hamza [aut] , Guido Schwarzer [aut, cre] , Georgia Salanti [aut]
Maintainer: Guido Schwarzer <guido.schwarzer@uniklinik-freiburg.de>

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Package CohortSurvival updated to version 0.6.1 with previous version 0.6.0 dated 2024-11-10

Title: Estimate Survival from Common Data Model Cohorts
Description: Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts.
Author: Edward Burn [aut, cre] , Kim Lopez-Gueell [aut] , Marti Catala [aut] , Xintong Li [aut] , Danielle Newby [aut] , Nuria Mercade-Besora [aut]
Maintainer: Edward Burn <edward.burn@ndorms.ox.ac.uk>

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New package TAD with initial version 1.0.0
Package: TAD
Title: Realize the Trait Abundance Distribution
Version: 1.0.0
Description: This analytical framework is based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).
License: BSD_3_clause + file LICENSE
URL: https://forgemia.inra.fr/urep/data_processing/tad
BugReports: https://forgemia.inra.fr/urep/data_processing/tad/-/issues
Encoding: UTF-8
Depends: R (>= 3.5)
Imports: doFuture, foreach, mblm (>= 0.12), methods, stats
Suggests: Cairo, covr, dplyr, devtools, future (>= 1.33), ggplot2 (>= 3.5), ggpubr (>= 0.6), knitr, Matrix (>= 1.6), pkgdown, rlang, rmarkdown, roxygen2, testthat (>= 3.0), tinytex
Language: en-US
VignetteBuilder: knitr
LazyData: true
LazyDataCompression: bzip2
NeedsCompilation: no
Packaged: 2024-11-28 09:59:57 UTC; lain
Author: Nathan Rondeau [aut], Yoann Le Bagousse-Pinguet [aut] , Raphael Martin [aut] , Lain Pavot [aut, cre], Pierre Liancourt [aut] , Nicolas Gross [aut] , INRAe/UREP [cph]
Maintainer: Lain Pavot <lain.pavot@inrae.fr>
Repository: CRAN
Date/Publication: 2024-11-28 12:20:02 UTC

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New package qVarSel with initial version 1.1
Package: qVarSel
Title: Select Variables for Optimal Clustering
Version: 1.1
Date: 2024-11-24
Description: Finding hidden clusters in structured data can be hindered by the presence of masking variables. If not detected, masking variables are used to calculate the overall similarities between units, and therefore the cluster attribution is more imprecise. The algorithm q-vars implements an optimization method to find the variables that most separate units between clusters. In this way, masking variables can be discarded from the data frame and the clustering is more accurate. Tests can be found in Benati et al.(2017) <doi:10.1080/01605682.2017.1398206>.
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.13), lpSolveAPI
Suggests: mclust
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2024-11-28 08:01:14 UTC; stefanobenati
Author: Stefano Benati [aut, cre]
Maintainer: Stefano Benati <stefano.benati@unitn.it>
Repository: CRAN
Date/Publication: 2024-11-28 12:10:02 UTC

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Package dfr updated to version 0.1.2 with previous version 0.1.1 dated 2024-11-16

Title: Dual Feature Reduction for SGL
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.
Author: Fabio Feser [aut, cre]
Maintainer: Fabio Feser <ff120@ic.ac.uk>

Diff between dfr versions 0.1.1 dated 2024-11-16 and 0.1.2 dated 2024-11-28

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New package samplezoo with initial version 1.1.0
Package: samplezoo
Title: Generate Samples with a Variety of Probability Distributions
Version: 1.1.0
Maintainer: Nicholas Vietto <nicholasvietto@gmail.com>
Description: The 'samplezoo' package streamlines the process of generating samples from various probability distributions, enabling users to quickly create data frames for demonstrations, troubleshooting, or teaching. By prioritizing simplicity and efficiency,'samplezoo' reduces the need for repetitive code, making it particularly useful for beginners or anyone seeking to save time. The package implements standard methods for generating random samples from probability distributions commonly available in base R, with no specific external references. For more details, visit the package documentation.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://github.com/nvietto/samplezoo, https://nvietto.github.io/samplezoo/
BugReports: https://github.com/nvietto/samplezoo/issues
NeedsCompilation: no
Packaged: 2024-11-27 16:30:58 UTC; nicholasvietto
Author: Nicholas Vietto [aut, cre, cph]
Repository: CRAN
Date/Publication: 2024-11-28 11:40:03 UTC

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New package saeHB.twofold with initial version 0.1.2
Package: saeHB.twofold
Title: Hierarchical Bayes Twofold Subarea Level Model SAE
Version: 0.1.2
Maintainer: Reyhan Saadi <reyhansaadi335@gmail.com>
Description: We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 2.10)
Imports: rjags, coda, stringr, stats, grDevices, graphics, data.table, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net)
VignetteBuilder: knitr
URL: https://github.com/reymath99/saeHB.twofold
BugReports: https://github.com/reymath99/saeHB.twofold/issues
NeedsCompilation: no
Packaged: 2024-11-27 10:39:18 UTC; USER10
Author: Reyhan Saadi [aut, cre], Azka Ubaidillah [aut]
Repository: CRAN
Date/Publication: 2024-11-28 11:20:03 UTC

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New package LLIC with initial version 3.0.0
Package: LLIC
Title: Likelihood Criterion (LIC) Analysis for Laplace Regression Model
Version: 3.0.0
Date: 2024-11-23
Description: Performs likelihood criterion analysis using the Laplace regression model to determine its optimal subset of variables. The methodology is based on Guo et al. (2023), LIC criterion for optimal subset selection in distributed interval estimation <doi:10.1080/02331888.2020.1823979>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: stats, VGAM, dplyr, LaplacesDemon, relliptical, ggplot2, rlang
NeedsCompilation: no
Packaged: 2024-11-23 10:48:14 UTC; Lenovo
Author: Guangbao Guo [aut, cre], Yaxuan Wang [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2024-11-28 12:00:02 UTC

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New package interpret with initial version 0.1.34
Package: interpret
Title: Fit Interpretable Machine Learning Models
Version: 0.1.34
Date: 2024-11-28
Description: Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
URL: https://github.com/interpretml/interpret
BugReports: https://github.com/interpretml/interpret/issues
License: MIT + file LICENSE
Depends: R (>= 3.0.0)
NeedsCompilation: yes
SystemRequirements: C++17
Packaged: 2024-11-28 10:45:55 UTC; runner
Author: Samuel Jenkins [aut], Harsha Nori [aut], Paul Koch [aut], Rich Caruana [aut, cre], The InterpretML Contributors [cph]
Maintainer: Rich Caruana <interpretml@outlook.com>
Repository: CRAN
Date/Publication: 2024-11-28 11:40:08 UTC

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New package HTSeedGLM with initial version 0.1.0
Package: HTSeedGLM
Title: Hydro Thermal Time Analysis of Seed Germination Using Generalised Linear Model
Description: Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is [...truncated...]
Author: Dr. Himadri Ghosh [aut, cre], Mr. Saikath Das [aut], Dr. Debopam Rakshit [aut]
Maintainer: Dr. Himadri Ghosh <hghosh@gmail.com>
Imports: stats
Version: 0.1.0
Encoding: UTF-8
License: GPL-3
NeedsCompilation: no
Packaged: 2024-11-27 14:50:58 UTC; Debopam
Repository: CRAN
Date/Publication: 2024-11-28 11:30:21 UTC

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New package epiparameterDB with initial version 0.1.0
Package: epiparameterDB
Title: Database of Epidemiological Parameters
Version: 0.1.0
Description: A data package containing a database of epidemiological parameters. It stores the data for the 'epiparameter' R package. Epidemiological parameter estimates are extracted from the literature.
License: CC0
URL: https://github.com/epiverse-trace/epiparameterDB/
BugReports: https://github.com/epiverse-trace/epiparameterDB/issues
Depends: R (>= 4.0.0)
Suggests: DT, jsonlite, knitr, rmarkdown, spelling, testthat (>= 3.0.0)
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-GB
LazyData: true
NeedsCompilation: no
Packaged: 2024-11-27 15:57:54 UTC; lshjl15
Author: Joshua W. Lambert [cre, aut, cph] , Adam Kucharski [aut] , Carmen Tamayo [aut]
Maintainer: Joshua W. Lambert <joshua.lambert@lshtm.ac.uk>
Repository: CRAN
Date/Publication: 2024-11-28 11:40:11 UTC

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New package bayeslist with initial version 0.0.1.4
Package: bayeslist
Title: Bayesian Analysis of List Experiments with Prior Information
Version: 0.0.1.4
Author: Xiao Lu [aut, cre], Richard Traunmueller [aut]
Maintainer: Xiao Lu <xiao.lu.research@gmail.com>
Description: Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2021) <doi:10.2139/ssrn.3871089> for examples and details of estimation.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Biarch: true
Depends: R (>= 3.5.0)
Imports: methods, Formula, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>= 2.1.1), ggplot2 (>= 3.3.3)
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2024-11-27 15:23:45 UTC; xiao
Repository: CRAN
Date/Publication: 2024-11-28 11:30:05 UTC

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Package BAS updated to version 1.7.5 with previous version 1.7.3 dated 2024-09-17

Title: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Description: Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform prior [...truncated...]
Author: Merlise Clyde [aut, cre, cph] , Michael Littman [ctb], Joyee Ghosh [ctb], Yingbo Li [ctb], Betsy Bersson [ctb], Don van de Bergh [ctb], Quanli Wang [ctb]
Maintainer: Merlise Clyde <clyde@duke.edu>

Diff between BAS versions 1.7.3 dated 2024-09-17 and 1.7.5 dated 2024-11-28

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Package SudokuDesigns updated to version 1.1.0 with previous version 1.0.0 dated 2024-10-31

Title: Sudoku as an Experimental Design
Description: Sudoku designs (Bailey et al., 2008<doi:10.1080/00029890.2008.11920542>) can be used as experimental designs which tackle one extra source of variation than conventional Latin square designs. Although Sudoku designs are similar to Latin square designs, only addition is the region concept. Some very important functions related to row-column designs as well as block designs along with basic functions are included in this package.
Author: Ashutosh Dalal [aut, cre], Cini Varghese [aut, ctb], Rajender Parsad [aut, ctb], Mohd Harun [aut, ctb]
Maintainer: Ashutosh Dalal <ashutosh.dalal97@gmail.com>

Diff between SudokuDesigns versions 1.0.0 dated 2024-10-31 and 1.1.0 dated 2024-11-28

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Package priorCON updated to version 0.1.3 with previous version 0.1.2 dated 2024-11-06

Title: Graph Community Detection Methods into Systematic Conservation Planning
Description: An innovative tool-set that incorporates graph community detection methods into systematic conservation planning. It is designed to enhance spatial prioritization by focusing on the protection of areas with high ecological connectivity. Unlike traditional approaches that prioritize individual planning units, 'priorCON' focuses on clusters of features that exhibit strong ecological linkages. The 'priorCON' package is built upon the 'prioritizr' package <doi:10.32614/CRAN.package.prioritizr>, using commercial and open-source exact algorithm solvers that ensure optimal solutions to prioritization problems.
Author: Christos Adam [aut, cre] , Aggeliki Doxa [aut] , Nikolaos Nagkoulis [aut] , Maria Papazekou [aut] , Antonios D. Mazaris [aut] , Stelios Katsanevakis [aut]
Maintainer: Christos Adam <econp266@econ.soc.uoc.gr>

Diff between priorCON versions 0.1.2 dated 2024-11-06 and 0.1.3 dated 2024-11-28

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Package mlr3misc updated to version 0.16.0 with previous version 0.15.1 dated 2024-06-24

Title: Helper Functions for 'mlr3'
Description: Frequently used helper functions and assertions used in 'mlr3' and its companion packages. Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'.
Author: Marc Becker [cre, aut] , Michel Lang [aut] , Patrick Schratz [aut]
Maintainer: Marc Becker <marcbecker@posteo.de>

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Package familial updated to version 1.0.6 with previous version 1.0.5 dated 2023-06-26

Title: Statistical Tests of Familial Hypotheses
Description: Provides functionality for testing familial hypotheses. Supports testing centers belonging to the Huber family. Testing is carried out using the Bayesian bootstrap. One- and two-sample tests are supported, as are directional tests. Methods for visualizing output are provided.
Author: Ryan Thompson [aut, cre]
Maintainer: Ryan Thompson <ryan.thompson-1@uts.edu.au>

Diff between familial versions 1.0.5 dated 2023-06-26 and 1.0.6 dated 2024-11-28

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Package GPIC (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:

2021-03-01 0.1.0

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Package dataCompare updated to version 1.0.5 with previous version 1.0.4 dated 2024-11-21

Title: A 'shiny' App to Compare Two Data Frames
Description: A tool developed with the 'Golem' framework which provides an easier way to check cells differences between two data frames. The user provides two data frames for comparison, selects IDs variables identifying each row of input data, then clicks a button to perform the comparison. Several 'R' package functions are used to describe the data and perform the comparison in the server of the application. The main ones are comparedf() from 'arsenal' and skim() from 'skimr'. For more details see the description of comparedf() from the 'arsenal' package and that of skim() from the 'skimr' package.
Author: Sergio Ewane Ebouele [aut, cre]
Maintainer: Sergio Ewane Ebouele <info@dataforknow.com>

Diff between dataCompare versions 1.0.4 dated 2024-11-21 and 1.0.5 dated 2024-11-28

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Package CompExpDes updated to version 1.0.6 with previous version 1.0.5 dated 2024-11-04

Title: Computer Experiment Designs
Description: In computer experiments space-filling designs are having great impact. Most popularly used space-filling designs are Uniform designs (UDs), Latin hypercube designs (LHDs) etc. For further references one can see Mckay (1979) <DOI:10.1080/00401706.1979.10489755> and Fang (1980) <https://cir.nii.ac.jp/crid/1570291225616774784>. In this package, we have provided algorithms for generate efficient LHDs and UDs. Here, generated LHDs are efficient as they possess lower value of Maxpro measure, Phi_p value and Maximum Absolute Correlation (MAC) value based on the weightage given to each criterion. On the other hand, the produced UDs are having good space-filling property as they always attain the lower bound of Discrete Discrepancy measure. Further, some useful functions added in this package for adding more value to this package.
Author: Ashutosh Dalal [aut, cre], Cini Varghese [aut, ctb], Rajender Parsad [aut, ctb], Mohd Harun [aut, ctb]
Maintainer: Ashutosh Dalal <ashutosh.dalal97@gmail.com>

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Package callme updated to version 0.1.11 with previous version 0.1.10 dated 2024-07-27

Title: Easily Compile and Call Inline 'C' Functions
Description: Compile inline 'C' code and easily call with automatically generated wrapper functions. By allowing user-defined headers and compilation flags (preprocessor, compiler and linking flags) the user can configure optimization options and linking to third party libraries. Multiple functions may be defined in a single block of code - which may be defined in a string or a path to a source file.
Author: Mike Cheng [aut, cre, cph]
Maintainer: Mike Cheng <mikefc@coolbutuseless.com>

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