Mon, 09 Dec 2024

Package ShapePattern updated to version 3.1.0 with previous version 3.0.1 dated 2023-08-22

Title: Tools for Analyzing Shapes and Patterns
Description: This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting th [...truncated...]
Author: Tarmo K. Remmel [aut, cre] , Marie-Josee Fortin [ctb], Ferenc Csillag [ctb], Sandor Kabos [ctb]
Maintainer: Tarmo K. Remmel <remmelt@yorku.ca>

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Package epiR updated to version 2.0.78 with previous version 2.0.77 dated 2024-11-19

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 [aut, cre] , Evan Sergeant [aut], Cord Heuer [ctb], Telmo Nunes [ctb], Cord Heuer [ctb], Jonathon Marshall [ctb], Javier Sanchez [ctb], Ron Thornton [ctb], Jeno Reiczigel [ctb], Jim Robison-Cox [ctb], Paola Sebastiani [ctb], Peter Soly [...truncated...]
Maintainer: Mark Stevenson <mark.stevenson1@unimelb.edu.au>

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Package familial updated to version 1.0.7 with previous version 1.0.6 dated 2024-11-28

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>

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Package DrugUtilisation updated to version 0.8.0 with previous version 0.7.1 dated 2024-09-06

Title: Summarise Patient-Level Drug Utilisation in Data Mapped to the OMOP Common Data Model
Description: Summarise patient-level drug utilisation cohorts using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. New users and prevalent users cohorts can be generated and their characteristics, indication and drug use summarised.
Author: Marti Catala [aut, cre] , Mike Du [ctb] , Yuchen Guo [aut] , Kim Lopez-Guell [aut] , Edward Burn [aut] , Xintong Li [ctb] , Marta Alcalde-Herraiz [ctb] , Nuria Mercade-Besora [aut] , Xihang Chen [aut]
Maintainer: Marti Catala <marti.catalasabate@ndorms.ox.ac.uk>

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Package crm12Comb updated to version 0.1.9 with previous version 0.1.8 dated 2024-11-03

Title: Phase I/II CRM Based Drug Combination Design
Description: Implements the adaptive designs for integrated phase I/II trials of drug combinations via continual reassessment method (CRM) to evaluate toxicity and efficacy simultaneously for each enrolled patient cohort based on Bayesian inference. It supports patients assignment guidance in a single trial using current enrolled data, as well as conducting extensive simulation studies to evaluate operating characteristics before the trial starts. It includes various link functions such as empiric, one-parameter logistic, two-parameter logistic, and hyperbolic tangent, as well as considering multiple prior distributions of the parameters like normal distribution, gamma distribution and exponential distribution to accommodate diverse clinical scenarios. Method using Bayesian framework with empiric link function is described in: Wages and Conaway (2014) <doi:10.1002/sim.6097>.
Author: Junying Wang [cre, aut], Song Wu [aut], Jie Yang [aut]
Maintainer: Junying Wang <junying.wang@stonybrook.edu>

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Package AtmChile updated to version 1.0.2 with previous version 1.0.1 dated 2022-08-29

Title: Download Air Quality and Meteorological Information of Chile
Description: Download air quality and meteorological information of Chile from the National Air Quality System (S.I.N.C.A.)<https://sinca.mma.gob.cl/> dependent on the Ministry of the Environment and the Meteorological Directorate of Chile (D.M.C.)<https://www.meteochile.gob.cl/> dependent on the Directorate General of Civil Aeronautics.
Author: Francisco Catalan Meyer [aut, cre] , Manuel Leiva [aut] , Richard Toro [aut]
Maintainer: Francisco Catalan Meyer <francisco.catalan@ug.uchile.cl>

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Package AssocBin updated to version 1.0-2 with previous version 1.0-0 dated 2024-06-28

Title: Measuring Association with Recursive Binning
Description: An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a complete description of the functionality and algorithm, see Salahub and Oldford (2023) <doi:10.48550/arXiv.2311.08561>.
Author: Chris Salahub [aut, cre]
Maintainer: Chris Salahub <chris.salahub@uwaterloo.ca>

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Package caret updated to version 7.0-1 with previous version 6.0-94 dated 2023-03-21

Title: Classification and Regression Training
Description: Misc functions for training and plotting classification and regression models.
Author: Max Kuhn [aut, cre] , Jed Wing [ctb], Steve Weston [ctb], Andre Williams [ctb], Chris Keefer [ctb], Allan Engelhardt [ctb], Tony Cooper [ctb], Zachary Mayer [ctb], Brenton Kenkel [ctb], R Core Team [ctb], Michael Benesty [ctb], Reynald Lescarbeau [ct [...truncated...]
Maintainer: Max Kuhn <mxkuhn@gmail.com>

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Package lmap (with last version 0.1.2) was removed from CRAN

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

2024-03-08 0.1.2

Permanent link
Package glmmrBase updated to version 0.11.1 with previous version 0.10.5 dated 2024-09-07

Title: Generalised Linear Mixed Models in R
Description: Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood and Laplace approximation model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more. See <https://samuel-watson.github.io/glmmr-web/> for a detailed manual.
Author: Sam Watson [aut, cre]
Maintainer: Sam Watson <S.I.Watson@bham.ac.uk>

Diff between glmmrBase versions 0.10.5 dated 2024-09-07 and 0.11.1 dated 2024-12-09

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Package robmed updated to version 1.2.0 with previous version 1.1.0 dated 2024-11-03

Title: (Robust) Mediation Analysis
Description: Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>.
Author: Andreas Alfons [aut, cre] , Nufer Y. Ates [ctb]
Maintainer: Andreas Alfons <alfons@ese.eur.nl>

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Package parsedate updated to version 1.3.2 with previous version 1.3.1 dated 2022-10-27

Title: Recognize and Parse Dates in Various Formats, Including All ISO 8601 Formats
Description: Parse dates automatically, without the need of specifying a format. Currently it includes the git date parser. It can also recognize and parse all ISO 8601 formats.
Author: Gabor Csardi [aut, cre], Linus Torvalds [aut]
Maintainer: Gabor Csardi <csardi.gabor@gmail.com>

Diff between parsedate versions 1.3.1 dated 2022-10-27 and 1.3.2 dated 2024-12-09

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Package move2 updated to version 0.4.0 with previous version 0.3.0 dated 2024-05-22

Title: Processing and Analysing Animal Trajectories
Description: Tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from 'movebank' <https://www.movebank.org/cms/movebank-main> and files is facilitated.
Author: Bart Kranstauber [aut, cre] , Kamran Safi [aut] , Anne K. Scharf [aut]
Maintainer: Bart Kranstauber <b.kranstauber@uva.nl>

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Package greybox updated to version 2.0.3 with previous version 2.0.2 dated 2024-08-27

Title: Toolbox for Model Building and Forecasting
Description: Implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.
Author: Ivan Svetunkov [aut, cre] , Yves R. Sagaert [ctb]
Maintainer: Ivan Svetunkov <ivan@svetunkov.com>

Diff between greybox versions 2.0.2 dated 2024-08-27 and 2.0.3 dated 2024-12-09

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Package arkhe updated to version 1.9.0 with previous version 1.8.0 dated 2024-11-07

Title: Tools for Cleaning Rectangular Data
Description: A dependency-free collection of simple functions for cleaning rectangular data. This package allows to detect, count and replace values or discard rows/columns using a predicate function. In addition, it provides tools to check conditions and return informative error messages.
Author: Nicolas Frerebeau [aut, cre] , Brice Lebrun [ctb] , Universite Bordeaux Montaigne [fnd], CNRS [fnd]
Maintainer: Nicolas Frerebeau <nicolas.frerebeau@u-bordeaux-montaigne.fr>

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Package REDCapTidieR updated to version 1.2.1 with previous version 1.2.0 dated 2024-10-23

Title: Extract 'REDCap' Databases into Tidy 'Tibble's
Description: Convert 'REDCap' exports into tidy tables for easy handling of 'REDCap' repeat instruments and event arms.
Author: Richard Hanna [aut, cre] , Stephan Kadauke [aut] , Ezra Porter [aut]
Maintainer: Richard Hanna <richardshanna91@gmail.com>

Diff between REDCapTidieR versions 1.2.0 dated 2024-10-23 and 1.2.1 dated 2024-12-09

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 13 files changed, 87 insertions(+), 51 deletions(-)

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Package kappaGold updated to version 0.4.0 with previous version 0.3.2 dated 2024-09-30

Title: Agreement of Nominal Scale Raters (with a Gold Standard)
Description: Estimate agreement of a group of raters with a gold standard rating on a nominal scale. For a single gold standard rater the average pairwise agreement of raters with this gold standard is provided. For a group of (gold standard) raters the approach of S. Vanbelle, A. Albert (2009) <doi:10.1007/s11336-009-9116-1> is implemented. Bias and standard error are estimated via delete-1 jackknife.
Author: Matthias Kuhn [aut, cre] , Jonas Breidenstein [aut]
Maintainer: Matthias Kuhn <matthias.kuhn@tu-dresden.de>

Diff between kappaGold versions 0.3.2 dated 2024-09-30 and 0.4.0 dated 2024-12-09

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Package worldbank updated to version 0.4.0 with previous version 0.3.0 dated 2024-08-26

Title: Client for World Banks's 'Indicators' and 'Poverty and Inequality Platform (PIP)' APIs
Description: Download and search data from the 'World Bank Indicators API', which provides access to nearly 16,000 time series indicators. See <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation> for further details about the API.
Author: Maximilian Muecke [aut, cre]
Maintainer: Maximilian Muecke <muecke.maximilian@gmail.com>

Diff between worldbank versions 0.3.0 dated 2024-08-26 and 0.4.0 dated 2024-12-09

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Package PakPC updated to version 0.3.0 with previous version 0.2.0 dated 2024-11-11

Title: 'shiny' App to Analyze Pakistan's Population Census Data
Description: Provides tools for analyzing Pakistan's Population Censuses data via the 'PakPC2023' and 'PakPC2017' R packages. Designed for researchers, policymakers, and professionals, the app enables in-depth numerical and graphical analysis, including detailed cross-tabulations and insights. With diverse statistical models and visualization options, it supports informed decision-making in social and economic policy. This tool enhances users' ability to explore and interpret census data, providing valuable insights for effective planning and analysis across various fields.
Author: Muhammad Yaseen [aut, cre, cph] , Muhammad Arfan Dilber [ctb], Zahid Asghar [ctb]
Maintainer: Muhammad Yaseen <myaseen208@gmail.com>

Diff between PakPC versions 0.2.0 dated 2024-11-11 and 0.3.0 dated 2024-12-09

 DESCRIPTION                                               |    6 
 MD5                                                       |   10 -
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 README.md                                                 |   16 ++
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 inst/shinyapp/ui.R                                        |   87 ++++++++++++--
 6 files changed, 105 insertions(+), 24 deletions(-)

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New package warehouseTools with initial version 0.1.2
Package: warehouseTools
Title: Heuristics for Solving the Traveling Salesman Problem in Warehouse Layouts
Version: 0.1.2
Description: Heuristic methods to solve the routing problems in a warehouse management. Package includes several heuristics such as the Midpoint, Return, S-Shape and Semi-Optimal Heuristics for designation of the picker’s route in order picking. The heuristics aim to provide the acceptable travel distances while considering warehouse layout constraints such as aisles and shelves. It also includes implementation of the COPRAS (COmplex PRoportional ASsessment) method for supporting selection of locations to be visited by the picker in shared storage systems. The package is designed to facilitate more efficient warehouse routing and logistics operations. see: Bartholdi, J. J., Hackman, S. T. (2019). "WAREHOUSE & DISTRIBUTION SCIENCE. Release 0.98.1." The Supply Chain & Logistics Institute. H. Milton Stewart School of Industrial and Systems Engineering. Georgia Institute of Technology. <https://www.warehouse-science.com/book/editions/wh-sci-0.98.1.pdf>.
Depends: R (>= 3.5.0)
License: GPL-3
Imports: dplyr,clusterSim
Suggests: graphics,testthat (>= 2.1.0)
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-12-07 18:34:19 UTC; andrzej
Author: Andrzej Dudek [aut, cre] , Krzysztof Dmytrow [aut]
Maintainer: Andrzej Dudek <andrzej.dudek@ue.wroc.pl>
Repository: CRAN
Date/Publication: 2024-12-09 19:50:02 UTC

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New package SBMTrees with initial version 1.1
Package: SBMTrees
Title: Sequential Imputation with Bayesian Trees Mixed-Effects Models for Longitudinal Data
Version: 1.1
Date: 2024-11-20
Author: Jungang Zou [aut, cre], Liangyuan Hu [aut], Robert McCulloch [ctb], Rodney Sparapani [ctb], Charles Spanbauer [ctb]
Maintainer: Jungang Zou <jungang.zou@gmail.com>
Description: Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.
License: GPL-2
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: Rcpp, lme4, Matrix, arm, dplyr, mvtnorm, sn, tidyr, mice, nnet
LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress
SystemRequirements: GNU make
Suggests: knitr, rmarkdown, mitml
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-12-08 07:36:54 UTC; jz3138
Repository: CRAN
Date/Publication: 2024-12-09 20:00:02 UTC

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New package paramix with initial version 0.0.1
Package: paramix
Title: Aggregate and Disaggregate Continuous Parameters for Compartmental Models
Version: 0.0.1
Description: A convenient framework for aggregating and disaggregating continuously varying parameters (for example, case fatality ratio, with age) for proper parametrization of lower-resolution compartmental models (for example, with broad age categories) and subsequent upscaling of model outputs to high resolution (for example, as needed when calculating age-sensitive measures like years-life-lost).
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (>= 3.5.0)
Suggests: lintr, knitr, rmarkdown, ggplot2, roxygen2, wpp2019, testthat (>= 3.0.0), patchwork
VignetteBuilder: knitr
Imports: data.table
URL: https://cmmid.github.io/paramix/
NeedsCompilation: no
Packaged: 2024-12-06 20:06:24 UTC; holism
Author: Carl Pearson [aut, cre] , Simon Proctor [aut] , Lucy Goodfellow [aut]
Maintainer: Carl Pearson <carl.ab.pearson@gmail.com>
Repository: CRAN
Date/Publication: 2024-12-09 19:30:02 UTC

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New package iClusterVB with initial version 0.1.4
Package: iClusterVB
Title: Fast Integrative Clustering and Feature Selection for High Dimensional Data
Version: 0.1.4
Author: Abdalkarim Alnajjar [aut, cre, cph] , Zihang Lu [aut]
Maintainer: Abdalkarim Alnajjar <abdalkarim.alnajjar@queensu.ca>
Description: A variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.
License: MIT + file LICENSE
URL: https://github.com/AbdalkarimA/iClusterVB
BugReports: https://github.com/AbdalkarimA/iClusterVB/issues
Depends: R (>= 4.0.0)
Imports: cluster, clustMixType, cowplot, ggplot2, graphics, grDevices, mclust, MCMCpack, mvtnorm, pheatmap, poLCA, Rcpp (>= 1.0.12), stats, utils, VarSelLCM
Suggests: knitr, rmarkdown, survival, survminer
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-12-07 22:15:58 UTC; Abdalkarim
Repository: CRAN
Date/Publication: 2024-12-09 19:50:06 UTC

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New package gg1d with initial version 0.1.0
Package: gg1d
Title: Exploratory Data Analysis using Tiled One-Dimensional Graphics
Version: 0.1.0
Description: Streamlines exploratory data analysis by providing a turnkey approach to visualising n-dimensional data which graphically reveals correlative or associative relationships between 2 or more features. Represents all dataset features as distinct, vertically aligned bar or tile plots, with plot types auto-selected based on whether variables are categorical or numeric.
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/selkamand/gg1d, https://selkamand.github.io/gg1d/
BugReports: https://github.com/selkamand/gg1d/issues
Imports: assertions (>= 0.2.0), cli, ggiraph, ggplot2, ggtext, patchwork (>= 1.3.0), rank (>= 0.1.1), rlang
Suggests: covr, knitr, rmarkdown, testthat (>= 3.0.0)
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2024-12-07 01:39:11 UTC; selkamand
Author: Sam El-Kamand [aut, cre] , Children's Cancer Institute Australia [cph]
Maintainer: Sam El-Kamand <sam.elkamand@gmail.com>
Repository: CRAN
Date/Publication: 2024-12-09 19:40:02 UTC

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New package AuxSurvey with initial version 0.9
Package: AuxSurvey
Title: Survey Analysis with Auxiliary Discretized Variables
Version: 0.9
Author: Jungang Zou [aut, cre], Yutao Liu [aut], Sharifa Williams [aut], Qixuan Chen [aut]
Maintainer: Jungang Zou <jungang.zou@gmail.com>
Description: Probability surveys often use auxiliary continuous data from administrative records, but the utility of this data is diminished when it is discretized for confidentiality. We provide a set of survey estimators to make full use of information from the discretized variables. See Williams, S.Z., Zou, J., Liu, Y., Si, Y., Galea, S. and Chen, Q. (2024) <doi:10.1002/sim.10270> for details.
Encoding: UTF-8
Depends: mgcv, rstanarm, stats
Imports: survey, gtools, coda, BART, dplyr, stringr, gridExtra, rlang
License: Apache License (>= 2)
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-12-08 22:34:57 UTC; zoujungang
Repository: CRAN
Date/Publication: 2024-12-09 20:00:06 UTC

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Package opticskxi updated to version 1.1.0 with previous version 0.1 dated 2019-07-18

Title: OPTICS K-Xi Density-Based Clustering
Description: Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in nois [...truncated...]
Author: Thomas Charlon [aut, cre]
Maintainer: Thomas Charlon <charlon@protonmail.com>

Diff between opticskxi versions 0.1 dated 2019-07-18 and 1.1.0 dated 2024-12-09

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Package rts2 updated to version 0.7.7 with previous version 0.7.6 dated 2024-09-06

Title: Real-Time Disease Surveillance
Description: Supports modelling real-time case data to facilitate the real-time surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2023) <doi:10.1007/s11222-022-10167-2> and the nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>. 'cmdstanr' can be downloaded at <https://mc-stan.org/cmdstanr/>.
Author: Sam Watson [aut, cre]
Maintainer: Sam Watson <s.i.watson@bham.ac.uk>

Diff between rts2 versions 0.7.6 dated 2024-09-06 and 0.7.7 dated 2024-12-09

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 28 files changed, 541 insertions(+), 534 deletions(-)

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New package tugboat with initial version 0.1.0
Package: tugboat
Title: Build a Docker Image from a Directory or Project
Version: 0.1.0
Description: Simple utilities to generate a Dockerfile from a directory or project, build the corresponding Docker image, and push the image to DockerHub.
Imports: dockerfiler (>= 0.2.2), here, renv (>= 1.0.0)
Suggests: yaml, rmarkdown, testthat (>= 3.0.0)
License: GPL (>= 3)
Encoding: UTF-8
URL: https://www.dmolitor.com/tugboat/
Language: en-US
NeedsCompilation: no
Packaged: 2024-12-06 17:40:33 UTC; dmolitor
Author: Daniel Molitor [aut, cph, cre]
Maintainer: Daniel Molitor <molitdj97@gmail.com>
Repository: CRAN
Date/Publication: 2024-12-09 13:40:02 UTC

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New package tinylens with initial version 0.1.0
Package: tinylens
Title: Minimal Implementation of Functional Lenses
Version: 0.1.0
Description: Provides utilities to create and use lenses to simplify data manipulation. Lenses are composable getter/setter pairs that provide a functional approach to manipulating deeply nested data structures, e.g., elements within list columns in data frames. The implementation is based on the earlier 'lenses' R package <https://github.com/cfhammill/lenses>, which was inspired by the Haskell 'lens' package by Kmett (2012) <https://github.com/ekmett/lens>, one of the most widely referenced implementations of lenses. For additional background and history on the theory of lenses, see the 'lens' package wiki: <https://github.com/ekmett/lens/wiki/History-of-Lenses>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: rlang, S7, vctrs
Suggests: tidyselect, tinytest
URL: https://github.com/arbelt/tinylens
BugReports: https://github.com/arbelt/tinylens/issues
NeedsCompilation: no
Packaged: 2024-12-06 15:27:24 UTC; albert
Author: Albert Wang [aut, cre, cph]
Maintainer: Albert Wang <albert_z_wang@harvard.edu>
Repository: CRAN
Date/Publication: 2024-12-09 13:30:02 UTC

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New package surveynnet with initial version 1.0.0
Package: surveynnet
Title: Neural Network for Complex Survey Data
Version: 1.0.0
Description: The goal of 'surveynnet' is to extend the functionality of 'nnet', which already supports survey weights, by enabling it to handle clustered and stratified data. It achieves this by incorporating design effects through the use of effective sample sizes as outlined by Chen and Rust (2017), <doi:10.1093/jssam/smw036>, and performed by 'deffCR' in the package 'PracTools' (Valliant, Dever, and Kreuter (2018), <doi:10.1007/978-3-319-93632-1>).
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://github.com/237triangle/surveynnet
BugReports: https://github.com/237triangle/surveynnet/issues
Imports: dplyr, nnet, PracTools, stats, survey, survival
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2024-12-06 18:16:37 UTC; aaroncohen
Author: Aaron Cohen [aut, cre] , Raul Cruz-Cano [aut]
Maintainer: Aaron Cohen <cohenaa@iu.edu>
Repository: CRAN
Date/Publication: 2024-12-09 13:40:05 UTC

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Package PerformanceAnalytics updated to version 2.0.8 with previous version 2.0.4 dated 2020-02-06

Title: Econometric Tools for Performance and Risk Analysis
Description: Collection of econometric functions for performance and risk analysis. In addition to standard risk and performance metrics, this package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.
Author: Brian G. Peterson [cre, aut, cph], Peter Carl [aut, cph], Kris Boudt [ctb, cph], Ross Bennett [ctb], Joshua Ulrich [ctb], Eric Zivot [ctb], Dries Cornilly [ctb], Eric Hung [ctb], Matthieu Lestel [ctb], Kyle Balkissoon [ctb], Diethelm Wuertz [ctb], An [...truncated...]
Maintainer: Brian G. Peterson <brian@braverock.com>

Diff between PerformanceAnalytics versions 2.0.4 dated 2020-02-06 and 2.0.8 dated 2024-12-09

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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/DESCRIPTION                                                          |   25 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/MD5                                                                  |  525 ++---
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/NAMESPACE                                                            |   23 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/ActivePremium.R                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/AdjustedSharpeRatio.R                                              |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/AppraisalRatio.R                                                   |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/BernadoLedoitratio.R                                               |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/BurkeRatio.R                                                       |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CAPM.alpha.R                                                       |  129 -
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CAPM.beta.R                                                        |  321 +--
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CAPM.epsilon.R                                                     |   26 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CAPM.jensenAlpha.R                                                 |   51 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CAPM.utils.R                                                       |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CDAR.alpha.R                                                       |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CDAR.beta.R                                                        |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CalmarRatio.R                                                      |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/CoMoments.R                                                        |   24 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/DRatio.R                                                           |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/DownsideDeviation.R                                                |   69 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/DownsideFrequency.R                                                |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/DownsideSharpeRatio.R                                              |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/DrawdownPeak.R                                                     |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Drawdowns.R                                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/ES.R                                                               |   92 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/FamaBeta.R                                                         |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Frequency.R                                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/HerfindahlIndex.R                                                  |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/HurstIndex.R                                                       |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/InformationRatio.R                                                 |    8 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Kappa.R                                                            |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/KellyRatio.R                                                       |    8 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/M2Sortino.R                                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/MM.NCE.R                                                           |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/MSquared.R                                                         |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/MSquaredExcess.R                                                   |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/MartinRatio.R                                                      |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/MeanAbsoluteDeviation.R                                            |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/MultivariateMoments.R                                              |   99 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/NetSelectivity.R                                                   |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Omega.R                                                            |   47 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/OmegaExcessReturn.R                                                |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/OmegaSharpeRatio.R                                                 |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/PainIndex.R                                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/PainRatio.R                                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/PerformanceAnalytics-package.R                                     |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/PortfolioRisk.R                                                    |   78 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/ProspectRatio.R                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/RPESE.control.R                                                    |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/RachevRatio.R                                                      |   40 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.Geltner.R                                                   |    8 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.annualized.R                                                |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.calculate.R                                                 |   36 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.clean.R                                                     |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.cumulative.R                                                |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.excess.R                                                    |   10 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.locScaleRob.R                                               |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.portfolio.R                                                 |   12 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.read.R                                                      |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Return.relative.R                                                  |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SFM.coefficients.R                                                 |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SFM.fit.models.R                                                   |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/Selectivity.R                                                      |    9 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SemiDeviation.R                                                    |   62 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SharpeRatio.R                                                      |  109 -
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SharpeRatio.annualized.R                                           |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SkewnessKurtosisRatio.R                                            |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SmoothingIndex.R                                                   |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SortinoRatio.R                                                     |   51 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SpecificRisk.R                                                     |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/StdDev.R                                                           |  215 +-
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/StdDev.annualized.R                                                |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/SystematicRisk.R                                                   |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/TotalRisk.R                                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/TrackingError.R                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/TreynorRatio.R                                                     |    8 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/UlcerIndex.R                                                       |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/UpDownRatios.R                                                     |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/UpsideFrequency.R                                                  |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/UpsidePotentialRatio.R                                             |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/UpsideRisk.R                                                       |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/VaR.Marginal.R                                                     |    2 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/VolatilitySkewness.R                                               |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/apply.fromstart.R                                                  |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/apply.rolling.R                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.ACF.R                                                        |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.ACFplus.R                                                    |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.Bar.R                                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.BarVaR.R                                                     |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.Boxplot.R                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.CaptureRatios.R                                              |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.Correlation.R                                                |   34 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.CumReturns.R                                                 |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.Drawdown.R                                                   |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.ECDF.R                                                       |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.Events.R                                                     |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.Histogram.R                                                  |   11 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.QQPlot.R                                                     |   14 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.Regression.R                                                 |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.RelativePerformance.R                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.RiskReturnScatter.R                                          |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.RollingCorrelation.R                                         |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.RollingMean.R                                                |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.RollingPerformance.R                                         |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.RollingQuantileRegression.R                                  |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.RollingRegression.R                                          |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.SFM.R                                                        |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.Scatter.R                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.SnailTrail.R                                                 |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.StackedBar.R                                                 |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.TimeSeries.R                                                 |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.TimeSeries.base.R                                            |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.TimeSeries.builtin.R                                         |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/chart.VaRSensitivity.R                                             |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/charts.Bar.R                                                       |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/charts.BarVaR.R                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/charts.PerformanceSummary.R                                        |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/charts.RollingPerformance.R                                        |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/charts.RollingRegression.R                                         |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/charts.TimeSeries.R                                                |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/checkData.R                                                        |    8 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/findDrawdowns.R                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/kurtosis.R                                                         |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/legend.R                                                           |   16 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/lpm.R                                                              |   33 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/maxDrawdown.R                                                      |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/mean.utils.R                                                       |   47 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/na.skip.R                                                          |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/replaceTabs.R                                                      |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/skewness.R                                                         |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/sortDrawdowns.R                                                    |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.AnnualizedReturns.R                                          |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.Arbitrary.R                                                  |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.Autocorrelation.R                                            |   94 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.CAPM.R                                                       |   52 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.CalendarReturns.R                                            |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.CaptureRatios.R                                              |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.Correlation.R                                                |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.Distributions.R                                              |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.DownsideRisk.R                                               |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.DownsideRiskRatio.R                                          |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.Drawdowns.R                                                  |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.DrawdownsRatio.R                                             |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.HigherMoments.R                                              |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.InformationRatio.R                                           |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.MonthlyReturns.R                                             |   11 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.RollingPeriods.R                                             |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.SpecificRisk.R                                               |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.UpDownRatios.R                                               |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/table.Variability.R                                                |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/textplot.R                                                         |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/to.period.contributions.R                                          |    8 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/utils.R                                                            |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/zerofill.R                                                         |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/R/zzz.R                                                              |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/README.md                                                            |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/build/vignette.rds                                                   |binary
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/data/edhec.rda                                                       |binary
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/data/managers.rda                                                    |binary
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/inst/doc/PA-charts.R                                                 |   10 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/AdjustedSharpeRatio.Rd                                           |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/CAPM.RiskPremium.Rd                                              |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/CAPM.jensenAlpha.Rd                                              |   17 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/CDD.Rd                                                           |    2 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/CoMoments.Rd                                                     |    8 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/ES.Rd                                                            |   20 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/EWMAMoments.Rd                                                   |    3 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/InformationRatio.Rd                                              |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/KellyRatio.Rd                                                    |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/MCA.Rd                                                           |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/NCE.Rd                                                           |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/Omega.Rd                                                         |    9 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/PerformanceAnalytics-package.Rd                                  |  995 ++++++++--
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/RPESE.control.Rd                                                 |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/RachevRatio.Rd                                                   |   17 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/Return.Geltner.Rd                                                |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/Return.calculate.Rd                                              |   32 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/Return.locScaleRob.Rd                                            |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/Return.portfolio.Rd                                              |    2 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/Selectivity.Rd                                                   |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/SharpeRatio.Rd                                                   |   13 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/ShrinkageMoments.Rd                                              |   13 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/SortinoRatio.Rd                                                  |   12 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/StdDev.Rd                                                        |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/StdDev.annualized.Rd                                             |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/StructuredMoments.Rd                                             |   15 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/SystematicRisk.Rd                                                |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/TreynorRatio.Rd                                                  |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/UlcerIndex.Rd                                                    |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/UpsidePotentialRatio.Rd                                          |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/VaR.Rd                                                           |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/centeredmoments.Rd                                               |   15 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.ACF.Rd                                                     |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.Correlation.Rd                                             |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.ECDF.Rd                                                    |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.Histogram.Rd                                               |    3 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.QQPlot.Rd                                                  |   11 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.RiskReturnScatter.Rd                                       |    3 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.RollingCorrelation.Rd                                      |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.RollingMean.Rd                                             |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.RollingPerformance.Rd                                      |    5 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.SFM.Rd                                                     |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.StackedBar.Rd                                              |    2 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/chart.TimeSeries.Rd                                              |    5 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/edhec.Rd                                                         |    9 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/legend.Rd                                                        |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/managers.Rd                                                      |    2 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.AnnualizedReturns.Rd                                       |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.Autocorrelation.Rd                                         |    7 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.CAPM.Rd                                                    |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.CalendarReturns.Rd                                         |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.Correlation.Rd                                             |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.Distributions.Rd                                           |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.DownsideRisk.Rd                                            |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.DownsideRiskRatio.Rd                                       |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.DrawdownsRatio.Rd                                          |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.HigherMoments.Rd                                           |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.InformationRatio.Rd                                        |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.MonthlyReturns.Rd                                          |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.RollingPeriods.Rd                                          |    5 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.SpecificRisk.Rd                                            |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/table.Variability.Rd                                             |    4 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/test_returns.Rd                                                  |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/test_weights.Rd                                                  |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/textplot.Rd                                                      |    4 
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 PerformanceAnalytics-2.0.8/PerformanceAnalytics/man/unique-comoments.Rd                                              |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/src/NCEHelper.c                                                      |only
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/src/comomentsEstimators.c                                            |    6 
 PerformanceAnalytics-2.0.8/PerformanceAnalytics/src/init.c                                                           |   11 
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New package FlexRL with initial version 0.1.0
Package: FlexRL
Title: A Flexible Model for Record Linkage
Version: 0.1.0
Description: Implementation of the Stochastic Expectation Maximisation (StEM) approach to Record Linkage described in the paper by K. Robach, S. L. van der Pas, M. A. van de Wiel and M. H. Hof (2024, <doi:10.48550/arXiv.2407.06835>); see 'citation("FlexRL")' for details. This is a record linkage method, for finding the common set of records among 2 data sources based on Partially Identifying Variables (PIVs) available in both sources. It includes modelling of dynamic Partially Identifying Variables (e.g. postal code) that may evolve over time and registration errors (missing values and mistakes in the registration). Low memory footprint.
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 4.4.0)
Imports: Matrix (>= 1.7), progress(>= 1.2.3), testit(>= 0.13), Rcpp(>= 1.0.13)
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2024-12-06 12:59:19 UTC; kayane
Author: Kayane ROBACH [aut, cre, cph]
Maintainer: Kayane ROBACH <k.c.robach@amsterdamumc.nl>
Repository: CRAN
Date/Publication: 2024-12-09 13:20:02 UTC

More information about FlexRL at CRAN
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Package CopernicusDEM updated to version 1.0.5 with previous version 1.0.4 dated 2024-08-17

Title: Copernicus Digital Elevation Models
Description: Copernicus Digital Elevation Model datasets (DEM) of 90 and 30 meters resolution using the 'awscli' command line tool. The Copernicus (DEM) is included in the Registry of Open Data on 'AWS (Amazon Web Services)' and represents the surface of the Earth including buildings, infrastructure and vegetation.
Author: Lampros Mouselimis [aut, cre]
Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>

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Package ClusterVAR updated to version 0.0.8 with previous version 0.0.7 dated 2024-07-10

Title: Fitting Latent Class Vector-Autoregressive (VAR) Models
Description: Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters.
Author: Anja Ernst [aut, cre], Jonas Haslbeck [aut]
Maintainer: Anja Ernst <a.f.ernst@rug.nl>

Diff between ClusterVAR versions 0.0.7 dated 2024-07-10 and 0.0.8 dated 2024-12-09

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Package qMRI updated to version 1.2.7.8 with previous version 1.2.7.6 dated 2024-05-22

Title: Methods for Quantitative Magnetic Resonance Imaging ('qMRI')
Description: Implementation of methods for estimation of quantitative maps from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013) <doi:10.3389/fnins.2013.00095>) and analysis of Inversion Recovery MRI data. Usage of the package is described in Polzehl and Tabelow (2023), "Magnetic Resonance Brain Imaging", 2nd Edition, Chapter 6 and 7, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8>. J. Polzehl and K. Tabelow (2023), "Magnetic Resonance Brain Imaging - Modeling and Data Analysis Using R: Code and Data." <doi:10.20347/WIAS.DATA.6> provides extensive example code and data.
Author: Joerg Polzehl [aut], Karsten Tabelow [aut, cre], WIAS Berlin [cph, fnd]
Maintainer: Karsten Tabelow <karsten.tabelow@wias-berlin.de>

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Package mssearchr updated to version 0.2.0 with previous version 0.1.1 dated 2024-06-25

Title: Library Search Against Electron Ionization Mass Spectral Databases
Description: Perform library searches against electron ionization mass spectral databases using either the API provided by 'MS Search' software (<https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:nistlibs>) or custom implementations of the Identity and Similarity algorithms.
Author: Andrey Samokhin [aut, cre, cph]
Maintainer: Andrey Samokhin <andrey.s.samokhin@gmail.com>

Diff between mssearchr versions 0.1.1 dated 2024-06-25 and 0.2.0 dated 2024-12-09

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Package network updated to version 1.19.0 with previous version 1.18.2 dated 2023-12-05

Title: Classes for Relational Data
Description: Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
Author: Carter T. Butts [aut, cre], David Hunter [ctb], Mark Handcock [ctb], Skye Bender-deMoll [ctb], Jeffrey Horner [ctb], Li Wang [ctb], Pavel N. Krivitsky [ctb] , Brendan Knapp [ctb] , Michal Bojanowski [ctb] , Chad Klumb [ctb]
Maintainer: Carter T. Butts <buttsc@uci.edu>

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Package BFS updated to version 0.5.12 with previous version 0.5.11 dated 2024-11-27

Title: Get Data from the Swiss Federal Statistical Office
Description: Search and download data from the Swiss Federal Statistical Office (BFS) APIs <https://www.bfs.admin.ch/>.
Author: Felix Luginbuhl [aut, cre, cph] , Janosch Brenzel-Weiss [ctb], Joao Martins [ctb], Philipp Baumann [ctb]
Maintainer: Felix Luginbuhl <felix.luginbuhl@protonmail.ch>

Diff between BFS versions 0.5.11 dated 2024-11-27 and 0.5.12 dated 2024-12-09

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New package multimolang with initial version 0.1.1
Package: multimolang
Title: 'multimolang': Multimodal Language Analysis
Version: 0.1.1
Description: Process 'OpenPose' human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA 'NewsScape' archive.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: arrow
Suggests: roxygen2 (>= 7.0.0), testthat (>= 3.0.0), knitr, rmarkdown, spelling
URL: https://github.com/daedalusLAB/multimolang
BugReports: https://github.com/daedalusLAB/multimolang/issues
VignetteBuilder: knitr
Copyright: Copyright (C) 2024 Universidad de Murcia
Language: en-US
NeedsCompilation: no
Packaged: 2024-12-05 23:57:13 UTC; bherreno
Author: Brian Herreno Jimenez [aut, cre]
Maintainer: Brian Herreno Jimenez <brian.herreno@um.es>
Repository: CRAN
Date/Publication: 2024-12-09 10:40:02 UTC

More information about multimolang at CRAN
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Package GaussSuppression updated to version 0.9.2 with previous version 0.9.0 dated 2024-09-24

Title: Tabular Data Suppression using Gaussian Elimination
Description: A statistical disclosure control tool to protect tables by suppression using the Gaussian elimination secondary suppression algorithm (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6>. A suggestion is to start by working with functions SuppressSmallCounts() and SuppressDominantCells(). These functions use primary suppression functions for the minimum frequency rule and the dominance rule, respectively. Novel functionality for suppression of disclosive cells is also included. General primary suppression functions can be supplied as input to the general working horse function, GaussSuppressionFromData(). Suppressed frequencies can be replaced by synthetic decimal numbers as described in Langsrud (2019) <doi:10.1007/s11222-018-9848-9>.
Author: Oeyvind Langsrud [aut, cre] , Daniel Lupp [aut] , Hege Boevelstad [ctb] , Vidar Norstein Klungre [rev] , Statistics Norway [cph]
Maintainer: Oeyvind Langsrud <oyl@ssb.no>

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Package SmallCountRounding updated to version 1.1.0 with previous version 1.0.8 dated 2024-10-22

Title: Small Count Rounding of Tabular Data
Description: A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.
Author: Oeyvind Langsrud [aut, cre], Johan Heldal [aut]
Maintainer: Oeyvind Langsrud <oyl@ssb.no>

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Package neuroimaGene updated to version 0.1.2 with previous version 0.1.1 dated 2024-09-20

Title: Transcriptomic Atlas of Neuroimaging Derived Phenotypes
Description: Contains functions to query and visualize the Neuroimaging features associated with genetically regulated gene expression (GReX). The primary utility, neuroimaGene(), relies on a list of user-defined genes and returns a table of neuroimaging features (NIDPs) associated with each gene. This resource is designed to assist in the interpretation of genome-wide and transcriptome-wide association studies that evaluate brain related traits. Bledsoe (2024) <doi:10.1016/j.ajhg.2024.06.002>. In addition there are several visualization functions that generate summary plots and 2-dimensional visualizations of regional brain measures. Mowinckel (2020).
Author: Xavier Bledsoe [aut, cre] , Eric Gamazon [aut]
Maintainer: Xavier Bledsoe <xavier.bledsoe@vanderbilt.edu>

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Package ggcompare updated to version 0.0.3 with previous version 0.0.2 dated 2024-10-07

Title: Mean Comparison in 'ggplot2'
Description: Add mean comparison annotations to a 'ggplot'. This package provides an easy way to indicate if two or more groups are significantly different in a 'ggplot'. Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).
Author: Hao Wang [aut, cre]
Maintainer: Hao Wang <wanghao8772@gmail.com>

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Package DiscreteGapStatistic updated to version 1.0.0 with previous version 0.1.0 dated 2024-10-25

Title: An Extension of the Gap Statistic for Ordinal/Categorical Data
Description: The gap statistic approach is extended to estimate the number of clusters for categorical response format data. This approach and accompanying software is designed to be used with the output of any clustering algorithm and with distances specifically designed for categorical (i.e. multiple choice) or ordinal survey response data.
Author: Jeffrey Miecznikowski [aut], Eduardo Cortes [aut, cre]
Maintainer: Eduardo Cortes <ecortesg@buffalo.edu>

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