Fri, 09 Sep 2022

Package PPforest updated to version 0.1.3 with previous version 0.1.2 dated 2021-10-14

Title: Projection Pursuit Classification Forest
Description: Implements projection pursuit forest algorithm for supervised classification.
Author: Natalia da Silva, Eun-Kyung Lee, Di Cook
Maintainer: Natalia da Silva <natalia.dasilva@fcea.edu.uy>

Diff between PPforest versions 0.1.2 dated 2021-10-14 and 0.1.3 dated 2022-09-09

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Package officer updated to version 0.4.4 with previous version 0.4.3 dated 2022-06-12

Title: Manipulation of Microsoft Word and PowerPoint Documents
Description: Access and manipulate 'Microsoft Word' and 'Microsoft PowerPoint' documents from R. The package focuses on tabular and graphical reporting from R; it also provides two functions that let users get document content into data objects. A set of functions lets add and remove images, tables and paragraphs of text in new or existing documents. The package does not require any installation of Microsoft products to be able to write Microsoft files.
Author: David Gohel [aut, cre], Frank Hangler [ctb] , Liz Sander [ctb] , Anton Victorson [ctb] , Jon Calder [ctb] , John Harrold [ctb] , John Muschelli [ctb] , Bill Denney [ctb] , Nikolai Beck [ctb] , Stefan Moog [ctb] , Greg Leleu [ctb]
Maintainer: David Gohel <david.gohel@ardata.fr>

Diff between officer versions 0.4.3 dated 2022-06-12 and 0.4.4 dated 2022-09-09

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Package IDSL.UFAx updated to version 1.5 with previous version 1.4 dated 2022-06-29

Title: Exhaustive Chemical Enumeration for United Formula Annotation
Description: A pipeline to annotate a number of peaks from the IDSL.IPA peaklists using an exhaustive chemical enumeration-based approach. This package can perform elemental composition calculations using the following 15 elements : C, B, Br, Cl, K, S, Se, Si, N, H, As, F, I, Na, O, and P.
Author: Sadjad Fakouri-Baygi [cre, aut] , Dinesh Barupal [aut]
Maintainer: Sadjad Fakouri-Baygi <sadjad.fakouri-baygi@mssm.edu>

Diff between IDSL.UFAx versions 1.4 dated 2022-06-29 and 1.5 dated 2022-09-09

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Package crch updated to version 1.1-1 with previous version 1.1-0 dated 2022-09-07

Title: Censored Regression with Conditional Heteroscedasticity
Description: Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects.
Author: Jakob Messner [aut, cre] , Achim Zeileis [aut] , Reto Stauffer [aut]
Maintainer: Jakob Messner <jakob.messner@posteo.net>

Diff between crch versions 1.1-0 dated 2022-09-07 and 1.1-1 dated 2022-09-09

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Package mixedCCA updated to version 1.6.2 with previous version 1.5.2 dated 2022-05-25

Title: Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data
Description: Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.
Author: Grace Yoon [aut] , Mingze Huang [ctb] , Irina Gaynanova [aut, cre]
Maintainer: Irina Gaynanova <irinag@stat.tamu.edu>

Diff between mixedCCA versions 1.5.2 dated 2022-05-25 and 1.6.2 dated 2022-09-09

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Package fddm updated to version 0.5-2 with previous version 0.5-1 dated 2022-03-15

Title: Fast Implementation of the Diffusion Decision Model
Description: Provides the probability density function (PDF), cumulative distribution function (CDF), and the partial derivatives of the PDF of the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>; Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>; Hartmann & Klauer, 2021, <doi:10.1016/j.jmp.2021.102550>) plus new approximations. All approximations are implemented purely in 'C++' providing faster speed than existing packages.
Author: Kendal B. Foster [aut], Henrik Singmann [ctb, cre]
Maintainer: Henrik Singmann <singmann@gmail.com>

Diff between fddm versions 0.5-1 dated 2022-03-15 and 0.5-2 dated 2022-09-09

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Package cmhc updated to version 0.2.1 with previous version 0.2.0 dated 2022-06-15

Title: Access, Retrieve, and Work with CMHC Data
Description: Wrapper around the Canadian Mortgage and Housing Corporation (CMHC) web interface. It enables programmatic and reproducible access to a wide variety of housing data from CMHC.
Author: Jens von Bergmann
Maintainer: Jens von Bergmann <jens@mountainmath.ca>

Diff between cmhc versions 0.2.0 dated 2022-06-15 and 0.2.1 dated 2022-09-09

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Package arkdb updated to version 0.0.16 with previous version 0.0.15 dated 2022-02-15

Title: Archive and Unarchive Databases Using Flat Files
Description: Flat text files provide a robust, compressible, and portable way to store tables from databases. This package provides convenient functions for exporting tables from relational database connections into compressed text files and streaming those text files back into a database without requiring the whole table to fit in working memory.
Author: Carl Boettiger [aut, cre, cph] , Richard FitzJohn [ctb], Brandon Bertelsen [ctb]
Maintainer: Carl Boettiger <cboettig@gmail.com>

Diff between arkdb versions 0.0.15 dated 2022-02-15 and 0.0.16 dated 2022-09-09

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Package IDSL.UFA updated to version 1.5 with previous version 1.4 dated 2022-06-13

Title: United Formula Annotation (UFA) for HRMS Data Processing
Description: A pipeline to annotate peaklists from the IDSL.IPA package with molecular formula using an isotopic profile matching approach. The IDSL.UFA pipeline is especially beneficial when MS/MS data is not available. The IDSL.UFA package has functions to process user-defined adduct formulas.
Author: Sadjad Fakouri-Baygi [cre, aut] , Dinesh Barupal [aut]
Maintainer: Sadjad Fakouri-Baygi <sadjad.fakouri-baygi@mssm.edu>

Diff between IDSL.UFA versions 1.4 dated 2022-06-13 and 1.5 dated 2022-09-09

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Package IDSL.IPA updated to version 2.2 with previous version 2.1 dated 2022-08-08

Title: Intrinsic Peak Analysis (IPA) for HRMS Data
Description: A sophisticated pipeline for processing LC/HRMS data to extract signals of organic compounds. The package performs isotope pairing, peak detection, alignment, RT correction, gap filling, peak annotation and visualization of extracted ion chromatograms (EIC) and total ion chromatograms (TIC).
Author: Sadjad Fakouri-Baygi [cre, aut] , Dinesh Barupal [aut]
Maintainer: Sadjad Fakouri-Baygi <sadjad.fakouri-baygi@mssm.edu>

Diff between IDSL.IPA versions 2.1 dated 2022-08-08 and 2.2 dated 2022-09-09

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Package cccp updated to version 0.2-9 with previous version 0.2-7 dated 2020-10-30

Title: Cone Constrained Convex Problems
Description: Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <http://cvxopt.org> for more information).
Author: Bernhard Pfaff [aut, cre], Lieven Vandenberghe [cph] , Martin Andersen [cph] , Joachim Dahl [cph]
Maintainer: Bernhard Pfaff <bernhard@pfaffikus.de>

Diff between cccp versions 0.2-7 dated 2020-10-30 and 0.2-9 dated 2022-09-09

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Package qsimulatR updated to version 1.1 with previous version 1.0 dated 2020-12-09

Title: A Quantum Computer Simulator
Description: A quantum computer simulator framework with up to 24 qubits. It allows to define general single qubit gates and general controlled single qubit gates. For convenience, it currently provides the most common gates (X, Y, Z, H, Z, S, T, Rx, Ry, Rz, CNOT, SWAP, Toffoli or CCNOT, Fredkin or CSWAP). 'qsimulatR' also implements noise models. 'qsimulatR' supports plotting of circuits and is able to export circuits to 'Qiskit' <https://qiskit.org/>, a python package which can be used to run on IBM's hardware <https://quantum-computing.ibm.com/>.
Author: Johann Ostmeyer [aut], Carsten Urbach [aut, cre]
Maintainer: Carsten Urbach <urbach@hiskp.uni-bonn.de>

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Package KrigInv updated to version 1.4.2 with previous version 1.4.1 dated 2018-09-11

Title: Kriging-Based Inversion for Deterministic and Noisy Computer Experiments
Description: Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.
Author: Clement Chevalier [aut], Dario Azzimonti [aut, cre] , David Ginsbourger [aut] , Victor Picheny [aut] , Yann Richet [ctb]
Maintainer: Dario Azzimonti <dario.azzimonti@gmail.com>

Diff between KrigInv versions 1.4.1 dated 2018-09-11 and 1.4.2 dated 2022-09-09

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Package hadron updated to version 3.2.0 with previous version 3.1.2 dated 2020-09-10

Title: Analysis Framework for Monte Carlo Simulation Data in Physics
Description: Toolkit to perform statistical analyses of correlation functions generated from Lattice Monte Carlo simulations. In particular, a class 'cf' for correlation functions and methods to analyse those are defined. This includes (blocked) bootstrap (based on the 'boot' package) and jackknife, but also an automatic determination of integrated autocorrelation times. 'hadron' also provides a very general function bootstrap.nlsfit() to bootstrap a non-linear least squares fit. More specific functions are provided to extract hadronic quantities from Lattice Quantum Chromodynamics simulations, a particular Monte Carlo simulation,(see e.g. European Twisted Mass Collaboration, P. Boucaud et al. (2008) <doi:10.1016/j.cpc.2008.06.013>). Here, to determine energy eigenvalues of hadronic states, specific fitting routines and in particular the generalised eigenvalue method (see e.g. B. Blossier et al. (2009) <doi:10.1088/1126-6708/2009/04/094> and M. Fischer et al. (2020) <https://inspire [...truncated...]
Author: Bartosz Kostrzewa [aut], Johann Ostmeyer [aut], Martin Ueding [aut], Carsten Urbach [aut, cre], Nikolas Schlage [ctb], Markus Werner [ctb], Ferenc Pittler [ctb], Matthias Fischer [ctb], Vittorio Lubicz [ctb]
Maintainer: Carsten Urbach <urbach@hiskp.uni-bonn.de>

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Package toRvik updated to version 1.1.0 with previous version 1.0.2 dated 2022-04-22

Title: Extensive and Tidy NCAA Men's College Basketball Data
Description: An API wrapper for cbbstat and a suite of functions to pull and clean detailed, extensive college basketball statistics from Barttorvik<https://barttorvik.com/>.
Author: Andrew Weatherman [aut, cre]
Maintainer: Andrew Weatherman <taw38@duke.edu>

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Package scam updated to version 1.2-13 with previous version 1.2-12 dated 2021-08-10

Title: Shape Constrained Additive Models
Description: Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>. Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of gam() in package 'mgcv' are used for setting up the model matrix, printing and plotting the results. Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with multiple smoothing parameter selection by GCV or UBRE/AIC.
Author: Natalya Pya <nat.pya@gmail.com>
Maintainer: Natalya Pya <nat.pya@gmail.com>

Diff between scam versions 1.2-12 dated 2021-08-10 and 1.2-13 dated 2022-09-09

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Package Morpho updated to version 2.10 with previous version 2.9 dated 2021-09-09

Title: Calculations and Visualisations Related to Geometric Morphometrics
Description: A toolset for Geometric Morphometrics and mesh processing. This includes (among other stuff) mesh deformations based on reference points, permutation tests, detection of outliers, processing of sliding semi-landmarks and semi-automated surface landmark placement.
Author: Stefan Schlager [aut, cre, cph], Gregory Jefferis [ctb], Dryden Ian [cph]
Maintainer: Stefan Schlager <zarquon42@gmail.com>

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Package powerly updated to version 1.8.6 with previous version 1.7.4 dated 2022-05-01

Title: Sample Size Analysis for Psychological Networks and More
Description: An implementation of the sample size computation method for network models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>. The implementation takes the form of a three-step recursive algorithm designed to find an optimal sample size given a model specification and a performance measure of interest. It starts with a Monte Carlo simulation step for computing the performance measure and a statistic at various sample sizes selected from an initial sample size range. It continues with a monotone curve-fitting step for interpolating the statistic across the entire sample size range. The final step employs stratified bootstrapping to quantify the uncertainty around the fitted curve.
Author: Mihai Constantin [aut, cre]
Maintainer: Mihai Constantin <mihai@mihaiconstantin.com>

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Package howzatR updated to version 1.0.1 with previous version 1.0.0 dated 2022-06-09

Title: Useful Functions for Cricket Analysis
Description: Helping to calculate cricket specific problems in a tidy & simple manner.
Author: Luke Lockley [aut, cre]
Maintainer: Luke Lockley <luke.lockley@btinternet.com>

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Package updater updated to version 0.1.1 with previous version 0.1.0 dated 2022-06-17

Title: Utilities for Updating R
Description: When updating major or minor R versions all packages should be re-installed. The utilities in this package assist in getting a user up-and-running again by installing all previously installed R packages. The package uses 'renv' to install; immediately replenishing your 'renv' package cache.
Author: Daniel D. Sjoberg [aut, cre, cph]
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>

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Package radiant.model updated to version 1.4.8 with previous version 1.4.7 dated 2022-08-24

Title: Model Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in 'radiant.data'.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

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Package lifecycle updated to version 1.0.2 with previous version 1.0.1 dated 2021-09-24

Title: Manage the Life Cycle of your Package Functions
Description: Manage the life cycle of your exported functions with shared conventions, documentation badges, and user-friendly deprecation warnings.
Author: Lionel Henry [aut, cre], Hadley Wickham [aut] , RStudio [cph, fnd]
Maintainer: Lionel Henry <lionel@rstudio.com>

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Package yuima updated to version 1.15.15 with previous version 1.15.3 dated 2022-01-31

Title: The YUIMA Project Package for SDEs
Description: Simulation and Inference for SDEs and Other Stochastic Processes.
Author: YUIMA Project Team
Maintainer: Stefano M. Iacus <siacus@iq.harvard.edu>

Diff between yuima versions 1.15.3 dated 2022-01-31 and 1.15.15 dated 2022-09-09

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New package truncnormbayes with initial version 0.0.2
Package: truncnormbayes
Title: Estimates Moments for a Truncated Normal Distribution using 'Stan'
Version: 0.0.2
Description: Finds the posterior modes for the mean and standard deviation for a truncated normal distribution with one or two known truncation points. The method used extends Bayesian methods for parameter estimation for a singly truncated normal distribution under the Jeffreys prior (see Zhou X, Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated data with applications to operational risk measurement". <doi:10.1080/14697688.2012.752103>). This package additionally allows for a doubly truncated normal distribution.
URL: https://github.com/leonkt/truncnormbayes
BugReports: https://github.com/leonkt/truncnormbayes/issues
License: GPL (>= 3)
Encoding: UTF-8
Biarch: true
Depends: R (>= 3.4.0)
Imports: methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), Rdpack, rstan (>= 2.18.1), rstantools (>= 2.2.0), stats
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)
Suggests: testthat (>= 3.0.0), truncnorm (>= 1.0), withr (>= 2.5.0)
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2022-09-08 02:37:09 UTC; mbraginsky
Author: Mika Braginsky [aut, cre], Leon Tran [aut], Maya Mathur [ctb]
Maintainer: Mika Braginsky <mika.br@gmail.com>
Repository: CRAN
Date/Publication: 2022-09-09 10:30:02 UTC

More information about truncnormbayes at CRAN
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Package mapme.biodiversity updated to version 0.2.1 with previous version 0.2.0 dated 2022-08-23

Title: Efficient Monitoring of Global Biodiversity Portfolios
Description: Biodiversity areas, especially primary forest, serve a multitude of functions for local economy, regional functionality of the ecosystems as well as the global health of our planet. Recently, adverse changes in human land use practices and climatic responses to increased greenhouse gas emissions, put these biodiversity areas under a variety of different threats. The present package helps to analyse a number of biodiversity indicators based on freely available geographical datasets. It supports computational efficient routines that allow the analysis of potentially global biodiversity portfolios. The primary use case of the package is to support evidence based reporting of an organization's effort to protect biodiversity areas under threat and to identify regions were intervention is most duly needed.
Author: Darius A. Goergen [aut, cre], Om Prakash Bhandari [aut]
Maintainer: Darius A. Goergen <darius2402@web.de>

Diff between mapme.biodiversity versions 0.2.0 dated 2022-08-23 and 0.2.1 dated 2022-09-09

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Package manymome updated to version 0.1.4.3 with previous version 0.1.4.0 dated 2022-09-06

Title: Mediation, Moderation and Moderated-Mediation After Model Fitting
Description: Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax. Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by structural equation modeling using lavaan() or regression using lm().
Author: Shu Fai Cheung [aut, cre] , Sing-Hang Cheung [aut]
Maintainer: Shu Fai Cheung <shufai.cheung@gmail.com>

Diff between manymome versions 0.1.4.0 dated 2022-09-06 and 0.1.4.3 dated 2022-09-09

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Package ipsecr updated to version 1.3.0 with previous version 1.2.0 dated 2022-08-29

Title: Spatially Explicit Capture-Recapture by Inverse Prediction
Description: Estimates the density of a spatially distributed animal population sampled with an array of passive detectors, such as traps. Models incorporating distance-dependent detection are fitted by simulation and inverse prediction as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.
Author: Murray Efford [aut, cre]
Maintainer: Murray Efford <murray.efford@otago.ac.nz>

Diff between ipsecr versions 1.2.0 dated 2022-08-29 and 1.3.0 dated 2022-09-09

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Package FactoMineR updated to version 2.6 with previous version 2.5 dated 2022-09-05

Title: Multivariate Exploratory Data Analysis and Data Mining
Description: Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Author: Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Maintainer: Francois Husson <francois.husson@agrocampus-ouest.fr>

Diff between FactoMineR versions 2.5 dated 2022-09-05 and 2.6 dated 2022-09-09

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Package DynNom updated to version 5.0.2 with previous version 5.0.1 dated 2019-06-20

Title: Visualising Statistical Models using Dynamic Nomograms
Description: Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, mgcv::gam and gam::gam model objects.
Author: Amirhossein Jalali, Davood Roshan, Alberto Alvarez-Iglesias, John Newell
Maintainer: Amirhossein Jalali <amir.jalali@ul.ie>

Diff between DynNom versions 5.0.1 dated 2019-06-20 and 5.0.2 dated 2022-09-09

 DESCRIPTION      |    9 -
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Package psychReport updated to version 3.0.2 with previous version 3.0.1 dated 2021-05-06

Title: Reproducible Reports in Psychology
Description: Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files.
Author: Ian G Mackenzie [cre, aut], Carolin Dudschig [aut]
Maintainer: Ian G Mackenzie <ian.mackenzie@uni-tuebingen.de>

Diff between psychReport versions 3.0.1 dated 2021-05-06 and 3.0.2 dated 2022-09-09

 DESCRIPTION         |    8 ++---
 LICENSE             |    2 -
 MD5                 |   18 +++++------
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 R/utils.R           |   15 ++++-----
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 man/printTable.Rd   |    3 -
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 10 files changed, 70 insertions(+), 138 deletions(-)

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Package mr.pivw updated to version 0.1.1 with previous version 0.1.0 dated 2022-08-16

Title: Penalized Inverse-Variance Weighted Estimator for Mendelian Randomization
Description: The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.
Author: Siqi Xu
Maintainer: Siqi Xu <sqxu@hku.hk>

Diff between mr.pivw versions 0.1.0 dated 2022-08-16 and 0.1.1 dated 2022-09-09

 DESCRIPTION |    6 +++---
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 4 files changed, 29 insertions(+), 11 deletions(-)

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New package wordbankr with initial version 1.0.0
Package: wordbankr
Title: Accessing the Wordbank Database
Description: Connecting to Wordbank, an open repository for developmental vocabulary data. For more information on the underlying data, see <http://wordbank.stanford.edu>.
Version: 1.0.0
Depends: R (>= 4.0)
License: GPL-3
URL: https://langcog.github.io/wordbankr/, https://github.com/langcog/wordbankr/
BugReports: https://github.com/langcog/wordbankr/issues/
Imports: assertthat (>= 0.2.1), DBI (>= 1.1.3), dbplyr (>= 2.2.1), dplyr (>= 1.0.9), glue (>= 1.6.2), jsonlite (>= 1.8.0), lifecycle (>= 1.0.1), purrr (>= 0.3.4), quantregGrowth (>= 1.4), rlang (>= 1.0.4), RMySQL (>= 0.10.23), robustbase (>= 0.95), stringr (>= 1.4.0), tidyr (>= 1.2.0)
Suggests: ggplot2, knitr, rmarkdown
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-09-08 19:00:01 UTC; mbraginsky
Author: Mika Braginsky [aut, cre], Daniel Yurovsky [ctb], Michael Frank [ctb], Danielle Kellier [ctb], Alvin Tan [ctb]
Maintainer: Mika Braginsky <mika.br@gmail.com>
Repository: CRAN
Date/Publication: 2022-09-09 08:10:02 UTC

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New package sqlstrings with initial version 1.0.0
Package: sqlstrings
Title: Map 'SQL' Code to R Lists
Version: 1.0.0
Description: Provides a helper function, to bulk read 'SQL' code from separate files and load it into an 'R' list, where the list elements contain the individual statements and queries as strings. This works by annotating the 'SQL' code with a name comment, which also will be the name of the list element.
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: testthat (>= 3.0.0)
Imports: fs, readr, stringr
NeedsCompilation: no
Packaged: 2022-09-08 18:36:59 UTC; Admin
Author: Dejan Prvulovic [aut, cre]
Maintainer: Dejan Prvulovic <dejan.prv@gmail.com>
Repository: CRAN
Date/Publication: 2022-09-09 08:10:05 UTC

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New package moose with initial version 0.0.1
Package: moose
Title: Mean Squared Out-of-Sample Error Projection
Version: 0.0.1
Description: Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>. It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.
License: MIT + file LICENSE
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-09-09 00:07:54 UTC; chris
Author: Chris Rohlfs [aut, cre]
Maintainer: Chris Rohlfs <car2228@columbia.edu>
Repository: CRAN
Date/Publication: 2022-09-09 08:20:02 UTC

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New package macrocol with initial version 0.1.0
Package: macrocol
Title: Colombian Macro-Financial Time Series Generator
Version: 0.1.0
Maintainer: Pedro Alejandro Cabra-Acela <jando2797@gmail.com>
Description: This repository aims to contribute to the econometric models' production with Colombian data, by providing a set of web-scrapping functions of some of the main macro-financial indicators. All the sources are public and free, but the advantage of these functions is that they directly download and harmonize the information in R's environment. No need to import or download additional files. You only need an internet connection!
License: Apache License 2.0
Encoding: UTF-8
Imports: openxlsx,httr,lubridate,readxl,stats,utils,R.utils
URL: <https://github.com/pedroCabraAcela/Scrapping-Colombian-Macrodata>
NeedsCompilation: no
Packaged: 2022-09-08 18:16:16 UTC; pc5297
Author: Pedro Alejandro Cabra-Acela [aut, cre]
Repository: CRAN
Date/Publication: 2022-09-09 08:13:01 UTC

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New package figuRes2 with initial version 1.0.0
Package: figuRes2
Title: Support for a Variety of Figure Production Tasks
Version: 1.0.0
Date: 2022-08-29
URL: https://github.com/gcicc/figures2
Maintainer: Greg Cicconetti <greg.cicconetti@gmail.com>
Description: We view a figure as a collection of graphs/tables assembled on a page and optionally annotated with metadata (titles, headers and footers). Functions and supporting documentation are offered to streamline a variety of figure production task.
License: GPL-2
Encoding: UTF-8
LazyLoad: no
Depends: R (>= 3.5.0)
Imports: survival, ggplot2, scales, stringr, plyr, grid, gridExtra, gtable, reshape2, grDevices, utils
Suggests: RColorBrewer, knitr, rmarkdown, tidyverse, latex2exp
ByteCompile: TRUE
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-09-08 15:30:45 UTC; eri7441
Author: Greg Cicconetti [aut, cre] , David Wade [aut]
Repository: CRAN
Date/Publication: 2022-09-09 08:02:55 UTC

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New package EvidenceSynthesis with initial version 0.3.0
Package: EvidenceSynthesis
Title: Synthesizing Causal Evidence in a Distributed Research Network
Version: 0.3.0
Date: 2022-09-02
Maintainer: Martijn Schuemie <schuemie@ohdsi.org>
Description: Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.
SystemRequirements: Java version 8 or higher (https://www.java.com/)
Depends: survival, R (>= 3.5.0)
Imports: ggplot2, gridExtra, meta, EmpiricalCalibration, rJava, BeastJar, Cyclops (>= 3.1.0), HDInterval, coda, rlang, methods
Suggests: knitr, rmarkdown, testthat, sn
License: Apache License 2.0
URL: https://ohdsi.github.io/EvidenceSynthesis/, https://github.com/OHDSI/EvidenceSynthesis
BugReports: https://github.com/OHDSI/EvidenceSynthesis/issues
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-09-09 05:22:39 UTC; mschuemi
Author: Martijn Schuemie [aut, cre], Marc A. Suchard [aut], Observational Health Data Science and Informatics [cph]
Repository: CRAN
Date/Publication: 2022-09-09 08:22:58 UTC

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New package DBIsqldf with initial version 0.9.9-2
Package: DBIsqldf
Version: 0.9.9-2
Date: 2022-09-07
Title: Manipulate R Data Frames Using SQL
Author: Neal Fultz <nfultz@gmail.com> and G. Grothendieck <ggrothendieck@gmail.com>
Maintainer: Neal Fultz <nfultz@gmail.com>
Description: Helper function sqldf() transparently initializes a database, imports data frames into it, and executes a query or other statement. This supports all DBI drivers to some degree, and parameterized queries.
ByteCompile: true
Imports: DBI
Suggests: RSQLite
License: GPL-2
BugReports: https://github.com/nfultz/DBIsqldf/issues
URL: https://github.com/nfultz/DBIsqldf
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-09-08 20:03:13 UTC; nfultz
Repository: CRAN
Date/Publication: 2022-09-09 08:13:04 UTC

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Package windAC updated to version 1.2.9 with previous version 1.2.8 dated 2022-04-24

Title: Area Correction Methods
Description: Post-construction fatality monitoring studies at wind facilities are based on data from searches for bird and bat carcasses in plots beneath turbines. Bird and bat carcasses can fall outside of the search plot. Bird and bat carcasses from wind turbines often fall outside of the searched area. To compensate, area correction (AC) estimations are calculated to estimate the percentage of fatalities that fall within the searched area versus those that fall outside of it. This package provides two likelihood based methods and one physics based method (Hull and Muir (2010) <doi:10.1080/14486563.2010.9725253>, Huso and Dalthorp (2014) <doi:10.1002/jwmg.663>) to estimate the carcass fall distribution. There are also functions for calculating the proportion of area searched within one unit annuli, log logistic distribution functions, and truncated distribution functions.
Author: Paul Rabie [aut], Daniel Riser-Espinoza [aut, cre], Trent McDonald [ctb]
Maintainer: Daniel Riser-Espinoza <despinoza@west-inc.com>

Diff between windAC versions 1.2.8 dated 2022-04-24 and 1.2.9 dated 2022-09-09

 DESCRIPTION                |   10 +++++-----
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 10 files changed, 16 insertions(+), 85 deletions(-)

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Package whippr updated to version 0.1.2 with previous version 0.1.0 dated 2022-03-09

Title: Tools for Manipulating Gas Exchange Data
Description: Set of tools for manipulating gas exchange data from cardiopulmonary exercise testing.
Author: Felipe Mattioni Maturana [aut, cre]
Maintainer: Felipe Mattioni Maturana <felipe.mattioni@med.uni-tuebingen.de>

Diff between whippr versions 0.1.0 dated 2022-03-09 and 0.1.2 dated 2022-09-09

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New package TSdeeplearning with initial version 0.1.0
Package: TSdeeplearning
Title: Deep Learning Model for Time Series Forecasting
Version: 0.1.0
Maintainer: Ronit Jaiswal <ronitjaiswal2912@gmail.com>
Description: RNNs are preferred for sequential data like time series, speech, text, etc. but when dealing with long range dependencies, vanishing gradient problems account for their poor performance. LSTM and GRU are effective solutions which are nothing but RNN networks with the abilities of learning both short-term and long-term dependencies. Their structural makeup enables them to remember information for a long period without any difficulty. LSTM consists of one cell state and three gates, namely, forget gate, input gate and output gate whereas GRU comprises only two gates, namely, reset gate and update gate. This package consists of three different functions for the application of RNN, LSTM and GRU to any time series data for its forecasting. For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: keras, tensorflow, reticulate, tsutils, BiocGenerics, utils, graphics, magrittr
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2022-09-08 11:50:23 UTC; kapil
Author: Ronit Jaiswal [aut, cre], Girish Kumar Jha [aut, ths, ctb], Rajeev Ranjan Kumar [aut, ctb], Kapil Choudhary [aut, ctb]
Repository: CRAN
Date/Publication: 2022-09-09 07:33:00 UTC

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Package sommer updated to version 4.1.9 with previous version 4.1.8 dated 2022-09-06

Title: Solving Mixed Model Equations in R
Description: Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the mixed-model-equations-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available in sommer.
Author: Giovanny Covarrubias-Pazaran
Maintainer: Giovanny Covarrubias-Pazaran <cova_ruber@live.com.mx>

Diff between sommer versions 4.1.8 dated 2022-09-06 and 4.1.9 dated 2022-09-09

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Package metabolic updated to version 0.1.1 with previous version 0.1.0 dated 2020-09-25

Title: Datasets and Functions for Reproducing Meta-Analyses
Description: Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. It contains all the data and functions to reproduce the analysis. "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis". Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) <doi:10.1249/MSS.0000000000002506>.
Author: Felipe Mattioni Maturana [aut, cre, cph]
Maintainer: Felipe Mattioni Maturana <felipe.mattioni@med.uni-tuebingen.de>

Diff between metabolic versions 0.1.0 dated 2020-09-25 and 0.1.1 dated 2022-09-09

 DESCRIPTION                     |    8 +--
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 NEWS.md                         |    4 +
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 README.md                       |   91 ++++++++++++++++++++++------------------
 build                           |only
 inst/CITATION                   |    2 
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 12 files changed, 92 insertions(+), 73 deletions(-)

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New package latentFactoR with initial version 0.0.2
Package: latentFactoR
Title: Data Simulation Based on Latent Factors
Version: 0.0.2
Date: 2022-09-08
Maintainer: Alexander Christensen <alexpaulchristensen@gmail.com>
Description: Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.
Depends: R (>= 3.6.0)
License: GPL (>= 3.0)
Imports: methods, mvtnorm, psych, rstudioapi
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-09-08 12:38:48 UTC; chria12
Author: Alexander Christensen [aut, cre] , Maria Dolores Nieto Canaveras [aut], Hudson Golino [aut] , Luis Eduardo Garrido [aut]
Repository: CRAN
Date/Publication: 2022-09-09 07:32:55 UTC

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Package JMbayes2 updated to version 0.3-0 with previous version 0.2-8 dated 2022-07-05

Title: Extended Joint Models for Longitudinal and Time-to-Event Data
Description: Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).
Author: Dimitris Rizopoulos [aut, cre] , Grigorios Papageorgiou [aut], Pedro Miranda Afonso [aut]
Maintainer: Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl>

Diff between JMbayes2 versions 0.2-8 dated 2022-07-05 and 0.3-0 dated 2022-09-09

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New package ExpGenetic with initial version 0.1.0
Package: ExpGenetic
Title: Non-Additive Expression Analysis of Hybrid Offspring
Version: 0.1.0
Description: Three functional modules, including genetic features, differential expression analysis and non-additive expression analysis were integrated into the package. And the package is suitable for RNA-seq and small RNA sequencing data. Besides, two methods of non-additive expression analysis were provided. One is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the gene in hybrid offspring with the expression level in parents. For non-additive expression analysis of RNA-seq data, it is only applicable to hybrid offspring (including two sub-genomes) species for the time being.
License: AGPL (>= 3)
Encoding: UTF-8
LazyData: true
Imports: DESeq2 (>= 1.34.0), futile.logger (>= 1.4.3), ggplot2 (>= 3.3.6), ggsci (>= 2.9), plyr (>= 1.8.7), VennDiagram (>= 1.7.3)
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2022-09-08 08:27:24 UTC; wuyuqing
Author: Yuqing Wu [aut, cre]
Maintainer: Yuqing Wu <wuyuqing0104@163.com>
Repository: CRAN
Date/Publication: 2022-09-09 07:13:01 UTC

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Package baseballr updated to version 1.3.0 with previous version 1.2.0 dated 2022-04-25

Title: Acquiring and Analyzing Baseball Data
Description: Provides numerous utilities for acquiring and analyzing baseball data from online sources such as Baseball Reference <https://www.baseball-reference.com/>, FanGraphs <https://www.fangraphs.com/>, and the MLB Stats API <https://www.mlb.com/>.
Author: Bill Petti [aut], Saiem Gilani [aut, cre], Ben Baumer [ctb], Ben Dilday [ctb], Robert Frey [ctb], Camden Kay [ctb]
Maintainer: Saiem Gilani <saiem.gilani@gmail.com>

Diff between baseballr versions 1.2.0 dated 2022-04-25 and 1.3.0 dated 2022-09-09

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 40 files changed, 1601 insertions(+), 553 deletions(-)

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New package binfunest with initial version 0.1.0
Package: binfunest
Title: Estimates Parameters of Functions Driving Binomial Random Variables
Version: 0.1.0
Maintainer: Philip Shea <philshea@gmail.com>
Description: Provides maximum likelihood estimates of the performance parameters that drive a binomial distribution of observed errors, and takes full advantage of zero error observations. High performance communications systems typically have inherent noise sources and other performance limitations that need to be estimated. Measurements made at high signal to noise ratios typically result in zero errors due to limitation in available measurement time. Package includes theoretical performance functions for common modulation schemes (Proakis, "Digital Communications" (1995, <ISBN:0-07-051726-6>)), polarization shifted QPSK (Agrell & Karlsson (2009, <DOI:10.1109/JLT.2009.2029064>)), and utility functions to work with the performance functions.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Suggests: covr, knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Imports: pracma, stats, stats4
URL: https://github.com/PhilShea/binfunest
BugReports: https://github.com/PhilShea/binfunest/issues
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2022-09-08 13:14:30 UTC; phils
Author: Philip Shea [aut, cre]
Repository: CRAN
Date/Publication: 2022-09-09 07:32:57 UTC

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New package allhomes with initial version 0.3.0
Package: allhomes
Title: Extract Past Sales Data from Allhomes.com.au
Version: 0.3.0
Description: Extract past sales data for specific suburb(s) and year(s) from the Australian property website <https://www.allhomes.com.au>. Allhomes data include the address and property details, date and price of the sale, block size and unimproved value of properties mainly in the ACT and NSW.
Language: en-AU
License: MIT + file LICENSE
Depends: R (>= 4.0)
Imports: dplyr, htmltab, httr, jsonlite, magrittr, purrr, readr, rlang, tibble, tidyr, stringr
Encoding: UTF-8
LazyData: true
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
URL: https://mevers.github.io/allhomes/
NeedsCompilation: no
Packaged: 2022-09-08 11:34:46 UTC; maurits
Author: Maurits Evers [aut, cre]
Maintainer: Maurits Evers <maurits.evers@gmail.com>
Repository: CRAN
Date/Publication: 2022-09-09 07:23:00 UTC

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Package WINS updated to version 1.3 with previous version 1.2 dated 2022-07-18

Title: The R WINS Package
Description: Calculate the win statistics (win ratio, net benefit and win odds) for prioritized multiple endpoints, plot the win statistics and win proportions over study time if at least one time-to-event endpoint is analyzed, and simulate datasets with dependent endpoints. The package can handle any type of outcomes (continuous, ordinal, binary, time-to-event) and allow users to perform stratified analysis and inverse probability of censoring weighting (IPCW) analysis.
Author: Ying Cui [aut, cre], Bo Huang [aut]
Maintainer: Ying Cui <cuiyingbeicheng@gmail.com>

Diff between WINS versions 1.2 dated 2022-07-18 and 1.3 dated 2022-09-09

 DESCRIPTION             |    6 +++---
 MD5                     |   16 ++++++++--------
 R/covipcw.adjusted.KL.R |    7 +++++--
 R/ipcw.adjusted.KL.R    |    7 +++++--
 R/win.stat.R            |    9 +++++++--
 inst/doc/vignette.pdf   |binary
 man/partition_t.plot.Rd |    2 +-
 man/stat_t.plot.Rd      |    2 +-
 man/win.stat.Rd         |    2 +-
 9 files changed, 31 insertions(+), 20 deletions(-)

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Package imputeTS updated to version 3.3 with previous version 3.2 dated 2021-01-16

Title: Time Series Missing Value Imputation
Description: Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.
Author: Steffen Moritz [aut, cre, cph] , Sebastian Gatscha [aut], Earo Wang [ctb] , Ron Hause [ctb]
Maintainer: Steffen Moritz <steffen.moritz10@gmail.com>

Diff between imputeTS versions 3.2 dated 2021-01-16 and 3.3 dated 2022-09-09

 imputeTS-3.2/imputeTS/R/ggplot_na_intervals.R                                          |only
 imputeTS-3.2/imputeTS/tests/testthat/test-ggplot_na_intervals.R                        |only
 imputeTS-3.3/imputeTS/DESCRIPTION                                                      |   24 -
 imputeTS-3.3/imputeTS/MD5                                                              |  128 ++---
 imputeTS-3.3/imputeTS/NAMESPACE                                                        |    4 
 imputeTS-3.3/imputeTS/NEWS.md                                                          |  100 ++--
 imputeTS-3.3/imputeTS/R/deprecated_defunct.R                                           |   34 +
 imputeTS-3.3/imputeTS/R/ggplot_na_distribution.R                                       |   10 
 imputeTS-3.3/imputeTS/R/ggplot_na_distribution2.R                                      |only
 imputeTS-3.3/imputeTS/R/ggplot_na_gapsize.R                                            |   26 -
 imputeTS-3.3/imputeTS/R/ggplot_na_imputations.R                                        |   18 
 imputeTS-3.3/imputeTS/R/imputeTS-package.R                                             |    4 
 imputeTS-3.3/imputeTS/R/internal_algorithm_interface.R                                 |    8 
 imputeTS-3.3/imputeTS/R/na_interpolation.R                                             |   84 ++-
 imputeTS-3.3/imputeTS/R/na_kalman.R                                                    |   65 +-
 imputeTS-3.3/imputeTS/R/na_locf.R                                                      |   92 ++-
 imputeTS-3.3/imputeTS/R/na_ma.R                                                        |   35 -
 imputeTS-3.3/imputeTS/R/na_mean.R                                                      |   47 +-
 imputeTS-3.3/imputeTS/R/na_random.R                                                    |   59 +-
 imputeTS-3.3/imputeTS/R/na_remove.R                                                    |   22 
 imputeTS-3.3/imputeTS/R/na_replace.R                                                   |   39 -
 imputeTS-3.3/imputeTS/R/na_seadec.R                                                    |   71 ++-
 imputeTS-3.3/imputeTS/R/na_seasplit.R                                                  |   53 +-
 imputeTS-3.3/imputeTS/R/statsNA.R                                                      |    4 
 imputeTS-3.3/imputeTS/R/tsAirgap.R                                                     |    4 
 imputeTS-3.3/imputeTS/R/tsAirgapComplete.R                                             |    4 
 imputeTS-3.3/imputeTS/README.md                                                        |   43 +
 imputeTS-3.3/imputeTS/build/partial.rdb                                                |binary
 imputeTS-3.3/imputeTS/build/vignette.rds                                               |binary
 imputeTS-3.3/imputeTS/inst/doc/Cheat_Sheet_imputeTS.pdf                                |binary
 imputeTS-3.3/imputeTS/inst/doc/gallery_visualizations.R                                |   20 
 imputeTS-3.3/imputeTS/inst/doc/gallery_visualizations.Rmd                              |   30 -
 imputeTS-3.3/imputeTS/inst/doc/gallery_visualizations.html                             |  235 ++++++----
 imputeTS-3.3/imputeTS/inst/doc/imputeTS-Time-Series-Missing-Value-Imputation-in-R.ltx  |   12 
 imputeTS-3.3/imputeTS/inst/doc/imputeTS-Time-Series-Missing-Value-Imputation-in-R.pdf  |binary
 imputeTS-3.3/imputeTS/man/ggplot_na_distribution.Rd                                    |   10 
 imputeTS-3.3/imputeTS/man/ggplot_na_distribution2.Rd                                   |only
 imputeTS-3.3/imputeTS/man/ggplot_na_gapsize.Rd                                         |   14 
 imputeTS-3.3/imputeTS/man/ggplot_na_imputations.Rd                                     |    6 
 imputeTS-3.3/imputeTS/man/ggplot_na_intervals.Rd                                       |  153 ------
 imputeTS-3.3/imputeTS/man/imputeTS-package.Rd                                          |   16 
 imputeTS-3.3/imputeTS/man/na.kalman.Rd                                                 |    8 
 imputeTS-3.3/imputeTS/man/na_interpolation.Rd                                          |   32 +
 imputeTS-3.3/imputeTS/man/na_kalman.Rd                                                 |    9 
 imputeTS-3.3/imputeTS/man/na_locf.Rd                                                   |   27 -
 imputeTS-3.3/imputeTS/man/na_ma.Rd                                                     |    1 
 imputeTS-3.3/imputeTS/man/na_mean.Rd                                                   |   13 
 imputeTS-3.3/imputeTS/man/na_remove.Rd                                                 |    1 
 imputeTS-3.3/imputeTS/man/na_replace.Rd                                                |    1 
 imputeTS-3.3/imputeTS/man/na_seadec.Rd                                                 |   31 +
 imputeTS-3.3/imputeTS/man/na_seasplit.Rd                                               |    1 
 imputeTS-3.3/imputeTS/man/plotNA.distributionBar.Rd                                    |    4 
 imputeTS-3.3/imputeTS/man/statsNA.Rd                                                   |    2 
 imputeTS-3.3/imputeTS/man/tsAirgap.Rd                                                  |    4 
 imputeTS-3.3/imputeTS/man/tsAirgapComplete.Rd                                          |    4 
 imputeTS-3.3/imputeTS/src/RcppExports.cpp                                              |    5 
 imputeTS-3.3/imputeTS/tests/testthat/test-apply_base_algorithm.R                       |    2 
 imputeTS-3.3/imputeTS/tests/testthat/test-error_handling.R                             |only
 imputeTS-3.3/imputeTS/tests/testthat/test-ggplot_na_distribution2.R                    |only
 imputeTS-3.3/imputeTS/tests/testthat/test-na_interpolation.R                           |   10 
 imputeTS-3.3/imputeTS/tests/testthat/test-na_kalman.R                                  |    9 
 imputeTS-3.3/imputeTS/tests/testthat/test-na_locf.R                                    |    4 
 imputeTS-3.3/imputeTS/tests/testthat/test-na_ma.R                                      |    4 
 imputeTS-3.3/imputeTS/tests/testthat/test-na_mean.R                                    |    2 
 imputeTS-3.3/imputeTS/tests/testthat/test-na_seadec.R                                  |    7 
 imputeTS-3.3/imputeTS/tests/testthat/test-na_seasplit.R                                |    2 
 imputeTS-3.3/imputeTS/vignettes/gallery_visualizations.Rmd                             |   30 -
 imputeTS-3.3/imputeTS/vignettes/imputeTS-Time-Series-Missing-Value-Imputation-in-R.ltx |   12 
 68 files changed, 993 insertions(+), 706 deletions(-)

More information about imputeTS at CRAN
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