Sat, 23 Jul 2022

Package orf updated to version 0.1.4 with previous version 0.1.3 dated 2020-01-31

Title: Ordered Random Forests
Description: An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>.
Author: Gabriel Okasa [aut, cre], Michael Lechner [ctb]
Maintainer: Gabriel Okasa <okasa.gabriel@gmail.com>

Diff between orf versions 0.1.3 dated 2020-01-31 and 0.1.4 dated 2022-07-23

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Package blsR updated to version 0.3.1 with previous version 0.3.0 dated 2022-04-11

Title: Make Requests from the Bureau of Labor Statistics API
Description: Implements v2 of the B.L.S. API for requests of survey information and time series data through 3-tiered API that allows users to interact with the raw API directly, create queries through a functional interface, and re-shape the data structures returned to fit common uses. The API definition is located at: <https://www.bls.gov/developers/api_signature_v2.htm>.
Author: Guillermo Roditi Dominguez [aut, cre]
Maintainer: Guillermo Roditi Dominguez <guillermo@newriverinvestments.com>

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Package BranchGLM updated to version 1.2.0 with previous version 1.1.3 dated 2022-06-29

Title: Efficient Branch and Bound Variable Selection for GLMs using 'RcppArmadillo'
Description: Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'.
Author: Jacob Seedorff [aut, cre]
Maintainer: Jacob Seedorff <jwseedorff@uiowa.edu>

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Package jinjar updated to version 0.3.0 with previous version 0.2.0 dated 2022-06-24

Title: Template Engine Inspired by 'Jinja'
Description: Template engine powered by the 'inja' C++ library. Users write a template document, using syntax inspired by the 'Jinja' Python package, and then render the final document by passing data from R. The template syntax supports features such as variables, loops, conditions and inheritance.
Author: David Hall [aut, cre, cph] , Lars Berscheid [cph] , Niels Lohmann [cph]
Maintainer: David Hall <david.hall.physics@gmail.com>

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Package radiant.multivariate updated to version 1.4.4 with previous version 1.4.1 dated 2021-11-22

Title: Multivariate Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in 'radiant.data'.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant.multivariate versions 1.4.1 dated 2021-11-22 and 1.4.4 dated 2022-07-23

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Package radiant.model updated to version 1.4.6 with previous version 1.4.4 dated 2022-05-25

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>

Diff between radiant.model versions 1.4.4 dated 2022-05-25 and 1.4.6 dated 2022-07-23

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Package radiant.design updated to version 1.4.4 with previous version 1.4.1 dated 2021-11-18

Title: Design Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Design menu includes interfaces for design of experiments, sampling, and sample size calculation. The application extends the functionality in 'radiant.data'.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant.design versions 1.4.1 dated 2021-11-18 and 1.4.4 dated 2022-07-23

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Package radiant.basics updated to version 1.4.4 with previous version 1.4.1 dated 2021-11-18

Title: Basics Menu for Radiant: Business Analytics using R and Shiny
Description: The Radiant Basics menu includes interfaces for probability calculation, central limit theorem simulation, comparing means and proportions, goodness-of-fit testing, cross-tabs, and correlation. The application extends the functionality in 'radiant.data'.
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>

Diff between radiant.basics versions 1.4.1 dated 2021-11-18 and 1.4.4 dated 2022-07-23

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