New package GauPro with initial version 0.2.5
Package: GauPro
Title: Gaussian Process Fitting
Version: 0.2.5
Author: Collin Erickson
Maintainer: Collin Erickson <collinberickson@gmail.com>
Description: Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel (not for Windows), and 'Rcpp' has been used to speed up calculations. Other R packages that perform similar calculations include 'laGP', 'DiceKriging', 'GPfit', and 'mlegp'.
License: GPL-3
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, R6, lbfgs
Suggests: testthat, knitr, rmarkdown, microbenchmark, numDeriv, ContourFunctions, dplyr, ggplot2, ggrepel, lhs, tidyr, MASS
VignetteBuilder: knitr
URL: https://github.com/CollinErickson/GauPro
BugReports: https://github.com/CollinErickson/GauPro/issues
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2022-11-11 01:55:26 UTC; colli
Repository: CRAN
Date/Publication: 2022-11-15 00:20:05 UTC

More information about GauPro at CRAN
Permanent link

New package fanc with initial version 2.3.9
Package: fanc
Title: Penalized Likelihood Factor Analysis via Nonconvex Penalty
Version: 2.3.9
Date: 2022-10-20
Depends: Matrix, ellipse, tcltk
Description: Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used. This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.
License: GPL (>= 2)
URL: https://doi.org/10.1007/s11222-014-9458-0, https://doi.org/10.1016/j.csda.2014.05.011, https://doi.org/10.1007/s41237-016-0007-3, https://keihirose.com
Packaged: 2022-11-08 08:27:40 UTC; hirosekei
Repository: CRAN
Date/Publication: 2022-11-15 00:20:02 UTC
NeedsCompilation: yes
Author: Kei Hirose [aut, cre] , Michio Yamamoto [aut], Haruhisa Nagata [aut]
Maintainer: Kei Hirose <mail@keihirose.com>

More information about fanc at CRAN
Permanent link

New package tip with initial version 0.1.0
Package: tip
Title: Bayesian Clustering Using the Table Invitation Prior (TIP)
Version: 0.1.0
Description: Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: rlang, igraph, network, ggplot2, GGally, LaplacesDemon, changepoint, parallel, doParallel, foreach, methods, mniw
Suggests: knitr, sna, mcclust, SMFilter, rmarkdown, spelling, testthat (>= 3.0.0)
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2022-11-12 01:56:07 UTC; CharlesHarrison
Author: Charles W. Harrison [cre, aut, cph], Qing He [aut, cph], Hsin-Hsiung Huang [aut, cph]
Maintainer: Charles W. Harrison <charleswharrison@knights.ucf.edu>
Repository: CRAN
Date/Publication: 2022-11-14 17:30:02 UTC

More information about tip at CRAN
Permanent link

New package RMT4DS with initial version 0.0.1
Package: RMT4DS
Title: Computation of Random Matrix Models
Version: 0.0.1
Description: We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model.
License: MIT + file LICENSE
Encoding: UTF-8
Repository: CRAN
Imports: MASS, RMTstat, lpSolve, mpoly, nleqslv, pracma, rARPACK, rootSolve, quadprog
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-11-11 19:51:47 UTC; Ethan
Author: Xiucai Ding [aut, cre, cph], Yichen Hu [aut, cph]
Maintainer: Xiucai Ding <xiucaiding89@gmail.com>
Date/Publication: 2022-11-14 17:30:05 UTC

More information about RMT4DS at CRAN
Permanent link

New package RFPM with initial version 1.0
Package: RFPM
Title: Floating Percentile Model
Version: 1.0
Date: 2022-11-11
Description: Floating Percentile Model with additional functions for optimizing inputs and evaluating outputs and assumptions.
Imports: stats, graphics, grDevices, dplyr, reshape2, lawstat
Suggests: knitr
VignetteBuilder: knitr
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Maintainer: Brian Church <brianc@windwardenv.com>
Depends: R (>= 3.5.0)
NeedsCompilation: no
Packaged: 2022-11-11 23:26:11 UTC; brianc
Author: Brian Church [aut, cre] , Claire Detering [aut]
Repository: CRAN
Date/Publication: 2022-11-14 17:30:08 UTC

More information about RFPM at CRAN
Permanent link

Package MLEce updated to version 1.0.1 with previous version 1.0.0 dated 2022-11-11

Title: Statistical Inference for Asymptotic Efficient Closed-Form Estimators
Description: Estimate asymptotic efficient closed-form estimators and provide goodness of fit, estimates, plot and etc. Yue, S. (2001) <doi:10.1002/hyp.259>. Mosimann, James E. (1962) <doi:10.1093/biomet/49.1-2.65>.
Author: Yu-Kwang Kim [aut, cre, com], Yu-Hyeong Jang [aut], Jae Ho Chang [aut], Sang Kyu Lee [aut], Jun Zhao [aut], Hyoung-Moon Kim [aut, ths]
Maintainer: Yu-Kwang Kim <lumiere_profuse@naver.com>

Diff between MLEce versions 1.0.0 dated 2022-11-11 and 1.0.1 dated 2022-11-14

 DESCRIPTION |   12 +++++++-----
 MD5         |    2 +-
 2 files changed, 8 insertions(+), 6 deletions(-)

More information about MLEce at CRAN
Permanent link

New package fetchSalesforceR with initial version 0.1.0
Package: fetchSalesforceR
Title: Get Data from Salesforce via the 'Windsor.ai' API
Version: 0.1.0
Description: Collect your data on digital marketing campaigns from Salesforce using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
License: GPL-3
URL: https://windsor.ai/
Depends: R (>= 3.5.0)
Imports: jsonlite (>= 1.7.2)
Suggests: knitr, rmarkdown, dplyr, ggplot2, tidyr, curl
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
LazyData: true
NeedsCompilation: no
Packaged: 2022-11-12 16:55:12 UTC; pablo
Author: Pablo Sanchez [cre, aut], Windsor.ai [cph]
Maintainer: Pablo Sanchez <pablosama@outlook.es>
Repository: CRAN
Date/Publication: 2022-11-14 17:40:01 UTC

More information about fetchSalesforceR at CRAN
Permanent link

New package diffval with initial version 1.0.0
Package: diffval
Title: Vegetation Patterns
Version: 1.0.0
Description: Find, visualize and explore patterns of differential taxa in vegetation data (namely in a phytosociological table), using the Differential Value (DiffVal). Patterns are searched through mathematical optimization algorithms. Ultimately, Total Differential Value (TDV) optimization aims at obtaining classifications of vegetation data based on differential taxa, as in the traditional geobotanical approach. The Gurobi optimizer, as well as the R package 'gurobi', can be installed from <https://www.gurobi.com/products/gurobi-optimizer/>. The useful vignette Gurobi Installation Guide, from package 'prioritizr', can be found here: <https://prioritizr.net/articles/gurobi_installation_guide.html>.
License: GPL (>= 3)
URL: https://gitlab.com/point-veg/diffval
BugReports: https://gitlab.com/point-veg/diffval/-/issues
Depends: R (>= 2.10)
Imports: graphics, stats
Suggests: gurobi, utils
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2022-11-11 18:13:22 UTC; tmh
Author: Tiago Monteiro-Henriques [aut, cre] , Jorge Orestes Cerdeira [aut] , Fundacao para a Ciencia e a Tecnologia, Portugal [fnd]
Maintainer: Tiago Monteiro-Henriques <tmh.dev@icloud.com>
Repository: CRAN
Date/Publication: 2022-11-14 17:20:02 UTC

More information about diffval at CRAN
Permanent link

New package rMOST with initial version 0.0.1
Package: rMOST
Title: Estimates Pareto-Optimal Solution for Hiring with 3 Objectives
Version: 0.0.1
Description: Estimates Pareto-optimal solution for personnel selection with 3 objectives using Normal Boundary Intersection (NBI) algorithm introduced by Das and Dennis (1998) <doi:10.1137/S1052623496307510>. Takes predictor intercorrelations and predictor-objective relations as input and generates a series of solutions containing predictor weights as output. Accepts between 3 and 10 selection predictors. Maximum 2 objectives could be adverse impact objectives. Partially modeled after De Corte (2006) TROFSS Fortran program <https://users.ugent.be/~wdecorte/trofss.pdf> and updated from 'ParetoR' package described in Song et al. (2017) <doi:10.1037/apl0000240>. For details, see Song et al. (in press).
License: MIT + file LICENSE
Encoding: UTF-8
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Imports: graphics, grDevices, nloptr, stats
NeedsCompilation: no
Packaged: 2022-11-10 20:16:49 UTC; kimye
Author: Chelsea Song [aut, cre]
Maintainer: Chelsea Song <qianqisong@gmail.com>
Repository: CRAN
Date/Publication: 2022-11-14 11:20:02 UTC

More information about rMOST at CRAN
Permanent link

New package npi with initial version 0.2.0
Package: npi
Title: Access the U.S. National Provider Identifier Registry API
Version: 0.2.0
Description: Access the United States National Provider Identifier Registry API <https://npiregistry.cms.hhs.gov/api/>. Obtain and transform administrative data linked to a specific individual or organizational healthcare provider, or perform advanced searches based on provider name, location, type of service, credentials, and other attributes exposed by the API.
License: MIT + file LICENSE
URL: https://github.com/ropensci/npi/, https://docs.ropensci.org/npi/, https://npiregistry.cms.hhs.gov/api/
BugReports: https://github.com/ropensci/npi/issues/
Depends: R (>= 3.1)
Imports: checkLuhn, checkmate, curl, dplyr, glue, httr, magrittr, purrr, rlang, stringr, tibble, tidyr, utils
Suggests: covr, httptest, knitr, mockery, rmarkdown, spelling, testthat (>= 2.1.0)
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
LazyData: true
NeedsCompilation: no
Packaged: 2022-11-11 16:09:38 UTC; frank
Author: Frank Farach [cre, aut, cph] , Sam Parmar [ctb], Matthias Grenie [rev] , Emily C. Zabor [rev]
Maintainer: Frank Farach <frank.farach@gmail.com>
Repository: CRAN
Date/Publication: 2022-11-14 11:30:02 UTC

More information about npi at CRAN
Permanent link

New package gtfs2emis with initial version 0.1.0
Package: gtfs2emis
Title: Estimating Public Transport Emissions from General Transit Feed Specification (GTFS) Data
Version: 0.1.0
Description: A bottom up model to estimate the emission levels of public transport systems based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) Public transport data in the GTFS standard format; and ii) Some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the package estimates several pollutants at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics. A full description of the methods used in the 'gtfs2emis' model is presented in Vieira, J. P. B.; Pereira, R. H. M.; Andrade, P. R. (2022) <doi:10.31219/osf.io/8m2cy>.
URL: https://ipeagit.github.io/gtfs2emis/ , https://github.com/ipeaGIT/gtfs2emis
BugReports: https://github.com/ipeaGIT/gtfs2emis/issues
License: MIT + file LICENSE
Depends: R (>= 3.6)
Imports: checkmate, data.table, furrr, future, gtfs2gps, methods, sf (>= 0.9-0), sfheaders, terra, units
Suggests: gtfstools, ggplot2, knitr, lwgeom, progressr, rmarkdown, testthat (>= 2.1.0)
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2022-11-11 17:54:43 UTC; joaobazzo
Author: Joao Bazzo [aut, cre] , Rafael H. M. Pereira [aut] , Pedro R. Andrade [aut] , Sergio Ibarra-Espinosa [ctb] , Ipea - Institute for Applied Economic Research [cph, fnd]
Maintainer: Joao Bazzo <joao.bazzo@gmail.com>
Repository: CRAN
Date/Publication: 2022-11-14 11:30:05 UTC

More information about gtfs2emis at CRAN
Permanent link

New package dbGaPCheckup with initial version 1.0.0
Package: dbGaPCheckup
Title: dbGaP Checkup
Version: 1.0.0
URL: https://lwheinsberg.github.io/dbGaPCheckup/, https://github.com/lwheinsberg/dbGaPCheckup
Description: Contains functions that check for formatting of the Subject Phenotype data set and data dictionary as specified by the National Center for Biotechnology Information (NCBI) Database of Genotypes and Phenotypes (dbGaP) <https://www.ncbi.nlm.nih.gov/gap/docs/submissionguide/>.
Imports: readxl, tidyr, dplyr, formatR, ggplot2, pander, magrittr, rmarkdown, purrr, questionr, tibble, rlang, labelled, stats, utils, graphics
License: GPL-2
Encoding: UTF-8
LazyData: true
Suggests: testthat, knitr
BugReports: https://github.com/lwheinsberg/dbGaPCheckup/issues
Depends: R (>= 3.5.0)
VignetteBuilder: knitr, rmarkdown, formatR
NeedsCompilation: no
Packaged: 2022-11-11 12:03:33 UTC; law145
Author: Lacey W. Heinsberg [aut, cre], Daniel E. Weeks [aut], University of Pittsburgh [cph]
Maintainer: Lacey W. Heinsberg <law145@pitt.edu>
Repository: CRAN
Date/Publication: 2022-11-14 11:20:05 UTC

More information about dbGaPCheckup at CRAN
Permanent link

New package rjtools with initial version 1.0.9
Package: rjtools
Title: Preparing, Checking, and Submitting Articles to the 'R Journal'
Version: 1.0.9
Description: Create an 'R Journal' 'Rmarkdown' template article, that will generate html and pdf versions of your paper. Check that the paper folder has all the required components needed for submission. Examples of 'R Journal' publications can be found at <https://journal.r-project.org>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: distill, stringr, cranlogs, purrr, hunspell, fs, cli, glue, whisker, xfun, callr, rlang, yaml, yesno, utils, tinytex, bookdown
Suggests: rmarkdown, knitr, pdftools, rstudioapi, testthat (>= 3.0.0)
VignetteBuilder: knitr
URL: https://github.com/rjournal/rjtools
BugReports: https://github.com/rjournal/rjtools/issues
NeedsCompilation: no
Packaged: 2022-11-09 20:43:18 UTC; cookd
Author: Mitchell O'Hara-Wild [aut], Stephanie Kobakian [aut], H. Sherry Zhang [aut], Di Cook [aut, cre] , Christophe Dervieux [aut]
Maintainer: Di Cook <dicook@monash.edu>
Repository: CRAN
Date/Publication: 2022-11-14 11:00:02 UTC

More information about rjtools at CRAN
Permanent link

New package rbmi with initial version 1.2.3
Package: rbmi
Title: Reference Based Multiple Imputation
Version: 1.2.3
Description: Implements reference based multiple imputation allowing for the imputation of longitudinal datasets using predefined strategies.
Encoding: UTF-8
LazyData: true
URL: https://insightsengineering.github.io/rbmi/, https://github.com/insightsengineering/rbmi
BugReports: https://github.com/insightsengineering/rbmi/issues
Suggests: dplyr, tidyr, nlme, testthat, emmeans, tibble, mvtnorm, knitr, rmarkdown, bookdown, lubridate, purrr, ggplot2, R.rsp
Biarch: true
Imports: mmrm, pkgload, Matrix, methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.1.1), R6, assertthat
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
Depends: R (>= 3.4.0)
License: Apache License (>= 2)
VignetteBuilder: R.rsp
NeedsCompilation: yes
Packaged: 2022-11-11 18:15:47 UTC; gowerc
Author: Craig Gower-Page [aut, cre], Alessandro Noci [aut], Marcel Wolbers [ctb], Roche [cph, fnd]
Maintainer: Craig Gower-Page <craig.gower-page@roche.com>
Repository: CRAN
Date/Publication: 2022-11-14 09:20:02 UTC

More information about rbmi at CRAN
Permanent link

New package harmony with initial version 0.1.1
Package: harmony
Title: Fast, Sensitive, and Accurate Integration of Single Cell Data
Version: 0.1.1
Description: Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.
URL: software.broadinstitute.org/harmony
License: GPL-3
Encoding: UTF-8
Depends: R(>= 3.4.0), Rcpp
LazyData: true
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
Imports: dplyr, cowplot, tidyr, ggplot2, irlba, Matrix, methods, tibble, rlang
Suggests: SingleCellExperiment, Seurat (>= 4.1.1), testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2022-11-12 13:18:25 UTC; ik97
Author: Ilya Korsunsky [cre, aut] , Nghia Millard [aut] , Jean Fan [aut, ctb] , Kamil Slowikowski [aut, ctb] , Miles Smith [ctb], Soumya Raychaudhuri [aut]
Maintainer: Ilya Korsunsky <ilya.korsunsky@gmail.com>
Repository: CRAN
Date/Publication: 2022-11-14 09:20:08 UTC

More information about harmony at CRAN
Permanent link

New package CDatanet with initial version 2.0.2
Package: CDatanet
Title: Modeling Count Data with Peer Effects
Version: 2.0.2
Date: 2022-10-31
Description: Likelihood-based estimation and data generation from a class of models used to estimate peer effects on count data by controlling for the network endogeneity. This class includes count data models with social interactions (Houndetoungan 2022; <doi:10.2139/ssrn.3721250>), spatial tobit models (Xu and Lee 2015; <doi:10.1016/j.jeconom.2015.05.004>), and spatial linear-in-means models (Lee 2004; <doi:10.1111/j.1468-0262.2004.00558.x>).
License: GPL-3
Language: en-US
Encoding: UTF-8
BugReports: https://github.com/ahoundetoungan/CDatanet/issues
URL: https://github.com/ahoundetoungan/CDatanet
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 1.0.0), Formula, formula.tools, ddpcr, Matrix
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppDist, RcppNumerical, RcppEigen
Suggests: ggplot2, MASS, knitr, rmarkdown
NeedsCompilation: yes
Packaged: 2022-11-11 23:47:33 UTC; haache
Author: Elysee Aristide Houndetoungan [cre, aut]
Maintainer: Elysee Aristide Houndetoungan <ariel92and@gmail.com>
Repository: CRAN
Date/Publication: 2022-11-14 09:20:11 UTC

More information about CDatanet at CRAN
Permanent link

New package BayesOrdDesign with initial version 0.1.2
Package: BayesOrdDesign
Title: Bayesian Group Sequential Design for Ordinal Data
Version: 0.1.2
Maintainer: Chengxue Zhong <czhong9106@gmail.com>
Description: The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios. Richard J. Barker, William A. Link (2013) <doi:10.1080/00031305.2013.791644>. Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) <doi:10.1111/biom.12842>. Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) <arXiv:2108.06568>.
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: ordinal, schoolmath, coda, gsDesign, superdiag, ggplot2, madness, rjmcmc, R2jags, rjags, methods,
Depends: R (>= 3.3.0)
Suggests: testthat (>= 3.0.0)
NeedsCompilation: no
Packaged: 2022-11-12 00:46:55 UTC; alexz
Author: Chengxue Zhong [aut, cre], Haitao Pan [aut], Hongyu Miao [aut]
Repository: CRAN
Date/Publication: 2022-11-14 09:20:14 UTC

More information about BayesOrdDesign at CRAN
Permanent link

Package vimp updated to version 2.3.0 with previous version 2.2.5 dated 2021-08-16

Title: Perform Inference on Algorithm-Agnostic Variable Importance
Description: Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).
Author: Brian D. Williamson [aut, cre] , Jean Feng [ctb], Noah Simon [ths] , Marco Carone [ths]
Maintainer: Brian D. Williamson <brian.d.williamson@kp.org>

Diff between vimp versions 2.2.5 dated 2021-08-16 and 2.3.0 dated 2022-11-14

 DESCRIPTION                                   |   17 
 MD5                                           |  145 +-
 NAMESPACE                                     |   11 
 NEWS.md                                       |  722 ++++++-----
 R/bootstrap_se.R                              |   11 
 R/cv_vim.R                                    | 1226 +++++++++---------
 R/est_predictiveness.R                        |  169 +-
 R/estimate.R                                  |only
 R/estimate.predictiveness_measure.R           |only
 R/estimate_type_predictiveness.R              |only
 R/extract_sampled_split_predictions.R         |  159 +-
 R/format.predictiveness_measure.R             |only
 R/measure_accuracy.R                          |  149 +-
 R/measure_anova.R                             |  176 +-
 R/measure_auc.R                               |  207 +--
 R/measure_average_value.R                     |only
 R/measure_cross_entropy.R                     |  155 --
 R/measure_deviance.R                          |  193 +-
 R/measure_mse.R                               |  122 -
 R/measure_r_squared.R                         |  165 +-
 R/predictiveness_measure.R                    |only
 R/print.predictiveness_measure.R              |only
 R/print.vim.R                                 |   26 
 R/sp_vim.R                                    |  824 ++++++------
 R/utils.R                                     | 1127 +++++++++--------
 R/vim.R                                       |  901 +++++++------
 R/vimp-package.R                              |  107 -
 R/vimp_ci.R                                   |  123 -
 build/partial.rdb                             |only
 build/vignette.rds                            |binary
 inst/doc/introduction-to-vimp.R               |  390 +++---
 inst/doc/introduction-to-vimp.Rmd             |  716 +++++------
 inst/doc/introduction-to-vimp.html            | 1683 ++++++++++++++------------
 inst/doc/ipcw-vim.R                           |only
 inst/doc/ipcw-vim.Rmd                         |only
 inst/doc/ipcw-vim.html                        |only
 inst/doc/precomputed-regressions.R            |  366 ++---
 inst/doc/precomputed-regressions.html         | 1386 ++++++++++-----------
 inst/doc/types-of-vims.R                      |   88 -
 inst/doc/types-of-vims.html                   | 1073 ++++++++--------
 man/bootstrap_se.Rd                           |   20 
 man/cv_vim.Rd                                 |  125 +
 man/est_predictiveness.Rd                     |   48 
 man/estimate.Rd                               |only
 man/estimate.predictiveness_measure.Rd        |only
 man/estimate_eif_projection.Rd                |only
 man/estimate_nuisances.Rd                     |only
 man/estimate_type_predictiveness.Rd           |only
 man/extract_sampled_split_predictions.Rd      |   19 
 man/format.predictiveness_measure.Rd          |only
 man/get_test_set.Rd                           |only
 man/measure_accuracy.Rd                       |   38 
 man/measure_anova.Rd                          |   26 
 man/measure_auc.Rd                            |   17 
 man/measure_average_value.Rd                  |only
 man/measure_cross_entropy.Rd                  |   19 
 man/measure_deviance.Rd                       |   23 
 man/measure_mse.Rd                            |   18 
 man/measure_r_squared.Rd                      |   23 
 man/predictiveness_measure.Rd                 |only
 man/print.predictiveness_measure.Rd           |only
 man/print.vim.Rd                              |    2 
 man/process_arg_lst.Rd                        |only
 man/run_sl.Rd                                 |    5 
 man/sp_vim.Rd                                 |   45 
 man/vim.Rd                                    |   37 
 man/vimp.Rd                                   |   11 
 man/vimp_accuracy.Rd                          |   85 -
 man/vimp_anova.Rd                             |   71 -
 man/vimp_auc.Rd                               |   85 -
 man/vimp_ci.Rd                                |    4 
 man/vimp_deviance.Rd                          |   85 -
 man/vimp_regression.Rd                        |   71 -
 man/vimp_rsquared.Rd                          |   85 -
 tests/testthat/test-average_vim.R             |  138 +-
 tests/testthat/test-avg_value.R               |only
 tests/testthat/test-binary_outcomes.R         |  356 ++---
 tests/testthat/test-bootstrap.R               |   21 
 tests/testthat/test-continuous_outcomes.R     |  112 -
 tests/testthat/test-cv_vim.R                  |  644 +++++----
 tests/testthat/test-ipcw.R                    |  225 +--
 tests/testthat/test-predictiveness_measures.R |only
 tests/testthat/test-sp_vim.R                  |  205 +--
 vignettes/introduction-to-vimp.Rmd            |  716 +++++------
 vignettes/ipcw-vim.Rmd                        |only
 vignettes/vimp_bib.bib                        |  153 +-
 86 files changed, 8291 insertions(+), 7678 deletions(-)

More information about vimp at CRAN
Permanent link

New package superml with initial version 0.5.6
Package: superml
Title: Build Machine Learning Models Like Using Python's Scikit-Learn Library in R
Version: 0.5.6
Maintainer: Manish Saraswat <manish06saraswat@gmail.com>
Description: The idea is to provide a standard interface to users who use both R and Python for building machine learning models. This package provides a scikit-learn's fit, predict interface to train machine learning models in R.
License: GPL-3 | file LICENSE
Encoding: UTF-8
LazyData: true
URL: https://github.com/saraswatmks/superml
BugReports: https://github.com/saraswatmks/superml/issues
Depends: R(>= 3.5), R6(>= 2.2)
Imports: data.table (>= 1.10), Rcpp (>= 1.0), assertthat (>= 0.2), Metrics (>= 0.1)
LinkingTo: Rcpp, BH, RcppArmadillo
Suggests: knitr, rlang, testthat, rmarkdown, naivebayes(>= 0.9), ClusterR(>= 1.1), FNN(>= 1.1), ranger(>= 0.10), caret(>= 6.0), xgboost(>= 0.6), glmnet(>= 2.0), e1071(>= 1.7)
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2022-11-12 10:29:39 UTC; manish.saraswat
Author: Manish Saraswat [aut, cre]
Repository: CRAN
Date/Publication: 2022-11-14 08:30:07 UTC

More information about superml at CRAN
Permanent link

Package gpboost updated to version 0.7.10 with previous version 0.7.9 dated 2022-08-25

Title: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models
Description: An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2022, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.
Author: Fabio Sigrist [aut, cre], Benoit Jacob [cph], Gael Guennebaud [cph], Nicolas Carre [cph], Pierre Zoppitelli [cph], Gauthier Brun [cph], Jean Ceccato [cph], Jitse Niesen [cph], Other authors of Eigen for the included version of Eigen [ctb, cph], Timot [...truncated...]
Maintainer: Fabio Sigrist <fabiosigrist@gmail.com>

Diff between gpboost versions 0.7.9 dated 2022-08-25 and 0.7.10 dated 2022-11-14

 DESCRIPTION                                                     |   10 -
 MD5                                                             |   47 ++++----
 R/GPModel.R                                                     |    7 -
 R/gpb.Booster.R                                                 |   48 ++++-----
 README.md                                                       |    4 
 configure.ac                                                    |    2 
 demo/00Index                                                    |    1 
 demo/GPBoost_algorithm.R                                        |   53 ++++++----
 demo/compare_usage_lme4_gpboost.R                               |only
 demo/generalized_linear_Gaussian_process_mixed_effects_models.R |   13 +-
 man/predict.GPModel.Rd                                          |    5 
 man/predict.gpb.Booster.Rd                                      |    5 
 src/gpboost_R.h                                                 |    2 
 src/include/GPBoost/likelihoods.h                               |   20 +--
 src/include/GPBoost/re_model.h                                  |    7 -
 src/include/GPBoost/re_model_template.h                         |   34 +++++-
 src/include/GPBoost/sparse_matrix_utils.h                       |   14 +-
 src/include/LightGBM/c_api.h                                    |    2 
 src/include/LightGBM/utils/log.h                                |   14 +-
 src/metric/binary_metric.hpp                                    |    2 
 src/metric/regression_metric.hpp                                |    6 -
 src/re_model.cpp                                                |    6 -
 tests/testthat/test_GPBoost_algorithm.R                         |   41 ++++---
 tests/testthat/test_GPModel_gaussian_process.R                  |   42 +++++--
 tests/testthat/test_GPModel_non_Gaussian_data.R                 |   49 +++++----
 25 files changed, 259 insertions(+), 175 deletions(-)

More information about gpboost at CRAN
Permanent link

New package AnnoProbe with initial version 0.1.7
Package: AnnoProbe
Title: Annotate the Gene Symbols for Probes in Expression Array
Version: 0.1.7
Date: 2022-11-12
Maintainer: Yonghe Xia <xiayh17@gmail.com>
Description: We curated 147 of expression array, from 3 species(human,mouse,rat), 3 companies('Affymetrix','Illumina','Agilent'), by aligning the 'Fasta' sequences of all probes of each platform to their corresponding reference genome, and then annotate them to genes.
License: Apache License (>= 2)
Encoding: UTF-8
URL: https://github.com/jmzeng1314/AnnoProbe
LazyData: true
Depends: R (>= 3.4.0)
Imports: ggplot2, DT, ggpubr, pheatmap, utils, methods, Biobase, stats, xml2, httr, curl
Suggests: limma, GEOquery, knitr, rmarkdown
NeedsCompilation: no
Packaged: 2022-11-12 11:39:35 UTC; xiayh
Author: Jianming Zeng [aut], Yujia Xiang [aut], Yonghe Xia [ctb, cre]
Repository: CRAN
Date/Publication: 2022-11-14 08:30:11 UTC

More information about AnnoProbe at CRAN
Permanent link

Package breakaway (with last version 4.8.2) was removed from CRAN

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

2022-10-29 4.8.2

Permanent link