r-forge r-universe repositoryhttps://r-forge.r-universe.devPackage updated in r-forgecranlike-server 0.11.2https://github.com/r-forge.png?size=400r-forge r-universe repositoryhttps://r-forge.r-universe.devFri, 13 May 2022 11:27:49 GMT[r-forge] GeneralizedHyperbolic 0.8-5d.scott@auckland.ac.nz (David Scott)Functions for the hyperbolic and related distributions.
Density, distribution and quantile functions and random number generation
are provided for the hyperbolic distribution, the generalized hyperbolic
distribution, the generalized inverse Gaussian distribution and
the skew-Laplace distribution. Additional functionality is
provided for the hyperbolic distribution, normal inverse
Gaussian distribution and generalized inverse Gaussian distribution,
including fitting of these distributions to data. Linear models with
hyperbolic errors may be fitted using hyperblmFit.https://github.com/r-universe/r-forge/actions/runs/2319948477Fri, 13 May 2022 11:27:49 GMTGeneralizedHyperbolic0.8-5successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] SkewHyperbolic 0.4-1d.scott@auckland.ac.nz (David Scott)Functions are provided for the density function,
distribution function, quantiles and random number
generation for the skew hyperbolic
t-distribution. There are also functions that fit
the distribution to data. There are functions for the
mean, variance, skewness, kurtosis and mode of a given
distribution and to calculate moments of any order about
any centre. To assess goodness of fit, there are
functions to generate a Q-Q plot, a P-P plot and a tail plot.https://github.com/r-universe/r-forge/actions/runs/2319953452Fri, 13 May 2022 11:27:49 GMTSkewHyperbolic0.4-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] coin 1.4-3Torsten.Hothorn@R-project.org (Torsten Hothorn)Conditional inference procedures for the general independence
problem including two-sample, K-sample (non-parametric ANOVA),
correlation, censored, ordered and multivariate problems described
in <doi:10.18637/jss.v028.i08>.https://github.com/r-universe/r-forge/actions/runs/2319946476Fri, 13 May 2022 07:42:10 GMTcoin1.4-3successhttps://r-forge.r-universe.devhttps://github.com/r-forge/coinLegoCondInf.RnwLegoCondInf.pdfA Lego System for Conditional Inference2015-01-12 14:22:252021-02-08 10:08:11coin.Rnwcoin.pdfcoin: A Computational Framework for Conditional Inference2015-01-12 14:22:252019-05-27 10:13:40Implementation.RnwImplementation.pdfImplementing a Class of Permutation Tests: The coin Package2018-11-09 12:23:342019-12-13 16:19:52MAXtest.RnwMAXtest.pdfOrder-restricted Scores Test2015-01-12 14:22:252021-02-08 10:08:11[r-forge] mlt 1.4-1Torsten.Hothorn@R-project.org (Torsten Hothorn)Likelihood-based estimation of conditional transformation
models via the most likely transformation approach described in
Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020)
<DOI:10.18637/jss.v092.i01>.https://github.com/r-universe/r-forge/actions/runs/2319690885Fri, 13 May 2022 06:53:43 GMTmlt1.4-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] tramnet 0.0-6lucasheinrich.kook@uzh.ch (Lucas Kook)Partially penalized versions of specific transformation models
implemented in package 'mlt'. Available models include a fully parametric version
of the Cox model, other parametric survival models (Weibull, etc.), models for
binary and ordered categorical variables, normal and transformed-normal (Box-Cox type)
linear models, and continuous outcome logistic regression. Hyperparameter tuning
is facilitated through model-based optimization functionalities from package 'mlrMBO'.
The accompanying vignette describes the methodology used in 'tramnet' in detail.
Transformation models and model-based optimization are described in
Hothorn et al. (2019) <doi:10.1111/sjos.12291> and
Bischl et al. (2016) <arxiv:1703.03373>, respectively.https://github.com/r-universe/r-forge/actions/runs/2319695320Fri, 13 May 2022 06:53:43 GMTtramnet0.0-6failurehttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] tram 0.7-1Torsten.Hothorn@R-project.org (Torsten Hothorn)Formula-based user-interfaces to specific transformation models
implemented in package 'mlt'. Available models include Cox models, some parametric
survival models (Weibull, etc.), models for ordered categorical variables,
normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
(Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to
transformation models for clustered data is provided (Hothorn, 2019, <arxiv:1910.09219>).
Multivariate conditional transformation models (<arxiv:1906.03151>) can be fitted as well.https://github.com/r-universe/r-forge/actions/runs/2319694610Fri, 13 May 2022 06:53:43 GMTtram0.7-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmmtram.Rnwmtram.pdfmtram2019-09-19 10:05:122022-01-14 09:08:56tram.Rnwtram.pdftram2017-12-17 16:36:532022-05-13 06:51:50[r-forge] tbm 0.3-5Torsten.Hothorn@R-project.org (Torsten Hothorn)Boosting the likelihood of conditional and shift transformation models as introduced in \doi{10.1007/s11222-019-09870-4}.https://github.com/r-universe/r-forge/actions/runs/2319694236Fri, 13 May 2022 06:53:43 GMTtbm0.3-5successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmtbm_supplement.Rnwtbm_supplement.pdftbm2018-06-22 14:18:152020-11-27 13:51:13[r-forge] variables 1.1-1Torsten.Hothorn@R-project.org (Torsten Hothorn)Abstract descriptions of (yet) unobserved variables.https://github.com/r-universe/r-forge/actions/runs/2319696395Fri, 13 May 2022 06:53:43 GMTvariables1.1-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] trtf 0.4-1Torsten.Hothorn@R-project.org (Torsten Hothorn)Recursive partytioning of transformation models with
corresponding random forest for conditional transformation models
as described in 'Transformation Forests' (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>)
and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).https://github.com/r-universe/r-forge/actions/runs/2319695571Fri, 13 May 2022 06:53:43 GMTtrtf0.4-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] mlt.docreg 1.1-3Torsten.Hothorn@R-project.org (Torsten Hothorn)Additional documentation, a package vignette and
regression tests for package mlt.https://github.com/r-universe/r-forge/actions/runs/2319691144Fri, 13 May 2022 06:53:43 GMTmlt.docreg1.1-3successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmmlt.Rnwmlt.pdfmlt2016-02-17 10:22:362022-05-13 06:52:43[r-forge] basefun 1.1-2Torsten.Hothorn@R-project.org (Torsten Hothorn)Some very simple infrastructure for basis functions.https://github.com/r-universe/r-forge/actions/runs/2319688033Fri, 13 May 2022 06:53:43 GMTbasefun1.1-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] tramME 1.0.1balint.tamasi@uzh.ch (Balint Tamasi)Likelihood-based estimation of mixed-effects transformation models using the Template
Model Builder ('TMB', Kristensen et al., 2016, <doi:10.18637/jss.v070.i05>). The technical details
of transformation models are given in Hothorn et al. (2018, <doi:10.1111/sjos.12291>). Likelihood
contributions of exact, randomly censored (left, right, interval) and truncated observations are
supported. The random effects are assumed to be normally distributed on the scale of the
transformation function, the marginal likelihood is evaluated using the Laplace approximation,
and the gradients are calculated with automatic differentiation (Tamasi and Hothorn, 2021,
<doi:10.32614/RJ-2021-075>). Penalized smooth shift terms can be defined using 'mgcv'.https://github.com/r-universe/r-forge/actions/runs/2319695021Fri, 13 May 2022 06:53:43 GMTtramME1.0.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmmixed-effects-additive-models.Rnwmixed-effects-additive-models.pdfExamples in statistical ecology2022-04-11 12:26:242022-04-11 19:59:58RJ-2021-075.RnwRJ-2021-075.pdfR Journal 20212022-04-11 12:26:242022-04-11 12:26:24[r-forge] cotram 0.3-2Torsten.Hothorn@R-project.org (Torsten Hothorn)Count transformation models featuring
parameters interpretable as discrete hazard ratios, odds ratios,
reverse-time discrete hazard ratios, or transformed expectations.
An appropriate data transformation for a count outcome and
regression coefficients are simultaneously estimated by maximising
the exact discrete log-likelihood using the computational framework
provided in package 'mlt', technical details are given in
Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>.
The package also contains an experimental implementation of
multivariate count transformation models with an application
to multi-species distribution models.https://github.com/r-universe/r-forge/actions/runs/2319688801Fri, 13 May 2022 06:53:43 GMTcotram0.3-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmcotram.Rnwcotram.pdfcotram2020-03-30 18:33:482021-09-09 15:09:04[r-forge] numDeriv 2020.2-1pgilbert.ttv9z@ncf.ca (Paul Gilbert)Methods for calculating (usually) accurate
numerical first and second order derivatives. Accurate calculations
are done using 'Richardson''s' extrapolation or, when applicable, a
complex step derivative is available. A simple difference
method is also provided. Simple difference is (usually) less accurate
but is much quicker than 'Richardson''s' extrapolation and provides a
useful cross-check.
Methods are provided for real scalar and vector valued functions.https://github.com/r-universe/r-forge/actions/runs/2304318473Tue, 10 May 2022 23:44:30 GMTnumDeriv2020.2-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/optimizerGuide.StexGuide.pdfnumDeriv Guide2011-11-17 01:59:242012-08-14 17:15:09[r-forge] GNE 0.99-3christophe.dutang@ensimag.fr (Christophe Dutang)Provide functions to compute standard and generalized Nash Equilibria.
Optimization methods are available nonsmooth reformulation, fixed-point formulation,
minimization problem and constrained-equation reformulation.
See e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.https://github.com/r-universe/r-forge/actions/runs/2304317171Tue, 10 May 2022 23:44:30 GMTGNE0.99-3successhttps://r-forge.r-universe.devhttps://github.com/r-forge/optimizerGNE-howto.RnwGNE-howto.pdfUser guide for the GNE package2012-07-26 07:09:252019-12-20 19:52:29[r-forge] GPArotation 2015.7-1pgilbert.ttv9z@ncf.ca (Paul Gilbert)Gradient Projection Algorithm Rotation for Factor Analysis. See '?GPArotation.Intro' for more details.https://github.com/r-universe/r-forge/actions/runs/2304317265Tue, 10 May 2022 23:44:30 GMTGPArotation2015.7-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/optimizerGuide.StexGuide.pdfgpa Guide2011-11-17 01:59:242012-08-14 17:15:09[r-forge] minqa 1.2.1katharine.mullen@nist.gov (Katharine M. Mullen)Derivative-free optimization by quadratic approximation based
on an interface to Fortran implementations by M. J. D. Powell.https://github.com/r-universe/r-forge/actions/runs/2304317995Tue, 10 May 2022 23:44:30 GMTminqa1.2.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/optimizer[r-forge] robustbase 0.95-1maechler@stat.math.ethz.ch (Martin Maechler)"Essential" Robust Statistics.
Tools allowing to analyze data with robust methods. This includes
regression methodology including model selections and multivariate
statistics where we strive to cover the book "Robust Statistics,
Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.https://github.com/r-universe/r-forge/actions/runs/2302772003Tue, 10 May 2022 17:12:46 GMTrobustbase0.95-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/robustbasefastMcd-kmini.RnwfastMcd-kmini.pdfcovMcd() -- Generalizing the FastMCD2014-11-22 13:52:202016-11-15 14:28:44psi_functions.Rnwpsi_functions.pdfDefinitions of Psi-Functions Available in Robustbase2013-07-22 14:46:032016-11-15 14:28:44lmrob_simulation.Rnwlmrob_simulation.pdfSimulations for Robust Regression Inference in Small Samples2013-07-22 14:46:032021-01-04 09:44:20[r-forge] rgdal 1.5-32Roger.Bivand@nhh.no (Roger Bivand)Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) and access to projection/transformation operations from the 'PROJ' library. Please note that 'rgdal' will be retired by the end of 2023, plan transition to sf/stars/'terra' functions using 'GDAL' and 'PROJ' at your earliest convenience. Use is made of classes defined in the 'sp' package. Raster and vector map data can be imported into R, and raster and vector 'sp' objects exported. The 'GDAL' and 'PROJ' libraries are external to the package, and, when installing the package from source, must be correctly installed first; it is important that 'GDAL' < 3 be matched with 'PROJ' < 6. From 'rgdal' 1.5-8, installed with to 'GDAL' >=3, 'PROJ' >=6 and 'sp' >= 1.4, coordinate reference systems use 'WKT2_2019' strings, not 'PROJ' strings. 'Windows' and 'macOS' binaries (including 'GDAL', 'PROJ' and their dependencies) are provided on 'CRAN'.https://github.com/r-universe/r-forge/actions/runs/2305521251Mon, 09 May 2022 16:42:30 GMTrgdal1.5-32successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rgdalCRS_projections_transformations.RmdCRS_projections_transformations.htmlCRS, projections and transformations2020-05-16 12:11:442022-03-07 09:52:12PROJ6_GDAL3.RmdPROJ6_GDAL3.htmlMigration to PROJ6/GDAL32019-11-12 14:16:322022-03-07 09:52:12OGR_shape_encoding.RnwOGR_shape_encoding.pdfOGR shapefile encoding2012-12-17 10:26:232012-12-19 13:12:13[r-forge] oompaBase 3.2.9krc@silicovore.com (Kevin R. Coombes)Provides the class unions that must be
preloaded in order for the basic tools in the OOMPA (Object-Oriented
Microarray and Proteomics Analysis) project to be defined and loaded.
It also includes vectorized operations for row-by-row means,
variances, and t-tests. Finally, it provides new color schemes.
Details on the packages in the OOMPA project can be found at
<http://oompa.r-forge.r-project.org/>.https://github.com/r-universe/r-forge/actions/runs/2305521150Mon, 09 May 2022 15:33:47 GMToompaBase3.2.9successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompaoompa.Rnwoompa.pdfOOMPA2014-02-10 15:51:102019-08-19 17:48:24[r-forge] Polychrome 1.5.1krc@silicovore.com (Kevin R. Coombes)Tools for creating, viewing, and assessing qualitative
palettes with many (20-30 or more) colors. See Coombes and colleagues
(2019) <doi:10.18637/jss.v090.c01>.https://github.com/r-universe/r-forge/actions/runs/2305521200Mon, 09 May 2022 15:33:47 GMTPolychrome1.5.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompacolor-deficits.Rmdcolor-deficits.htmlColor Deficits2017-11-19 14:58:032018-04-11 13:09:15creatingPalettes.RmdcreatingPalettes.htmlCreating Palettes with Polychrome2017-04-27 19:43:022018-05-17 12:51:39polychrome.Rmdpolychrome.htmlPolychrome2016-05-11 13:07:592018-05-17 12:51:39testgg.Rmdtestgg.htmlUsing Polychrome With ggplot2020-11-10 15:35:472020-11-10 15:35:47[r-forge] TailRank 3.2.1krc@silicovore.com (Kevin R. Coombes)Implements the tail-rank statistic for selecting biomarkers
from a microarray data set, an efficient nonparametric test focused
on the distributional tails. See
<http://bioinformatics.mdanderson.org/TailRank/tolstoy-new.pdf>.https://github.com/r-universe/r-forge/actions/runs/2305521163Mon, 09 May 2022 15:33:47 GMTTailRank3.2.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompabetabinomial.Rnwbetabinomial.pdfBeta-Binomial Distribution2014-05-09 13:11:102016-05-06 12:50:35tailrank.Rnwtailrank.pdfTailRank2014-02-10 15:51:102016-05-06 12:50:35[r-forge] ClassComparison 3.2.0krc@silicovore.com (Kevin R. Coombes)Defines the classes used for "class comparison" problems
in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
comparison includes tests for differential expression; see Simon's
book for details on typical problem types.https://github.com/r-universe/r-forge/actions/runs/2305521090Mon, 09 May 2022 15:33:47 GMTClassComparison3.2.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompaoompa-cc.Rnwoompa-cc.pdfOOMPA ClassComparison2014-02-10 15:51:102014-05-05 19:51:49[r-forge] oompaData 3.1.1krc@silicovore.com (Kevin R. Coombes)This is a data-only package to provide example data for
other packages that are part of the "Object-Oriented Microrray and
Proteomics Analysis" suite of packages. These are described in more
detail at the package URL.https://github.com/r-universe/r-forge/actions/runs/2305521055Mon, 09 May 2022 15:33:47 GMToompaData3.1.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompa[r-forge] ClassDiscovery 3.4.3krc@silicovore.com (Kevin R. Coombes)Defines the classes used for "class discovery" problems
in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
discovery primarily consists of unsupervised clustering methods with
attempts to assess their statistical significance.https://github.com/r-universe/r-forge/actions/runs/2305521035Mon, 09 May 2022 15:33:47 GMTClassDiscovery3.4.3successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompaoompa-cd.Rnwoompa-cd.pdfOOMPA ClassDiscovery2014-02-10 15:51:102014-02-10 15:51:10maha-test.Rnwmaha-test.pdfOOMPA Mahalanobis Distance2014-02-10 15:51:102014-02-10 15:51:10[r-forge] PreProcess 3.1.7krc@silicovore.com (Kevin R. Coombes)Provides classes to pre-process microarray gene
expression data as part of the OOMPA collection of packages
described at <http://oompa.r-forge.r-project.org/>.https://github.com/r-universe/r-forge/actions/runs/2305521229Mon, 09 May 2022 15:33:47 GMTPreProcess3.1.7successhttps://r-forge.r-universe.devhttps://github.com/r-forge/oompaoompa-prep.Rnwoompa-prep.pdfOOMPA PreProcessing2014-02-10 15:51:102017-04-25 13:56:14[r-forge] modEvA 3.4ana.marcia.barbosa@gmail.com (A. Marcia Barbosa)Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.https://github.com/r-universe/r-forge/actions/runs/2305520030Fri, 06 May 2022 15:39:05 GMTmodEvA3.4successhttps://r-forge.r-universe.devhttps://github.com/r-forge/modeva[r-forge] CHNOSZ 1.9.9-18j3ffdick@gmail.com (Jeffrey Dick)An integrated set of tools for thermodynamic calculations in
aqueous geochemistry and geobiochemistry. Functions are provided for writing
balanced reactions to form species from user-selected basis species and for
calculating the standard molal properties of species and reactions, including
the standard Gibbs energy and equilibrium constant. Calculations of the
non-equilibrium chemical affinity and equilibrium chemical activity of species
can be portrayed on diagrams as a function of temperature, pressure, or
activity of basis species; in two dimensions, this gives a maximum affinity or
predominance diagram. The diagrams have formatted chemical formulas and axis
labels, and water stability limits can be added to Eh-pH, oxygen fugacity-
temperature, and other diagrams with a redox variable. The package has been
developed to handle common calculations in aqueous geochemistry, such as
solubility due to complexation of metal ions, mineral buffers of redox or pH,
and changing the basis species across a diagram ("mosaic diagrams"). CHNOSZ
also implements a group additivity algorithm for the standard thermodynamic
properties of proteins.https://github.com/r-universe/r-forge/actions/runs/2305518311Fri, 06 May 2022 10:19:39 GMTCHNOSZ1.9.9-18successhttps://r-forge.r-universe.devhttps://github.com/r-forge/chnoszanintro.Rmdanintro.htmlAn Introduction to CHNOSZ2017-02-04 12:55:302022-03-27 03:25:02customizing.Rmdcustomizing.htmlCustomizing the thermodynamic database2022-03-24 00:37:092022-05-06 10:09:14multi-metal.Rmdmulti-metal.htmlDiagrams with multiple metals2020-07-16 01:32:592022-04-09 09:26:30equilibrium.Rmdequilibrium.htmlEquilibrium in CHNOSZ2020-07-06 02:19:042021-07-09 13:08:55OBIGT.RmdOBIGT.htmlOBIGT thermodynamic database2020-07-04 04:53:422022-04-11 02:27:40[r-forge] Matrix 1.4-2mmaechler+Matrix@gmail.com (Martin Maechler)A rich hierarchy of matrix classes, including triangular,
symmetric, and diagonal matrices, both dense and sparse and with
pattern, logical and numeric entries. Numerous methods for and
operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries.https://github.com/r-universe/r-forge/actions/runs/2305518165Fri, 06 May 2022 03:33:28 GMTMatrix1.4-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/matrixIntro2Matrix.RnwIntro2Matrix.pdf2nd Introduction to the Matrix Package2012-12-31 10:14:202021-01-06 11:29:58Comparisons.RnwComparisons.pdfComparisons of Least Squares calculation speeds2012-12-31 10:14:202012-12-31 10:14:20Design-issues.RnwDesign-issues.pdfDesign Issues in Matrix package Development2012-12-31 10:14:202018-03-19 17:21:14Introduction.RnwIntroduction.pdfIntroduction to the Matrix Package2012-12-31 10:14:202012-12-31 10:14:20sparseModels.RnwsparseModels.pdfSparse Model Matrices2012-12-31 10:14:202021-01-06 11:29:58[r-forge] MatrixModels 0.5-0mmaechler+Matrix@gmail.com (Martin Maechler)Modelling with sparse and dense 'Matrix' matrices, using
modular prediction and response module classes.https://github.com/r-universe/r-forge/actions/runs/2305518146Fri, 06 May 2022 03:33:28 GMTMatrixModels0.5-0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/matrix[r-forge] anacor 1.1-3mair@fas.harvard.edu (Patrick Mair)Performs simple and canonical CA (covariates on rows/columns) on a two-way frequency table (with missings) by means of SVD. Different scaling methods (standard, centroid, Benzecri, Goodman) as well as various plots including confidence ellipsoids are provided.https://github.com/r-universe/r-forge/actions/runs/2305518052Thu, 05 May 2022 21:38:04 GMTanacor1.1-3successhttps://r-forge.r-universe.devhttps://github.com/r-forge/psychor[r-forge] WRS2 1.1-3mair@fas.harvard.edu (Patrick Mair)A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.https://github.com/r-universe/r-forge/actions/runs/2305518109Thu, 05 May 2022 21:38:04 GMTWRS21.1-3successhttps://r-forge.r-universe.devhttps://github.com/r-forge/psychorWRS2.RnwWRS2.pdfWRS22015-10-01 01:37:342020-06-09 21:26:28[r-forge] Gifi 0.3-9mair@fas.harvard.edu (Patrick Mair)Implements categorical principal component analysis ('PRINCALS'), multiple correspondence analysis ('HOMALS'), monotone regression analysis ('MORALS'). It replaces the 'homals' package.https://github.com/r-universe/r-forge/actions/runs/2305518047Thu, 05 May 2022 21:38:04 GMTGifi0.3-9successhttps://r-forge.r-universe.devhttps://github.com/r-forge/psychor[r-forge] isotone 1.1-0mair@fas.harvard.edu (Patrick Mair)Contains two main functions: one for
solving general isotone regression problems using the
pool-adjacent-violators algorithm (PAVA); another one provides
a framework for active set methods for isotone optimization
problems with arbitrary order restrictions. Various types of
loss functions are prespecified.https://github.com/r-universe/r-forge/actions/runs/2305518019Thu, 05 May 2022 21:38:04 GMTisotone1.1-0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/psychorisotone.Rnwisotone.pdfIsotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods2015-07-24 08:22:022015-07-24 08:22:02[r-forge] aspect 1.0-6mair@fas.harvard.edu (Patrick Mair)Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.https://github.com/r-universe/r-forge/actions/runs/2305520260Thu, 05 May 2022 21:38:04 GMTaspect1.0-6successhttps://r-forge.r-universe.devhttps://github.com/r-forge/psychor[r-forge] Rmpfr 0.8-8maechler@stat.math.ethz.ch (Martin Maechler)Arithmetic (via S4 classes and methods) for
arbitrary precision floating point numbers, including transcendental
("special") functions. To this end, the package interfaces to
the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library
which itself is based on the 'GMP' (GNU Multiple Precision) Library.https://github.com/r-universe/r-forge/actions/runs/2305517719Fri, 29 Apr 2022 20:49:27 GMTRmpfr0.8-8failurehttps://r-forge.r-universe.devhttps://github.com/r-forge/rmpfr[r-forge] DPQ 0.5-2maechler@stat.math.ethz.ch (Martin Maechler)Computations for approximations and alternatives for the 'DPQ'
(Density (pdf), Probability (cdf) and Quantile) functions for probability
distributions in R.
Primary focus is on (central and non-central) beta, gamma and related
distributions such as the chi-squared, F, and t.
--
This is for the use of researchers in these numerical approximation
implementations, notably for my own use in order to improve standard
R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.https://github.com/r-universe/r-forge/actions/runs/2305518295Mon, 25 Apr 2022 17:10:30 GMTDPQ0.5-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/specfuncomp-beta.Rnwcomp-beta.pdfComputing Beta(a,b) for Large Arguments2019-08-15 15:54:122019-08-31 21:18:50log1pmx-etc.Rnwlog1pmx-etc.pdflog1pmx, bd0, stirlerr - Probability Computations in R2021-04-30 13:38:162022-04-20 21:50:57Noncentral-Chisq.RnwNoncentral-Chisq.pdfNoncentral Chi-Squared Probabilities -- Algorithms in R2019-09-02 19:47:182021-05-07 15:59:24[r-forge] Bessel 0.6-0maechler@stat.math.ethz.ch (Martin Maechler)Computations for Bessel function for complex, real and partly
'mpfr' (arbitrary precision) numbers; notably interfacing TOMS 644;
approximations for large arguments, experiments, etc.https://github.com/r-universe/r-forge/actions/runs/2305518247Mon, 25 Apr 2022 17:10:30 GMTBessel0.6-0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/specfunother-Bessels.Rnwother-Bessels.pdfBessel Functions in other CRAN Packages2013-12-10 08:52:032018-11-29 17:53:56[r-forge] party 1.3-10Torsten.Hothorn@R-project.org (Torsten Hothorn)A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on
parametric models (e.g. linear models, GLMs or survival
regression) employing parameter instability tests for split
selection. Extensible functionality for visualizing tree-structured
regression models is available. The methods are described in
Hothorn et al. (2006) <doi:10.1198/106186006X133933>,
Zeileis et al. (2008) <doi:10.1198/106186008X319331> and
Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.https://github.com/r-universe/r-forge/actions/runs/2305517434Mon, 25 Apr 2022 14:35:50 GMTparty1.3-10successhttps://r-forge.r-universe.devhttps://github.com/r-forge/partyMOB.RnwMOB.pdfparty with the mob2012-01-26 11:45:372019-11-25 14:32:22party.Rnwparty.pdfparty: A Laboratory for Recursive Partytioning2012-01-26 11:45:372021-02-08 10:04:24[r-forge] multcomp 1.4-19Torsten.Hothorn@R-project.org (Torsten Hothorn)Simultaneous tests and confidence intervals
for general linear hypotheses in parametric models, including
linear, generalized linear, linear mixed effects, and survival models.
The package includes demos reproducing analyzes presented
in the book "Multiple Comparisons Using R" (Bretz, Hothorn,
Westfall, 2010, CRC Press).https://github.com/r-universe/r-forge/actions/runs/2305518277Mon, 25 Apr 2022 14:35:22 GMTmultcomp1.4-19successhttps://r-forge.r-universe.devhttps://github.com/r-forge/multcompmultcomp-examples.Rnwmultcomp-examples.pdfAdditional Examples2012-02-17 13:09:372021-02-08 10:06:08generalsiminf.Rnwgeneralsiminf.pdfSimultaneous Inference in General Parametric Models2012-02-17 13:09:372020-04-08 06:38:19chfls1.Rnwchfls1.pdfSupplementary Material for "A re-evaluation of the model selection procedure in Pollet \& Nettle (2009)"2012-02-17 13:09:372018-02-26 12:18:06[r-forge] car 3.0-13jfox@mcmaster.ca (John Fox)Functions to Accompany J. Fox and S. Weisberg,
An R Companion to Applied Regression, Third Edition, Sage, 2019.https://github.com/r-universe/r-forge/actions/runs/2305518288Mon, 25 Apr 2022 13:28:38 GMTcar3.0-13successhttps://r-forge.r-universe.devhttps://github.com/r-forge/carembedding.Rnwembedding.pdfUsing car functions inside user functions2017-08-27 16:26:392021-05-15 17:42:25[r-forge] carData 3.0-5jfox@mcmaster.ca (John Fox)Datasets to Accompany J. Fox and S. Weisberg,
An R Companion to Applied Regression, Third Edition, Sage (2019).https://github.com/r-universe/r-forge/actions/runs/2305518231Mon, 25 Apr 2022 13:28:38 GMTcarData3.0-5successhttps://r-forge.r-universe.devhttps://github.com/r-forge/car[r-forge] setRNG 2015.7-1pgilbert.ttv9z@ncf.ca (Paul Gilbert)Provides utilities to help set and record the setting of
the seed and the uniform and normal generators used when a random
experiment is run. The utilities can be used in other functions
that do random experiments to simplify recording and/or setting all the
necessary information for reproducibility.
See the vignette and reference manual for examples.https://github.com/r-universe/r-forge/actions/runs/2305518833Fri, 22 Apr 2022 10:49:11 GMTsetRNG2015.7-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distrsetRNG.StexsetRNG.pdfsetRNG Guide2011-11-17 01:53:482017-04-23 12:11:21[r-forge] distrEllipse 2.8.0peter.ruckdeschel@uni-oldenburg.de (Peter Ruckdeschel)Distribution (S4-)classes for elliptically contoured distributions (based on
package 'distr').https://github.com/r-universe/r-forge/actions/runs/2305519361Fri, 22 Apr 2022 10:49:11 GMTdistrEllipse2.8.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distrDoc 2.8.0peter.ruckdeschel@uni-oldenburg.de (Peter Ruckdeschel)Provides documentation in form of a common vignette to packages 'distr',
'distrEx', 'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.https://github.com/r-universe/r-forge/actions/runs/2305519506Fri, 22 Apr 2022 10:49:11 GMTdistrDoc2.8.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distrdistr.Rnwdistr.pdfdistr - manual2011-11-18 11:34:592019-03-01 16:00:34[r-forge] distrTeach 2.8.1peter.ruckdeschel@uni-oldenburg.de (Peter Ruckdeschel)Provides flexible examples of LLN and CLT for teaching purposes in secondary
school.https://github.com/r-universe/r-forge/actions/runs/2305519078Fri, 22 Apr 2022 10:49:11 GMTdistrTeach2.8.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distrEx 2.8.0Matthias.Kohl@stamats.de (Matthias Kohl)Extends package 'distr' by functionals, distances, and conditional distributions.https://github.com/r-universe/r-forge/actions/runs/2305519080Fri, 22 Apr 2022 10:49:11 GMTdistrEx2.8.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distrTEst 2.8.0peter.ruckdeschel@uni-oldenburg.de (Peter Ruckdeschel)Evaluation (S4-)classes based on package distr for evaluating procedures
(estimators/tests) at data/simulation in a unified way.https://github.com/r-universe/r-forge/actions/runs/2305519161Fri, 22 Apr 2022 10:49:11 GMTdistrTEst2.8.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distrSim 2.8.0peter.ruckdeschel@uni-oldenburg.de (Peter Ruckdeschel)S4-classes for setting up a coherent framework for simulation within the distr
family of packages.https://github.com/r-universe/r-forge/actions/runs/2305518582Fri, 22 Apr 2022 10:49:11 GMTdistrSim2.8.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distrRmetrics 2.8.0peter.ruckdeschel@uni-oldenburg.de (Peter Ruckdeschel)S4-distribution classes based on package distr for distributions from packages
'fBasics' and 'fGarch'.https://github.com/r-universe/r-forge/actions/runs/2305520246Fri, 22 Apr 2022 10:49:11 GMTdistrRmetrics2.8.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr