r-forge r-universe repositoryhttps://r-forge.r-universe.devPackage updated in r-forgecranlike-server 0.16.70https://github.com/r-forge.png?size=400r-forge r-universe repositoryhttps://r-forge.r-universe.devWed, 29 Nov 2023 12:01:48 GMT[r-forge] CHNOSZ 2.0.0-38j3ffdick@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/7033740921Wed, 29 Nov 2023 12:01:48 GMTCHNOSZ2.0.0-38successhttps://r-forge.r-universe.devhttps://github.com/r-forge/chnoszanintro.Rmdanintro.htmlAn Introduction to CHNOSZ2017-02-04 12:55:302023-11-29 12:01:48FAQ.RmdFAQ.htmlCHNOSZ FAQ2023-05-17 08:54:572023-11-29 12:01:48custom_data.Rmdcustom_data.htmlCustomizing the thermodynamic database2023-03-03 04:56:392023-11-29 12:01:48multi-metal.Rmdmulti-metal.htmlDiagrams with multiple metals2020-07-16 01:32:592023-11-29 12:01:48equilibrium.Rmdequilibrium.htmlEquilibrium in CHNOSZ2020-07-06 02:19:042023-11-29 12:01:48OBIGT.RmdOBIGT.htmlOBIGT thermodynamic database2020-07-04 04:53:422023-11-15 04:21:12eos-regress.Rmdeos-regress.htmlRegressing thermodynamic data2017-02-06 13:30:102023-11-29 12:01:48[r-forge] MatrixModels 0.5-3mmaechler+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/7033744842Wed, 29 Nov 2023 11:35:09 GMTMatrixModels0.5-3successhttps://r-forge.r-universe.devhttps://github.com/r-forge/matrix[r-forge] Matrix 1.6-4mmaechler+Matrix@gmail.com (Martin Maechler)A rich hierarchy of sparse and dense matrix classes,
including general, symmetric, triangular, and diagonal matrices
with numeric, logical, or pattern entries. Efficient methods
for operating on such matrices, often wrapping the 'BLAS',
'LAPACK', and 'SuiteSparse' libraries.https://github.com/r-universe/r-forge/actions/runs/7033743320Wed, 29 Nov 2023 11:35:09 GMTMatrix1.6-4successhttps://r-forge.r-universe.devhttps://github.com/r-forge/matrixIntro2Matrix.RnwIntro2Matrix.pdf2nd Introduction to the Matrix Package2012-12-31 10:14:202023-06-21 06:27:04Comparisons.RnwComparisons.pdfComparisons of Least Squares calculation speeds2012-12-31 10:14:202023-06-21 06:27:04Design-issues.RnwDesign-issues.pdfDesign Issues in Matrix package Development2012-12-31 10:14:202023-06-21 06:27:04Introduction.RnwIntroduction.pdfIntroduction to the Matrix Package2012-12-31 10:14:202012-12-31 10:14:20sparseModels.RnwsparseModels.pdfSparse Model Matrices2012-12-31 10:14:202023-06-21 06:27:04[r-forge] robustbase 0.99-1maechler@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/7023999107Tue, 28 Nov 2023 15:58:52 GMTrobustbase0.99-1successhttps://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] surveillance 1.22.1.9000seb.meyer@fau.de (Sebastian Meyer)Statistical methods for the modeling and monitoring of
time series of counts, proportions and categorical data, as
well as for the modeling of continuous-time point processes of
epidemic phenomena. The monitoring methods focus on aberration
detection in count data time series from public health
surveillance of communicable diseases, but applications could
just as well originate from environmetrics, reliability
engineering, econometrics, or social sciences. The package
implements many typical outbreak detection procedures such as
the (improved) Farrington algorithm, or the negative binomial
GLR-CUSUM method of Hoehle and Paul (2008)
<doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach
combining logistic and multinomial logistic modeling is also
included. The package contains several real-world data sets,
the ability to simulate outbreak data, and to visualize the
results of the monitoring in a temporal, spatial or
spatio-temporal fashion. A recent overview of the available
monitoring procedures is given by Salmon et al. (2016)
<doi:10.18637/jss.v070.i10>. For the retrospective analysis of
epidemic spread, the package provides three endemic-epidemic
modeling frameworks with tools for visualization, likelihood
inference, and simulation. hhh4() estimates models for
(multivariate) count time series following Paul and Held (2011)
<doi:10.1002/sim.4177> and Meyer and Held (2014)
<doi:10.1214/14-AOAS743>. twinSIR() models the
susceptible-infectious-recovered (SIR) event history of a fixed
population, e.g, epidemics across farms or networks, as a
multivariate point process as proposed by Hoehle (2009)
<doi:10.1002/bimj.200900050>. twinstim() estimates
self-exciting point process models for a spatio-temporal point
pattern of infective events, e.g., time-stamped geo-referenced
surveillance data, as proposed by Meyer et al. (2012)
<doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of
the implemented space-time modeling frameworks for epidemic
phenomena is given by Meyer et al. (2017)
<doi:10.18637/jss.v077.i11>.https://github.com/r-universe/r-forge/actions/runs/7020766223Tue, 28 Nov 2023 11:57:36 GMTsurveillance1.22.1.9000successhttps://r-forge.r-universe.devhttps://github.com/r-forge/surveillanceglrnb.Rnwglrnb.pdfalgo.glrnb: Count data regression charts using the generalized likelihood ratio statistic2012-07-25 20:25:232023-03-19 12:22:59surveillance.Rnwsurveillance.pdfGetting started with outbreak detection2012-07-24 12:20:222022-02-08 09:55:12hhh4_spacetime.Rnwhhh4_spacetime.pdfhhh4 (spatio-temporal): Endemic-epidemic modeling of areal count time series2016-03-29 22:46:432023-10-21 21:48:59hhh4.Rnwhhh4.pdfhhh4: An endemic-epidemic modelling framework for infectious disease counts2012-07-25 20:25:232023-10-25 16:07:41monitoringCounts.RnwmonitoringCounts.pdfMonitoring count time series in R: Aberration detection in public health surveillance2016-05-14 15:51:192023-03-08 13:31:35twinSIR.RnwtwinSIR.pdftwinSIR: Individual-level epidemic modeling for a fixed population with known distances2016-03-24 13:33:252023-05-16 13:48:35twinstim.Rnwtwinstim.pdftwinstim: An endemic-epidemic modeling framework for spatio-temporal point patterns2016-03-04 23:43:272023-05-05 08:37:15[r-forge] fAssets 4023.85Stefan.Theussl@R-project.org (Stefan Theussl)A collection of functions to manage, to investigate and to
analyze data sets of financial assets from different points of
view.https://github.com/r-universe/r-forge/actions/runs/7020748951Tue, 28 Nov 2023 11:47:35 GMTfAssets4023.85successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] fPortfolio 4023.84Stefan.Theussl@R-project.org (Stefan Theussl)A collection of functions to optimize portfolios and to
analyze them from different points of view.https://github.com/r-universe/r-forge/actions/runs/7020753910Tue, 28 Nov 2023 11:47:35 GMTfPortfolio4023.84successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] GeneralizedHyperbolic 0.8-6d.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/7020758015Tue, 28 Nov 2023 11:47:35 GMTGeneralizedHyperbolic0.8-6successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] SkewHyperbolic 0.4-2d.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/7020763325Tue, 28 Nov 2023 11:47:35 GMTSkewHyperbolic0.4-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] stabledist 0.7-2maechler@stat.math.ethz.ch (Martin Maechler)Density, Probability and Quantile functions, and random
number generation for (skew) stable distributions, using the
parametrizations of Nolan.https://github.com/r-universe/r-forge/actions/runs/7020764806Tue, 28 Nov 2023 11:47:35 GMTstabledist0.7-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] NormalLaplace 0.3-1d.scott@auckland.ac.nz (David Scott)Functions for the normal Laplace distribution. Currently,
it provides limited functionality. Density, distribution and
quantile functions, random number generation, and moments are
provided.https://github.com/r-universe/r-forge/actions/runs/7020759331Tue, 28 Nov 2023 11:47:35 GMTNormalLaplace0.3-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] fUnitRoots 4021.81georgi.boshnakov@manchester.ac.uk (Georgi N. Boshnakov)Provides four addons for analyzing trends and unit roots
in financial time series: (i) functions for the density and
probability of the augmented Dickey-Fuller Test, (ii) functions
for the density and probability of MacKinnon's unit root test
statistics, (iii) reimplementations for the ADF and MacKinnon
Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's
unit root test suite.https://github.com/r-universe/r-forge/actions/runs/7020756514Tue, 28 Nov 2023 11:47:35 GMTfUnitRoots4021.81successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] fExtremes 4021.83p.northrop@ucl.ac.uk (Paul J. Northrop)Provides functions for analysing and modelling extreme
events in financial time Series. The topics include: (i) data
pre-processing, (ii) explorative data analysis, (iii) peak over
threshold modelling, (iv) block maxima modelling, (v)
estimation of VaR and CVaR, and (vi) the computation of the
extreme index.https://github.com/r-universe/r-forge/actions/runs/7020750039Tue, 28 Nov 2023 11:47:35 GMTfExtremes4021.83successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] randtoolbox 2.0.5dutangc@gmail.com (Christophe Dutang)Provides (1) pseudo random generators - general linear
congruential generators, multiple recursive generators and
generalized feedback shift register (SF-Mersenne Twister
algorithm (<doi:10.1007/978-3-540-74496-2_36>) and WELL
(<doi:10.1145/1132973.1132974>) generators); (2) quasi random
generators - the Torus algorithm, the Sobol sequence, the
Halton sequence (including the Van der Corput sequence) and (3)
some generator tests - the gap test, the serial test, the poker
test, see, e.g., Gentle (2003) <doi:10.1007/b97336>. Take a
look at the Distribution task view of types and tests of random
number generators. The package can be provided without the
'rngWELL' dependency on demand. Package in Memoriam of Diethelm
and Barbara Wuertz.https://github.com/r-universe/r-forge/actions/runs/7020761232Tue, 28 Nov 2023 11:47:35 GMTrandtoolbox2.0.5successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetricsfullpres.Rnwfullpres.pdfA note on random number generation2012-04-29 13:46:262022-05-18 06:14:11shortintro.Rnwshortintro.pdfQuick introduction of randtoolbox2012-04-29 13:46:262021-10-28 12:56:57[r-forge] fRegression 4021.83p.northrop@ucl.ac.uk (Paul J. Northrop)A collection of functions for linear and non-linear
regression modelling. It implements a wrapper for several
regression models available in the base and contributed
packages of R.https://github.com/r-universe/r-forge/actions/runs/7020755094Tue, 28 Nov 2023 11:47:35 GMTfRegression4021.83successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] fGarch 4031.90.9000georgi.boshnakov@manchester.ac.uk (Georgi N. Boshnakov)Analyze and model heteroskedastic behavior in financial
time series.https://github.com/r-universe/r-forge/actions/runs/7020751078Tue, 28 Nov 2023 11:47:35 GMTfGarch4031.90.9000successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] fImport 4021.86.9000georgi.boshnakov@manchester.ac.uk (Georgi N. Boshnakov)Provides a collection of utility functions to download and
manage data sets from the Internet or from other sources.https://github.com/r-universe/r-forge/actions/runs/7020752359Tue, 28 Nov 2023 11:47:35 GMTfImport4021.86.9000successhttps://r-forge.r-universe.devhttps://github.com/r-forge/rmetrics[r-forge] expm 0.999-8maechler@stat.math.ethz.ch (Martin Maechler)Computation of the matrix exponential, logarithm, sqrt,
and related quantities, using traditional and modern methods.https://github.com/r-universe/r-forge/actions/runs/7023998579Tue, 28 Nov 2023 10:21:50 GMTexpm0.999-8successhttps://r-forge.r-universe.devhttps://github.com/r-forge/expmexpm.Rnwexpm.pdfUsing expm in packages2013-07-04 15:15:322013-07-04 15:15:32[r-forge] deSolve 1.40thomas.petzoldt@tu-dresden.de (Thomas Petzoldt)Functions that solve initial value problems of a system of
first-order ordinary differential equations ('ODE'), of partial
differential equations ('PDE'), of differential algebraic
equations ('DAE'), and of delay differential equations. The
functions provide an interface to the FORTRAN functions
'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK'
collection, to the FORTRAN functions 'dvode', 'zvode' and
'daspk' and a C-implementation of solvers of the 'Runge-Kutta'
family with fixed or variable time steps. The package contains
routines designed for solving 'ODEs' resulting from 1-D, 2-D
and 3-D partial differential equations ('PDE') that have been
converted to 'ODEs' by numerical differencing.https://github.com/r-universe/r-forge/actions/runs/7015353239Mon, 27 Nov 2023 22:17:40 GMTdeSolve1.40successhttps://r-forge.r-universe.devhttps://github.com/r-forge/desolvedeSolve.RnwdeSolve.pdfR Package deSolve: Solving Initial Value Differential Equations in R2013-08-14 13:15:032021-09-22 13:40:59compiledCode.RnwcompiledCode.pdfR Package deSolve: Writing Code in Compiled Languages2013-08-14 13:15:032021-09-22 13:29:04[r-forge] zoo 1.8-13Achim.Zeileis@R-project.org (Achim Zeileis)An S3 class with methods for totally ordered indexed
observations. It is particularly aimed at irregular time series
of numeric vectors/matrices and factors. zoo's key design goals
are independence of a particular index/date/time class and
consistency with ts and base R by providing methods to extend
standard generics.https://github.com/r-universe/r-forge/actions/runs/7013129481Mon, 27 Nov 2023 20:18:19 GMTzoo1.8-13successhttps://r-forge.r-universe.devhttps://github.com/r-forge/zoozoo-read.Rnwzoo-read.pdfReading Data in zoo2012-04-28 20:21:172016-08-05 22:16:14zoo-design.Rnwzoo-design.pdfzoo Design2012-04-28 20:21:172012-04-28 20:21:17zoo-faq.Rnwzoo-faq.pdfzoo FAQ2012-04-28 20:21:172019-03-18 21:59:28zoo-quickref.Rnwzoo-quickref.pdfzoo Quick Reference2012-04-28 20:21:172019-03-18 21:59:28zoo.Rnwzoo.pdfzoo: An S3 Class and Methods for Indexed Totally Ordered Observations2012-04-28 20:21:172020-01-02 14:46:24[r-forge] party 1.3-14Torsten.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/7007711378Mon, 27 Nov 2023 11:55:44 GMTparty1.3-14successhttps://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] mvtnorm 1.2-4Torsten.Hothorn@R-project.org (Torsten Hothorn)Computes multivariate normal and t probabilities,
quantiles, random deviates, and densities. Log-likelihoods for
multivariate Gaussian models and Gaussian copulae parameterised
by Cholesky factors of covariance or precision matrices are
implemented for interval-censored and exact data, or a mix
thereof. Score functions for these log-likelihoods are
available. A class representing multiple lower triangular
matrices and corresponding methods are part of this package.https://github.com/r-universe/r-forge/actions/runs/7006219912Mon, 27 Nov 2023 11:41:03 GMTmvtnorm1.2-4successhttps://r-forge.r-universe.devhttps://github.com/r-forge/mvtnormlmvnorm_src.Rnwlmvnorm_src.pdfMultivariate Normal Log-likelihoods2023-03-28 15:22:542023-06-07 14:44:45MVT_Rnews.RnwMVT_Rnews.pdfUsing mvtnorm2013-09-04 09:04:322013-09-04 09:04:32[r-forge] distrDoc 2.8.1peter.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/7006200548Mon, 27 Nov 2023 06:13:43 GMTdistrDoc2.8.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distrdistr.Rnwdistr.pdfdistr - manual2011-11-18 11:34:592022-11-12 13:51:25[r-forge] distrSim 2.8.1peter.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/7006213131Mon, 27 Nov 2023 06:13:43 GMTdistrSim2.8.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distrTEst 2.8.1peter.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/7006218355Mon, 27 Nov 2023 06:13:43 GMTdistrTEst2.8.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distrTeach 2.9.0peter.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/7006215654Mon, 27 Nov 2023 06:13:43 GMTdistrTeach2.9.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distrMod 2.9.0peter.ruckdeschel@uni-oldenburg.de (Peter Ruckdeschel)Implements S4 classes for probability models based on
packages 'distr' and 'distrEx'.https://github.com/r-universe/r-forge/actions/runs/7006209252Mon, 27 Nov 2023 06:13:43 GMTdistrMod2.9.0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distrdistrMod.RnwdistrMod.pdfR Package distrMod: S4 Classes and Methods for Probability Models2011-11-18 12:33:042019-03-31 17:14:24[r-forge] distrRmetrics 2.8.1peter.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/7006211098Mon, 27 Nov 2023 06:13:43 GMTdistrRmetrics2.8.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] distr 2.9.2peter.ruckdeschel@uni-oldenburg.de (Peter Ruckdeschel)S4-classes and methods for distributions.https://github.com/r-universe/r-forge/actions/runs/7006199186Mon, 27 Nov 2023 06:13:43 GMTdistr2.9.2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distrnewDistributions-knitr.RnwnewDistributions-knitr.pdfnewDistributions2018-07-08 14:29:452022-11-12 13:50:48[r-forge] distrEx 2.9.1Matthias.Kohl@stamats.de (Matthias Kohl)Extends package 'distr' by functionals, distances, and
conditional distributions.https://github.com/r-universe/r-forge/actions/runs/7006206658Mon, 27 Nov 2023 06:13:43 GMTdistrEx2.9.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[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/7006222582Mon, 27 Nov 2023 06:13:43 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 2.8.1peter.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/7006203399Mon, 27 Nov 2023 06:13:43 GMTdistrEllipse2.8.1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/distr[r-forge] modEvA 3.11ana.marcia.barbosa@gmail.com (A. Marcia Barbosa)Analyses species distribution models and evaluates their
performance. It includes functions for variation partitioning,
extracting variable importance, computing several metrics of
model discrimination and calibration performance, optimizing
prediction thresholds based on a number of criteria, performing
multivariate environmental similarity surface (MESS) analysis,
and displaying various analytical plots. Initially described in
Barbosa et al. (2013) <doi:10.1111/ddi.12100>.https://github.com/r-universe/r-forge/actions/runs/6994085301Sat, 25 Nov 2023 19:41:55 GMTmodEvA3.11successhttps://r-forge.r-universe.devhttps://github.com/r-forge/modeva[r-forge] R2MLwiN 0.8-8zhengzheng236@gmail.com (Zhengzheng Zhang)An R command interface to the 'MLwiN' multilevel modelling
software package.https://github.com/r-universe/r-forge/actions/runs/6984498860Fri, 24 Nov 2023 15:41:25 GMTR2MLwiN0.8-8successhttps://r-forge.r-universe.devhttps://github.com/r-forge/r2mlwin[r-forge] colorspace 2.1-1Achim.Zeileis@R-project.org (Achim Zeileis)Carries out mapping between assorted color spaces
including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV),
CIELAB, and polar CIELAB. Qualitative, sequential, and
diverging color palettes based on HCL colors are provided along
with corresponding ggplot2 color scales. Color palette choice
is aided by an interactive app (with either a Tcl/Tk or a shiny
graphical user interface) and shiny apps with an HCL color
picker and a color vision deficiency emulator. Plotting
functions for displaying and assessing palettes include color
swatches, visualizations of the HCL space, and trajectories in
HCL and/or RGB spectrum. Color manipulation functions include:
desaturation, lightening/darkening, mixing, and simulation of
color vision deficiencies (deutanomaly, protanomaly,
tritanomaly). Details can be found on the project web page at
<https://colorspace.R-Forge.R-project.org/> and in the
accompanying scientific paper: Zeileis et al. (2020, Journal of
Statistical Software, <doi:10.18637/jss.v096.i01>).https://github.com/r-universe/r-forge/actions/runs/6978326270Fri, 24 Nov 2023 01:02:46 GMTcolorspace2.1-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/colorspacecolorspace.Rmdcolorspace.htmlcolorspace: A Toolbox for Manipulating and Assessing Colors and Palettes2018-10-06 00:15:392021-01-08 13:42:16hcl-colors.Rnwhcl-colors.pdfHCL-Based Color Palettes in R2013-01-23 06:07:542021-05-04 00:31:34[r-forge] cops 1.10-1thomas.rusch@wu.ac.at (Thomas Rusch)Multidimensional scaling (MDS) methods that aim at
pronouncing the clustered appearance of the configuration
(Rusch, Mair & Hornik, 2021,
<doi:10.1080/10618600.2020.1869027>). They achieve this by
transforming proximities/distances with explicit power
functions and penalizing the fitting criterion with a
clusteredness index, the OPTICS Cordillera (Rusch, Hornik &
Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two
variants: One for finding the configuration directly (COPS-C)
with given explicit power transformations and implicit ratio,
interval and nonmetric optimal scaling transformations (Borg &
Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the
augmented fitting criterion to find optimal hyperparameters for
the explicit transformations (P-COPS). The package contains
various functions, wrappers, methods and classes for fitting,
plotting and displaying a large number of different MDS models
(most of the functionality in smacofx) in the COPS framework.
The package further contains a function for pattern search
optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch,
Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a
functions to calculate the phi-distances for count data or
histograms.https://github.com/r-universe/r-forge/actions/runs/6966592096Wed, 22 Nov 2023 17:18:48 GMTcops1.10-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/stopscops.html.asiscops.htmlA Tutorial on Cluster Optimized Proximity Scaling (COPS)2020-09-22 21:40:182020-09-30 20:22:40[r-forge] cordillera 1.0-2thomas.rusch@wu.ac.at (Thomas Rusch)Functions for calculating the OPTICS Cordillera. The
OPTICS Cordillera measures the amount of 'clusteredness' in a
numeric data matrix within a distance-density based framework
for a given minimum number of points comprising a cluster, as
described in Rusch, Hornik, Mair (2018)
<doi:10.1080/10618600.2017.1349664>. We provide an R native
version with methods for printing, summarizing, and plotting
the result.https://github.com/r-universe/r-forge/actions/runs/6966592446Wed, 22 Nov 2023 17:18:48 GMTcordillera1.0-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/stopscordillera.html.asiscordillera.htmlUsing the OPTICS Cordillera2020-10-02 09:47:552020-10-05 15:26:26[r-forge] smacofx 1.3-2thomas.rusch@wu.ac.at (Thomas Rusch)Flexible multidimensional scaling (MDS) methods and
extensions to the package 'smacof'. This package contains
various functions, wrappers, methods and classes for fitting,
plotting and displaying a large number of different flexible
MDS models (some as of yet unpublished). These are: Torgerson
scaling (Torgerson, 1958, ISBN:978-0471879459) with powers,
Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>)
with ratio and interval optimal scaling, Multiscale MDS
(Ramsay, 1977, <doi:10.1007/BF02294052>) with ratio and
interval optimal scaling, S-stress MDS (ALSCAL; Takane, Young &
De Leeuw, 1977, <doi:10.1007/BF02293745>) with ratio and
interval optimal scaling, elastic scaling (McGee, 1966,
<doi:10.1111/j.2044-8317.1966.tb00367.x>) with ratio and
interval optimal scaling, r-stress MDS (De Leeuw, Groenen &
Mair, 2016, <https://rpubs.com/deleeuw/142619>) with ratio,
interval and non-metric optimal scaling, power-stress MDS
(POST-MDS; Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>)
with ratio and interval optimal scaling, restricted
power-stress (Rusch, Mair & Hornik, 2021,
<doi:10.1080/10618600.2020.1869027>) with ratio and interval
optimal scaling, approximate power-stress with ratio optimal
scaling (Rusch, Mair & Hornik, 2021,
<doi:10.1080/10618600.2020.1869027>), Box-Cox MDS (Chen & Buja,
2013, <https://jmlr.org/papers/v14/chen13a.html>), local MDS
(Chen & Buja, 2009, <doi:10.1198/jasa.2009.0111>), curvilinear
component analysis (Demartines & Herault, 1997,
<doi:10.1109/72.554199>) and curvilinear distance analysis
(Lee, Lendasse & Verleysen, 2004,
<doi:10.1016/j.neucom.2004.01.007>). There also are
experimental models (e.g., sparsified MDS and sparsified
POST-MDS). Some functions are suitably flexible to allow any
other sensible combination of explicit power transformations
for weights, distances and input proximities with implicit
ratio, interval or non-metric optimal scaling of the input
proximities. Most functions use a Majorization-Minimization
algorithm. Currently the methods are only available for
one-mode data (symmetric dissimiliarity matrices).https://github.com/r-universe/r-forge/actions/runs/6966592826Wed, 22 Nov 2023 17:18:48 GMTsmacofx1.3-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/stops[r-forge] stops 1.4-2thomas.rusch@wu.ac.at (Thomas Rusch)Methods that use flexible variants of multidimensional
scaling (MDS) which incorporate parametric nonlinear distance
transformations and trade-off the goodnes-of-fit fit with
structure considerations to find optimal hyperparameters, also
known as structure optimized proximity scaling (STOPS) (Rusch,
Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The
package contains various functions, wrappers, methods and
classes for fitting, plotting and displaying different 1-way
MDS models with ratio, interval, ordinal optimal scaling in a
STOPS framework. These cover essentially the functionality of
the package smacofx, including Torgerson (classical) scaling
with power transformations of dissmiliarities, SMACOF MDS with
powers of dissmiliarities, Sammon mapping with powers of
dissmiliarities, elastic scaling with powers of
dissmiliarities, spherical SMACOF with powers of
dissmiliarities, (ALSCAL) s-stress MDS with powers of
dissmiliarities, r-stress MDS, MDS with powers of
dissmiliarities and configuration distances, elastic scaling
powers of dissmiliarities and configuration distances, Sammon
mapping powers of dissmiliarities and configuration distances,
power stress MDS (POST-MDS), approximate power stress, Box-Cox
MDS, local MDS, Isomap, curvilinear component analysis (CLCA),
curvilinear distance analysis (CLDA) and sparsified (power)
multidimensional scaling and (power) multidimensional distance
analysis (experimental models from smacofx influenced by CLCA).
All of these models can also be fit by optimizing over
hyperparameters based on godness-of-fit fit only (i.e., no
structure considerations). The package further contains
functions for optimization, specifically the adaptive
Luus-Jaakola algorithm and a wrapper for Bayesian optimization
with treed Gaussian process with jumps to linear models, and
functions for various c-structuredness indices.https://github.com/r-universe/r-forge/actions/runs/6966593198Wed, 22 Nov 2023 17:18:48 GMTstops1.4-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/stopsstops.html.asisstops.htmlA tutorial on STOPS2015-07-08 11:32:242015-07-08 11:32:24[r-forge] basefun 1.1-4Torsten.Hothorn@R-project.org (Torsten Hothorn)Some very simple infrastructure for basis functions.https://github.com/r-universe/r-forge/actions/runs/6908103790Fri, 17 Nov 2023 15:26:33 GMTbasefun1.1-4successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[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/6908110216Fri, 17 Nov 2023 15:26:33 GMTvariables1.1-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] tramME 1.0.4balint.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 & 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/6908107584Fri, 17 Nov 2023 15:26:33 GMTtramME1.0.4failurehttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] tram 1.0-1Torsten.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 (Barbanti and Hothorn, 2022,
<DOI:10.1093/biostatistics/kxac048>). Multivariate conditional transformation models
(Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023,
<DOI:10.1080/00031305.2023.2203177>) can be fitted as well.https://github.com/r-universe/r-forge/actions/runs/6908106829Fri, 17 Nov 2023 15:26:33 GMTtram1.0-1successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmmtram.Rnwmtram.pdfmtram2019-09-19 10:05:122022-05-18 12:20:26tram.Rnwtram.pdftram2017-12-17 16:36:532022-05-13 06:51:50[r-forge] cotram 0.5-0siegfried.sandra@protonmail.com (Sandra Siegfried)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 <arXiv:2201.13095>.https://github.com/r-universe/r-forge/actions/runs/6908104295Fri, 17 Nov 2023 15:26:33 GMTcotram0.5-0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmcotram.Rnwcotram.pdfcotram2020-03-30 18:33:482023-09-05 08:38:11[r-forge] tramnet 0.1-0lucasheinrich.kook@gmail.com (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 'mlr3MBO'.
The methodology is described in Kook et al. (2021) <doi:10.32614/RJ-2021-054>.
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/6908108264Fri, 17 Nov 2023 15:26:33 GMTtramnet0.1-0successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] mlt.docreg 1.1-7Torsten.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/6908105477Fri, 17 Nov 2023 15:26:33 GMTmlt.docreg1.1-7successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmmlt.Rnwmlt.pdfmlt2016-02-17 10:22:362022-08-15 11:17:40[r-forge] trtf 0.4-2Torsten.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/6908109502Fri, 17 Nov 2023 15:26:33 GMTtrtf0.4-2successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[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/6908106228Fri, 17 Nov 2023 15:26:33 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] mlt 1.4-10Torsten.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/6908104841Fri, 17 Nov 2023 15:26:33 GMTmlt1.4-10successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctm[r-forge] tramvs 0.0-5lucasheinrich.kook@uzh.ch (Lucas Kook)Greedy optimal subset selection for transformation models
(Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess
algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to
models from packages 'tram' and 'cotram'.https://github.com/r-universe/r-forge/actions/runs/6908108916Fri, 17 Nov 2023 15:26:33 GMTtramvs0.0-5successhttps://r-forge.r-universe.devhttps://github.com/r-forge/ctmtramvs.Rnwtramvs.pdftramvs2022-01-19 16:52:572023-03-06 13:38:37