bayestestR (>= 0.9.0), insight (>= 0.14.0), datawizard (>=
0.2.0), methods, stats, utils
AER, afex, BayesFactor, betareg, bigutilsr, brms, car,
CompQuadForm, correlation, cplm, dbscan, dplyr, fixest,
forecast, gamm4, ggplot2, glmmTMB, ICS, ICSOutlier, ISLR,
lavaan, lme4, lmtest, loo, magrittr, MASS, Matrix, mclust,
metafor, mgcv, mlogit, multimode, nlme, nonnest2, ordinal,
parallel, parameters, patchwork, pscl, psych, randomForest,
rmarkdown, rstanarm, rstantools, sandwich, see, spelling,
survey, survival, testthat, tweedie, VGAM

bayestestR (>= 0.11.5), insight (>= 0.17.0), datawizard (>=
0.3.0), methods, stats, utils
AER, afex, BayesFactor, bayesplot, betareg, bigutilsr,
blavaan, brms, car, CompQuadForm, correlation, cplm, dbscan,
fixest, forecast, gamm4, ggplot2, glmmTMB, httr, ICS,
ICSOutlier, ISLR, lavaan, lme4, lmtest, loo, magrittr, MASS,
Matrix, mclust, metafor, mgcv, mlogit, multimode, nlme,
nonnest2, ordinal, parallel, parameters, patchwork, poorman,
pscl, psych, randomForest, rmarkdown, rstanarm, rstantools,
sandwich, see, spelling, survey, survival, testthat, tweedie,
VGAM

bayestestR (>= 0.11.5), insight (>= 0.17.0), datawizard (>=
0.4.1), methods, stats, utils
AER, afex, BayesFactor, bayesplot, betareg, bigutilsr,
blavaan, brms, car, CompQuadForm, correlation, cplm, dbscan,
fixest, forecast, gamm4, ggplot2, glmmTMB, httr, ICS,
ICSOutlier, ISLR, lavaan, lme4, lmtest, loo, magrittr, MASS,
Matrix, mclust, metafor, mgcv, mlogit, multimode, nlme,
nonnest2, ordinal, parallel, parameters, patchwork, poorman,
pscl, psych, randomForest, rmarkdown, rstanarm, rstantools,
sandwich, see, survey, survival, testthat, tweedie, VGAM

bayestestR (>= 0.11.5), insight (>= 0.18.0), datawizard (>=
0.4.1.10), methods, stats, utils

An infra-structure for performance estimation of predictive
models
R(>= 3.0), methods, graphics, ggplot2 (>= 0.9.3)
This package provides an  infra-structure for performance estimation and experimental comparison and selection of predictive models

An Infra-Structure for Performance Estimation of Predictive
Models
An  infra-structure for estimating the predictive performance
of predictive models. In this context, it can also be used to compare and/or select among
different alternative ways of solving one or more predictive tasks. The main goal of the
package is to provide a generic infra-structure to estimate the values of different metrics
of predictive performance using different estimation procedures. These estimation tasks can be
applied to any solutions (workflows) to the predictive tasks. The package provides easy to use
standard workflows that allow the usage of any available R modeling algorithm together with
some pre-defined data pre-processing steps and also prediction post-processing methods. It also provides means for addressing issues related with the statistical significance of the observed differences.