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Merged
strengejacke merged 3 commits into from
Jun 30, 2026
Merged

Prepare CRAN release #920

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d3fa20e
Prepare CRAN release
strengejacke Jun 29, 2026
59672ed
update tests, fix mising arg
strengejacke Jun 29, 2026
5ff040c
fix missing RD link
strengejacke Jun 29, 2026
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3 changes: 1 addition & 2 deletions DESCRIPTION
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Type: Package
Package: performance
Title: Assessment of Regression Models Performance
Version: 0.17.0.6
Version: 0.17.1
Authors@R:
c(person(given = "Daniel",
family = "Lüdecke",
Expand Down Expand Up @@ -168,4 +168,3 @@ Config/Needs/website:
easystats/easystatstemplate
Config/rcmdcheck/ignore-inconsequential-notes: true
Config/roxygen2/version: 8.0.0
Remotes: easystats/see
2 changes: 1 addition & 1 deletion NEWS.md
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# performance (devel)
# performance 0.17.1

## Changes

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1 change: 1 addition & 0 deletions R/check_overdispersion.R
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#' using the **DHARMa** package. For `glm`, the default is `"simulated"` for
#' bernoulli, binomial and negative-binomial models. Set `residual_type = "normal"`
#' to always use regular (i.e. non-simulated) residuals to assess overdispersion.
#' @param verbose Toggle warnings and messages.
#'
#' @inheritParams check_zeroinflation
#'
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5 changes: 3 additions & 2 deletions R/check_priors.R
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#' overlaid with boxplots indicating where the probability mass of the prior
#' distribution is located.
#'
#' @seealso See documentation of [`see::plot.see_check_priors()`] for available
#' arguments to change the plot appearance.
#' @seealso See documentation of
#' [`?see::plot.see_check_priors`](https://easystats.github.io/see/reference/plot.see_check_priors.html)
#' for available arguments to change the plot appearance.
#'
#' @references
#' Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019).
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2 changes: 2 additions & 0 deletions man/check_overdispersion.Rd
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5 changes: 3 additions & 2 deletions man/check_priors.Rd
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10 changes: 5 additions & 5 deletions tests/testthat/_snaps/item_omega.md
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Expand Up @@ -47,7 +47,7 @@
wt | 0.43 | -0.79 | -0.04 | 0.31 | 0.91 | 0.09 | 0.20 | 1.87
qsec- | 0.81 | 0.19 | 0.50 | 0.06 | 0.95 | 0.05 | 0.70 | 1.81
vs- | 0.74 | -0.27 | 0.38 | 0.05 | 0.77 | 0.23 | 0.71 | 1.81
am- | 8.38e-03 | -0.89 | -0.15 | -9.51e-03 | 0.81 | 0.19 | 8.63e-05 | 1.06
am- | 8.34e-03 | -0.89 | -0.15 | -9.52e-03 | 0.81 | 0.19 | 8.55e-05 | 1.06
gear | 0.03 | 0.87 | 9.01e-03 | 0.32 | 0.87 | 0.13 | 9.03e-04 | 1.27
carb | 0.68 | 0.06 | 0.10 | 0.63 | 0.87 | 0.13 | 0.53 | 2.06

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Composite | Total (%) | General Factor (%) | Group Factor (%)
-------------------------------------------------------------
g | 97.28 | 56.64 | 26.42
F1* | 90.12 | 31.07 | 59.05
F2* | 91.37 | 69.32 | 22.04
F3* | 87.36 | 59.65 | 27.71
g | 97.28 | 56.64 | 26.43
F1* | 90.12 | 31.06 | 59.05
F2* | 91.37 | 69.33 | 22.04
F3* | 87.36 | 59.64 | 27.72

4 changes: 2 additions & 2 deletions tests/testthat/_snaps/model_performance.psych.md
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Model | Chi2 | df | p (Chi2) | RMSR | RMSR_corrected | TLI | RMSEA | RMSEA 90% CI | BIC | R2 | Correlation
-----------------------------------------------------------------------------------------------------------------------------------
3-factor solution | 31.796 | 25 | 0.164 | 0.011 | 0.016 | | 0.087 | [0.000, 0.181] | -54.8 | |
g-model | 264.781 | 44 | < .001 | 0.278 | 0.311 | 0.195 | 0.395 | [0.356, 0.450] | 112.3 | 0.886 | 0.941
g-model | 264.785 | 44 | < .001 | 0.278 | 0.311 | 0.195 | 0.395 | [0.356, 0.450] | 112.3 | 0.886 | 0.941

Compare the model fit of the 3-factor solution with the g-only model.
If the g-model has smaller RMSR and RMSEA then your items are more
Expand All @@ -132,7 +132,7 @@
Model | Chi2 | df | p (Chi2) | RMSR | RMSR_corrected | TLI | RMSEA | RMSEA 90% CI | BIC | R2 | Correlation
-----------------------------------------------------------------------------------------------------------------------------------
3-factor solution | 31.796 | 25 | 0.164 | 0.011 | 0.016 | | 0.087 | [0.000, 0.181] | -54.8 | |
g-model | 264.781 | 44 | < .001 | 0.278 | 0.311 | 0.195 | 0.395 | [0.356, 0.450] | 112.3 | 0.886 | 0.941
g-model | 264.785 | 44 | < .001 | 0.278 | 0.311 | 0.195 | 0.395 | [0.356, 0.450] | 112.3 | 0.886 | 0.941

Compare the model fit of the 3-factor solution with the g-only model.
If the g-model has smaller RMSR and RMSEA then your items are more
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