BFpack handles common statistical analyses with default priors and data missing at random¶

Claim · All claims
Evidence · 1 study · design r1 · q2
Currency · newest study 2021 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? BFpack supports common statistical analyses (linear regression, generalized linear models, (multivariate) analysis of (co)variance, correlation analysis, random intercept models) using default priors and while allowing data to contain missing observations that are missing at random. → Mulder 2021

Evidence¶

Mulder 2021¶

Mulder, J., Williams, D., Gu, X., Tomarken, A., Böing-Messing, F., Olsson-Collentine, A., Meijerink-Bosman, M., Menke, J., van Aert, R., Fox, J.-P., Hoijtink, H., Rosseel, Y., Wagenmakers, E.-J., & van Lissa, C. (2021). BFpack: Flexible Bayes factor testing of scientific theories in R. Journal of Statistical Software, 100(18), 1–63. https://doi.org/10.18637/jss.v100.i18

q2 · i? · design · r1

Descriptive statement from the article's abstract enumerating the analyses BFpack covers. The package uses "default priors" and allows "data to contain missing observations that are missing at random." This is a capability description, not an empirical test.

"common statistical analyses, such as linear regression, generalized linear models, (multivariate) analysis of (co)variance, correlation analysis, and random intercept models, (iv) using default priors, and (v) while allowing data to contain missing observations that are missing at random"

Discussion¶

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