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Description
- insight
- CRAN annoucement
- Difference between terms, parameters, predictors etc.
- bayestestR
- CRAN annoucement
-
rnorm_perfect -
distribution - maybe elaborate more on
equivalence_test(), based on the discussion with Aki - Introduce the
bayesfactorfunction (@mattansb) -
describe_posteriors - Bayesian approach to frequentist algorithms (lmer and arm::sim instead of stan_glmer on large data? bayestestR#219)
- ...
- performance
- CRAN annoucement
- Variance components / R2 / ICC for mixed models
-
check_model - present the
check_family - Overview / comparison of performance functions
- ...
- parameters
- CRAN annoucement
-
find_distributionand distribution classification - Data standardization vs. data normalization: also introduce bayetestR::estimate_density.df and see
- Present
.*.and parameters_selection - How to intepret coefficients in a regression (interactions and nested models)
- Parameters standardization
-
n_factors -
psychsupport -
efa_to_cfaand graph plots for lavaan plots -
check_factorstructure - parameters is also interesting for developpers:
parameters_type - ...
- report
- CRAN annoucement
-
report_participants - ...
- correlation
- CRAN annoucement
- How to plot correlations
- ...
- estimate
- CRAN annoucement
- lighthouse plots
- The world is non-linear: polynomial, splines and GAMs (and their linear segmentation interpretation)
- Signal processing features: smoothing, find_inversions
- ...
- see
- CRAN annoucement
- Plotting examples for bayestestR functions (not all, just a small scope like p_direction, rope, ...)
- ...
- easystats
- "we are growing, now 5 packages on CRAN"
-
easystats_update()
- effectsize
- CRAN annoucement
- Data standardization (normal vs. robust)
If you guys have ideas about posts feel free to add :)
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