Changes in version 0.0.4
o FIX: correction to `EKC` (used `factor_forest`'s version of EKC which used reference values rather than eigenvalues); `EKC` uses eigenvalues (proper implementation) whereas `factor_forest` uses reference (which was what the random forest model was trained on)
o FIX: cross-loadings with population error are screened for communalities >= 0.80; communalities near 0.90 prior to population error would often get stuck and not converge
o ADD: `skew` argument for continuous data
Changes in version 0.0.3
o ADD: `add_population_error` will add population error, using {bifactor}, from a simulated factor model
o ADD: `data_to_zipfs` to transform data to Zipf's distribution from `simulate_factors`
o ADD: `obtain_zipfs_parameters` to obtain a dataset's best fitting Zipf's distribution parameters
o ADD: `NEST` Next Eigenvalue Sufficiency Test to estimate dimensions
o ADD: `estimate_dimensions` provides a single function to estimate dimensions using state-of-the-art methods: Exploratory Graph Analysis (EGA),
Exploratory Factor Analysis with out-of-sample prediction (FSPE), Next Eigenvalue Sufficiency Test (NEST), parallel analysis (PA), and Factor Forest
Changes in version 0.0.2
o UPDATE: skews for categories now include 6 categories
o UPDATE: available skew increments are now 0.05 (were 0.50 previously)
o ADD: `add_local_dependence` will add local dependence between variables from a simulated factor model
Initial commit version 0.0.1