NEWS

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latentFactoR 0.0.7

• FIX: error check for positive definite in 'add_population_error'
• FIX: correction when adding negative skew for continuous data (internal function: 'skew_continuous')
• UPDATE: better ML population error in 'add_population_error' (update from {bifactor})
• UPDATE: some optimizations to 'add_population_error'
• UPDATE: some optimiztaions to 'NEST'

latentFactoR 0.0.6 (2024-04-18)

• MOVE: 'add_wording_effects' methods moved from 'utils-latentFactoR' to 'add_wording_effects-helpers' to facilitate ease of finding code (no observable changes to the user)
• FIX: categories greater than 7 were not previously allowed (they are now)
• FIX: correlations in 'EKC' were not used appropriately and led to an error
• UPDATE: switched on "Byte-Compile" (byte-compiles on our end and not when the user installs)

latentFactoR 0.0.5

• FIX: bug in skew when only providing 1 value
• FIX: further correction to 'EKC' (uses 'cumsum(eigenvalues)' rather than 'sum(eigenvalues)')
• ADD: 'add_wording_effects' will add wording effects such as acquiescence, difficulty, random careless, straight line, or some combination of the four to a simulated factor model
• ADD: 'ESEM' to perform Exploratory Structural Equation Modeling using {lavaan} (allows wording effects to be estimated)
• FIX: 'factor_forest' uses raw data in 'psych::fa.parallel' rather than correlation matrix
• ADD: internal functions for computing effect sizes across conditions are included (see 'simulation_helpers.R')
• ADD: skew in 'add_local_dependence' is guaranteed to be same direction for locally dependent variables

latentFactoR 0.0.4 (2022-11-22)

• FIX: correction to ‘EKC' (used 'factor_forest'’s version of EKC which used reference values rather than eigenvalues); 'EKC' uses eigenvalues whereas 'factor_forest' uses reference (which was what the random forest model was trained on)
• 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
• ADD: 'skew' argument for continuous data

latentFactoR 0.0.3 (2022-10-13)

• ADD: 'add_population_error' will add population error, using {bifactor}, to a simulated factor model
• ADD: ‘data_to_zipfs' to transform data to Zipf’s distribution from 'simulate_factors'
• ADD: ‘obtain_zipfs_parameters' to obtain a dataset’s best fitting Zipf's distribution parameters
• ADD: 'NEST' Next Eigenvalue Sufficiency Test to estimate dimensions
• 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

latentFactoR 0.0.2 (2022-09-09)

• UPDATE: skews for categories now include 6 categories
• UPDATE: available skew increments are now 0.05 (were 0.50 previously)
• ADD: 'add_local_dependence' will add local dependence between variables from a simulated factor model Initial commit version 0.0.1