## semEff 0.6.0

12/10/2021

### New features:

• New formatted table output for effect summaries for semEff() and bootCI(), accessed via new print() and summary() methods.
• Bootstrap standard errors and bias (mean minus original estimate) now included with effect summaries and fitted values from predEff().
• Support for "psem" objects (piecewiseSEM::psem()) in bootEff()/semEff().
• New package vignettes demonstrating some examples.

### Other changes:

• All calculated indirect effects (individual, not summed) now also returned as part of "semEff" object (for reference; does not include bootstrapped effects). Extract using getAllInd().
• Unnamed model lists can now be supplied to bootEff()/semEff() (names are generated automatically).
• Updates to R2() (control of negative values, new improved default method for adjusted R-squared – Olkin-Pratt exact estimator).
• New function getX(), for more flexible construction of model design matrices (mostly for internal use).
• Removed … argument of stdEff() – arguments to R2() are now passed as named list to R2.arg.
• Renamed extractor functions for effects (e.g. dirEff() -> getDirEff()) and added some new ones.
• Various other code and documentation updates.

### Bugs fixed:

• Amendment to previous incomplete fix for issue where xNam() was not evaluating factor/character terms correctly. The function now explicitly treats all non-numeric predictor variables as factors and coerces where necessary. It also has improved handling of factor contrasts when evaluating names.
• pSapply() did not work with parallel = "multicore", due to relying completely on parallel::parSapply() for parallel processing (which is "snow" only). The function now wraps parallel::mcmapply() for "multicore" (not available on Windows systems).

## semEff 0.5.0

09/04/2021

### New features:

• R2() can now calculate R-squared based on Spearman’s Rho.
• New function RVIF(), to calculate ‘root variance inflation factors’ (square root of VIFs).

### Other changes:

• Renamed unique.x argument of stdEff() to unique.eff (old name temporarily allowed).
• Removed default value of R argument of bootEff(). The number of bootstrap resamples must now be explicitly specified, which is probably better practice (10,000 is often recommended for confidence intervals).
• Added type argument to bootEff(), to specify the type of bootstrapping to perform (for mixed models). This replaces ran.eff = "crossed", previously used to indicate parametric bootstrapping (although it’s temporarily allowed).
• bootEff() will now treat a list containing both mixed and non-mixed models as all mixed (with a warning). Previously all such models were treated as non-mixed (unintentionally). This is presumably a relatively rare scenario.

### Bugs fixed:

• R2() did not calculate adjusted R-squared correctly for beta regression models (i.e. did not incorporate the ‘phi’ parameter in degrees of freedom calculations).
• xNam() produced an error when attempting to evaluate factor contrasts in data, expecting that character vectors were factors (related to the change to stringsAsFactors = FALSE as default in R 4.0.0, but would have occurred in some cases regardless).

## semEff 0.4.0

01/10/2020

### New features:

• Standardised vs raw effects: added incl.raw argument to stdEff() (stdCoeff()), to append raw effects (unstandardised coefficients) to the output. This facilitates simultaneous bootstrapping of both sets of effects, allowing raw effects to be used alternatively for calculating (semEff(..., use.raw = TRUE)) or predicting (predEff(..., use.raw = TRUE)) effects/CIs.

### Other changes:

• Renamed function stdCoeff() to stdEff(), to better reflect the concept of standardised model coefficients as ‘effects’ (calling stdCoeff() will still work – with a warning – until the next version at least).
• Added offset argument to getY() and R2(), to explicitly retain/remove an offset (where present) in/from the response variable or fitted values. Offsets are removed by default, which ensures, for example, that standardised effects are scaled appropriately.
• Added env argument to multiple functions, for explicitly specifying the location of data used to fit models (not necessary in most circumstances). This replaces the ... argument in many instances, which was previously used to pass an environment to eval() (via getData()). env (and data) can also now be passed (...) to bootEff() and predEff().
• Added confidence interval attributes to bootCI()/semEff() output (i.e. confidence level, type).
• R2() no longer calculates predictive R-squared for GLMMs, as the interpretation of the hat matrix used in calculations is not reliable (see https://rdrr.io/cran/lme4/man/hatvalues.merMod.html).
• Removed ability to pass arguments from getY() to glt(), allowing more controlled output of getY(..., link = TRUE).
• Various minor updates to function code and documentation, improvement and addition of some new internal helper functions.

### Bugs fixed:

• bootEff() specified with correlated errors failed for mixed models of class "lmerModLmerTest" (issue with re-fitting models using update()).
• predEff() failed to evaluate some complex model terms (e.g. polynomials).
• stdEff() (stdCoeff()) did not re-fit model properly to calculate correct VIFs for a fully ‘centred’ model (i.e. did not account sufficiently for complex terms such as polynomials or transformations, where mean-centring should occur as the final step).
• xNam() generated incorrect term names for categorical predictors under certain circumstances (different contrast types, interactive effects with no ‘main’ effects).
• stdEff() (stdCoeff()) incorrectly calculated ‘centred’ intercept for models with an offset specified.
• predEff() failed when a nested list of models and list of numeric weights were supplied (i.e. a model averaging scenario).
• stdEff() (stdCoeff()) did not return the ‘phi’ parameter(s) for beta regression models.

## semEff 0.3.0

25/03/2020

### New features:

• Support for mixed models of class "lmerModLmerTest".
• New function glt(), for calculating ‘generalised’ link transformations for non-gaussian variables.

### Other changes:

• Transfer of some functionality from getY() to glt().
• Minor changes to arguments in bootEff() and getY().
• Added ability in stdCoeff() to use variables not present in the model design matrix (e.g. a ‘missing’ main effect for an interaction).
• Added ability to pass a boot object (from bootEff()) to the effects argument of predEff().
• Added a refit.x argument to stdCoeff(), allowing control over whether to refit the model with centred predictors (for correct VIFs).

### Bugs fixed:

• xNam() did not generate correct term names for categorical variables with contrast types other than contr.treatment().
• stdCoeff() did not correctly adjust for multicollinearity for a model containing categorical variables when centring was specified (cen.x = TRUE).
• getY() failed to generate an estimated working response when a variable with missing values (NA) was supplied (this functionality now in glt()).
• predEff() failed for models with categorical variables (did not access dummy variables in model matrix).

## semEff 0.2.1

15/01/2020

### Bugs fixed:

• Function semEff() did not output effects properly.

## semEff 0.2.0

08/01/2020

### New features:

• Added support for generalised least squares models (class "gls").
• Added support for beta regression models (class "betareg").

### Bugs fixed:

• Function xNam() did not generate correct term names for interactions involving multi-coefficient terms (e.g. factors).
• Function xNam() did not generate correct term names for factors when the model intercept is suppressed.
• Function R2() with argument pred = TRUE threw an error for models where any weights = 0.

## semEff 0.1.0

04/11/2019

New package semEff, allowing the automatic calculation of effects for ‘piecewise’ structural equation models.