dpm 1.2.0

Enhancement

• For cases in which the proper lag structure is ambiguous, you may now use the partial.pre argument to dpm() to use the strategy proposed by Allison (2022).

dpm 1.1.2

Bug fixes:

• The ability to do arbitrary variable transformations introduced 1.1.0 has been made to actually work since lavaan does not allow non-syntactic variable names.

Feature updates:

• Specifying interactions in model formulae is now supported. To make them predetermined, just make sure one of the terms has the pre() tag surrounding it. Do not put the entire interaction inside pre().
• You may now include sampling weights via the weights argument.

dpm 1.1.1

Bug fixes:

• Fixed a bug that caused an error when printing summaries for models with y.free = TRUE.

Feature updates:

• dpm() now has the x.free argument, which can allow predictor variables to have coefficients that vary by wave.
• dpm objects now have a glance() method for the broom package.
• You can now get wave-by-wave coefficient tables when either x.free or y.free are used with dpm().

dpm 1.1.0

This release contains several important updates and a 1.1.x release is likely to be the one submitted to CRAN.

Bug fixes:

• Fixed an error in models with no constants.
• Fixed an error that sometimes caused predetermined variables to be treated incorrectly. (Vague, I know)
• Summaries now include coefficients for each wave of the lagged DV(s) when y.free is TRUE.

Feature updates:

• Models without lagged dependent variables are now supported (use y.lag = 0)
• Variable transformations in the model formula are now supported (e.g., y ~ scale(x)).
• You can now update dpm models with update.
• dpm objects now a have a tidy method via the broom package.

As a side note, there is now a testing suite in place to check models for accuracy/consistency with xtdpdml. That doesn’t mean there will be no bugs, but it should help prevent any regressions.

dpm 1.0.0 — major release

This is a major release with several breaking changes compared to the initial development release.

Most noticeably, the name of the package has been changed from clfe to dpm. This was done to better reflect the scope of the package — the cross-lagged fixed effects specification (CFLE) is a special case of the larger group of dynamic panel model (DPM) specifications availed to users of the package.

Accordingly, what was once the clfe function is now called dpm.

Internally, the dpm class is now an S4 object that contains the lavaan class. This means that any method implemented for lavaan objects that isn’t explicitly defined by this package should simply treat dpm objects as if they were lavaan objects.

The summary method now has more options and is more similar to lavaan’s summary in that regard. Of course, the summary output is much cleaner and more succinct.

The following arguments have been added to dpm():

• y.lag: Equivalent to xtdpdml’s ylag. Specify which lags of the DV to use.
• y.free: Equivalent to xtdpdml’s yfree. Allow stability coefficients for the lagged DV to vary over time.
• fixed.effects: Comparable to xtdpdml’s re. Use fixed effects (TRUE) or random effects (FALSE) specification.
• alpha.free: Equivalent to xtdpdml’s alphafree. Allow the fixed effects to vary over time.

clfe 0.3.0

• Added a NEWS.md file to track changes to the package.

The panelr package was added as a dependency, which has some downstream effects.

• The panel_data function now belongs to panelr.
• The panel_data object type now inherits from tibble, which has pluses and minuses but I believe more upside than downside.
• The WageData example data is now part of the panelr package.