margins 0.3.26

• Remove unconditional use of cairo_pdf in vignette, per CRAN policy.

margins 0.3.25

• Setup a cplot.default() method and modified documentation of cplot(), image(), and persp() methods slightly. (#84, h/t Luke Sonnet)
• Improve the documentation the behavior of cplot() for generalized linear models, which can generate unexpected confidence intervals (albeit ones consistent with base R’s behavior). (#92)
• Fix bug that caused spurious NAs and errors in margins() when vce was "bootstrap" or "simulation" and variables had a length of 1. (#112)
• Models fit using the lme4 package can now have variance estimation via bootstrap and simulation (#105).
• Updated documentation to be more explicit about what the package does for users unfamiliar with Stata. (#119)

margins 0.3.24

• Added new function margins_summary() which provides a single-function expression of summary(margins(...)). (#94, h/t Mike DeCrescenzo)
• Added variances of marginal effects to “polr” objects from MASS. (#98, @eijoac)
• Fix a bug in persp() related to attempting to take the mean of a factor variable. (#93, h/t Jared Knowles)

margins 0.3.23

• Fix a small issue in print() and summary() methods related to the release of prediction 0.3.6.

margins 0.3.22

• Expanded support for objects of class “merMod” from lme4, including support for variance estimation and an expanded test suite. (#56)

margins 0.3.21

• Modified the internals of gradient_factory() to be more robust to an expanded set of model classes through the introduction of an internal function reset_coefs(). A test suite for this function has been added.

margins 0.3.20

• Added support for objects of class “ivreg” from AER.
• margins.default() now attempts to calculate marginal effect variances in order to, by default, support additional model classes.

margins 0.3.19

• Added support for objects of class “betareg” from betareg. (#90)

margins 0.3.18

• margins() now returns attributes “vcov” and “jacobian” (the latter only when vce = "delta"), which contain the full variance-covariance matrix for the average marginal effects and jacobian for the same. This is different behavior from the previous draft (v0.3.17) because the attributes now always contain a single matrix; again use the vcov() method rather than accessing the attribute directly lest it change in the future. This allows calculation combination of marginal effects, such as the difference between two AMEs. Some internal functions have been renamed and code reorganized to make this possible. (#87, h/t Trenton Mize)
• The “at” attribute returned by margins() now contains the input value passed to the at argument to the function. New attribute “at_vars” returns a character vector of variables specified therein.
• The data frame returned by margins() now contains an added column "_at_number", which specifies which at combination a row comes from. This may be changed or removed in the future, but is useful for matching subsets of the data frame to corresponding entries in the “vcov” and “jacobian” matrices.

margins 0.3.17

• margins() now returns an attribute (“vcov”) containing the variance-covariance matrix for the average marginal effects and a new vcov.margins() method is provided for extracting it. Behavior when using at specifications is unspecified and may change in the future. (#87, h/t Trenton Mize)
• Updated examples in README.Rmd. (#83)

margins 0.3.16

• Fixed a bug in cplot() when xvar was of class “ordered”. (#77, h/t Francisco Llaneras)
• Fixed a bug in plot.margins() when at contained only one variable. (#78, h/t @cyberbryce)

margins 0.3.15

• Tried to improve the handling of edge case model specifications like y ~ I(x^2), y ~ x + I(2*x), and those involving RHS interactions between factors where some cells are not observed in the data. Added a test suite to cover these cases. (#82)
• Continued to update behavior of internal function find_terms_in_model().

margins 0.3.14

• Fixed a bug in survey-weighted objects involving weights and expanded the test suite to cover these cases.

margins 0.3.13

• Fixed a bug in all functions (ultimately in internal utility clean_terms()) that occurred when formulae contained variables with backticked names that contained spaces. (#80)

margins 0.3.12

• dydx() now uses the performance-enhancing prediction::prediction(..., calculate_se = FALSE) setting, where possible (introduced in prediction 0.2.4)
• data.table::rbindlist() is used instead of base::rbind() inside dydx().

margins 0.3.11

• Changed some internal representations from data frames to matrices in an effort to improve performance. marginal_effects() and dydx() gain an as.data.frame argument to regulate the class of their responses.
• Internal calls to prediction::prediction() were halved by stacking data frames used in calculating numerical derivatives (inside dydx() methods) and then splitting the resulting predicted value vectors.

margins 0.3.10

• Added an (internal use only) argument, varslist, to marginal_effects() and several internal functions that significantly improves performance. The performance gain is due to computational cost of identifying terms in model formulae each time marginal_effects() was called, which occurred repeatedly (e.g., during variance estimation). By performing this once at the margins()-level and passing the argument throughout, margins() is perhaps twice as fast as in versions <= 0.3.9. But, importantly, note that this argument should not be specified by end users!
• Some internal edits were made to the formula-processing functions find_terms_in_model() and clean_terms(), removing many regex calls with the goal of improving performance.
• Removed compiler dependency, which appeared to not improve performance.

margins 0.3.9

• Fixed a bug wherein model formulae involving non-standard variables names with spaces in them led to errors. (#80)

margins 0.3.8

• Added method for “svyglm” from survey.
• Improved handling of survey-weighted estimates. Removed weight-related warnings from margins() for unweighted models.
• print() and summary() now handle survey-weighted marginal effects.

margins 0.3.7

• margins() and marginal_effects() gain a variables argument to request marginal effects for a subset of variables included in a model. (#65, h/t Vincent Arul-Bundock)

margins 0.3.6

• Export margins.merMod(). (#56)

margins 0.3.5

• Added a cplot.clm() method. (#63, h/t David Barron)

margins 0.3.4

• Fixed a bug in cplot.polr(). (#62, h/t David Barron)

margins 0.3.3

• Fixed “margins” object structure in margins.merMod().
• Switched print() and summary() methods to using weighted.mean() instead of mean(). (#45)

margins 0.3.2

• Added method for class “polr” from MASS. (#60)

margins 0.3.1

• Added method for class “nnet” from nnet as an initial implementation of multi-category outcome models. (#60)

margins 0.3.0

• Significantly modified the data structure returned by margins(). It now returns a data frame with an added at attribute, specifying the names of the variables that have been fixed by build_datalist(). (#58)
• Renamed marginal effects, variance, and standard error columns returned by margins(). Marginal effects columns are prefixed by dydx_. Variances of the average marginal effect are stored (repeatedly, across observations) in new Var_dydx_ columns. Unit-specific standard errors, if requested, are stored as SE_dydx_ columns. (#58)
• summary.margins() now returns a single data frame of marginal effect estimates. Column names have also changed to avoid use of special characters (thus making it easier to use column names in plotting with, for example, ggplot2). Row-order can be controlled by the by_factor attribute, which by default sorts the data frame by the factor/term. If set to by_factor = FALSE, the data frame is sorted by the at variables. This behavior cascades into the print.summary.margins() method. (#58)
• print.margins() now presents (but does not return) effect estimates as a condensed data frame with some auxiliary information. Its behavior when using at is improved and tidied. (#58)
• build_margins() is no longer exported. Arguments used to control its behavior have been exposed in margins() methods.
• plot.margins() now displays marginal effects across each level of at. (#58)
• build_margins() and thus margins() no longer returns the original data twice (a bug introduced by change in behavior of prediction()). (#57)
• All methods for objects of class "marginslist" have been removed. (#58)
• The at argument in plot.margins() has been renamed to pos, to avoid ambiguity with at as used elsewhere in the package.
• persp() and image() methods gain a dx argument (akin to that in cplot()) to allow visualization of marginal effects of a variable across levels of two other variables. The default behavior remains unchanged.
• Cleaned up documentation and add some examples.

margins 0.2.26

• Added support for "merMod" models from lme4, though no variance estimation is currently supported.
• Imported prediction::mean_or_mode() for use in cplot() methods.

margins 0.2.25

• cplot.polr() now allows the display of “stacked” (cumulative) predicted probabilities. (#49)
• Added an example of cplot(draw = "add") to display predicted probabilities across a third factor variable. (#46)
• Moved the build_datalist() and seq_range() functions to the prediction package.
• A tentative cplot.multinom() method has been added.

margins 0.2.24

• The internal code of cplot.lm() has been refactored so that the actual plotting code now relies in non-exported utility functions, which can be used in other methods. This should make it easier to maintain existing methods and add new ones. (#49)
• A new cplot() method for objects of class "polr" has been added (#49).

margins 0.2.23

• The extract_marginal_effects() function has been removed and replaced by marginal_effects() methods for objects of classes "margins" and "marginslist".
• Added a dependency on prediction v.0.1.3 and, implicitly, an enhances suggestion of survey v3.31-5 to resolve an underlying prediction() issue for models of class "svyglm". (#47, h/t Carl Ganz)

margins 0.2.20

• A warning is now issued when a model uses weights, indicating that they are ignored. (#4)
• Various errors and warnings that occurred when applying margins() to a model with weights have been fixed.
• cplot() now issues an error when attempting to display the effects of a factor (with > 2 levels).

margins 0.2.20

• Fixed a bug in get_effect_variances(vce = "bootstrap"), wherein the variance of the marginal effects was always zero.

margins 0.2.20

• Factored the prediction() generic and methods into a separate package, prediction, to ease maintainence.
• Added a print.summary.margins() method to separate construction of the summary data frame the printing thereof.
• The “Technical Details” vignette now describes the package functionality and computational approach in near-complete detail.

margins 0.2.19

• Plotting functions cplot(), persp(), and image() gain a vcov argumetn to pass to `build_margins(). (#43)
• cplot() now allows for the display of multiple conditional relationships by setting draw = "add". (#32)
• The package Introduction vignette has improved examples, including ggplot2 examples using cplot() data. (#31)

margins 0.2.18

• Added support in dydx.default() to allow the calculation of various discrete changes rather than only numerical derivatives.

margins 0.2.17

• Fixes to handling of factors and ordered variables converted within formulae. (#38)
• Reconfigured the data argument in margins() and prediction() to be clearer about what is happening when it is set to missing.

margins 0.2.16

• Switched to using a more reliable “central difference” numerical differentiation and updated the calculation of the step size to follow marfx (#31, h/t Jeffrey Arnold)
• Added some functionality prediction() methods to, hopefully, reduce memory footprint of model objects. (#26)
• Changed the capitalization of the variances field in “margins” objects (to lower case), for consistency.
• Fixed some small errors in documentation and improved width of examples.

margins 0.2.15

• Expose previously internal dydx() generic and methods to provide variable-specific marginal effects calculations. (#31)
• Added example dataset from marfx package. (#31)

margins 0.2.13

• Added support for calculating marginal effects of logical terms, treating them as factors. (#31)

margins 0.2.12

• Added an image() method for “lm”, “glm”, and “loess” objects, as a flat complement to existing persp() methods. (#42)

margins 0.2.11

• Added a prediction() method for “gls” objects (from MASS::gls()). (#3)

margins 0.2.10

• Replaced numDeriv::jacobian() with an internal alternative. (#41)

margins 0.2.8

• Added a prediction() method for “ivreg” objects (from AER::ivreg()). (#3)
• Added a prediction() method for “survreg” objects (from survival::survreg()). (#3)

margins 0.2.7

• Added a prediction() method for “polr” objects (from MASS::polr()). (#3)
• Added a prediction() method for “coxph” objects (from survival::coxph()). (#3)

margins 0.2.7

• marginal_effects() and prediction() are now S3 generics, with methods for “lm” and “glm” objects, improving extensability. (#39, #40)
• prediction() returns a new class (“prediction”) and gains a print() method.
• Added preliminary support for “loess” objects, including methods for prediction(), marginal_effects(), cplot(), and persp(). No effect variances are currently calculated. (#3)
• Added a prediction() method for “nls” objects. (#3)
• Internal function get_effect_variances() gains a “none” option for the vce argument, to skip calculation of ME variances.

margins 0.2.7

• marginal_effects() issues a warning (rather than fails) when trying to extract the marginal effect of a factor variable that was coerced to numeric in a model formula via I(). (#38)

margins 0.2.5

• Added better support for factor x variables in cplot().
• Added (rudimentary) tests of variance methods. (#21)
• Removed .build_predict_fun() factory function, as it was no longer needed.
• Fix vignettes so package can be built with them. (#16)

margins 0.2.4

• Modified marginal_effects() to use a vectorized approach to simple numerical differentiation. (#36/#37, h/t Vincent Arel-Bundock)
• Removed margins.plm() method, which didn’t actually work because “plm” does not provide a predict() method.
• Updated Stata/R comparison documents included in inst/doc.
• Expanded tests of unit-specific variances. (#21)

margins 0.2.3

• Added a logical argument to enable/disable calculation of unit-specific marginal effect variances and set it to FALSE by default. (#36, h/t Vincent Arel-Bundock)

margins 0.2.2

• Removed support for “marginal effects at means” (MEMs) and the atmeans argument throughout package. (#35)
• Renamed the vc argument to vcov for consistency with other packages. (#34)

margins 0.2.1

• build_margins() now returns columns containing unit-specific standard errors of marginal effects.
• Added a vc argument to build_margins() to allow the passing of arbitrary variance-covariance matrices. (#16, h/t Alex Coppock & Gijs Schumacher)
• cplot() now draws confidence intervals for “effect” plots.
• Fixed a bug in get_marginal_effects() wherein the method argument was ignored. This improves performance significantly when using method = "simple" (the default differentiation method).

margins 0.2.0

• Added persp() methods for “lm” and “glm” class objects to display 3-dimensional representations of predicted values and marginal effects.
• Added plot.margins() method for mimicking Stata’s marginsplot behavior.
• Added cplot() generic and methods for “lm” and “glm” class objects to display conditional predictions and conditional marginal effects in the style of the interplot and plotMElm packages.
• Added various variance estimation procedures for marginal effects: delta method (the default), bootstrap, and simulation (ala Clarify).
• Fixed estimation of marginal effect variances for generalized linear models, so that they are correct on both “link” and “response” scales.
• Exposed two internal marginal effect estimation functions. First, build_margins() is called by margins() methods (perhaps repeatedly) and actually assembles a “margins” object from a model and data. It is never necessary to call this directly, but may be useful for very simple marginal effect estimation procedures (i.e., using original data with no at specification). Second, marginal_effects() is the very low level function that differentiates a model with respect to some input data (or calculate discrete changes in the outcome with respect to factor variables). This is the fastest way to obtain marginal effects without the overhead of creating a “margins” object (for which variance estimation is fairly time-consuming).
• Implemented estimation of “discrete change” representations of marginal effects of factor variables in models, ala Stata’s default settings.
• Re-implemented marginal effects estimation using numeric derivatives provided by numDeriv::grad() rather than symbolic differentiation. This allows margins() to handle almost any model that can be specified in R, including models that cannot be specified in Stata.
• Used compiler to byte compile prediction and gradient fucntions, thereby improving estimation speed.
• The internal build_datalist() now checks for specification of illegal factor levels in at and errors when these are encountered.
• Use the webuse package to handle examples.

margins 0.1.0

• Initial package released.