- under new maintainership
- various cosmetic/CRAN check updates

`build_datalist()`

now works correctly with data.table datasets. (#34, #35, h/t Dan Schrage)`build_datalist()`

dropped factor levels when replacing a factor variable. (#39, h/t Tomasz Żółtak)`find_data()`

now respects`subset`

and`na.actions`

arguments for`svyglm()`

models. (#37, h/t Tomasz Żółtak)

- Fixed a bug in
`prediction_glm`

with the`data`

argument (Issue #32).

- Remove mnlogit dependency, as it has been removed from CRAN.

- Remove bigFastLm dependency, as it has been removed from CRAN.

- Added tests for
`find_data()`

and`prediction.lm()`

to check for correct behavior in the presence of missing data (`na.action`

) and`subset`

arguments. (#28)

- Provisional support for variances of average predictions for GLMs. (#17)
- Added an example dataset,
`margex`

, borrowed from Stata’s identically named data.

`summary(prediction(...))`

now reports variances of average predictions, along with test statistics, p-values, and confidence intervals, where supported. (#17)- Added a function
`prediction_summary()`

which simply calls`summary(prediction(...))`

. - All methods now return additional attributes.

- Small fixes for failing CRAN checks. (#25)
- Remove
`prediction.bigglm()`

method (from**biglm**) due to failing tests. (#25)

- Fixed a bug that required specifying
`stats::poly()`

rather than just`poly()`

in model formulae. (#22)

- Added
`prediction.glmnet()`

method for “glmnet” objects from**glmnet**. (#1)

`prediction.merMod()`

gains an`re.form`

argument to pass forward to`predict.merMod()`

.

- Fix typo in “speedglm” that was overwriting “glm” method.

- CRAN release.

- Added
`prediction.glmML()`

method for “glimML” objects from**aod**. (#1) - Added
`prediction.glmQL()`

method for “glimQL” objects from**aod**. (#1) - Added
`prediction.truncreg()`

method for “truncreg” objects from**truncreg**. (#1) - Noted implicit support for “tobit” objects from
**AER**. (#1)

- Added
`prediction.bruto()`

method for “bruto” objects from**mda**. (#1) - Added
`prediction.fda()`

method for “fda” objects from**mda**. (#1) - Added
`prediction.mars()`

method for “mars” objects from**mda**. (#1) - Added
`prediction.mda()`

method for “mda” objects from**mda**. (#1) - Added
`prediction.polyreg()`

method for “polyreg” objects from**mda**. (#1)

- Added
`prediction.speedglm()`

and`prediction.speedlm()`

methods for “speedglm” and “speedlm” objects from**speedglm**. (#1) - Added
`prediction.bigLm()`

method for “bigLm” objects from**bigFastlm**. (#1) - Added
`prediction.biglm()`

and`prediction.bigglm()`

methods for “biglm” and “bigglm” objects from**biglm**, including those based by`"ffdf"`

from**ff**. (#1)

- Changed internal behavior of
`build_datalist()`

. The function now returns an an`at_specification`

attribute, which is a data frame representation of the`at`

argument.

- Due to a change in gam_1.15,
`prediction.gam()`

is now`prediction.Gam()`

for “Gam” objects from**gam**. (#1)

- Added
`prediction.train()`

method for “train” objects from**caret**. (#1)

- The
`at`

argument in`build_datalist()`

now accepts a data frame of combinations for limiting the set of levels.

- Most
`prediction()`

methods gain a (experimental)`calculate_se`

argument, which regulates whether to calculate standard errors for predictions. Setting to`FALSE`

can improve performance if they are not needed.

`build_datalist()`

gains an`as.data.frame`

argument, which - if`TRUE`

- returns a stacked data frame rather than a list. This argument is now used internally in most`prediction()`

functions in an effort to improve performance. (#18)

- Expanded test suite scope and fixed a few small bugs.
- Added a
`summary.prediction()`

method to interact with the average predicted values that are printed when`at != NULL`

.

- Added
`prediction.knnreg()`

method for “knnreg” objects from**caret**. (#1) - Added
`prediction.gausspr()`

method for “gausspr” objects from**kernlab**. (#1) - Added
`prediction.ksvm()`

method for “ksvm” objects from**kernlab**. (#1) - Added
`prediction.kqr()`

method for “kqr” objects from**kernlab**. (#1) - Added
`prediction.earth()`

method for “earth” objects from**earth**. (#1) - Added
`prediction.rpart()`

method for “rpart” objects from**rpart**. (#1)

- CRAN Release.
- Added
`mean_or_mode.data.frame()`

and`median_or_mode.data.frame()`

methods.

- Added
`prediction.zeroinfl()`

method for “zeroinfl” objects from**pscl**. (#1) - Added
`prediction.hurdle()`

method for “hurdle” objects from**pscl**. (#1) - Added
`prediction.lme()`

method for “lme” and “nlme” objects from**nlme**. (#1) - Documented
`prediction.merMod()`

.

- Added
`prediction.plm()`

method for “plm” objects from**plm**. (#1)

- Expanded test suite considerably and updated
`CONTRIBUTING.md`

to reflect expected test-driven development. - A few small code tweaks and bug fixes resulting from the updated test suite.

- Added
`prediction.mnp()`

method for “mnp” objects from**MNP**. (#1) - Added
`prediction.mnlogit()`

method for “mnlogit” objects from**mnlogit**. (#1) - Added
`prediction.gee()`

method for “gee” objects from**gee**. (#1) - Added
`prediction.lqs()`

method for “lqs” objects from**MASS**. (#1) - Added
`prediction.mca()`

method for “mca” objects from**MASS**. (#1) - Noted (built-in) support for “brglm” objects from
**brglm**via the`prediction.glm()`

method. (#1)

- Added a
`category`

argument to`prediction()`

methods for models of multilevel outcomes (e.g., ordered probit, etc.) to be dictate which level is expressed as the`"fitted"`

column. (#14) - Added an
`at`

argument to`prediction()`

methods. (#13) - Made
`mean_or_mode()`

and`median_or_mode()`

S3 generics. - Fixed a bug in
`mean_or_mode()`

and`median_or_mode()`

where incorrect factor levels were being returned.

- Added
`prediction.princomp()`

method for “princomp” objects from**stats**. (#1) - Added
`prediction.ppr()`

method for “ppr” objects from**stats**. (#1) - Added
`prediction.naiveBayes()`

method for “naiveBayes” objects from**e1071**. (#1) - Added
`prediction.rlm()`

method for “rlm” objects from**MASS**. (#1) - Added
`prediction.qda()`

method for “qda” objects from**MASS**. (#1) - Added
`prediction.lda()`

method for “lda” objects from**MASS**. (#1) `find_data()`

now respects the`subset`

argument in an original model call. (#15)`find_data()`

now respects the`na.action`

argument in an original model call. (#15)`find_data()`

now gracefully fails when a model is specified without a formula. (#16)`prediction()`

methods no longer add a “fit” or “se.fit” class to any columns. Fitted values are identifiable by the column name only.

`build_datalist()`

now returns`at`

value combinations as a list.

- Added
`prediction.nnet()`

method for “nnet” and “multinom” objects from**nnet**. (#1)

`prediction()`

methods now return the value of`data`

as part of the response data frame. (#8, h/t Ben Whalley)- Slight change to
`find_data()`

methods for`"crch"`

and`"hxlr"`

. (#5) - Added
`prediction.glmx()`

and`prediction.hetglm()`

methods for “glmx” and “hetglm” objects from**glmx**. (#1) - Added
`prediction.betareg()`

method for “betareg” objects from**betareg**. (#1) - Added
`prediction.rq()`

method for “rq” objects from**quantreg**. (#1) - Added
`prediction.gam()`

method for “gam” objects from**gam**. (#1) - Expanded basic test suite.

- Added
`prediction()`

and`find_data()`

methods for`"crch"`

`"hxlr"`

objects from**crch**. (#4, h/t Carl Ganz)

- Added
`prediction()`

and`find_data()`

methods for`"merMod"`

objects from**lme4**. (#1)

- Moved the
`seq_range()`

function from**margins**to**prediction**. - Moved the
`build_datalist()`

function from**margins**to**prediction**. This will simplify the ability to calculate arbitrary predictions.

- Added
`prediction.svm()`

method for objects of class`"svm"`

from**e1071**. (#1) - Fixed a bug in
`prediction.polr()`

when attempting to pass a`type`

argument, which is always ignored. A warning is now issued when attempting to override this.

- Added
`mean_or_mode()`

and`median_or_mode()`

functions, which provide a simple way to aggregate a variable of factor or numeric type. (#3) - Added
`prediction()`

methods for various time-series model classes: “ar”, “arima0”, and “Arima”.

`find_data()`

is now a generic, methods for “lm”, “glm”, and “svyglm” classes. (#2, h/t Carl Ganz)

- Added support for “svyglm” class from the
**survey**package. (#1) - Added tentative support for “clm” class from the
**ordinal**package. (#1)

- Initial package released.