- Avoid using
`isFALSE()`

for compatability with R < 3.5. - Don’t test number of iterations when comparing models on grouped and ungrouped rankings.

- Higher tolerance in tests of
`vcov()`

for CRAN Windows test machine.

`PlackettLuce()`

now supports MAP estimation with a multivariate normal prior on log-worths and/or a gamma prior on ranker adherence.`PlackettLuce()`

now returns log-likelihood and degrees of freedom for the null model (where all outcomes, including ties, have equal probability).- There is now a
`vcov`

method for Plackett-Luce trees.

`itempar.PlackettLuce()`

now always returns a matrix, even for a single node tree.

`pltree()`

or`PlackettLuce()`

with grouped rankings now work correctly with weights.

- Print methods for
`"PlackettLuce"`

and`"summary.PlacketLuce"`

objects now respect`options("width")`

.

`fitted`

always returns`n`

which is now weighted count of rankings (previously only returned unweighted count with argument`aggregate = TRUE`

).

- Correct vcov for weighted rankings of more than two items.
- Enable
`AIC.pltree`

to work on`"pltree"`

object with one node.

- Add
`AIC.pltree`

to enable computation of AIC on new observations (e.g. data held out in cross-validation). - Add
`fitted.pltree`

to return combined fitted probabilities for each choice within each ranking, for each node in a Plackett-Luce tree.

`vcov.PlackettLuce`

now works for models with non-integer weights (fixes #25).`plot.pltree`

now works for`worth = TRUE`

with psychotree version 0.15-2 (currently pre-release on https://r-forge.r-project.org/R/?group_id=330)`PlackettLuce`

and`plfit`

now work when`start`

argument is set.`itempar.PlackettLuce`

now works with`alias = FALSE`

- Add
**pkgdown**site. - Add content to README (fixes #5).
- Add
`plot.PlackettLuce`

method so that plotting works for a saved`"PlackettLuce"`

object

- Improved vignette, particularly example based on
`beans`

data (which has been updated). - Improved help files particularly
`?PlackettLuce`

and new`package?PlackettLuce`

. (Fixes #14 and #21).

`maxit`

defaults to 500 in`PlackettLuce`

.- Steffensen acceleration only applied in iterations where it will increase the log-likelihood (still only attempted once iterations have reached a solution that is “close enough” as specified by
`steffensen`

argument).

`coef.pltree()`

now respects`log = TRUE`

argument (fixes #19).- Fix bug causes lack of convergence with iterative scaling plus pseudo-rankings.
`[.grouped_rankings]`

now works for replicated indices.

- Add vignette.
- Add data sets
`pudding`

,`nascar`

and`beans`

. - Add
`pltree()`

function for use with`partykit::mob()`

. Requires new objects of type`"grouped_rankings"`

that add a grouping index to a`"rankings"`

object and store other derived objects used by`PlackettLuce`

. Methods to print, plot and predict from Plackett-Luce tree are provided. - Add
`connectivity()`

function to check connectivity of a network given adjacency matrix. New`adjacency()`

function computes adjacency matrix without creating edgelist, so remove`as.edgelist`

generic and method for `“PlackettLuce” objects. - Add
`as.data.frame`

methods so that rankings and grouped rankings can be added to model frames. - Add
`format`

methods for rankings and grouped_rankings, for pretty printing. - Add
`[`

methods for rankings and grouped_rankings, to create valid rankings from selected rankings and/or items. - Add method argument to offer choices of iterative scaling (default), or direct maximisation of the likelihood via BFGS or L-BFGS.
- Add
`itempar`

method for “PlackettLuce” objects to obtain different parameterizations of the worth parameters. - Add
`read.soc`

function to read Strict Orders - Complete List (.soc) files from http://www.preflib.org.

Old behaviour should be reproducible with arguments

`npseudo = 0, steffensen = 0, start = c(rep(1/N, N), rep(0.1, D))`

where `N`

is number of items and `D`

is maximum order of ties.

- Implement pseudo-data approach - now used by default.
- Improve starting values for ability parameters
- Add Steffensen acceleration to iterative scaling algorithm
- Dropped
`ref`

argument from`PlackettLuce`

; should be specified instead when calling`coef`

,`summary`

,`vcov`

or`itempar`

. `qvcalc`

generic now imported from**qvcalc**

- Refactor code to speed up model fitting and computation of fitted values and vcov.
- Implement ranking weights and starting values in
`PlackettLuce`

. - Add package tests
- Add
`log`

argument to`coef`

so that worth parameters (probability of coming first in strict ranking of all items) can be obtained easily.

- GitHub-only release of prototype package.