NEWS | R Documentation |

Add infrastructure for shift-scale transformation models.

`Gradient`

and`estfun`

always returned negative scores, this is documented now.

Arguments passed to

`mlt`

via dots were silently ignored.Remove nloptr dependency for the time being.

Add

`as.double`

for`response`

objects (replaces`trtf:::.R2vec`

).

Improved numerical stability for censored data.

New argument

`as.R.ordered`

allowing numeric and survival responses to be coded as ordered factors, for nonparametric maximum likelihood estimation.Allow sparse model matrices. This is useful for nonparametric maximum likelihood estimation with many distinct outcomes.

Some speed-ups.

Plotting of quantiles sometimes failed because inversion of cdf was not possible for certain quantiles. These are now removed before plotting.

Fitting models to interval censored responses containing intervals

`c(-Inf, Inf)`

failed.Always return names score matrices and residuals.

Improve documentation.

Sampling from unconditional models did not pay attention to number of observations.

Quantiles and thus simulations are now computationally more exact and more robust. The unnecessary

`interpolate`

argument to`predict`

and`simulate`

is now ignored.Adjust contrasts a fixed parameter contributes to.

Return numerically determined Hessians upon request.

Implement frailty error distributions, experimentally and internal only.

Implement cure mixture models, experimentally and internal only.

Improve computations of log-probabilities.

Discrete hazard functions were incorrect.

Add exponential distribution (for Aalen additive hazards models).

Pay attention to model class when computing cumulative hazards.

Add log-cumulative hazards, log-odds, and odds for predictions and plots.

Allow permutations of single variables.

Update citation info.

Try harder to invert Hessians.

Update reference output.

Add support for nloptr (still experimental and thus switched off by default).

Make sure

`coef()`

always returns named argument.Fix problem in

`as.Surv`

reported by Balint Tamasi.

Less paranoia in ‘bugfixes.R’.

Return Hessian for fixed parameters if requested.

Fix subsetting problem in

`R.numeric`

.Allow to

`update`

offsets.

Add a

`bread`

method.Check response variable against observations in

`data`

.Make sure integers larger zero are handled correctly in

`R`

.Implement

`resid`

method, ie the score wrt a constant.Cox examples with Bernstein polynomials of log-time.

Arguments

`K`

and`cheat`

where ignored by`confband`

when`newdata`

had multiple rows.Computation of starting values more robust now.

Order of fixed parameters (

`fixed`

argument to`mlt`

) might have been wrong due to incomplete matching.

Add

`lty`

argument to`plot.ctm`

.`update`

needs free coefficients only.Internal interface changes.

Make sure transformation functions outside

`bounds`

are minus or plus`Inf`

.Initial guestimates for ordered responses were incorrect and may potentially have led to nonsense results.

Some smaller improvements in computation of log-likelihoods and scores with respect to accuracy and speed.

`print`

respects`options(digits)`

.

`estfun, parm = coef(object, fixed = TRUE))`

evaluates scores for all model parameters, including fixed ones.`logLik(..., newdata, w)`

ignored weights`w`

when`newdata`

was given. Same problem was also fixed for`estfun`

.

A paper describing version 1.0-0 of the mlt, basefun, and variables packages was accepted for publication in the Journal of Statistical Software 2018-03-05.

Documentation updates.

Use coneprog for getting the starting values.

`logLik`

and`estfun`

accept matrices as`parm`

argument for the evalution of log-likelihoods and scores with subject-specific parameters (for example in transformation trees or forests and boosting procedures.

`q`

is forwarded to`qmlt`

by`predict.ctm`

now.`p`

is now`prob`

in`qmlt`

and thus`predict.ctm`

.Update citation.

Most Likely Transformations will be published in the Scandinavian Journal of Statistics.

Import package alabama.

`as.Surv(R(Surv(...)))`

returns`Surv(...)`

, useful for converting output by`simulate`

to`Surv`

objects.

Add

`subset`

argument to`update`

(for faster transformation trees and forests).Sum over score contributions with positive weight only when evaluating the gradient.

Having all response observations being interval-censored is allowed again (too heavy checking was in place).

Don't try to numerically check KKT conditions automatically.

Check for unused arguments in dots where necessary.

Make sure the score doesn't get too large (avoid division by near zero probabilities).

Improve

`survfit`

to compute non-parametric unconditional probabilities for obtaining starting values in the presence of censoring and truncation.

`logLik`

with`newdata`

argument ignored`parm`

and`weights`

arguments.`estfun`

now also has a`newdata`

argument.Correct axes labelling when plotting quantile functions.

make sure names are correct in

`coef(model, fixed = FALSE)`

.check if any exact or interval-censored response with non-zero weight exists before trying to fit the model.

make checks a little more robust against huge diffs under Windows.

Fix two bugs in computation of log-likelihood for possibly left-truncated responses such as

`Surv(start, time, status)`

.

Add augmented lagrangian minimization (

`auglag()`

from package alabama).Make optimiation procedure more general and adaptive, allow users to change defaults and even add their own optimiser.

fix bug when calling

`survfit`

for computing initial probabilities.add

`bysim`

argument to`simulate`

.make sure

`checkGrad`

is respected by`update`

.`predict`

computes`q`

with`K`

elements if not given (as`plot`

always did).

Make sure

`times`

are ordered before calling`survival::summary.survfit`

.

Introduce

`as.mlt`

generic.Introduce a

`coef`

slot in`ctm`

objects and a corresponding`coef<-`

and`coef`

method for setting and extracting coefficients to and from unfitted conditional transformation models.`predict`

,`simulate`

and`plot`

work on`ctm`

objects (with meaningful coefficients) now.

Some small improvements wrt run time and memory consumption.

Use

`theta = coef(object)`

as default starting parameters in`update()`

.`logLik`

has a new`newdata`

argument.`simulate`

has a new`q`

argument.

The mlt package was first published on CRAN.