R(Surv(...), as.R.ordered = TRUE) matches
Kaplan-Meier for right-censored and possibly tied observations.
Such responses give NPML estimates.
R(Surv(...), as.R.numeric = TRUE) can be used to fit
smooth models by maximising the nonparametric likelihood.
Better support for frailty parameter.
Starting values might end up being NA; set to zero.
mlt(..., theta = theta, dofit = TRUE) returns a "fitted"
mlt model with coefficients
theta is missing, an unfitted model is returned.
Register internal methods.
Re-add support for nloptr in
Fix S3 argument mismatches.
Try to detect negative standard errors in case optimisation failed silently.
Improve numerical stability in
plot have new types involving logs.
as.R.interval allows evaluation of nonparametric
likelihood for smoothly defined models.
New Laplace link function, contributed by Ainesh Sewak.
New cauchit link function.
drop = TRUE in score for shift-scale models.
Eliminate fallout of fix of PR#17616 in Rout.save files.
update allows fixing parameters.
Fix discrete gradient for shift-scale models.
Add infrastructure for shift-scale transformation models.
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.
Improved numerical stability for censored data.
as.R.ordered allowing numeric and survival
responses to be coded as ordered factors, for nonparametric maximum
Allow sparse model matrices. This is useful for nonparametric maximum likelihood estimation with many distinct outcomes.
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.
Sampling from unconditional models did not pay attention to number of observations.
Quantiles and thus simulations are now computationally more exact and more robust.
interpolate argument to
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).
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
Check response variable against observations in
Make sure integers larger zero are handled correctly in
resid method, ie the score wrt a constant.
Cox examples with Bernstein polynomials of log-time.
had multiple rows.
Computation of starting values more robust now.
Order of fixed parameters (
fixed argument to
might have been wrong due to incomplete matching.
lty argument to
update needs free coefficients only.
Internal interface changes.
Make sure transformation functions outside
bounds are minus
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.
estfun, parm = coef(object, fixed = TRUE)) evaluates
scores for all model parameters, including fixed ones.
logLik(..., newdata, w) ignored weights
newdata was given. Same problem was also fixed for
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.
Use coneprog for getting the starting values.
estfun accept matrices as
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
p is now
qmlt and thus
Most Likely Transformations will be published in the Scandinavian Journal of Statistics.
Import package alabama.
useful for converting output by
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).
survfit to compute non-parametric unconditional
probabilities for obtaining starting values in the presence of
censoring and truncation.
newdata argument ignored
estfun now also has a
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.
bysim argument to
checkGrad is respected by
if not given (as
plot always did).
times are ordered before calling
coef slot in
and a corresponding
method for setting and extracting coefficients
to and from unfitted conditional transformation models.
ctm objects (with meaningful coefficients)
Some small improvements wrt run time and memory consumption.
theta = coef(object) as default starting parameters in
logLik has a new
simulate has a new
The mlt package was first published on CRAN.