-*- text -*-
USER-VISIBLE CHANGES
--------------------
(For detailed changes see https://github.com/chjackson/msm from Nov 2016 onwards, and the ChangeLog in the source package before that)
Version 1.6.8 (2019-12-16)
-------------
o Bug fix for bootstrapping with factor subject IDs.
o Fix of bug that broke piecewise-constant intensities with "pci" and transition-specific covariates.
o Bug fix for multivariate hidden Markov models and hmmIdent.
Version 1.6.7 (2019-03-15)
-------------
o Beta-binomial outcome model in HMMs added.
o Fix of bug that affected HMMs with categorical outcomes and multiple outcomes, where outcome probabilites did not add up to 1.
o Fix of bug affecting bootstrapping with factor covariates.
Version 1.6.6 (2018-02-02)
-------------
o New function updatepars.msm() to overwrite the estimates in a fitted model object to a given vector of values.
o Fix of bug in pearson.msm, triggered by r-devel.
o Fix of random memory crashes for models with censoring, revealed by asan testing.
Version 1.6.5 (2017-12-05)
-------------
o New feature viterbi.msm(..., normboot=TRUE) to return Viterbi results for a parameter estimate randomly sampled from the distribution of the MLEs.
o Bug fix to prevalence.msm with factor subject IDs.
o Bug fix to observed state prevalences in prevalence.msm for "ematrix"-style misclassification models with censoring - censored states were not being imputed correctly.
o Bug fix to plot.survfit.msm, which had been assuming that everyone starts at time zero.
o plot.survfit.msm gets a speed-up for bigger datasets, and "from" is now handled properly in the empirical curve.
o Bug fix to qpexp, and new "special" argument to qgeneric.
Version 1.6.4 (2016-10-02)
-------------
o CRAN release. Vignette source included in vignettes directory, on
request of CRAN.
Version 1.6.3 (2016-06-03)
-------------
o r-forge release only. Fix of bug for qtnorm with vectorised
arguments. Thanks to James Gibbons for the report.
Version 1.6.2 (2016-03-17)
-------------
o r-forge release only. Fix of bug for Pearson test with censored
states. Thanks to Casimir Sofeu for the report.
Version 1.6.1 (2016-03-09)
-------------
o Fix of bug introduced in 1.5.2 for models with "obstrue" and
"ematrix". This affected the first misclassification model
presented in the PDF manual. Documented behaviour of "obstrue"
clarified: with "ematrix" models, the state data are assumed to
contain the true state if "obstrue" is turned on at the
corresponding observation, and with "hmodel" models, the state data
are generated from the HMM outcome model conditionally on the true
state.
o Fix of minor bugs in draic.msm and output printing.
Version 1.6 (2015-11-17)
------------
o CRAN release. Includes the changes from versions 1.5.1 - 1.5.3, plus
also:
o Analytic derivatives for HMMs with multiple outcomes.
o Bug fix for printing model output when only one transition rate is
affected by covariates. Thanks to Jordi Blanch for the report.
Version 1.5.3 (2015-09-14)
-------------
o More underflow correction for probabilities of hidden states in
viterbi.msm. Thanks to Hannah Linder for the report.
o "death" argument in msm() is deprecated and renamed to "deathexact".
o censor.states now defaults to all transient states if not supplied,
instead of complaining, even if there is no absorbing state. Thanks
to Jonathan Williams for the report.
Version 1.5.2 (2015-02-17)
-------------
o HMMs can now have multiple observations at each time generated from
different distributions. See new function hmmMV().
o obstrue can now contain the actual true state, instead of an
indicator. This allows the information from HMM outcomes generated
conditionally on this state to be included in the model.
Version 1.5.1 (2015-01-15)
-------------
o R-forge only release.
o HMMs can now have multiple observations at each time from the same
distribution. The "state" in the "formula" argument of msm() is
supplied as a matrix.
o Up-to-date version of the vignette included in the package.
Version 1.5 (2015-01-05)
-----------
o CRAN release. Includes the changes from versions 1.4.1 - 1.4.3.
Version 1.4.3 (2014-12-12)
-------------
o R-forge only release.
o Phase type models now allow an extra hidden Markov model on top.
Version 1.4.2 (2014-12-11)
-------------
o R-forge only release.
o viterbi.msm now returns the "posterior" probability of each hidden
state at each time, given the full data.
o Bug fixes to misclassification models where some states were
misclassified as other states with probability 1, for both ematrix
and hmmCat specifications. Thanks to Li Su.
Version 1.4.1 (2014-12-10)
-------------
o R-forge only release.
o Experimental facility for two-phase semi-Markov models.
o Memory leaks in C code fixed. Thanks to Brian Ripley.
o Don't print CIs for fixed parameters.
o Documented that factors are allowed as the state variable as long as
their levels are called "1", "2",...
o Bug fixes for covariates on initial state occupancy probabilities
with structural zeros. Thanks to Jeffrey Eaton and Tara Mangal.
o Bug fixes for drlcv.msm. Thanks to Howard Thom.
o Give warning that polynomial contrasts aren't supported.
o Three and four-state versions of the BOS data provided.
Version 1.4 (2014-07-04)
------------
o CRAN release. Includes the major changes from versions 1.3.2 and
1.3.3 below, previously only released on R-forge, plus:
o Default confidence interval method for pnext.msm changed to
"normal", since delta method may not respect probability <1
constraint.
Version 1.3.3 (2014-06-23)
--------------
o R-forge only release.
o C interface changed from .C to .Call, giving a slight speed
improvement.
o Probabilities of passage, see ppass.msm.
Version 1.3.2 (2014-06-19)
--------------
o R-forge only release.
o The new compact format for printing results from fitted models is
now the default. The underlying numbers can be accessed from the
functions msm.form.qoutput or msm.form.eoutput, or from the object
returned by the print function, in the same tidy matrix form. The
old print method is still available as "printold.msm".
o Analytic derivatives available for most hidden Markov models and
models with censored states (excluding unknown initial state
probabilities, constraints on misclassification / categorical
outcome probabilities and their covariates, and truncated or
measurement error distributions). This should speed up optimisation
with the BFGS or CG methods. The corresponding Fisher information
matrix is also available for misclassification (categorical/identity
outcome) and censored state models.
o The BFGS optimisation method is now the default, rather than
Nelder-Mead.
o The internal code that deals with reading the data and passing it to
models has been rewritten to use formulae, model frames and model
matrices more efficiently. As a result the "data" component of msm
objects now has a different structure. The data can be extracted
with the new model.frame() and model.matrix() methods for msm
objects. Also see help(recreate.olddata) for a utility to get the
old (undocumented) format back, but this will not be supported in
the long term.
o New methods (draic.msm, drlcv.msm) for comparing models with
differently-aggregated states. Thanks to Howard Thom.
o Parallel processing supported for bootstrapping and bootstrap
confidence intervals (ci="boot"), if the "doParallel" package is
installed.
o If msm is called with hessian=FALSE, then the Fisher (expected)
information is used to obtain standard errors and CIs, though this
is only available for non-hidden and misclassification models.
This may be preferable if the observed Hessian is very intensive to
approximate.
o Optimisation code tidied, making it easier to add new methods. As
an example, the "bobyqa" algorithm, a fast derivative-free method,
is now supported if the "minqa" package is installed.
o Give informative warning for initial outcomes in HMMs which are
impossible for given initial state probabilites and outcome models.
o Internal centering of "timeperiod" covariates around their means for
inhomogeneous models specified with "pci" is now done consistently
with other covariates, by omitting subjects' last observations
before calculating the mean, since they don't contribute to the
likelihood. Therefore for these models, the initial values (with
"covariates centered around their means in the data") and outputs
for covariates="mean", have a very slightly different meaning from
previous versions.
o When calculating the likelihood for hidden Markov or censoring
models, P matrices are not recalculated when the same one occurs
more than once. This may speed up some models.
o Test suite tidied up and converted to use "testthat" package.
o Data consistency check added to crudeinits.msm().
o Bug fix for misclassification models with constraints on baseline
misclassification probabilities and fixed parameters.
o Bug fix for bootstrap CIs with efpt.msm
o Miscellaneous minor bug fixes, see Changelog.
Version 1.3.1 (2014-04-04)
--------------
o R-forge only release.
o Time-dependent covariates supported in totlos.msm.
o New function envisits.msm() for expected number of visits to each
state over a period, calculated as a corollary of totlos.msm().
o New utility "msm2Surv" to export data from msm format to counting
process format for use with the survival and mstate packages. This
assumes the exact transition times of the process are known.
o More informative messages from model fits which have not converged.
In particular, a warning is now given when the optimiser iteration
limit was reached without convergence, which previously happened
silently.
o Miscellaneous minor bug fixes, see Changelog.
Version 1.3 (2014-01-15)
-------------
o CRAN release. Includes the changes below from R-forge versions
1.2.1 up to 1.2.7, plus:
o Fix of bug introduced in 1.2.3 which broke models with non-standard
state ordering.
o Datasets now lazy loaded so data() not required.
o New "start" argument to efpt.msm, allowing averaging over a set of
starting states.
Version 1.2.7 (2013-10-07)
-------------
o R-forge only release
o Fix of bug for logLik.msm with by.subject=TRUE.
Version 1.2.6 (2013-09-13)
-------------
o R-forge only release
o Row numbers reported in error message about different states at the
same time corrected to account for missing data. Thanks to Lucy
Leigh for the report.
o An informative error is now shown if trying to use gen.inits with a
hidden Markov model, and it is now documented that this is not
supported.
Version 1.2.5 (2013-07-30)
-------------
o R-forge only release
o Analytic formula for totlos.msm implemented, which is vastly more
efficient than the numerical integration used previously. Debugging
outputs left in 1.2.3 also removed.
o Matrix exponentials, in MatrixExp and non-analytic likelihood
calculations, are now calculated using expm from the expm package by
default. As a result msm now depends on the expm package.
Version 1.2.4 (2013-07-23)
-------------
o R-forge only release
o Range constraints can now be given for HMM outcome parameters,
through a new argument "hranges" to msm. This may improve HMM
identifiability.
Version 1.2.3 (2013-06-06)
-------------
o R-forge only release
o New interface for easily specifiying different covariates for each
transition intensity, through a named list in the "covariates"
argument to msm. Previously this required "fixedpars".
o Major restructuring of the internal code, mainly so that parameters
are adjusted for covariates in R rather than C. There should be no
differences visible to the user.
o Initial state occupancy probabilities are estimated on the
multivariate logit scale, not univariate, and confidence intervals
are calculated using a simulation-based method (with 10000
simulations, so there will be a small Monte Carlo error).
o When centering covariates around their means for the default
likelihood calculation, the means used are now after dropping
missing values and subjects with one observation, not before. Thanks
to Howard Thom for the report.
o Relatedly, the covariate values for subjects' last observations are
not included in this mean, since they don't contribute to the
likelihood, so interpretation of initial values for the qmatrix, and
outputs for covariates="mean", will now be very slightly different.
o Bug fix in totlos.msm: calculations were wrong for fromt > 0.
o Memory bug in Viterbi, which could crash R, fixed.
Version 1.2.2 (2013-05-21)
-------------
o R-forge only release
o Can now examine subject-specific -2 log likelihoods at the maximum
likelihood estimates, via logLik.msm().
o The state can now be a factor with levels (1:nstates), as well as
numeric. Previously supplying a factor state led to unpredictable
behaviour and potential crashes.
Version 1.2.1 (2013-05-16)
-------------
o R-forge only release
o A matrix of fixed patient-specific initial state distributions can
now be supplied as "initprobs" in hidden Markov models.
Version 1.2 (2013-05-14)
-----------
o Implemented accurate p-value for the Pearson-type test from Titman
(Lifetime Data Analysis, 2009). Non-hidden Markov models for pure
panel data only.
o A Fisher scoring algorithm can now be used to maximise the
likelihood for panel data without censored / hidden states.
Thanks to Andrew Titman for help with this.
o New function efpt.msm for expected first passage times for
time-homogeneous models.
o prevalence.msm now produces expected values by integrating model
predictions over the covariate histories observed in the data, if
'covariates="population"' is supplied. This is the default, but the
old behaviour is available by supplying fixed covariates in the
"covariates" argument.
o In prevalence.msm and plot.prevalence.msm, subjects reaching the
absorbing state can be removed from the risk set after they have
reached an optional censoring time. Thanks to Andrew Titman.
o Newly user-accessible function simfitted.msm for simulating from a
model defined by the estimates from a model fitted in msm.
o Subjects with only one observation are dropped from the data stored
in fitted model objects. This gives more accurate numbers at risk
in prevalence.msm.
o Arguments can be passed through summary.msm to prevalence.msm.
o pmatrix.piecewise.msm allows time-homogeneous models with change
point vector "times" of length 0.
o Fixes for bugs in the the Pearson test introduced in 0.9.5.
o Misclassification models where some off-diagonal misclassification
probabilities are 1 are now handled properly. Thanks to Howard Thom
for uncovering this.
o Bug fix for interp="midpoint" method in calculation of observed
prevalences (prevalence.msm). Thanks to Erica Liu.
o Bug fix for Viterbi algorithm with obstrue. Thanks to Linda
Sharples.
Version 1.1.4 (2012-12-10)
-------------
o Minor modification of package tests to enable R CMD check to pass
with the forthcoming release of mvtnorm.
Version 1.1.3 (2012-09-28)
-------------
o Bug fix: qmatrix.msm and ematrix.msm were returning inaccurate delta
method standard errors / CIs with center=FALSE, covariates and
user-supplied covariate values. Thanks to Vikki O'Neill for the
report.
o Use BFGS method for one-parameter optimisation unless method
supplied explicitly, avoiding warning about unreliability of
Nelder-Mead.
Version 1.1.2 (2012-07-31)
-------------
o New Student t distribution for hidden Markov model outcomes. Thanks
to Darren Gillis.
o Removed debugging browser which had been inadvertently left in
pearson.msm. Thanks to Chyi-Hung Hsu.
o Corrected equation 5 in the PDF manual for the likelihood under
exact transition times. The code was unaffected. Thanks to Simon
Bond.
Version 1.1.1 (2012-05-11)
-------------
o Fix of bug in calculation of confidence intervals using "ci=normal".
Affected models were those with fixed parameters or HMMs. Users are
advised to check their results with the corrected package -
apologies.
o If user supplies an ematrix with all misclassification probabilities
zero, this degrades gracefully to a non-misclassification model.
Thanks to Sharareh Taghipour for the report.
o Bug fix for error messages when model inconsistent with data, and
when subject IDs not adjacent. Thanks to Kelly Williams-Sieg for
the report.
o Bug fix in pearson.msm for models where transitions are only allowed
from one state. Thanks to Gavin Chan for the report.
o qtnorm fixed for p=0 or 1 and upper < lower. Thanks to Art Owen for
the report.
Version 1.1 (2011-09-09)
-------------
o New function "pnext.msm" to compute a matrix of probabilities for
the next state of the process.
o New "[" method to intuitively extract a row and column of
matrix-based estimates and confidence intervals, for example
qmatrix.msm(x)[1,2]
o Miscellaneous doc and minor bug fixes, see Changelog.
Version 1.0.1 (2011-05-26)
-------------
o Fix of a bug which made pmatrix.msm break for time-inhomogeneous
models with non-integer time cut points "pci". Thanks to Christos
Argyropoulos for the report.
o Return -Inf in dtnorm when outside truncation bounds and log=TRUE.
Version 1.0 (2010-11-24)
-------------
o 1.0 release to accompany the forthcoming Journal of Statistical
Software paper about msm.
o Line types, colours and widths can be configured in plotprog.msm,
plot.survfit.msm and plot.prevalence.msm.
o Added warning for multiple observations at the same time on the same
person with different states, which leads to zero likelihood and
the dreaded "cannot be evaluated at initial values" message.
o If center=FALSE, the $Qmatrices$baseline, $Ematrices$baseline and
$sojourn components of msm objects are evaluated with covariate
values of 0, for consistency with "logbaseline". Documentation and
printed output corrected accordingly. These issues caused problems
with viterbi.msm. Thanks to Kenneth Gundersen for the report.
Version 0.9.7 (2010-05-18)
-------------
o Bug fixes for bootstrapping with totlos, covariates on HMM outcomes
and fixedpars. Thanks to Li Su for the report.
Version 0.9.6 (2010-02-09)
-------------
o Fix of a bug which caused occasional wrong likelihood calculations
for models with "exacttimes". Thanks to Brian Tom for the report.
o Fix for "NA in probability vector" error in pearson.msm. Thanks to
Wen-Wen Yang for the report.
Version 0.9.5 (2009-11-25)
-------------
o Fix for a bug in pearson.msm triggered by a change in R version
2.10.0, which caused all expected values to be returned as zero.
Thanks to Brian Tom for the report.
Version 0.9.4 (2009-11-13)
-------------
o Bug fix for calculation error in scoreresid.msm. Thanks to Aidan
O'Keeffe for the report.
o Options to MatrixExp for calculating the matrix exponential can be
passed through from pmatrix.msm and pmatrix.piecewise.msm. Thanks
to Peter Adamson for the suggestion.
o Missing data handling bug fixes, in particular, crudeinits.msm and
gen.inits no longer give errors if there are missing values in the
subject, time or state variable.
o Other minor bug fixes, see ChangeLog.
Version 0.9.3 (2009-08-20)
-------------
o Bug fix - estimates of covariate effects in matrices outputted by
msm were ordered wrongly in models with "qconstraint". Thanks to
Brian Tom for the report.
o Bug fix - "gradient in optim evaluated to wrong length" was still
affecting certain models with fixed parameters. Thanks to Aidan
O'Keeffe for the report.
o Fix to pearson.msm for R versions >= 2.9.1 ("replacement has 0 rows"
error)
Version 0.9.2 (2009-07-07)
-------------
o Bug fix for models with fixed parameters fitted using optimisation
methods with derivatives ("BFGS"), which failed with the error
"gradient in optim evaluated to wrong length". Thanks to Isaac
Dinner for the report.
Version 0.9.1 (2009-06-12)
-------------
o Minor update to the test suite to allow build on Fedora / Red Hat
Linux.
Version 0.9 (2009-06-09)
-----------
o Time-inhomogeneous models fitted with the "pci" argument to msm are
now fully supported in all output functions.
pmatrix.msm can now compute transition probabilities over any given
time interval for time-inhomogeneous models fitted with "pci". A
new argument "t1" to pmatrix.msm specifies the starting time, while
"t" still specifies the interval length.
All functions which call on pmatrix.msm, such as plot.msm,
plot.survfit.msm, prevalence.msm and totlos.msm, now account for
time-inhomogeneity in models fitted using "pci".
o Extractor functions are now more tolerant. If a list of covariate
values is supplied, unknown covariates are ignored and covariates
with unspecified values are set to zero. Factor values can be
specified either by factor levels or by 0/1 contrasts.
o Bug fix - score residuals were being calculated wrongly for models
with covariates.
o Derivatives are now used in the optimisation by default (use.deriv=TRUE)
for optimisation methods such as BFGS which employ them.
o Licence clarified as GPL-2 or later, to enable packaging of msm for
Fedora/Red Hat Linux.
Version 0.8.2 (2009-04-08)
-------------
o Bug fix - extractor functions were not being calculated for models
with interactions between covariates.
o Sources for the PDF manual included in the source package, to enable
inclusion of msm in Debian GNU/Linux.
Version 0.8.1 (2008-07-25)
-------------
o New option "pci" to msm, which automatically constructs a model with
piecewise-constant transition intensities which change at the
supplied times.
o The HMM outcome model is assumed to apply to censored states in
HMMs, unless obstrue = 1.
o totlos.msm now calculates total length of stay for all states, not
just transient states. New argument "end" added.
o Bug fix in the likelihood calculation for data containing a mixture
of obstype = 1 and obstype = 2. Thanks to Peter Jepsen for
uncovering this.
Version 0.8 (2008-03-28)
-------------
o New function "pearson.msm" implementing the Pearson-type
goodness-of-fit test for multi-state models fitted to panel data
(Aguirre-Hernandez and Farewell, Statistics in Medicine 2002;
Titman and Sharples, Statistics in Medicine 2007). Thanks to
Andrew Titman for his work on this.
o New function "scoreresid.msm" to compute and plot score residuals
for detecting influential subjects.
o New function "plotprog.msm" to plot Kaplan-Meier estimates of time
to first occurrence of each state.
o New function "plot.survfit.msm" to plot Kaplan-Meier estimate of
survival probabilty compared with the fitted survival probability from
a model.
o New convenience function "lrtest.msm" for comparing a set of models
with likelihood ratio tests.
o logLik method returns the log-likelihood, not the minus
log-likelihood, for consistency with methods in other
packages. Thanks to Jay Rotella.
o msm now depends on the "survival" package.
o Data "heart" renamed to "cav" to avoid clashing with the dataset in
the "survival" package.
Version 0.7.6 (2007-12-10)
-------------
o Covariates on misclassification probabilities can now be specified
in simmulti.msm. Simulation bug introduced in 0.7.5 fixed.
o quantile functions (qtnorm,qmenorm,qmeunif,qpexp) made more robust
for small probabilities.
Version 0.7.5 (2007-11-20)
-------------
o The Viterbi algorithm can now be used to impute the most likely true
state for censored states, as well as for HMMs
o prevalence.msm now handles models with censored states correctly,
using the Viterbi algorithm to determine the observed states.
o Bug fix: account for extra arguments supplied to "prevalence.msm"
when producing the plot of prevalences against time. Thanks to
Peter Jepsen for the report.
o Bug fixes involving factor covariates in bootstrapping and
qratio.msm. Thanks to Peter Jepsen.
o New beta outcome distribution for hidden Markov models.
Version 0.7.4 (2007-10-01)
-------------
o Minor changes to satisfy the package-building tools in the new R
version 2.6.0.
Version 0.7.3 (2007-08-15)
-------------
o Confidence intervals in various output functions can now be
calculated by simulating from the asymptotic normal distribution of
the maximum likelihood estimates of the Q matrix and transforming.
The "ci.boot" argument in these functions has been replaced by the
"ci" argument, which can take values "none", "normal" and
"bootstrap". This is implemented for qmatrix.msm, ematrix.msm,
sojourn.msm, qratio.msm, pmatrix.msm, pmatrix.piecewise.msm,
totlos.msm and prevalence.msm. Such CIs are expected to be more
accurate than the delta method, but less accurate than
bootstrapping. There is a similar compromise in computation time.
Thanks to Andrew Titman for the suggestion.
o As a result, msm now depends on the mvtnorm package.
o In prevalence.msm, observed and expected prevalences can now be
plotted against time. Thanks to Andrew Titman for the suggestion.
o In prevalence.msm, observed states can be interpolated using the
assumption that they change at the midpoints between observation
times.
o Matrix exponential routines now handle matrices with complex
eigenvalues. Thanks to Véronique Bouchard for uncovering the bug.
o Bug fix to surface.msm for HMMs. Thanks to Michael Sweeting.
o Bug fix for bootstrapping - now handles models with obstype and
obstrue. Thanks to Peter Jepsen for the report.
Version 0.7.2 (2007-05-31)
-------------
o An error in the calculation of multinomial logistic regression
probabilities has been fixed. This will change the results of
misclassification models where there were both a) three or more
possible classifications for a particular underlying state and b)
covariates on the corresponding classification probabilities. Any
changes are not expected to be substantial.
o Misclassification probabilities are now estimated on a different
scale during the optimisation: log relative to baseline probability,
instead of on a univariate logit scale. Therefore maximum likelihood
estimates for misclassification models may be very slightly
different from previous versions.
o Confidence intervals for probabilities are now more appropriately
calculated using a delta method approximation to the variance of
logit(p), instead of log(p).
o New argument "initcovariates" and "initcovinits" to msm, to allow
covariate effects on initial state probabilities in hidden Markov
models to be estimated through multinomial logistic regression.
o Initial state probabilities initialised to zero are now fixed at
zero during optimisation, if initprobs is being estimated
("structural zeroes").
o New argument "obstrue" to msm, to allow some observations to be
observed without error in misclassification models.
o Constraints on covariate effects on transition intensities are now
allowed such that some effects are equal to other effects multiplied
by -1.
o New option "ci.boot" to prevalence.msm. This is a helper to
calculate bootstrap confidence limits for the expected prevalences
using "boot.msm".
o rtnorm() for sampling from the truncated normal distribution now
uses the efficient rejection sampling methods by Christian Robert.
Version 0.7.1 (2007-04-18)
-------------
o Maintainer's email address is now chris.jackson@mrc-bsu.cam.ac.uk
o msm now gives a warning when the standard errors cannot be
calculated due to the Hessian at the converged "solution" being
non-positive-definite. This issue had been causing a lot of user
confusion.
o prevalence.msm can now calculate expected prevalences for models
with piecewise-constant intensities, in the same manner as
pmatrix.piecewise.msm. Intensities must still be common to all
individuals.
o Bug fix for presentation of intensity matrices in print.msm and
qmatrix.msm when center = FALSE. These had been returning matrices
with covariates set to zero, when they should have been set to their
means. Thanks to Ross Boylan.
o Covariates on transition process which are missing at an
individual's last observation are not dropped, because they are not
used in the analysis. Thanks to Jonathan Williams. This has the
consequence that output from prevalence.msm may be different from
earlier versions (0.7 or earlier) if there are missing values in the
data. Users are advised to deal with missing values in their data
appropriately before using msm.
o Miscellaneous other bug fixes, see ChangeLog.
Version 0.7 (2006-11-21)
-----------
o Initial state occupancy probabilities in hidden Markov models can
now be estimated. See new argument "est.initprobs" to msm.
o Bootstrap resampling is implemented. This may be used to calculate
confidence intervals or standard errors for quantities such as the
transition probability matrix where this was previously not possible
with msm, or as an alternative to Hessian-based standard errors or the
delta method for other quantities. See new function boot.msm.
o Bootstrap confidence intervals can be calculated directly from
pmatrix.msm and totlos.msm.
o Bug fix in estimation of observed prevalences at maximum observed
time. Thanks to Jeremy Penn for the report. The function has also
been rewritten so that the calculation of these prevalences is now
much faster.
o prevalence.msm is adapted sensibly to handle data where not all
individuals start at a common time.
o The values of categorical (factor) covariates in output functions,
such as qmatrix.msm, are now specified in an intuitive way. For
example, to calculate a statistic with the categorical covariate
"smoke" at the level "CURRENT", just supply list(smoke="CURRENT") as
the "covariates" argument to the output function.
Version 0.6.4 (2006-09-21)
-------------
o Bug fix to rtnorm for vector parameters. Thanks to Jean-Baptiste
Denis for the report.
o Bug fix to sim.msm: multiply covariates by baseline intensities in
the correct order. Thanks to Stephan Lenz for the report.
Version 0.6.3 (2006-06-28)
-------------
o Correction to version 0.6.2 with the references reinstated in the
manual.
Version 0.6.2 (2006-06-23)
-------------
o The likelihood for certain transient 2, 3, 4 and 5 state models is
now calculated using analytic expressions for the transition
probability matrix, instead of by numerically calculating the matrix
exponential. This can give big speed improvements.
o Various bug fixes, including support for character subject IDs.
Version 0.6.1 (2006-03-26)
-------------
o Bug fix release. In Viterbi algorithm, don't ignore initial state
occupancy probabilities. Thanks to Melanie Wall for reporting this.
For other bug fixes see the ChangeLog.
Version 0.6 (2005-11-25)
-------------
o New argument "use.deriv" to msm. If TRUE, then analytic derivatives
are used in the algorithm to maximise the likelihood, where an
appropriate algorithm is being used, such as optim's BFGS. These
derivatives are also used to calculate the Hessian at the
maximum. Not supported for hidden Markov models or models with
censoring. This may substantially speed up convergence, especially
for larger models.
o The Newton-type algorithm (Dennis and Schnabel) from the R function
"nlm" can also be used to maximise the likelihood, as an alternative
to the algorithms in "optim".
o New function "surface.msm" to plot likelihood surfaces, for example,
in the region of a suspected maximum. Includes methods for the
generic R functions contour(), persp() and image(), to produce each
respective type of surface plot for a "msm" object.
Version 0.5.2 (2005-10-11)
-------------
o Bug fix in Viterbi algorithm. It didn't handle underlying Markov
models with progressive and regressive states properly. Thanks to
Rochelle Watkins.
o Negative binomial hidden Markov output distribution added.
o Miscellaneous other bug fixes, see ChangeLog.
Version 0.5.1 (2005-05-25)
-------------
o Bug fix in simulation functions (sim.msm, simmulti.msm). Models
with time dependent covariates were not being simulated properly,
the covariate changes were not fully accounted for. Thanks to Mike
Sweeting for the report.
o New functions dpexp, ppexp, qpexp, rpexp for the exponential
distribution with piecewise-constant rates.
o Bug fix. covariates with the same names as internal msm variable
names, such as "subject", "time" and "state", are now allowed.
o Argument "hessian" added to msm, to avoid calculating standard
errors, for example when bootstrapping.
o Miscellaneous internal edits and fixes, see ChangeLog.
Version 0.5 (2005-03-06)
-----------
o Major update. Much of the internal R and C code has been re-written.
o General continuous-time hidden Markov models can now be fitted with
msm, as well as misclassification models. Allowed response
distributions conditionally on the hidden state include categorical,
normal, Poisson, exponential and others. See the new "hmodel"
argument. Misclassification models can either be fitted in the old
style using an ematrix, or using a general HMM with a categorical
response distribution. Covariates can be fitted to many of the new
hidden response processes via generalized regressions. See
"hcovariates", "hcovinits" arguments.
o Per-observation observation schemes, generalising the "exacttimes"
and "death" concepts. An optional new variable in the data can
specify whether each observation is a snapshot of the process, an
exactly-observed transition time, or a death state. Observations
are allowed to be at identical times, for example, a snapshot
followed instantly by an exact transition time.
o Various syntax changes for cleaner moder specification.
- Instead of 0/1 indicators, qmatrix and ematrix should contain the
initial values for the transition intensity / misclassification
matrix. These matrices can be named with names for the states of
the Markov chain.
- The inits argument is abolished. Initial values are estimated
automatically if the new argument to msm "gen.inits = TRUE" is
supplied. This uses the initial values calculated by
crudeinits.msm.
- misc no longer needs to be specified if an ematrix is supplied.
- fixedpars=TRUE fixes all parameters, or specific parameters can
be fixed as before.
- crudeinits.msm takes a state ~ time formula instead of two
separate state, time arguments, for consistency with the msm
function.
- Initial values for covariate effects on transition rates /
misclassification probabilities are assumed to be zero unless
otherwise specified by the new "covinits" / "misccovinits" argument.
o Support for 'from-to' style data has been withdrawn. Storing data in
this format is inadvisable as it destroys the longitudinal nature of
the data.
o Speed improvements. The algorithm for calculating the likelihood
for non-hidden multi-state models has changed so that the matrix
exponential of the Q matrix is only calculated once for each time
difference / covariate combination. Therefore, users should see
speed improvements for data where the same from-state, to-state,
time difference, covariates combination appears many times.
o Confidence intervals are now presented instead of standard errors
for uncertainty in parameter estimates.
o New method of calculating matrix exponentials when the eigenvector
matrix is not invertible. It now uses the more robust method of
Pade approximants with scaling and squaring, instead of power
series. Faster LAPACK routines are now used for matrix inversion.
o covmatch argument to msm has been abolished. To take a
time-dependent covariate value from the end of the relevant
transition instead of the default start, users are expected to
manipulate their data accordingly before calling msm, shifting the
positions of the covariate back by one within each subject.
o Syntax changes for simmulti.msm.
Bug fixes
---------
o The likelihood is now calculated correctly for individuals with
censored intermediate states, as well as censored initial and final states.
Thanks to Michael Sweeting for reporting this.
o hazard.scale and odds.scale were interpreted wrongly in hazard.msm
and odds.msm respectively.
o time-dependent covariate values now taken from the start instead of
end of the transition under hidden Markov models.
Version 0.41 (2005-01-28)
------------
o Censored outcomes in misclassification models are assumed to be not
subject to misclassification.
o A couple of bug fixes for exact transition times.
Version 0.4 (2005-01-07)
-----------
o Censored observations are now supported, via new "censor" and
"censor.states" arguments. A censored observation is unknown, but
known to be one of a particular set of states.
A major update to msm is under development, for release in the first
half of 2005. This will support hidden Markov models with general
response distributions.
Version 0.3.3 (2004-09-18)
-------------
o Maintenance release with minor fixes and enhancements ready for
R-2.0.0.
Version 0.3.2 (2003-03-25)
-------------
o More than one death state is now permitted, through the "death"
argument. Death states are those whose exact entry time is known,
but the state at the previous instant before death is unknown.
o The "tunit" argument has been abolished. Death times are now
assumed to be exact rather than known within one day. This makes
more sense since for longitudinal studies, all observations are
usually recorded to within one basic time unit, not just death
times.
o Cleanups of the manual and minor fixes, as detailed in ChangeLog.
Version 0.3.1 (2003-10-14)
-------------
o Bug fix. The likelihood was being wrongly calculated in cases when
both the data represent exact transition times and the transition
intensity matrix had repeated eigenvalues.
o The "death" argument is no longer ignored when exacttimes=TRUE, as
it is reasonable to have the entry time into one state accurate to
within one day, and all other times exactly accurate.
o More memory problems should be fixed.
Version 0.3 (2003-09-29)
-------------
o Two errors in the calculation of the likelihood for a multi-state
model have been corrected. These bugs affect only models with
reversible transition matrices, that is, models which allow
progression and regression between states.
o The first bug occurred when death times were known to within one time
unit (death = TRUE) - the likelihood calculation did not account for
reversible states.
o The second bug occurred when the data represent exact transition times
(exacttimes = TRUE). The likelihood calculation did not properly
account for reversible states.
o Baseline transition intensities, or misclassification probabilities,
can now be constrained to be equal to each other, in the same manner
as covariate effects. Specified by new arguments "qconstraint" or
"econstraint".
o The memory allocation problems of version 0.2 have been fixed.
Version 0.22 (2003-06-30)
--------------
o Fixed some minor bugs, as detailed in ChangeLog.
o New function, pmatrix.piecewise.msm, for calculating transition
probability matrices for processes with piecewise-constant
intensities.
Version 0.21 (2003-06-03)
--------------
o Fixed a handful of minor bugs, as detailed in ChangeLog.
o Minor edits and additions to the manual.
o The subject ID can now be factor or character.
Version 0.2 (2003-01)
-------------
o A full manual in PDF format is included in the doc directory. This
gives the mathematical background behind multi-state modelling, and a
tutorial in the typical use of the functions in the msm package.
o Many more methods for extracting summary statistics from the fitted
model are included. These are generally called with the fitted model
as the argument, plus an optional argument indicating the assumed
covariate values. The functions include qmatrix.msm, ematrix.msm,
pmatrix.msm, qratio.msm, sojourn.msm, totlos.msm, hazard.msm,
odds.msm, prevalence.msm.
o New function statetable.msm to calculate frequencies of transitions
between pairs of states observed in the data.
o New function crudeinits.msm to estimate transition intensities
assuming the data represent the exact transition times of the Markov
process. These can be used as initial values in the msm function for
fitting the model.
o prevalencemisc.msm has been removed, as its methodology was
overcomplicated and confusing. The methods used in prevalence.msm
have been extended naturally to deal with misclassification models.
o Fix of a bug in the likelihood calculation for misclassification
models (the number of non-death states was assumed to be the same as
the number of states that could be misclassified, leading to failure
to calculate the likelihood for models where some states are observed
without error, but are not death states. ) Thanks to Martyn Plummer
for reporting this.
o Fix of a bug in the simulation routines (getobs.msm, called by
simmulti.msm), where for models with absorbing states, the absorbing
state is not retained in the simulated data.
o New heart transplant example data set, as used in the manual, so
that all the examples given in the manual can be run by the user.
o Tidying of the help pages.
Version 0.1 (2002-11)
-------------
o First release.