TemporalHazard 1.2.9

Bug fixes

• The phase-identifiability warning no longer names a cause that did not
  occur. variation is the relative range of a phase's contribution across
  the observed times, so it collapses for every phase when those times carry
  nothing that separates them. The saturated scan fired anyway: with
  time = rep(2, 20) it told a constant phase that its shape parameters were
  unidentified, when a constant phase has none, and blamed a short half-life.
  A single-row fit produced the same text.

  That case is now reported in its own words, under a condition that takes two
  things together: the observed times' own relative range is below the
  tolerance, and no phase's contribution varies across them. Neither half is
  sufficient. The range alone would condemn times bunched far from the origin,
  where a steep phase can be fully identified; the phase measures alone would
  condemn well-spread times whenever a single phase saturates. Because the
  range is relative, near-ties reach the condition as exact ties do --
  c(100, 100.000001) produced the old wording verbatim. Per-phase saturation
  and absence over well-spread times are reported exactly as before, and the
  condition is now caught even when every phase carries covariates, where the
  per-phase measures are withheld and previously nothing was said at all.

  Two related corrections. A phase that has not started is reported as absent
  even in a left-truncated fit, since that test rests on the share and not on
  how the times are spread. And when the counting-process entry times or
  interval bounds supply enough evaluation points, the degenerate-times verdict
  is withheld -- and "enough" is counted rather than assumed. With the event
  times tied, each added point supplies one more functional of the parameters,
  so one more than the number of added points must reach the number of free
  parameters. The package's default two-phase model has five, and pinning a
  phase's shapes lowers the bar because those parameters are no longer free.
  Points that add nothing do not count: a zero entry time, a bound equal to an
  event time -- both of which Surv(type = "interval") synthesises for every
  row -- or a bound the objective never reads, since time_upper is live only
  for left-censored and interval rows. Counting any of those silenced this
  warning on data that needed it while changing no likelihood value.

  The message also no longer names shape parameters for a model that has none.
  A single constant phase is now silent, since one functional determines its
  mu exactly; two constant phases still warn, since only their sum is
  determined, but the wording says the phases cannot be told apart rather than
  naming shapes they do not have.

  Where the times are NOT degenerate, the per-phase saturation message is
  reported regardless of the bounds, so adding a start column no longer
  removes a correct diagnostic (#211).

  Known limitation, unchanged by this work and stated rather than implicit: the
  per-phase measures are taken over the event times alone, and the count of
  evaluation points is deliberately conservative -- it does not credit the
  extra functional a zero entry time makes separately observable, so a fit can
  be warned about while being identified. That cuts both ways.
  A phase can be flat across the event times while its shape is identified
  through the bounds, so the saturated message can overstate what is lost; and
  the counting rule above is a local identification argument for tied event
  times, so a fit can clear it and still have a flat direction the guard does
  not see, in which case nothing is said at all. Tracked in #228.

• The SAS nomogram parser no longer deletes a whole-year time column. The
  PROC PRINT observation counter is identified as a leading column running
  exactly 1..n, which rested on no measurement column ever being a gapless
  1-based integer run -- true of the corpus in hand, not of SAS listings. A
  counter-suppressed nomogram on a whole-year grid prints YEARS as 1.0000, 2.0000, 3.0000, and the parser deleted the time key. Because the remaining
  columns then matched the header count, the shape check that would have caught
  it passed: the listing parsed, and the comparison ran against a table with no
  time column.

  The header now decides: a leading column the header names as a counter is
  dropped whatever values it carries, which also fixes the reverse case -- page
  two of a paginated listing starts at 41, and requiring a 1..n run left that
  counter in the data. The labels are matched case-insensitively, after the
  leading underscore the parser strips, so SAS's _N_ is recognised. A 1..n
  run under a name the parser does not know is now kept and warned about rather
  than silently dropped: the listing cannot distinguish a counter spelled a new
  way from a measurement that happens to run 1..n, and an extra column is
  visible where a deleted one is not (#212).

• A phase named total is now rejected rather than silently losing its
  contribution. total is the key the multiphase cumulative-hazard
  accumulator is stored under, so phases = list(total = hzr_phase("cdf"), ...)
  overwrote that phase's own contribution vector with the summed hazard. The fit
  returned normally; the phase then reported a contribution share of exactly 1
  and the identifiability diagnostic read it as dominating the model. The name is
  reserved across the decomposition and prediction output as well, where it
  labels the total column. hazard() and hzr_theta_names() -- which applies
  the same validation -- now stop with an explanatory message, and the
  reservation is documented on the phases argument (#214).

• objective = "sas" data defects are reported as data defects. The two
  conditions the SAS objective imposes -- no left-censored rows, and a positive
  width on every interval-censored row -- are pure functions of the data, but
  were checked inside the objective. The optimizer's per-start handler caught
  them and reported "produced no usable fit from N starts", which reads as a
  convergence problem and invites raising n_starts: a remedy that cannot work,
  because the condition is identical at every start. hazard() now checks both
  before any optimization, and the interval-width message reports the offending
  row in the data rather than its position among the interval rows. The guards
  inside the objective and gradient are unchanged, since the gradient is
  reachable without hazard() (#213).

• Behaviour change: because those preconditions are properties of the data
  and not of the fit, they are now checked whenever objective = "sas" is
  specified, including fit = FALSE. hazard(..., fit = FALSE, objective = "sas") on left-censored or zero-width-interval data previously returned a
  hazard object carrying an objective it could never be fitted with; it now
  stops. An NA in status under objective = "sas" is also reported by
  argument and row, rather than as a bare missing value where TRUE/FALSE needed.

  Note this reaches only the codes hazard() is given. On the vector
  interface a survival::Surv() object is unclassed without translating its
  codes, so they mean something else on arrival, and what goes wrong depends on
  type: under type = "left" a left-censored row is coded 0 and is fitted
  as right-censored; under type = "interval" it is coded 2 and is read as
  an interval of zero width, which this guard then rejects; and a genuine
  interval row, coded 3, matches no branch of the likelihood and contributes
  nothing. The formula interface translates and is guarded. That asymmetry is a
  pre-existing defect of the vector path, tracked in #226.