docs: fix stale v2.0 documentation and remove LLM artefacts

NEWS.md

@@ -133,7 +133,7 @@
 - `attr(dataset, "weight")` now accepts non-integer character weights.  The
   C++ scoring engine still stores `int` weights internally; fractional
   inputs are rescaled to integer with a configurable precision (default
-  0.001, controlled by `getOption("TreeSearch.fractional.scale", 1000L)`).
+  ~0.001, controlled by `getOption("TreeSearch.fractional.scale", 1260L)`).
   Previously, fractional weights were silently truncated at the Rcpp
   boundary (e.g. `c(0.5, 1.7)` became `c(0L, 1L)`, dropping 50% / 41% of
   the respective characters' contributions).  Integer weights pass
@@ -174,9 +174,6 @@ faster; inapplicable character handling (Brazeau _et al._ 2019) is built in.
 
 ### New features
 
-- `PaintCharacters()` colours each character in a morphological dataset by its
-  most concordant tree edges.
-
 - `ScoreSpectrum()`: Chao1-style landscape coverage estimator.  Treats
   distinct parsimony scores found across replicates as "species" and estimates
   how thoroughly the parsimony landscape has been sampled (Good-Turing sample
@@ -206,8 +203,6 @@ faster; inapplicable character handling (Brazeau _et al._ 2019) is built in.
 - **MPT enumeration**: after the main search converges, a TBR plateau walk
   from each pool tree discovers additional most-parsimonious topologies on the
   same and neighbouring score plateaus, up to `poolMaxSize`.
-  This addresses a common complaint that the previous implementation returned
-  only one tree when multiple MPTs exist.
 - `LeastSquaresTree()` and `LeastSquaresFit()` search for, and
   fit branch lengths to, the tree that best matches a target distance matrix
   under a least-squares criterion, reusing the optimised C++ rearrangement
@@ -224,7 +219,7 @@ faster; inapplicable character handling (Brazeau _et al._ 2019) is built in.
 - `attr(dataset, "weight")` now accepts non-integer character weights.  The
   C++ scoring engine still stores `int` weights internally; fractional
   inputs are rescaled to integer with a configurable precision (default
-  0.001, controlled by `getOption("TreeSearch.fractional.scale", 1000L)`).
+  ~0.001, controlled by `getOption("TreeSearch.fractional.scale", 1260L)`).
   Previously, fractional weights were silently truncated at the Rcpp
   boundary (e.g. `c(0.5, 1.7)` became `c(0L, 1L)`, dropping 50% / 41% of
   the respective characters' contributions).  Integer weights pass
@@ -401,8 +396,6 @@ faster; inapplicable character handling (Brazeau _et al._ 2019) is built in.
 - Dataset-adaptive timeout default (1–15 minutes based on dataset size).
 - Internal modularization of the Shiny app into proper Shiny modules.
 
-## Other improvements
-
 # TreeSearch 1.8.0.9001 (2026-04-23)
 
 - Reorder parameters in `Q[A]Col(quality, amount)`.

R/Concordance.R

@@ -1002,9 +1002,6 @@ SharedPhylogeneticConcordance <- function(tree, dataset) {
 #' consistent with a specified phylogenetic tree, and the signal:noise
 #' ratio of the character matrix implied if the tree is true.
 #'
-#' Presently restricted to datasets whose characters contain a maximum of
-#' two parsimony-informative states.
-#'
 #' @return `ConcordantInformation()` returns a named vector with elements:
 #'
 #' - `informationContent`: cladistic information content of `dataset`
@@ -1018,8 +1015,7 @@ SharedPhylogeneticConcordance <- function(tree, dataset) {
 #' matrix to the cladistic information content of the tree, a measure of the
 #' redundancy of the matrix
 #' - `ignored`: information content of characters whose signal and noise could
-#' not be calculated (too many states) and so are not included in the totals
-#' above.
+#' not be calculated, and so are not included in the totals above.
 #'
 #' @inheritParams TreeTools::Renumber
 #' @inheritParams MaximizeParsimony

R/MaximizeParsimony.R

@@ -366,11 +366,8 @@
 #'     `maxSeconds`, or raise `enumTimeFraction`, for a more complete set.
 #'   \item **TBR-island coverage.** The plateau walk only explores islands of
 #'     equal-score trees that a main-loop replicate actually landed on.  MPTs
-#'     lying in unvisited islands are never enumerated, however large
-#'     `poolMaxSize` is.  Increase `maxReplicates` to seed more islands.  Two
-#'     independent runs (or runs at different `strategy` effort) can therefore
-#'     return the *same* count if they exhaust the same connected island — this
-#'     reflects island structure, not a cap.
+#'     in unvisited islands are never enumerated, however large `poolMaxSize`
+#'     is; increase `maxReplicates` to seed more islands.
 #' }
 #' By default (`collapse = TRUE`), zero-length (unsupported) branches are
 #' contracted into polytomies and the returned set is deduplicated on the
@@ -387,11 +384,10 @@
 #'
 #' Implied weighting is supported natively: set `concavity` to a numeric
 #' value (e.g.\sspace{}10).
-#' Profile parsimony (`concavity = "profile"`) is supported natively:
-#' characters are simplified to binary (max 2 informative states),
-#' inapplicable tokens are treated as ambiguous, and per-character
-#' information profiles are used for scoring
-#' \insertCite{Faith2001}{TreeSearch}.
+#' Profile parsimony (`concavity = "profile"`) is supported natively.
+#' Inapplicable tokens are treated as ambiguous, and each character is scored
+#' by its information profile \insertCite{Faith2001}{TreeSearch}; see
+#' [`PrepareDataProfile()`] for how multi-state profiles are computed.
 #'
 #' @param dataset A phylogenetic data matrix of \pkg{phangorn} class
 #' \code{phyDat}, whose names correspond to the labels of any accompanying tree.
@@ -406,7 +402,7 @@
 #' @param concavity Determines the degree to which extra steps beyond the first
 #' are penalized.  Specify a numeric value to use implied weighting
 #' \insertCite{Goloboff1993}{TreeSearch}; `concavity` specifies _k_ in
-#'  _k_ / _e_ + _k_. A value of 10 is recommended;
+#'  _k_ / (_e_ + _k_). A value of 10 is recommended;
 #' TNT sets a default of 3, but this is too low in some circumstances
 #' \insertCite{Goloboff2018,Smith2019}{TreeSearch}.
 #' Better still explore the sensitivity of results under a range of
@@ -479,18 +475,20 @@
 #'       character patterns; `"default"` otherwise.}
 #'     \item{`"sprint"`}{Fast search: 3 ratchet cycles, no drift, minimal
 #'       sectorial. Good for small datasets or quick surveys.}
-#'     \item{`"default"`}{Balanced: 12 ratchet + sectorial + fusing.}
+#'     \item{`"default"`}{Balanced: 6 ratchet cycles, sectorial search and
+#'       fusing.}
 #'     \item{`"thorough"`}{Intensive: 20 ratchet cycles, adaptive
 #'       perturbation, extra sectorial rounds, drift (2 cycles) and 5 Wagner
 #'       starts, outer cycle loop. Best for datasets with 65-119 tips and 100+
 #'       character patterns; the drift cycles also recover equal-score trees on
 #'       TBR-disconnected islands that random restarts alone miss.}
-#'     \item{`"large"`}{Large-tree search (>=120 tips): reduced cycle
-#'       counts scaled for expensive per-replicate cost, no NNI
-#'       perturbation, single biased Wagner start (Goloboff 2014), larger
-#'       sector sizes, 1-cycle simulated annealing instead of drift
-#'       (linear cooling from T=20 to T=0 over 5 phases).  Empirically matches
-#'       or exceeds `"thorough"` at 180 tips across all time budgets.}
+#'     \item{`"large"`}{Large-tree search (>=120 tips): the `"thorough"`
+#'       settings with `maxReplicates` raised to 500 to suit the higher
+#'       per-replicate cost.}
+#'     \item{`"intensive"`}{Deprecated alias of `"thorough"`, retained for
+#'       backward compatibility.  The extra Wagner starts (5) that once
+#'       distinguished it are now folded into `"thorough"`, so the two are
+#'       identical.}
 #'     \item{`"none"`}{Use only the explicitly supplied parameter values.}
 #'   }
 #'   Presets stop on `targetHits` and the `perturbStopFactor` no-improvement

R/SearchControl.R

@@ -295,8 +295,7 @@ SearchControl <- function(
     # Ratchet
     # Default 12->6 (T-P5d, 2026-06-19): the ratchet was over-provisioned;
     # halving cycles saved 20-38% wall at zero quality loss on the mid-size EW
-    # benchmarks.  The `large` preset keeps 12 (deliberate large-tree tradeoff,
-    # T-179) and is unaffected by this formal-default change.
+    # benchmarks.  The `thorough` preset (which `large` now rebases on) sets 20.
     ratchetCycles = 6L,
     ratchetPerturbProb = 0.25,
     ratchetPerturbMode = 0L,

R/WideSample.R

@@ -85,7 +85,7 @@
 #'   Default `60`.
 #'
 #' @return A `multiPhylo` object of length `min(n, length(trees))` containing
-#' a uniform sample of `trees`.
+#' a topologically diverse (Max-Min) subset of `trees`.
 #' If `n == 1`, the single most central tree (the medoid) is returned.
 #' Attributes of the input (e.g. `score`, `hits_to_best`) are preserved.
 #'

R/data_manipulation.R

@@ -37,11 +37,12 @@
 #' Maddison & Slatkin (1991), falling back to a Monte Carlo approximation for
 #' large or complex characters.
 #' 
-#' Characters are simplified where necessary, with a warning:
-#' - inapplicable tokens will be replaced with the ambiguous token
-#'    (i.e. `-` \ifelse{html}{\out{→}}{\eqn{\rightarrow}{-->}} `?`);
-#' - Ambiguous tokens will be treated as fully ambiguous
-#'   (i.e. `{02}` \ifelse{html}{\out{→}}{\eqn{\rightarrow}{-->}} `?`)
+#' Characters are simplified where necessary:
+#' - inapplicable tokens are replaced with the ambiguous token
+#'    (i.e. `-` \ifelse{html}{\out{→}}{\eqn{\rightarrow}{-->}} `?`),
+#'    reported with a message;
+#' - ambiguous tokens are treated as fully ambiguous
+#'   (i.e. `{02}` \ifelse{html}{\out{→}}{\eqn{\rightarrow}{-->}} `?`).
 #' 
 #' @param dataset dataset of class \code{phyDat}
 #' @param approx Character string controlling how profile information amounts
@@ -63,9 +64,9 @@
 #'   - `informative`: logical specifying which characters contain any
 #'     phylogenetic information.
 #'   
-#'   - `bootstrap`: The character vector 
-#'     \code{c("info.amounts", "split.sizes")}, indicating attributes to sample
-#'      when bootstrapping the dataset (e.g. in Ratchet searches).
+#'   - `bootstrap`: A character vector naming the attributes to resample when
+#'     bootstrapping the dataset (e.g. in Ratchet searches); `PrepareDataProfile`
+#'     adds `"info.amounts"`.
 #'
 #' `PrepareDataIW` adds the attribute:
 #'