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Francisco Bischoff
16 Oct 2026
write_json()
and read_json() to avoid masking
base::write(). JSON round trips now preserve profile
vectors and the origin and partial
attributes.mstomp() and
mstomp_par()): Delegated computation to the
high-performance native C++ engine in
matrixprofiler::mstomp(), providing massive speedups
(~200x) and native multi-threading while removing legacy dependencies on
doSNOW and foreach.mode = "mpx" to tsmp() as a
first-class citizen algorithm delegating to
matrixprofiler::mpx().compute() now delegates
internally to tsmp(mode = "mpx") and pmp(),
preserving 100% compatibility with Matrix Profile Foundation unified API
(Python/Go) and standard MatrixProfile / PMP
S3 classes.mpdist() now delegates to
native C++ engine in matrixprofiler::mpdist(), delivering
major speedups and lower memory footprint to mpdist() and
find_snippet().fluss_cac() using tabulate(),
eliminating slow \(O(N)\) R loops.n_workers parameter to
find_snippet(), enabling multithreaded MPdist
computation.find_snippet() when time
series length is an exact multiple of snippet size.dist_profile(), compute(),
find_snippet() and pmp() with hardened
multi-worker validation against detectCores() == NA.::: calls in
dist_profile.R with public API calls to
matrixprofiler::mass().na_strategy = c("monolithic", "segmented", "segmented_native")
to tsmp() with end-to-end missing value protection for
discords and motifs.paa() and ipaa()) implemented and
maintained in pure R.audio dependency and audio completion
signals.NeedsCompilation: no) delegating heavy computations to
matrixprofiler.mpx().find_snippet() that uses MPdist to show
representative data.mpx() algorithm that doesn’t depends on FFT.pmp() pan-matrix profile.compute(), analyze() and
visualize(). Starting point in the unified API from
MPF.mass() to dist_profile(),
including options to different algorithms.dist_profile()
algorithms.dist_profile() allows Query with Gap (QwG) and
Approximate Distance Profile (ADP, with PAA)stompi_update() that updates the matrix profile
allowing real-time computation.floss() which can do real-time FLUSS
computation.[ for tsmp
objects.tail() and head() for
tsmp objects.find_motif() for
MultiMatrixProfile to report Motifs correctly.find_discord() and its print() and
plot() functions.plot() for motifs to show where are the
neighbors. Same for discord.valmod() for Variable Length Motif
Discovery.find_motif() for compatibility with
valmod().find_chains() not returning the longest chain.
Issue #33vars. Issue #23progress
package.tsmp()
output for example.%>% (pipe),
e.g. tsmp() %>% find_motif(). Except for SDTS that has a
proper way to work.tsmp() that handles the
several algorithms available.as.* functions to allow you to switch classes if
you want, e.g.: as.matrixprofile().as.* functions).beepr to audio
(actually beepr depends on audio, so fewer
dependencies).NEWS.md file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.