Nestimate 0.4.3
CRAN
resubmission (addresses incoming-check NOTEs on 0.4.2)
- Tarball now ships
build/vignette.rds (the vignette
index). Previous 0.4.2 build used
R CMD build --no-build-vignettes, which preserved pre-built
inst/doc/*.html but stripped the index — CRAN flagged
“VignetteBuilder field but no prebuilt vignette index.”
test-gimme.R now skip_on_cran(). GIMME
tests fit a lavaan SEM per subject and took ~50s locally (2-3× on
Windows), pushing total check time to 11 min on win-devel. Full test
suite still runs in CI and local dev.
Nestimate 0.4.2
CRAN resubmission
- Full
--as-cran --run-donttest audit pass.
- Purged stale
.Rcheck/ and Meta/ build
artifacts from working tree; added explicit
^Nestimate\.Rcheck$ and ^\.\.Rcheck$ entries
to .Rbuildignore as belt-and-suspenders against
repeat-submission contamination.
Nestimate 0.4.1
CRAN resubmission
- Pre-built vignettes included in
inst/doc/ as required
by CRAN.
- Fixed 301-redirect URLs in README.
- Added
skip_on_cran() to slow test block to keep check
time under 10 minutes.
Nestimate 0.4.0
New functions
build_mlvar() — multilevel VAR networks from ESM/EMA
panel data. Estimates temporal (directed), contemporaneous (undirected),
and between-subjects (undirected) networks matching
mlVAR::mlVAR() at machine precision.
build_mmm() / compare_mmm() — mixture of
Markov models via EM, with BIC/AIC/ICL model selection and optional
covariate regression.
cooccurrence() — standalone co-occurrence network
builder supporting 6 input formats and 8 similarity methods.
sequence_compare() — k-gram pattern comparison across
groups with optional permutation testing.
sequence_plot() / distribution_plot() —
base-R sequence index and state distribution plots with clustering
integration.
build_simplicial(), persistent_homology(),
q_analysis() — topological analysis of networks via
simplicial complexes.
nct() — Network Comparison Test matching
NetworkComparisonTest::NCT() at machine precision.
build_gimme() — group iterative mean estimation for
idiographic networks via lavaan.
passage_time(), markov_stability() —
Markov chain passage times and stability analysis.
predict_links() / evaluate_links() — link
prediction with 6 structural similarity methods.
association_rules() — Apriori association rule mining
from sequences or binary matrices.
predictability() — node predictability for
glasso/pcor/cor networks.
build_hon(), build_honem(),
build_hypa(), build_mogen() — higher-order
network methods (HON, HONEM, HYPA, MOGen) now
cograph_network-compatible.
New datasets
human_long, ai_long — canonical
long-format human–AI pair programming interaction sequences (10,796
turns, 429 sessions).
chatgpt_srl — ChatGPT-generated SRL scale scores for
psychological network analysis.
trajectories — 138-student engagement trajectory matrix
(15 timepoints, 3 states).
API
build_clusters(), network_reliability(),
permutation(), and prepare() replace earlier
internal names for consistency with the build_* naming
convention.
mgm estimator added (method = "mgm") for
mixed continuous + categorical data via nodewise lasso, matching
mgm::mgm() at machine precision.
Bug fixes
build_mmm() no longer crashes on platforms where
parallel::detectCores() returns NA (macOS
ARM64 CRAN check failure).
gimme convergence filter now correctly handles all
typed NA variants (NA_character_,
NA_real_, etc.).
NaN values in numeric metadata aggregation
(all-NA sessions) normalized to NA_real_.
- HYPA p-values corrected;
hypa_score column renamed to
p_value.
CRAN compliance
.data pronoun added to
globalVariables().
base::.rowSums() / base::.colSums()
replaced with rowSums() / colSums().
dev.new() guarded by interactive() — no
side effects under knitr or CI.
- Equivalence test files excluded from the built tarball.
do.call(rbind, ...) replaced with
data.table::rbindlist() in mcml.R and
sequence_compare.R.
Nestimate 0.3.4
- HYPA: Renamed
hypa_score column to p_value
for clarity. Added $over, $under,
$n_over, $n_under fields to
net_hypa objects. Scores are now pre-sorted with anomalous
paths first.
- HYPA:
summary.net_hypa() now shows
over/under-represented paths separately with a configurable
n parameter.
pathways.netobject(): New S3 method to extract
higher-order pathways directly from a netobject (builds HON or HYPA
internally).
path_counts(): Now handles NAs in trajectories by
stripping them before k-gram counting.
Nestimate 0.2.15
- Preparing for publication
Nestimate 0.2.0
- Reduced hard dependencies from 6 to 4 Imports (ggplot2, glasso,
data.table, cluster).
- Removed igraph from Imports —
centrality_stability()
and boot_glasso() now accept a centrality_fn
parameter for external centrality computation.
- Removed tna from Imports — moved to Suggests (only used for input
class detection).
- Implemented
graphical_var() from scratch using
coordinate descent lasso + graphical lasso with EBIC model selection,
eliminating the graphicalVAR dependency.
- Dropped
ml_graphical_var() — users should use
mlvar() for multilevel VAR.
- Removed cograph plot wrappers —
plot.netobject(),
plot.net_bootstrap(), plot.net_permutation(),
plot.net_hon(), plot.net_hypa() and
as_cograph() removed. Users call cograph plotting functions
directly on netobjects.
- Added
attention estimator for decay-weighted transition
networks.
- Increased test coverage from 84.5% to 96.1% (2780 tests).
- Passes R CMD check with 0 errors, 0 warnings, 0 notes.
Nestimate 0.1.0
- Initial release. Split from Saqrlab v0.3.0.
- Core estimation via
build_network() with 8 built-in
estimators.
- Bootstrap inference (
bootstrap_network()), permutation
testing (permutation()), EBICglasso bootstrap
(boot_glasso()).
- Higher-order networks: HON, HONEM, HYPA, MOGen.
- GIMME, MCML, multilevel VAR, graphical VAR.
- Temporal network analysis and velocity TNA.
- Dual-class
c("netobject", "cograph_network") output for
cograph compatibility.