Two-way and nested clustering via
cluster = c("g1","g2") and
nested = TRUE/FALSE, generating (1|g1/g2) or
(1|g1) + (1|g2) random-effects structures in
lme4.
Automatic knot / df selection
(df = "auto" in nl_fit() or via
nl_knots()) using AIC or BIC over a user-specified
grid.
Multilevel R-squared decomposition
(nl_r2()): Nakagawa-Schielzeth marginal R2m and conditional
R2c, plus a level-specific variance partition table (r2_mlm style) for
LMM, GLMM, and single-level OLS / GAM models.
Full postestimation suite:
nl_derivatives() — first and second derivatives with
delta-method confidence bands.nl_turning_points() — local maxima, minima, inflection
regions, and slope-direction regions.nl_plot() gains type = "slope",
"curvature", and "combo" in addition to the
original "trajectory".Built-in model comparison workflow
(nl_compare()): contrasts linear, polynomial, and spline
fits by AIC, BIC, log-likelihood, and likelihood-ratio tests.
B-spline basis (method = "bs",
bs_degree argument).
Random spline slopes
(random_slope = TRUE) to allow the nonlinear effect to vary
across clusters.
Cluster heterogeneity analysis
(nl_het()): plots cluster-specific trajectories (BLUPs) and
performs an LRT comparing random-slope vs random-intercept
models.
CI for glmerMod: approximate
confidence intervals via the delta method on the link scale (default,
fast) or parametric bootstrap (glmer_ci = "boot").
None. All v0.1.0 calls remain valid.
nl_r2() variance partition now correctly excludes NA
entries that could appear when lme4 internal row names are
ambiguous in nested models.nl_predict() now correctly computes CI when control
variables are stored as character (not factor) in the original
data.nl_plot() no longer errors when
time = NULL and the data frame has no time column.%||% is now imported from rlang rather
than defined internally, avoiding namespace masking.reformulas moved from Imports to Suggests (used
opportunistically for nobars(); falls back to
lme4::nobars() if unavailable).nl_fit(), nl_predict(),
nl_plot(), nl_summary(),
nl_icc().