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().