NEWS

---

lande 0.1.0 (2026-10-16)

First release.

• Selection differentials and linear, quadratic and correlational gradients on relative fitness, with p-values from the logistic model for survival and from a Poisson or negative binomial model for counts.
• Cubic spline fitness functions with a bootstrap band, two-trait fitness surfaces drawn only where there are data, and adaptive landscapes of mean fitness for one or two traits.
• count_family = "quasipoisson" or "nb" in univariate_spline(), correlated_fitness_surface() and temporal_landscape() for overdispersed counts; the spline and the surface report the dispersion.
• by_group = TRUE in the spline and the surface fits each group on its own and returns a list of fits.
• Several groups on one surface, with each group's mean and highest point marked, and interior peaks told from maxima at the edge of the data.
• Fitness functions and landscapes fitted period by period.
• Bootstrap standard errors and percentile intervals for the gradients, and leave-one-out (HC3) standard errors with se_type = "hc3".
• Assumption checks in one table: multivariate normality of the traits, VIF, rows per term, residuals or dispersion of the gradient model.
• Bumpus's sparrows, Martin's pupfish and two sets of Galapagos finches bundled as datasets, with CSV copies of the pupfish and finch data in extdata.
• A Shiny app that runs the analyses in a browser, opened with run_app(), which writes out the R calls that repeat an analysis with the current settings.
• canonical_analysis() rotates the gamma matrix to its canonical axes, with double-regression standard errors, permutation p-values (Reynolds et al. 2010) and an optional bootstrap, and plot_canonical_axes() draws fitness along them.
• Standard errors and a band on the GAM fitness surface, drawn with the plots' uncertainty option, and peak_difference() to compare two points of a surface, or each with the pass between them (route = "line" for the straight line).
• The adaptive landscape reports how much of each simulated population falls outside the data, and flags an optimum on the edge of the grid; the plot can mark where the population leaves the data.
• A clamp option on the landscape and the thin-plate surface holds predicted fitness inside the range of the fitness type; on by default.