Skip to contents

Changelog

Source: NEWS.md

emaxnls 0.1.1.9000

Bug fixes

  • simulate() and confint(simultaneous = TRUE) / summary(simultaneous = TRUE) now degrade gracefully on platforms where the mvtnorm shared object is installed but fails to link at runtime (observed on some clang-based Rhub builders). A warning is issued and the computation continues using base-R fallbacks: Cholesky-based multivariate normal sampling for simulate(), and a Bonferroni-corrected normal quantile for simultaneous confidence intervals. The fallback intervals are conservative but valid (#52).

  • The tibble package is now listed under Suggests rather than Imports, making it a genuine optional dependency. All package functionality works with or without tibble installed (#24).

New features

  • Adds a max_time argument to emax_nls_options() and emax_logistic_options() that sets a maximum elapsed time (in seconds) for model fitting. If the optimizer has not converged within the limit it is terminated and the model is treated as non-converged, consistent with any other convergence failure. Defaults to Inf (no limit). This is particularly useful when running many models in an SCM procedure, where a single pathological fit can otherwise stall the entire covariate search (#16).

  • Adds emax_logistic() for fitting binary-outcome Emax models using iterative reweighted least squares (IRLS), along with emax_logistic_init() and emax_logistic_options() for initialization and configuration. All standard S3 methods (coef(), vcov(), confint(), residuals(), fitted(), predict(), anova(), logLik(), AIC(), BIC(), deviance(), simulate()) are supported for the new emaxlogistic class.

  • Redesigns print() and summary() methods for emaxnls and emaxlogistic objects:

    • print() is now a concise model overview showing structural and covariate formulas, fit statistics (observations, residual df, sigma or deviance, AIC), and a coefficient table with estimates, standard errors, and confidence intervals. Hypothesis tests are not shown by print(); a footer line directs users to summary(). A conf_level argument controls the interval level (default 0.95).

    • summary() gains three new arguments:

      • suppress_nonsensical = TRUE: suppresses the test statistic and p-value for logEC50_Intercept by default. The logEC50 intercept is estimated on the log-concentration scale, so testing H0: logEC50 = 0 corresponds to testing EC50 = 1 on the concentration scale — a threshold with no pharmacometric meaning. The confidence interval for logEC50 is always reported. Pass suppress_nonsensical = FALSE to restore the raw test.
      • p_adjust = "none": adjusts non-NA p-values via p.adjust() for multiple-comparison correction; suppressed p-values are excluded from the adjustment set.
      • simultaneous = FALSE: when TRUE, computes simultaneous Wald confidence intervals using mvtnorm::qmvnorm() on the correlation matrix from vcov(), giving intervals with joint coverage at conf_level across all parameters simultaneously.

    • summary(back_transform = TRUE) now also sets the test statistic to NA for back-transformed parameters, consistent with the standard error already being NA on the back-transformed scale.

  • confint() now falls back to Wald intervals with a warning if profile likelihood computation fails (which can occur for sigmoidal models).

Documentation

  • Expands the summary() documentation to explain the relationship between the p_adjust and simultaneous arguments. The two are independent tools for multiplicity — p_adjust corrects the hypothesis-test p-values, while simultaneous widens the confidence intervals — and they use different machinery, so their reject/retain decisions need not agree. The new “Multiplicity: p-value adjustment versus simultaneous intervals” section spells out what each argument changes, why the adjusted p-values and the simultaneous intervals may disagree, and which tool to reach for (#47).

Bug fixes

  • Fixes AIC() and BIC() when called with multiple model arguments (#37).

  • Fixes na.action parameter not being passed through correctly in emax_nls() and emax_logistic() (#38).

  • Fixes predict() omitting residual.scale from the return value when se.fit = TRUE (#39).

  • Fixes crashes in emax_logistic_init() and prevents Inf parameter bounds arising during initialization (#40).

  • Hardens .nls_call() to avoid cryptic errors when optimization fails, and tightens argument validation in emax_fun() (#41).

  • Adds input validation for the binary response variable in emax_logistic(), with informative errors for non-binary or out-of-range values (#42).

  • Fixes validator error messages to reference public API parameter names rather than internal names (#43).

  • Fixes NaN produced by .binomial_deviance() when predicted probabilities are exactly 0 or 1 (boundary cases) (#44).

  • confint() now accepts a simultaneous argument, mirroring summary(). Previously confint(object, simultaneous = TRUE) silently ignored the argument (swallowed by ...) and returned pointwise intervals. Setting simultaneous = TRUE now returns simultaneous (joint) Wald intervals that match those reported by summary(object, simultaneous = TRUE) (#46).

emaxnls 0.1.1

CRAN release: 2026-06-30

  • Expanded description of package.
  • Fixes bug when emax_nls_init() is called manually.
  • anova() now warns rather than errors when fewer than two converged models are supplied.
  • Additional examples in documentation.
  • Improved unit tests.

emaxnls 0.1.0

Initial CRAN submission. The package provides tools for fitting and analyzing Emax dose-response models via nonlinear least squares.

Model fitting

  • emax_nls() fits continuous-response Emax models using NLS, supporting both the hyperbolic (E0 + Emax * x / (EC50 + x)) and sigmoidal (E0 + Emax * x^Hill / (EC50^Hill + x^Hill)) model forms.

  • emax_nls_options() configures the optimization algorithm and control parameters. Three algorithms are supported via the optim_method argument: "gauss" (Gauss-Newton, default), "port" (bounded nl2sol), and "levenberg" (Levenberg-Marquardt via minpack.lm).

  • emax_nls_init() generates starting values and parameter bounds automatically from the data, including support for categorical covariates. Users can also call it directly to inspect or override the initialization before fitting.

Covariate modeling

  • Covariates can be added to any structural parameter (E0, Emax, logEC50, logHill) via a formula interface, e.g. emax_nls(rsp ~ dose, data = df, E0 = ~ group + age).

  • emax_add_term() and emax_remove_term() update a fitted model by adding or removing a single covariate term without refitting from scratch.

Stepwise covariate modeling (SCM)

  • emax_scm_forward() performs forward covariate selection, adding one term at a time while the likelihood-ratio test p-value stays below a threshold.

  • emax_scm_backward() performs backward elimination, removing terms while the p-value exceeds a threshold.

  • emax_scm_history() returns the sequence of models tested during an SCM procedure, including AIC, BIC, and convergence status for each candidate.

S3 methods

  • print() displays a brief model summary.

  • summary() produces a coefficient table with standard errors, confidence intervals, and hypothesis tests. A back_transform = TRUE argument re-expresses log-scaled parameters (logEC50, logHill) on their natural scales (EC50, Hill) in the output.

  • coef() extracts the parameter vector; supports back_transform = TRUE.

  • vcov() returns the estimated variance-covariance matrix.

  • confint() computes profile-likelihood confidence intervals.

  • residuals() and fitted() return model residuals and fitted values.

  • predict() generates predictions, optionally with standard errors.

  • anova() compares nested models by likelihood-ratio test.

  • logLik(), AIC(), BIC(), sigma(), nobs(), and df.residual() return standard model-fit statistics.

  • simulate() draws Monte Carlo replicates by resampling parameters from the asymptotic normal distribution of the estimates (requires mvtnorm).

  • emax_fun() extracts a standalone prediction function from a fitted model that can be evaluated at arbitrary dose values and parameter vectors.

  • emax_converged() returns TRUE or FALSE indicating whether the optimizer converged. All S3 methods handle non-convergent models gracefully by returning an emaxnls_null object rather than erroring.

Data

  • emax_df: a synthetic dataset of 400 observations across four dose groups (0, 100, 200, 300), with a continuous response, a binary response, two exposure metrics, continuous and binary covariates, and a categorical covariate.