nlme — Linear and Nonlinear Mixed-Effects Models in R. Fit hierarchical and repeated-measures data with lme() for linear models and nlme() for nonlinear models. Supports nested random effects, within-
Use with AI
Install the MCP server or CLI to instantly fetch nlme documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/nlme
cmprsk — Subdistribution Analysis of Competing Risks. R package providing non-parametric cumulative incidence estimation via cuminc() with Gray's K-sample test for group comparisons, Fine & Gray propo
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DAGitty — graphical analysis of causal models using directed acyclic graphs (DAGs). Create DAGs with dagitty(), identify minimal adjustment sets for unbiased causal effect estimation (adjustmentSets),
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glmmTMB — R package for fitting generalized linear mixed models (GLMMs) and extensions using Template Model Builder. Supports zero-inflation via ziformula, dispersion modeling via dispformula, and 15+
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rstpm2 — R package for generalized survival models (GSMs), smooth accelerated failure time (AFT) models, and Markov multi-state models. Flexible parametric survival analysis via stpm2() with natural s
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scipy.optimize — Unified interface for numerical optimization in Python. Provides local and global minimization (minimize, differential_evolution, dual_annealing), root finding (root, root_scalar), cu
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