NoLimits.jl provides a macro-based composable framework for nonlinear mixed-effects models supporting ODEs, neural networks, flexible distributions, and unified frequentist/Bayesian inference in Julia.
, author MacDonald, I.L
4 Pith papers cite this work. Polarity classification is still indexing.
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Tropical Viterbi tubes are defined as superlevel sets of complete-data log-scores around the Viterbi optimum in HMMs, computed exactly via max-plus recursions and illustrated on bat-tracking data.
An HSMM integrated with discrete-time survival analysis is applied to four years of Shanghai metro smart card data to identify five mobility states, directional transitions, and state-dependent exit/re-entry hazards.
libhmm is a C++20 library implementing correct MLE emission M-steps for 16 distributions in HMMs via Baum-Welch, with log-space forward-backward/Viterbi and SIMD acceleration.
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NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
NoLimits.jl provides a macro-based composable framework for nonlinear mixed-effects models supporting ODEs, neural networks, flexible distributions, and unified frequentist/Bayesian inference in Julia.