API
The API describes chi’s classes and functions. Below is an aphabetically ordered list of all classes and functions. The menu bar can be used to explore chi’s functionality in a more structured way.
Table of Contents
Summary of all functions and classes in chi
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A base class for predictive models whose parameters are drawn from a distribution. |
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Computes the pointwise log-likelihood for each observation and each parameter sample from the posterior distribution. |
An error model which assumes that the model error is a mixture between a Gaussian base-level noise and a Gaussian heteroscedastic noise. |
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A base class for covariate models. |
A base class for error models for the one-dimensional output of |
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A collection of Erlotinib PKPD datasets. |
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Contains references to pharmacokinetic and pharmacodynamic models in SBML file format. |
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A linear covariate model. |
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A log-likelihood that quantifies the likelihood of parameter values to capture the measurements within the model approximation of the data-generating process. |
An error model which assumes that the model error follows a Log-normal distribution. |
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A population model which models parameters across individuals with a lognormal distribution. |
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A log-posterior constructed from a log-likelihood and a log-prior. |
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A population model which imposes no relationship on the model parameters across individuals. |
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A hierarchical log-likelihood consists of structurally identical log-likelihoods whose parameters are governed by a population model. |
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A hierarchical log-posterior is defined by a hierarchical log-likelihood and a log-prior for the population (or top-level) parameters. |
An error model which assumes that the model error follows a Gaussian distribution. |
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A population model which models parameters across individuals with a Gaussian distribution. |
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A base class for inference controllers. |
A base class for time series models of the form |
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An error model which assumes that the model error is a Gaussian heteroscedastic noise. |
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Sets up an optimisation routine that attempts to find the parameter values that maximise a |
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A model that is defined by the probabilistic average of posterior predictive models. |
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Instantiates a PKPD model from a SBML specification. |
A figure class that visualises the marginal posterior probability for each parameter across individuals. |
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A figure class that visualises parameter maximum a posteriori probability estimates across multiple optimisation runs. |
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A figure class that visualises measurements of a pharmacodynamic observables across multiple individuals. |
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A figure class that visualises measurements of a pharmacokinetic observable across multiple individuals. |
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A figure class that visualises the predictions of a predictive pharmacodynamic model. |
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A figure class that visualises the predictions of a predictive pharmacokinetic model. |
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A figure class that visualises the residual error between the predictions of a predictive model and measured observations. |
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A population model which pools the model parameters across individuals. |
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A base class for population models. |
Implements a model of a data-generating process. |
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A class that can be used to permanently fix model parameters of an |
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Implements the posterior predictive model of the modelled data-generating process and the associated parameter posterior distribution. |
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Implements a model of a data-generating process. |
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Implements a model that predicts the change of observable biomarkers over time based on the provided distribution of model parameters prior to the inference. |
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A problem modelling controller which simplifies the model building process of a pharmacokinetic and pharmacodynamic problem. |
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A wrapper class for a |
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A class that can be used to permanently fix model parameters of a |
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Sets up a sampling routine that attempts to find the posterior distribution of parameters defined by a |
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Instantiates a mechanistic model from a SBML specification. |
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A population model which models model parameters across individuals as Gaussian random variables which are truncated at zero. |