solve_ode

Solve ODE with Runge-Kutta method (RK4)

Various methods for targeted and semiparametric inference including
augmented inverse probability weighted (AIPW) estimators for missing data and
causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
variable importance and conditional average treatment effects (CATE)
(van der Laan (2006) <doi:10.2202/1557-4679.1008>),
estimators for risk differences and relative risks (Richardson et al. (2017)
<doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Klaus Holst

targeted

Targeted Inference

Klaus K. Holst

Andreas Nordland

solve_ode function

<dl><dt>ode_ptr</dt>
<dd>pointer (externalptr) to C++ function or an R function</dd>
<dt>input</dt>
<dd>Input matrix. 1st column specifies the time points</dd>
<dt>init</dt>
<dd>Initial conditions</dd>
<dt>par</dt>
<dd>Parameters defining the ODE (parsed to ode_ptr)</dd></dl>

Arguments

Author

Solve ODE — solve_ode

<dl>

<dt>ode_ptr</dt>
<dd>pointer (externalptr) to C++ function or an R function</dd>


<dt>input</dt>
<dd>Input matrix. 1st column specifies the time points</dd>


<dt>init</dt>
<dd>Initial conditions</dd>


<dt>par</dt>
<dd>Parameters defining the ODE (parsed to ode_ptr)</dd>

</dl>

solve_ode: Solve ODE

Description

Usage

Value

Arguments

Author

Details

See Also

Examples