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zH5On5F^Ds6hNZS~YX_MDXU1~nZ%h>X?=%w(i4=$b6Gs8K{yb`n$zLFUVGPEB=mjDg z$W=I?bn9RKq;5zrO{rU-tR10E$X~CvMtB7b-ow$VQ8loFGCZ)U49weKuO5(*N={YEgOqubj%ocv8xBw<8VN$g=puTyEYPY5j6!^=PDHd8GT} zNE#1QM7jUT$ofYjo6bZArtNx`hymCN+h7p3gMD)i!7%KE5!eNzup7o;4~)ZJ*a!RJ z033uvFaeWr7^dI|Ov4P=Kfz-#tMcGbyxpm e.g. in R, demo4\_1.R: - {\scriptsize -\begin{lstlisting}[] +\begin{minted}[fontsize=\footnotesize]{r} bioassayfun <- function(w, df) { z <- w[1] + w[2]*df$x -sum(df$y*(z) - df$n*log1p(exp(z))) @@ -459,8 +456,7 @@ optimres <- optim(w0, bioassayfun, gr=NULL, df1, hessian=T) thetahat <- optimres$par Sigma <- solve(optimres$hessian) -\end{lstlisting} - } +\end{minted} \end{itemize} \end{itemize} \end{frame} @@ -566,7 +562,7 @@ \begin{itemize} \item e.g. $\theta \in [0,1]$ presenting probability \item<+-> Stan code can include constraints\\ - \texttt{real theta;} + \rinline/real theta;/ \item<+-> for this, Stan does the inference in unconstrained space using logit transformation \item<+-> density of the transformed parameter needs to include @@ -817,12 +813,6 @@ fit1 <- brm(..., %\highlight{algorithm = "fullrank"}%) \end{minted} -% \begin{minted}[fontsize=\footnotesize]{r} -% fit1 <- brm(..., algorithm = "laplace") - -% fit1 <- brm(..., algorithm = "pathfinder") -% \end{minted} - \end{frame} \end{document}