Few format related things

05-origami.Rmd

@@ -7,20 +7,32 @@ by _Jeremy Coyle, Nima Hejazi, Ivana Malenica and Rachael Phillips_.
 
 Updated: `r Sys.Date()`
 
-## Learning Objectives
+`r if (knitr::is_latex_output()) '\\begin{VT1}\n\\VH{Learning Objectives}'`
+
+`r if (knitr::is_html_output()) '## Learning Objectives{-}'`
+
+By the end of this chapter you will be able to:
 
 1. Differentiate between training, validation and test sets.
+
 2. Understand the concept of a loss function, risk and cross-validation.
+
 3. Select a loss function that is appropriate for the functional parameter to be
    estimated.
+
 4. Understand and contrast different cross-validation schemes for i.i.d. data.
+
 5. Understand and contrast different cross-validation schemes for time dependent
    data.
+
 6. Setup the proper fold structure, build custom fold-based function, and
    cross-validate the proposed function using the `origami` `R` package.
+
 7. Setup the proper cross-validation structure for the use by the Super Learner
    using the the `origami` `R` package.
 
+`r if (knitr::is_latex_output()) '\\end{VT1}'`
+
 ## Introduction
 
 In this chapter, we start elaborating on the estimation step outlined in the
@@ -38,14 +50,18 @@ guides the choice of the final learning method, and it gives us a quantitative
 assessment of how good the chosen algorithm is doing. In order to assess the
 performance of an algorithm, we introduce the concept of a **loss** function,
 which helps us define the **risk**, also referred to as the **expected
-prediction error**.  Our goal, as further specified in the next chapter, will be
+prediction error**.  
+
+`r if (knitr::is_latex_output()) '\\begin{shortbox}\n\\Boxhead{Constructing a library that is consistent with the data-generating distribution}'`
+Our goal, as further specified in the next chapter, will be
 to estimate the true risk of the proposed statistical learning method. Our
 goal(s) consist of:
 
 1. Estimating the performance of different algorithms in order to choose the
    best one.
 2. Having chosen a winner, estimate the true risk of the proposed
    statistical learning method.
+`r if (knitr::is_latex_output()) '\\end{shortbox}'`
 
 In the following, we propose a method to do so using the observed data and
 **cross-validation** procedure using the `origami` package [@coyle2018origami].