Imputation of missing data: Recursive 1D discrete KNN algorithm

Any generated data is often have missing components or values. Probably, the most common occurrence manifest in time series data where there is no value available on the given time point, hence a NaN is placed in general (or NA in R). There is a large literature on how a statistical analysis must be performed in such data sets. For example, a seminal book by Little & Rubin called Statistical Analysis With Missing Data provides very detailed exposure to the field. Probably the simplest of all methods is called imputation. For example there are high quality R packages like imputation or mi that does the job for you. Similarly knnimpute from MATLAB bioinformatics toolbox provides similar solution.
  
k-nearest neighbour algorithm (KNN) is the most common approach to discover the closest available value in the data vector. However, often implementations of KNN contains a lot of options that are not needed for simple imputations in 1D and Euclidean metric. Here I propose 1D discrete KNN recursive algorithm that scans a given vector and determines the closest available value to given index). The main idea is assuming periodic boundary conditions for the vector index boundaries (Two-way linear search on the ring, see simple sketch). This is an $O(\bf{N})$ algorithm, we scan the vector twice in the worst case scenario. I have implemented this idea in MATLAB, for a given matrix. Files are available on the matworks file exchange [link]. This tool could be useful in imputing data in regression design matrices.