A fast reimplementation of several density-based algorithms of
    the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based 
    spatial clustering of applications with noise) and HDBSCAN (hierarchical 
    DBSCAN), the ordering algorithm OPTICS (ordering points to identify the 
    clustering structure), shared nearest neighbor clustering, and the outlier 
    detection algorithms LOF (local outlier factor) and GLOSH (global-local 
    outlier score from hierarchies). The implementations use the kd-tree data 
    structure (from library ANN) for faster k-nearest neighbor search. An R 
    interface to fast kNN and fixed-radius NN search is also provided. 
    Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.

Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.

First using 'dada2' R tools to analyse metabarcode data, the 'DBTC' package then uses the BLAST algorithm to search unknown sequences against local databases, and then takes reduced matched results and provides best taxonomic assignments.

Metabarcoding analysis using the 'DBTC' package is implemented here using 'shiny' in an interactive graphical user interface to conduct Metabarcode analyses and visualize and filter results.

Implements the doubly robust distribution balancing weighting proposed by Katsumata (2024) <doi:10.1017/psrm.2024.23>, which improves the augmented inverse probability weighting (AIPW) by estimating propensity scores with estimating equations suitable for the pre-specified parameter of interest (e.g., the average treatment effects or the average treatment effects on the treated) and estimating outcome models with the estimated inverse probability weights. It also implements the covariate balancing propensity score proposed by Imai and Ratkovic (2014) <doi:10.1111/rssb.12027> and the entropy balancing weighting proposed by Hainmueller (2012) <doi:10.1093/pan/mpr025>, both of which use covariate balancing conditions in propensity score estimation. The point estimate of the parameter of interest and its uncertainty as well as coefficients for propensity score estimation and outcome regression are produced using the M-estimation. The same functions can be used to estimate average outcomes in missing outcome cases.