Includes the ga.lts() function that estimates
        LTS (Least Trimmed Squares) parameters using genetic algorithms
        and C-steps. ga.lts() constructs a genetic algorithm to form a
        basic subset and iterates C-steps as defined in Rousseeuw and
        van-Driessen (2006) to calculate the cost value of the LTS
        criterion. OLS (Ordinary Least Squares) regression is known to
        be sensitive to outliers. A single outlying observation can
        change the values of estimated parameters. LTS is a resistant
        estimator even the number of outliers is up to half of the
        data. This package is for estimating the LTS parameters with
        lower bias and variance in a reasonable time. Version >=1.3 
        includes the function medmad for fast outlier detection in
        linear regression.

An R interface to the Galvanize 'Highbond' API <https://docs-apis.highbond.com>.

Functions for fitting and working with generalized
		additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).

Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of gam()in 'mgcv' package, applying the algorithm in this paper: Lai(2022) <doi:10.1093/jpe/rtac096>.

The full form is Garai and Mantri Biological Material Diversity. It is an R package designed for the calculation of biological diversity using sequence data. It simplifies the process by requiring only sample IDs and accession numbers. Whether you're analyzing genetic or microbial diversity, It provides efficient tools for diversity analysis. Serially one should go for the functions as presented here expand_accession_ranges(), get_sequence_information(), preprocess_for_alignment(), write_fasta(), SampleID_vs_NumSequences(), data_sampling(), alignment_info(), compute_average_similarity_matrix(), generate_heatmaps(), clustering_average_similarity(), clustering_percent_similarity(), bubble_plot_count(), bubble_plot_percentage(), tree_average_similarity(), tree_percent_similarity(). Till date there are total 15 functions. More details can be found in Faith (1992) <doi:10.1016/0006-3207(92)91201-3>.