Identification of putative causal variants in genome-wide association studies using hybrid analysis of both the trio and population designs. The package implements the method in the paper: Yang, Y., Wang, Q., Wang, C., Buxbaum, J., & Ionita-Laza, I. (2024). KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies. The American Journal of Human Genetics, in press.

Identification of putative causal variants in genome-wide association studies with trio and duo families. The package calculates the W feature statistics from KnockoffTrio and p-values from the family-based association test (FBAT) using trio and/or duo data. Compared to previous versions, a significant improvement has been made in Version 1.1.0 to allow the package to be applied not only to trio families but also to duo families. The package implements the methods in the paper: "Yang, Y., Wang, C., Liu, L., Buxbaum, J., He, Z., & Ionita-Laza, I. (2022). KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design. The American Journal of Human Genetics, 109(10), 1761-1776."

Makes visually pleasing diagrams of knot projections using optimized Bezier curves.

It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.

Retrieve data from 'kintone' (<https://www.kintone.com/>) via its API.
    'kintone' is an enterprise application platform.

Wrapper for 'Kobotoolbox' APIs ver 2 mentioned at <https://support.kobotoolbox.org/api.html>, to download data from 'Kobotoolbox' to R. Small and simple package that adds immense convenience for the data professionals using 'Kobotoolbox'.

A self-guided, weakly supervised learning algorithm for feature extraction from noisy and 
  high-dimensional data. It facilitates the identification of patterns that reflect underlying group 
  structures across all samples in a dataset. The method incorporates a novel strategy to integrate 
  spatial information, improving the clarity of results in spatially resolved data.