Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.

Provides automated downloading, parsing, cleaning, unit
    conversion and formatting of Global Surface Summary of the Day
    ('GSOD') weather data from the from the USA National Centers for
    Environmental Information ('NCEI').  Units are converted from from
    United States Customary System ('USCS') units to International System
    of Units ('SI').  Stations may be individually checked for number of
    missing days defined by the user, where stations with too many missing
    observations are omitted.  Only stations with valid reported latitude
    and longitude values are permitted in the final data.  Additional
    useful elements, saturation vapour pressure ('es'), actual vapour
    pressure ('ea') and relative humidity ('RH') are calculated from the
    original data using the improved August-Roche-Magnus approximation
    (Alduchov & Eskridge 1996) and included in the final data set.  The
    resulting metadata include station identification information,
    country, state, latitude, longitude, elevation, weather observations
    and associated flags.  For information on the 'GSOD' data from 'NCEI',
    please see the 'GSOD' 'readme.txt' file available from,
    <https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.

Proposes a new file format ('gson') for storing gene set and related information, and provides read, write and other utilities to process this file format.

Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017".