Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).

Nonparametric maximum likelihood estimation or Gaussian
        quadrature for overdispersed generalized linear models and
        variance component models.

Fit multinomial logistic regression with a penalty on the nuclear
    norm of the estimated regression coefficient matrix, using proximal
    gradient descent.

Performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks' Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable.  The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.

Posterior sampling in several commonly used distributions using
    normalized power prior as described in 
    Duan, Ye and Smith (2006) <doi:10.1002/env.752> and 
    Ibrahim et.al. (2015) <doi:10.1002/sim.6728>. 
    Sampling of the power parameter is achieved via 
    either independence Metropolis-Hastings or random walk Metropolis-Hastings 
    based on transformation.

Calculates phenological cycle and anomalies using a non-parametric
    approach applied to time series of vegetation indices derived from remote sensing data 
    or field measurements. The package implements basic and high-level functions for 
    manipulating vector data (numerical series) and raster data (satellite derived products).
    Processing of very large raster files is supported. For more information, please check 
    the following paper:
    Chávez et al. (2023) <doi:10.3390/rs15010073>.