Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.

Spectral and Average Autocorrelation Zero Distance Density
    ('sazed') is a method for estimating the season length of a 
    seasonal time series. 'sazed' is aimed at practitioners, as it employs only 
    domain-agnostic preprocessing and does not depend on parameter tuning or 
    empirical constants. The computation of 'sazed' relies on the efficient 
    autocorrelation computation methods suggested by Thibauld Nion (2012, URL: 
    <https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by 
    Bob Carpenter (2012, URL: 
    <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).

Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.

Implementation of several recent multivariate bias correction
			 methods with a unified interface to facilitate their use. A
			 description and comparison between methods can be found
			 in <doi:10.5194/esd-11-537-2020>.

Fitting and plotting parametric or non-parametric size-biased 
    non-negative distributions, with optional covariates if parametric. 
    Rowcliffe, M. et al. (2016) <doi:10.1002/rse2.17>.

Offers Bayesian semiparametric density estimation 
             and tail-index estimation for heavy tailed data, by
             using a parametric, tail-respecting transformation
             of the data to the unit interval and then modeling
             the transformed data with a purely nonparametric
             logistic Gaussian process density prior. Based on 
             Tokdar et al. (2022) <doi:10.1080/01621459.2022.2104727>.

Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi: 10.1002/sim.8549>.