Retrieves historical versions of clinical trial registry
    entries from <https://ClinicalTrials.gov>. Package functionality
    and implementation for v 1.0.0 is documented in Carlisle (2022)
    <DOI:10.1371/journal.pone.0270909>.

Estimation and inference methods for the continuous threshold expectile regression.
    It can fit the continuous threshold expectile regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted."

Identification and network inference of genetic loci associated
  with correlation changes in quantitative traits (called correlated trait loci, CTLs).
  Arends et al. (2016) <doi:10.21105/joss.00087>.

Implements clinical tolerance limits (CTL) methodology for assessing 
    agreement between two measurement methods. Estimates the true latent trait using 
    Best Linear Unbiased Predictors (BLUP), models bias and variance components, and 
    calculates overall and conditional agreement probabilities. Provides visualization 
    tools including tolerance limit plots and conditional probability of agreement plots 
    with confidence bands. 
    This package is based on methods described in Taffé (2016)
    <doi:10.1177/0962280216666667>, Taffé (2019) 
    <doi:10.1177/0962280219844535>, and 'Stata' package Taffé (2025) 
    <doi:10.1177/1536867X251365501>.

Estimation of Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) <doi:10.1111/1467-9965.00114> as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) <doi:10.1111/j.1467-9868.2005.00508.x> are included.

Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.