Estimate the lower and upper bound of asymptomatic cases in an 
    epidemic using the capture/recapture methods from Böhning et al. (2020) 
    <doi:10.1016/j.ijid.2020.06.009> and Rocchetti et al. (2020)
    <doi:10.1101/2020.07.14.20153445>. Note there is currently some discussion
    about the validity of the methods implemented in this package. You should
    read carefully the original articles, alongside this answer from Li et al.
    (2022) <doi:10.48550/arXiv.2209.11334> before using this package in your
    project.

Estimation of regression models for sparse asynchronous 
    longitudinal observations, where time-dependent response and covariates 
    are mismatched and observed intermittently within subjects. Kernel 
    weighted estimating equations are used for generalized linear models 
    with either time-invariant or time-dependent coefficients. Cao, H.,
    Li, J., and Fine, J. P. (2016) <doi:10.1214/16-EJS1141>. Cao, H., 
    Zeng, D., and Fine, J. P. (2015) <doi:10.1111/rssb.12086>.

Provides a collection of psychometric methods to process item metadata
 and use target assessment and measurement blueprint constraints to assemble a test form. Currently two automatic
 test assembly (ata) approaches are enabled. For example, the weighted (positive) deviations method, wdm(), proposed
 by Swanson and Stocking (1993) <doi:10.1177/014662169301700205> was implemented in its full specification allowing
 for both item selection as well as test form refinement. The linear constraint programming approach, atalp(), uses the 
 linear equation solver by Berkelaar et. al (2014) <http://lpsolve.sourceforge.net/5.5/>
 to enable a variety of approaches to select items.

Create Tables for Reporting Clinical Trials.
  Calculates descriptive statistics and hypothesis tests, 
  arranges the results in a table ready for reporting with LaTeX, HTML or Word.

The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>,
	Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the
	initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy
	implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend
	and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.