Data sets from the book "Forecasting with exponential smoothing: the state space approach" by 
	Hyndman, Koehler, Ord and Snyder (Springer, 2008).

Package computes and displays tables with support for 'SPSS'-style 
        labels, multiple and nested banners, weights, multiple-response variables 
        and significance testing. There are facilities for nice output of tables 
        in 'knitr', 'Shiny', '*.xlsx' files, R and 'Jupyter' notebooks. Methods 
        for labelled variables add value labels support to base R functions and to 
        some functions from other packages. Additionally, the package brings 
        popular data transformation functions from 'SPSS' Statistics and 'Excel': 
        'RECODE', 'COUNT', 'COUNTIF', 'VLOOKUP' and etc. 
        These functions are very useful for data processing in marketing research 
        surveys. Package intended to help people to move data 
        processing from 'Excel' and 'SPSS' to R.

Experiences studies are an integral component of the actuarial 
    control cycle. Regardless of the decrement or policyholder behavior of 
    interest, the analyses conducted is often the same. Ultimately, this 
    package aims to reduce time spent writing the same code used for 
    different experience studies, therefore increasing the time for to uncover
    new insights inherit within the relevant experience.

Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.

Estimation for high conditional quantiles based on quantile regression.

A system for batch-marking data analysis to estimate survival probabilities, capture probabilities, and enumerate the population abundance for both marked and unmarked individuals. The estimation of only marked individuals can be achieved through the batchMarkOptim() function. Similarly, the combined marked and unmarked can be achieved through the batchMarkUnmarkOptim() function. The algorithm was also implemented for the hidden Markov model encapsulated in batchMarkUnmarkOptim() to estimate the abundance of both marked and unmarked individuals in the population. The package is based on the paper: "Hidden Markov Models for Extended Batch Data" of Cowen et al. (2017) <doi:10.1111/biom.12701>.