An interactive document on  the topic of classification tree analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/CTShiny/>.

Perform state and parameter inference, and forecasting, in
    stochastic state-space systems using the 'ctsmTMB' R6 class. This
    class provides a user-friendly interface for working with stochastic
    state space models. Inference is based on maximum likelihood
    estimation, with derivatives efficiently computed through automatic
    differentiation enabled by the 'TMB'/'RTMB' packages (Kristensen et
    al., 2016) <doi:10.18637/jss.v070.i05>. The available inference
    methods include Kalman filters, in addition to a Laplace
    approximation-based smoothing method. For further details of these
    methods refer to the documentation of the 'CTSMR' package
    <https://ctsm.info/ctsmr-reference.pdf> and Thygesen (2025)
    <doi:10.48550/arXiv.2503.21358>. Forecasting capabilities include
    moment predictions and stochastic path simulations implemented in
    'C++' using 'Rcpp' (Eddelbuettel et al., 2018)
    <doi:10.1080/00031305.2017.1375990> for computational efficiency.

A collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. Functions provide reliability analyses (alpha), item statistics, disctractor analyses, disattenuated correlations, scoring routines, and empirical ICCs.

Interactive shiny application for running classical test theory (item analysis).

Visualizes results of item analysis such as item difficulty, 
    item discrimination, and coefficient alpha for ease of result communication.

Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated
             packages (client-side tools), generating HTML pages and storing task view information in the repository
	     (server-side tools).