It predicts any attribute (categorical) given a set of input numeric predictor values. Note that only numeric input predictors should be given.
The k value can be chosen according to accuracies provided. The attribute to be predicted can be selected from the dropdown provided (select categorical attribute).
This is because categorical attributes cannot be given as inputs here. A 'handsontable' is also provided to enter the input predictor values.

Predicts any variable in any categorical dataset for given values of predictor variables.
If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user.
The user can upload their own datasets and select what variable they want to predict.
A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown.
shiny, rhandsontable, dplyr, caret, e1071,

Knowledge domain visualization using 'mpa' co-words method as the word clustering method and network graphs with 'D3.js' library as visualization tool.
htmlwidgets, igraph, mpa, networkD3, rvest, stringr, tm, xml2

Kernel-Smoothed Nonparametric Methods for Environmental Exposure
Data Subject to Detection Limits
Yuchen Yang <yuchen.y@uky.edu>, Li Chen <lichenuky@uky.edu>
Calculate the kernel-smoothed nonparametric estimator for the exposure distribution in presence of detection limits.

Expectile Regression in Reproducing Kernel Hilbert Space
An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.

Multivariate Kalman filter and smoother, simulation smoother and
forecasting
Fast multivariate Kalman filter, smoother, simulation
smoother and forecasting. Uses exact diffuse initialisation
when distributions of some or all elements of initial state
vector are unknown.

Package KFAS provides functions for fast Kalman filtering,
smoothing, forecasting and simulation of multivariate
time-variant state space models. All functions can use exact
diffuse initialisation when distributions of some or all
elements of initial state vector are unknown. Functions use
sequential processing algorithm, which is faster than standard
approach, and it also allows singularity of prediction error
variance matrix.