Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer <http://dataexplorer.wittgensteincentre.org/>.

A flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.

Analyze given data frame with multiple endpoints and return Kaplan-Meier survival probabilities together with the specified confidence interval. See Nabipoor M, Westerhout CM, Rathwell S, and Bakal JA (2023) <doi:10.1186/s12874-023-01857-0>.

K-means clustering, hierarchical clustering, and PCA with observational 
    weights and/or variable weights. It also includes the corresponding functions 
    for data nuggets which serve as representative samples of large datasets.
    Cherasia et al., (2022) <doi:10.1007/978-3-031-22687-8_20>. 
    Amaratunga et al., (2009) <doi:10.1002/9780470317129>.

Retrieval the leaf area index (LAI) and soil moisture (SM) from  microwave backscattering data using water cloud model (WCM) model . The WCM algorithm attributed to Pervot et al.(1993) <doi:10.1016/0034-4257(93)90053-Z>. The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work.

Implements inferential and graphic procedures for the semiparametric proportional 
  means regression of weighted composite endpoint of recurrent event and death (Mao and Lin, 
  2016, <doi:10.1093/biostatistics/kxv050>).

Allows users to create weighted confusion matrices and accuracy
    metrics that help with the model selection process for classification
    problems, where distance from the correct category is important. The
    package includes several weighting schemes which can be parameterized, as
    well as custom configuration options. Furthermore, users can decide
    whether they wish to positively or negatively affect the accuracy score
    as a result of applying weights to the confusion matrix. Functions are
    included to calculate accuracy metrics for imbalanced data. Finally,
    'wconf' integrates well with the 'caret' package, but it can also work
    standalone when provided data in matrix form.
    References:
    Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package"
    <doi:10.18637/jss.v028.i05>
    Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization
    (and the mewto R package)" <doi:10.2139/ssrn.3805911>
    Monahov, A. (2024) "Improved Accuracy Metrics for Classification with
    Imbalanced Data and Where Distance from the Truth Matters, with the wconf R
    Package" <doi:10.2139/ssrn.4802336>
    Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced
    Data Classification Results. Pattern Recognition and Image Analysis, 295–302
    Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023)
    "A general framework for implementing distances for categorical variables"
    <doi:10.48550/arXiv.2301.02190>.