Provides a systematic framework for neural network–based model selection and forecasting using single hidden layer feed-forward networks. It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest.

Wraps 'dplyr' verbs (mutate, summarise, filter) to automatically
    capture variable metadata (type, source columns, categories, and source
    code), producing a codebook and eligibility tracking table with zero
    manual documentation. Works with both 'sparklyr' (tbl_spark) and local
    data frames. Adds big-data optimizations (caching, assume-unique
    counting, checkpointing) and a standardized report module with an
    eligibility flowchart, editable codebook export (HTML, DOCX, XLSX), and
    cross-sectional or longitudinal variable inspection. The eligibility
    flowchart follows the CONSORT statement (Schulz, Altman and Moher (2010)
    <doi:10.1136/bmj.c332>) and the reporting of observational cohort studies
    follows the STROBE recommendations (von Elm and others (2007)
    <doi:10.1371/journal.pmed.0040296>).

Automatically calculates cognostic groups for plot objects
    and list column plot objects.  Results are returned in a nested data
    frame.

Contains functions to implement automated covariate selection using methods described in the
             high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for
             for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score 
             matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for 
             confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates 
             in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in. 
             The functions in this package help to implement the three major steps of automated covariate selection as described by
             Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate 
             selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates(). 
             In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is 
             also available for running examples and also for further exploration. The original article where the algorithm is described 
             by Schneeweiss et.al. (2009) <doi:10.1097/EDE.0b013e3181a663cc> .