No packages match

dataProfilerR - Automated Exploratory Data Analysis and Dataset Profiling

Profiles a data frame with minimal input: column type inference, missing-value analysis, distributional summary statistics (including skewness and kurtosis), normality tests, outlier detection, correlation and categorical-association analysis, date-column profiling, grouped comparisons and an overall data-quality score, alongside a set of 'ggplot2' visualisations. A single entry point, profile_data(), returns a structured S3 object holding metadata, statistics, diagnostics and plots, with print(), summary() and plot() methods, and report() renders the whole profile to a self-contained HTML file. Statistical methods include the Shapiro-Wilk normality test as implemented by Royston (1995) <doi:10.2307/2986146> and the Anderson-Darling test following Stephens (1974) <doi:10.1080/01621459.1974.10480196>, with power comparisons of these tests in Yap and Sim (2011) <doi:10.1080/00949655.2010.520163>, and the categorical association measure of Cramer (1946, ISBN:9780691080048).

Last updated

4.18 score

GSbench - Benchmarking Genomic Selection and Machine-Learning Prediction Models

A unified interface to fit, cross-validate and benchmark genomic prediction models from SNP marker data. It implements genomic best linear unbiased prediction (GBLUP) and ridge-regression BLUP in base R, and offers a common interface to machine-learning predictors (elastic net, random forest and gradient boosting) through optional packages, together with a stacked ensemble. Cross-validation uses breeding-relevant schemes and reports prediction accuracy honestly, so models can be compared fairly. The genomic relationship matrix follows VanRaden (2008) <doi:10.3168/jds.2007-0980>; the mixed-model solver follows Endelman (2011) <doi:10.3835/plantgenome2011.08.0024>; the genomic-selection framework follows Meuwissen, Hayes and Goddard (2001) <doi:10.1093/genetics/157.4.1819>.

Last updated

2.70 score