beautils 0.3.0

beautils 0.2.0

New Features

Statistical Analysis & Inference

• Added conf_int() to calculate confidence intervals for the mean with or without grouping variables. Computes sample size, mean, standard deviation, standard error, degrees of freedom, calculated t-statistic, tabulated/critical t-value, and confidence limits (LI and LS), with optional elegant horizontal confidence interval plots (forest plots).
• Added t_test() to perform independent samples t-tests for all pairwise combinations with automated significance brackets, descriptive statistics, and boxplot/violin visualizations.
• Added t_test_paired() to perform paired samples t-tests with sequential pairing verification and paired line visualizations.

Experimental Planning & Designs

• Added square_lattice(): Generates Square Lattice designs (\(t = k^2\)) with fieldbook creation, UUID tagging, and visual field maps with incomplete block and replicate delimitation.
• Added rectangular_lattice(): Generates Rectangular Lattice designs for resolvable incomplete block experiments.
• Added prep_design(): Generates partially replicated (p-rep) designs for trials with unreplicated, partially replicated, and high-replication check entries.

Utilities

• Added uuid_random() and uuid_deterministic() for generating random (v4) and reproducible deterministic (v5) UUIDs.

Bug Fixes & Minor Improvements

• Ensured 100% ASCII compliance across all R source files and documentation.
• Registered all global variables to ensure clean R CMD check without variable binding notes.
• Added explicit imports for stats::complete.cases and utils::combn.
• Updated test suite in tests/testthat/ with 48 passing tests covering all new features and edge cases.

beautils 0.1.0

• Initial release of beautils, a package providing a collection of utility functions for biostatistics, agricultural trial planning, and experimental design.

Features

Experimental Planning & Designs

• Added functions to generate experimental designs:
  • alpha_lattice(): Generates Alpha-Lattice designs.
  • augmented(): Generates Augmented Block designs.
  • full_factorial(): Generates Full Factorial designs.
  • latin_square(): Generates Latin Square designs.
  • split_plot(): Generates Split-Plot designs.
  • strip_plot(): Generates Strip-Plot designs.
  • unifatorial(): Generates Randomized Complete Block (RCBD) or Completely Randomized (CRD) designs.

Biostatistics & Summary Tools

• Added core statistical tools:
  • hmean(): Calculates the harmonic mean.
  • gmean(): Calculates the geometric mean.
  • kurt() and skew(): Measure kurtosis and skewness.
  • ci_mean_t() and ci_mean_z(): Calculate confidence intervals using Student’s t or standard normal distributions.
  • sem(): Computes the standard error of the mean.
  • pseudo_sigma(): Computes pseudo-sigma for robust scale estimation.
  • Deviation helpers: av_dev(), ave_dev(), sum_dev(), sum_sq(), sum_sq_dev().
• Added by-group summary helpers: mean_by(), sd_by(), var_by(), sum_by(), min_by(), max_by(), n_by(), sem_by(), and cv_by().
• Added frequency distribution tools:
  • freq_table(): Generates frequency distribution tables.
  • freq_hist(): Plots frequency histograms.

Labeling & QR Codes

• Added labeling utility functions:
  • create_label(): Generates print-ready labels for experimental units.
  • labelize(): Formats variables to display nicely on labels.
  • make_qrcode(): Generates QR codes.