From 'R' 4.5.0, the 'datasets' package includes the penguins and 
    penguins_raw data sets popularised in the 'palmerpenguins' package.
    'basepenguins' takes files that use the 'palmerpenguins' package and 
    converts them to work with the versions from 'datasets' ('R' >= 4.5.0). 
    It does this by removing calls to library(palmerpenguins) and making the 
    necessary changes to column names. Additionally, it provides helper 
    functions to define new files paths for saving the output and a directory
    of example files to experiment with.

Primarily created as an easy and understanding way to do basic sequences surrounding the central dogma of molecular biology.

Download typicality rating datasets, generate new stereotype-based typicality ratings using large language models via the Inference Providers API (<https://huggingface.co/docs/inference-providers>), and evaluate them against human-annotated validation data. Also includes functions to extract stereotype strength and base-rate items from typicality matrices. For more details see Beucler et al. (2025) <doi:10.31234/osf.io/eqrfu_v1>.

We perform linear, logistic, and cox regression using the base functions lm(), 
    glm(), and coxph() in the R software and the 'survival' package. Likewise, we 
    can use ols(), lrm() and cph() from the 'rms' package for the same 
    functionality. Each of these two sets of commands has a different focus.
  In many cases, we need to use both sets of commands in the same situation, 
    e.g. we need to filter the full subset model using AIC, and we need to build 
    a visualization graph for the final model. 'base.rms' package can help you to switch 
    between the two sets of commands easily.

Provides various basis expansions for flexible regression modeling,
    including random Fourier features (Rahimi & Recht, 2007)
    <https://proceedings.neurips.cc/paper_files/paper/2007/file/013a006f03dbc5392effeb8f18fda755-Paper.pdf>,
    exact kernel / Gaussian process feature maps, Bayesian Additive Regression
    Trees (BART) (Chipman et al., 2010) <doi:10.1214/09-AOAS285> prior features,
    and a helpful interface for n-way interactions. The provided functions may
    be used within any modeling formula, allowing the use of kernel methods and
    other basis expansions in modeling functions that do not otherwise support
    them. Along with the basis expansions, a number of kernel functions are also
    provided, which support kernel arithmetic to form new kernels. Basic ridge
    regression functionality is included as well.