Provides a dataset of functions in all base and recommended packages of R versions 0.50 onwards.

Provides algorithms to locate multiple
    distributional change-points in piecewise stationary time series. The
    algorithms are provably consistent, even in the presence of long-range
    dependencies. Knowledge of the number of change-points is not
    required. The code is written in Go and interfaced with R.

A set of functions to generate, access and analyze standard data products from archival tagging data.

An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.

Collection of portable choice dialog widgets.

Client for 'ChromaDB', a vector database for storing and querying embeddings. This package provides a convenient interface to interact with the REST API of 'ChromaDB' <https://docs.trychroma.com>.

Provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see <http://www.chronomodel.fr> for more information).  ChronoModel is a friendly software to construct a chronological model in a Bayesian framework.  Its output is  a sampled Markov chain from the posterior distribution of dates component  the chronology. The functions can also be applied  to the  analyse of mcmc output generated by Oxcal software.

The concept of reliable and clinically significant change
    (Jacobson & Truax, 1991) helps you answer the following questions for a
    sample with two measurements at different points in time (pre & post):
    Which proportion of my sample has a (considering the reliability of the
    instrument) probably not-just-by-chance difference in pre- vs. post-scores?
    Which proportion of my sample does not only change in a statistically
    significant way (see question one), but also in a clinically significant way
    (e.g. change from a test score regarded "dysfunctional" to a score regarded
    "functional")?
    This package allows you to very easily create a scatterplot of your sample
    in which the x-axis maps to the pre-scores, the y-axis maps to the
    post-scores and several graphical elements (lines, colors) allow you to gain
    a quick overview about reliable changes in these scores.
    An example of this kind of plot is Figure 2 of Jacobson & Truax (1991).
    Referenced article:
    Jacobson, N. S., & Truax, P. (1991) <doi:10.1037/0022-006X.59.1.12>.