An interface to the 'fastText' library
	<https://github.com/facebookresearch/fastText>. The package
	can be used for text classification and to learn word vectors.
	An example how to use 'fastTextR' can be found in the 'README' file.

Fast functions for timestamp
	manipulation that avoid system calls and take shortcuts
	to facilitate operations on very large data.

Implements fast, scalable optimization algorithms for
    fitting topic models ("grade of membership" models) and
    non-negative matrix factorizations to count data. The methods
    exploit the special relationship between the multinomial topic
    model (also, "probabilistic latent semantic indexing") and Poisson
    non-negative matrix factorization. The package provides tools to
    compare, annotate and visualize model fits, including functions to
    efficiently create "structure plots" and identify key features in
    topics. The 'fastTopics' package is a successor to the
    'CountClust' package. For more information, see
    <doi:10.48550/arXiv.2105.13440> and
    <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub
    repository for additional vignettes not included in the package on
    CRAN.

Implements the algorithm by Briefs and Bläser (2025) <https://openreview.net/forum?id=8PHOPPH35D>, based on the approach of Gupta and Bläser (2024) <doi:10.1609/aaai.v38i18.30023>. It determines, for a structural causal model (SCM) whose directed edges form a tree, whether each parameter is unidentifiable, 1-identifiable or 2-identifiable (other cases cannot occur), using a randomized algorithm with provable running time O(n^3 log^2 n).

An implementation of sparsity-ranked lasso and related methods 
    for time series data. This methodology is especially useful for 
    large time series with exogenous features and/or complex 
    seasonality. Originally described in Peterson and Cavanaugh 
    (2022) <doi:10.1007/s10182-021-00431-7> in the context of variable 
    selection with interactions and/or polynomials, ranked sparsity is 
    a philosophy with methods useful for variable selection in the 
    presence of prior informational asymmetry. This situation exists for time 
    series data with complex seasonality, as shown in Peterson and Cavanaugh 
    (2024) <doi:10.1177/1471082X231225307>, which also describes this package
    in greater detail. The sparsity-ranked penalization methods for time series
    implemented in 'fastTS' can fit large/complex/high-frequency time series
    quickly, even with a high-dimensional exogenous feature set. The method is
    considerably faster than its competitors, while often producing more 
    accurate predictions. Also included is a long hourly series of arrivals 
    into the University of Iowa Emergency Department with concurrent local 
    temperature.