Defines S4 classes for single-cell genomic data and associated
  information, such as dimensionality reduction embeddings, nearest-neighbor
  graphs, and spatially-resolved coordinates. Provides data access methods and
  R-native hooks to ensure the Seurat object is familiar to other R users. See
  Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>,
  Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>,
  and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>,
  Hao Y, Hao S, et al (2021) <doi:10.1016/j.cell.2021.04.048> and
  Hao Y, et al (2023) <doi:10.1101/2022.02.24.481684> for
  more details.

R client and utilities for 'Seven Bridges Platform' API, from 'Cancer Genomics Cloud' 
    to other 'Seven Bridges' supported platforms. API documentation is hosted publicly 
    at <https://docs.sevenbridges.com/docs/the-api>.

Customise 'Shiny' disconnected screens as well as sanitize error messages to make them clearer and friendlier to the user.

Provides a simple interface to developing complex data pipelines which can be executed in a single call.
    'sewage' makes it easy to test, debug, and share data pipelines through it's interface and visualizations.

Support for simple features, a standardized way to
    encode spatial vector data. Binds to 'GDAL' for reading and writing
    data, to 'GEOS' for geometrical operations, and to 'PROJ' for
    projection conversions and datum transformations. Uses by default the 's2'
    package for spherical geometry operations on ellipsoidal (long/lat) coordinates.

Stochastic frontier analysis with advanced methods.
 In particular, it applies the approach proposed by Latruffe et al. (2017) 
 <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical 
 inefficiency effects when one input is endogenous.

Maximum likelihood estimation for stochastic frontier
    analysis (SFA) of production (profit) and cost functions. The package
    includes the basic stochastic frontier for cross-sectional or pooled
    data with several distributions for the one-sided error term (i.e.,
    Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential
    and truncated skewed Laplace), the latent class stochastic frontier
    model (LCM) as described in Dakpo et al. (2021)
    <doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data,
    and the sample selection model as described in Greene (2010)
    <doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021)
    <doi:10.1111/agec.12683>.  Several possibilities in terms of
    optimization algorithms are proposed.