Simple class to hold contents of a SMET file as specified in
    Bavay (2021) <https://code.wsl.ch/snow-models/meteoio/-/blob/master/doc/SMET_specifications.pdf>.
    There numerical meteorological measurements are all based on MKS (SI) units
    and timestamp is standardized to UTC time.

Implements stochastic metafrontier analysis for productivity and
    performance benchmarking across firms operating under different
    technologies. Contains routines for the deterministic metafrontier envelope
    of O'Donnell et al. (2008) <doi:10.1007/s00181-007-0119-4> via linear and
    quadratic programming, and the stochastic metafrontier of Huang et al.
    (2014) <doi:10.1007/s11123-014-0402-2>. Also supports latent class
    stochastic metafrontier analysis and sample selection correction stochastic
    metafrontier models. Depends on the 'sfaR' package by Dakpo et al. (2023)
    <https://CRAN.R-project.org/package=sfaR>.

Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.

Discovery of spatial patterns with Hidden Markov Random Field. This package is designed for spatial transcriptomic data and single molecule fluorescent in situ hybridization (FISH) data such as sequential fluorescence in situ hybridization (seqFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH). The methods implemented in this package are described in Zhu et al. (2018) <doi:10.1038/nbt.4260>.

Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.

Functions that provide statistical methods for interval-censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval-censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval-censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Walter, P. (2019) <doi:10.17169/refubium-1621>).