Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Extends the functionality of the 'RTMB' <https://kaskr.r-universe.dev/RTMB> package by providing a collection of non-standard probability distributions compatible with automatic differentiation (AD). While 'RTMB' enables flexible and efficient modelling, including random effects, its built-in support is limited to standard distributions. The package adds additional AD-compatible distributions, broadening the range of models that can be implemented and estimated using 'RTMB'. Automatic differentiation and Laplace approximation are described in Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>.

The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv)
    is a two-stage estimation method that reformulates structured table-based
    systems - such as allocation problems, transaction matrices, and
    input-output tables - as structured least-squares problems. Based on the
    Convex Least Squares Programming (CLSP) framework, TMPinv solves systems
    with row and column constraints, block structure, and optionally reduced
    dimensionality by (1) constructing a canonical constraint form and applying
    a pseudoinverse-based projection, followed by (2) a convex-programming
    refinement stage to improve fit, coherence, and regularization (e.g., via
    Lasso, Ridge, or Elastic Net).

Provides an interactive wrapper for the 'tmpinv()' function from
    the 'rtmpinv' package with options extending its functionality to pre-
    and post-estimation processing and streamlined incorporation of prior cell
    information. The Tabular Matrix Problems via Pseudoinverse Estimation
    (TMPinv) is a two-stage estimation method that reformulates structured
    table-based systems - such as allocation problems, transaction matrices,
    and input-output tables - as structured least-squares problems. Based
    on the Convex Least Squares Programming (CLSP) framework, TMPinv solves
    systems with row and column constraints, block structure, and optionally
    reduced dimensionality by (1) constructing a canonical constraint form
    and applying a pseudoinverse-based projection, followed by (2) a
    convex-programming refinement stage to improve fit, coherence, and
    regularization (e.g., via Lasso, Ridge, or Elastic Net).