Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) <doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019) <doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996) <doi:10.1214/ss/1038425655>.

The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

Provides functions to non-parametrically estimate the off-pulse interval of a source
    function originating from a pulsar. The technique is based on a sequential application of P-values
    obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
    Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.

Computes sequential A-, MV-, D- and E-optimal or near-optimal block and row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.

Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.

Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.  The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates.  The second regression uses this estimated probability to calculate the mean population response given covariates.