This package provides functions to access and download data from various NASA APIs <https://api.nasa.gov/#browseAPI>, including: Astronomy Picture of the Day (APOD), Mars Rover Photos, Earth Polychromatic Imaging Camera (EPIC), Near Earth Object Web Service (NeoWs), Earth Observatory Natural Event Tracker (EONET), and NASA Earthdata CMR Search. Most endpoints require a NASA API key for access. Data is retrieved, cleaned for analysis, and returned in a dataframe-friendly format.

Predicting the structure of a graph including new nodes and edges using a time series of graphs. Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2025) <doi:10.48550/arXiv.2507.05806>.

This package provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.

This package provides functions complementary to packages nicheROVER and SIBER allowing the user to extract Bayesian estimates from data objects created by the packages nicheROVER and SIBER'. Please see the following publications for detailed methods on nicheROVER and SIBER Hansen et al. (2015) <doi:10.1890/14-0235.1>, Jackson et al. (2011) <doi:10.1111/j.1365-2656.2011.01806.x>, and Layman et al. (2007) <doi:10.1890/0012-9658(2007)88[42:CSIRPF]2.0.CO;2>, respectfully.

Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.

Generates LaTeX code for drawing well-formatted neural network diagrams with TikZ'. Users have to define number of neurons on each layer, and optionally define neuron connections they would like to keep or omit, layers they consider to be oversized and neurons they would like to draw with lighter color. They can also specify the title of diagram, color, opacity of figure, labels of layers, input and output neurons. In addition, this package helps to produce LaTeX code for drawing activation functions which are crucial in neural network analysis. To make the code work in a LaTeX editor, users need to install and import some TeX packages including TikZ in the setting of TeX file.

Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.

Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.

This package provides a finite-population significance test of the sharp causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. A generalized likelihood ratio test statistic is used, and the resulting p-value is exact. Currently, it is assumed that there are only Compliers and Never Takers in the population.

Makes NCBI taxonomic data locally available and searchable as an R object.

Classify occurrence records based on confidence levels of species identification. In addition, implement tools to filter occurrences inside grid cells and to manually check for possibles errors with an interactive shiny application.

Estimation of structural equation models with nonlinear effects and underlying nonnormal distributions.

Th-U-Pb electron microprobe age dating of monazite, as originally described in <doi:10.1016/0009-2541(96)00024-1>.

Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).

Simulates the extinction of species in ecological networks and it analyzes its cascading effects, described in Dunne et al. (2002) <doi:10.1073/pnas.192407699>.

Fits Bayesian regularized varying coefficient models with the Nonparametric Varying Coefficient Spike-and-Slab Lasso (NVC-SSL) introduced by Bai et al. (2023) <https://jmlr.org/papers/volume24/20-1437/20-1437.pdf>. Functions to fit frequentist penalized varying coefficients are also provided, with the option of employing the group lasso penalty of Yuan and Lin (2006) <doi:10.1111/j.1467-9868.2005.00532.x>, the group minimax concave penalty (MCP) of Breheny and Huang <doi:10.1007/s11222-013-9424-2>, or the group smoothly clipped absolute deviation (SCAD) penalty of Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>.

Implementation of network integration approaches comprising unweighted and weighted integration methods. Unweighted integration is performed considering the average, per-edge average, maximum and minimum of networks edges. Weighted integration takes into account a weight for each network during the fusion process, where the weights express the predictiveness strength of each network considering a specific predictive task. Weights can be learned using a machine learning algorithm able to associate the weights to the assessment of the accuracy of the learning algorithm trained on the network itself. The implemented methods can be applied to effectively integrate different biological networks modelling a wide range of problems in bioinformatics (e.g. disease gene prioritization, protein function prediction, drug repurposing, clinical outcome prediction).

The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.

Subsampling methods for big data under different models and assumptions. Starting with linear regression and leading to Generalised Linear Models, softmax regression, and quantile regression. Specifically, the model-robust subsampling method proposed in Mahendran, A., Thompson, H., and McGree, J. M. (2023) <doi:10.1007/s00362-023-01446-9>, where multiple models can describe the big data, and the subsampling framework for potentially misspecified Generalised Linear Models in Mahendran, A., Thompson, H., and McGree, J. M. (2025) <doi:10.48550/arXiv.2510.05902>.

This package provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHS Making Data Count programme, and allows users to draw XmR charts, use change points and apply rules with summary indicators for when rules are breached.

Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are used, one based on the count matrix, and one using the alignment BAM files directly. Contains several options for every step of the process, as well as tools to quality check and assess the stability of output.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for support functions like preconditioned fits <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate selection.

An application for the empirical extrapolation of time features selecting and summarizing the most relevant patterns in time sequences.

This package provides functions to access NASA's Earth Imagery and Assets API and the Earth Observatory Natural Event Tracker (EONET) webservice.