To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.

Sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for visualizing pedigrees.

Automated pain scoring from paw withdrawal tracking data. Based on Jones et al. (2020) "A machine-vision approach for automated pain measurement at millisecond timescales" <doi:10.7554/eLife.57258>.

We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions.

Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias.

This package provides tools for reshaping, plotting, and manipulating matrices of orthogonal polynomials.

Permutation based Kolmogorov-Smirnov test for paired samples. The test was proposed by Wang W.S., Amsler C. and Schmidt, P. (2025) <doi:10.1007/s00181-025-02779-0>.

An easy-to-use tool for implementing Neural Ordinary Differential Equations (NODEs) in pharmacometric software such as Monolix', NONMEM', and nlmixr2', see Bräm et al. (2024) <doi:10.1007/s10928-023-09886-4> and Bräm et al. (2025) <doi:10.1002/psp4.13265>. The main functionality is to automatically generate structural model code describing computations within a neural network. Additionally, parameters and software settings can be initialized automatically. For using these additional functionalities with Monolix', pmxNODE interfaces with MonolixSuite via the lixoftConnectors package. The lixoftConnectors package is distributed with MonolixSuite (<https://monolixsuite.slp-software.com/r-functions/2024R1/package-lixoftconnectors>) and is not available from public repositories.

This package implements propensity score weighting methods for estimating counterfactual survival functions and marginal hazard ratios in observational studies with time-to-event outcomes. Supports binary and multiple treatment groups with average treatment effect on the combined full population (ATE), average treatment effect on the treated or target group (ATT), and overlap weighting estimands. Includes symmetric (Crump) and asymmetric (Sturmer) trimming options for extreme propensity scores. Variance estimation via analytical M-estimation or bootstrap. Methods based on Cheng et al. (2022) <doi:10.1093/aje/kwac043> and Li & Li (2019) <doi:10.1214/19-AOAS1282>.

Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.

Computational infrastructure for biogeography, community ecology, and biodiversity conservation (Daru et al. 2020) <doi:10.1111/2041-210X.13478>. It is based on the methods described in Daru et al. (2020) <doi:10.1038/s41467-020-15921-6>. The original conceptual work is described in Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013> on patterns and processes of biogeographical regionalization. Additionally, the package contains fast and efficient functions to compute more standard conservation measures such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness and global endangerment, as well as compositional turnover (e.g., beta diversity).

Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor.

This package provides functions and data sets for the text Probability and Statistics with R.

Returns almost all features that has been extracted from Position Specific Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) and 20 columns produced by PSI-BLAST which is a program to produce PSSM Matrix from multiple sequence alignment of proteins see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some of these features are described in Zahiri, J., et al.(2013) <DOI:10.1016/j.ygeno.2013.05.006>, Saini, H., et al.(2016) <DOI:10.17706/jsw.11.8.756-767>, Ding, S., et al.(2014) <DOI:10.1016/j.biochi.2013.09.013>, Cheng, C.W., et al.(2008) <DOI:10.1186/1471-2105-9-S12-S6>, Juan, E.Y., et al.(2009) <DOI:10.1109/CISIS.2009.194>.

Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist. These difficulties are related to those arising from logistic and multinomial regression. References for methods are given in the separate function documents. Supported by grant NSF DMS 1712839.

Likelihood based population viability analysis in the presence of observation error and missing data. The package can be used to fit, compare, predict, and forecast various growth model types using data cloning.

Spectral response data for broadband ultraviolet and visible radiation sensors. Angular response data for broadband ultraviolet and visible radiation sensors and diffusers used as entrance optics. Data obtained from multiple sources were used: author-supplied data from scientific research papers, sensor-manufacturer supplied data, and published sensor specifications. Part of the r4photobiology suite Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio (2019) <doi:10.5506/APhysPolBSupp.12.25>. The suggested geiger package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/geiger/>, suggested to take latest version. Otherwise its required code is present in the pcmabc package. The suggested distory package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/distory/>, suggested to take latest version.

An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) <doi:10.48550/arXiv.2304.00290>. Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only C++ 14 leveraging the Eigen library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided.

This package performs partial principal component analysis of a large sparse matrix. The matrix may be stored as a list of matrices to be concatenated (implicitly) horizontally. Useful application includes cases where the number of total nonzero entries exceed the capacity of 32 bit integers (e.g., with large Single Nucleotide Polymorphism data).

This package provides a collection of methods for commonly undertaken analytical tasks, primarily developed for Public Health Scotland (PHS) analysts, but the package is also generally useful to others working in the healthcare space, particularly since it has functions for working with Community Health Index (CHI) numbers. The package can help to make data manipulation and analysis more efficient and reproducible.

Parametric linkage analysis of monogenic traits in medical pedigrees. Features include singlepoint analysis, multipoint analysis via MERLIN (Abecasis et al. (2002) <doi:10.1038/ng786>), visualisation of log of the odds (LOD) scores and summaries of linkage peaks. Disease models may be specified to accommodate phenocopies, reduced penetrance and liability classes. paramlink2 is part of the pedsuite package ecosystem, presented in Pedigree Analysis in R (Vigeland, 2021, ISBN:9780128244302).

Following the method of Bailey et al., computes for a collection of candidate models the probability of backtest overfitting, the performance degradation and probability of loss, and the stochastic dominance.

This package implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.