Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.

Construct a principal surface that are two-dimensional surfaces that pass through the middle of a p-dimensional data set. They minimise the distance from the data points, and provide a nonlinear summary of data. The surfaces are nonparametric and their shape is suggested by the data. The formation of a surface is found using an iterative procedure which starts with a linear summary, typically with a principal component plane. Each successive iteration is a local average of the p-dimensional points, where an average is based on a projection of a point onto the nonlinear surface of the previous iteration. For more information on principal surfaces, see Ganey, R. (2019, "https://open.uct.ac.za/items/4e655d7d-d10c-481b-9ccc-801903aebfc8").

This package provides functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.

Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, ART and SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included.

R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means and for all latitudes, and polar night dates.

Streamlines the steps for adding colour scales and associated legends when working with base R graphics, especially for interactive use. Popular palettes are included and pretty legends produced when mapping a large variety of vector classes to a colour scale. An additional helper for adding axes and grid lines complements the base::plot() work flow.

This package provides a collection of software provides R support for ADMB (Automatic Differentiation Model Builder) and a GUI interface facilitates the conversion of ADMB template code to C code followed by compilation to a binary executable. Stand-alone functions can also be run by users not interested in clicking a GUI'.

Facilitates analysis of paleontological sequences of trait values. Functions are provided to fit, using maximum likelihood, simple evolutionary models (including unbiased random walks, directional evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and complex models (punctuation, mode shifts).

Run population simulations using an Individual-Based Model (IBM) compiled in C.

This package contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.

Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test, which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give biased results due to using the pooled standard deviation, unlike the Welch's t-test.

Computes penalized regression calibration (PRC), a statistical method for the dynamic prediction of survival when many longitudinal predictors are available. See Signorelli (2024) <doi:10.32614/RJ-2024-014> and Signorelli et al. (2021) <doi:10.1002/sim.9178> for details.

Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.

This package provides a tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.

This package implements the method described at the UCLA Statistical Consulting site <https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/> for checking if the proportional odds assumption holds for a cumulative logit model.

Implementation of T. Hailperin's procedure to calculate lower and upper bounds of the probability for a propositional-logic expression, given equality and inequality constraints on the probabilities for other expressions. Truth-valuation is included as a special case. Applications range from decision-making and probabilistic reasoning, to pedagogical for probability and logic courses. For more details see T. Hailperin (1965) <doi:10.1080/00029890.1965.11970533>, T. Hailperin (1996) "Sentential Probability Logic" ISBN:0-934223-45-9, and package documentation. Requires the lpSolve package.

This package provides a unified interface to access and manipulate various Philippine statistical classifications. It allows users to retrieve, filter, and harmonize classification data, making it easier to work with Philippine statistical data in R.

This package provides an implementation of piecewise normalisation techniques useful when dealing with the communication of skewed and highly skewed data. It also provides utilities that recommends a normalisation technique based on the distribution of the data.

Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties. The details of the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>.

This package provides functions to get prediction intervals and prediction points of future observations from mixture distributions like gamma, beta, Weibull and normal.

Identification, model fitting and estimation for time series with periodic structure. Additionally, procedures for simulation of periodic processes and real data sets are included. Hurd, H. L., Miamee, A. G. (2007) <doi:10.1002/9780470182833> Box, G. E. P., Jenkins, G. M., Reinsel, G. (1994) <doi:10.1111/jtsa.12194> Brockwell, P. J., Davis, R. A. (1991, ISBN:978-1-4419-0319-8) Bretz, F., Hothorn, T., Westfall, P. (2010, ISBN: 9780429139543) Westfall, P. H., Young, S. S. (1993, ISBN:978-0-471-55761-6) Bloomfield, P., Hurd, H. L.,Lund, R. (1994) <doi:10.1111/j.1467-9892.1994.tb00181.x> Dehay, D., Hurd, H. L. (1994, ISBN:0-7803-1023-3) Vecchia, A. (1985) <doi:10.1080/00401706.1985.10488076> Vecchia, A. (1985) <doi:10.1111/j.1752-1688.1985.tb00167.x> Jones, R., Brelsford, W. (1967) <doi:10.1093/biomet/54.3-4.403> Makagon, A. (1999) <https://www.math.uni.wroc.pl/~pms/files/19.2/Article/19.2.5.pdf> Sakai, H. (1989) <doi:10.1111/j.1467-9892.1991.tb00069.x> Gladyshev, E. G. (1961) <https://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=dan&paperid=24851> Ansley (1979) <doi:10.1093/biomet/66.1.59> Hurd, H. L., Gerr, N. L. (1991) <doi:10.1111/j.1467-9892.1991.tb00088.x>.

This package provides a unified framework for generating, submitting, and analyzing pairwise comparisons of writing quality using large language models (LLMs). The package supports live and/or batch evaluation workflows across multiple providers ('OpenAI', Anthropic', Google Gemini', Together AI', and locally-hosted Ollama models), includes bias-tested prompt templates and a flexible template registry, and offers tools for constructing forward and reversed comparison sets to analyze consistency and positional bias. Results can be modeled using Bradleyâ Terry (1952) <doi:10.2307/2334029> or Elo rating methods to derive writing quality scores. For information on the method of pairwise comparisons, see Thurstone (1927) <doi:10.1037/h0070288> and Heldsinger & Humphry (2010) <doi:10.1007/BF03216919>. For information on Elo ratings, see Clark et al. (2018) <doi:10.1371/journal.pone.0190393>.

This package provides a unified method, called M statistic, is provided for detecting phylogenetic signals in continuous traits, discrete traits, and multi-trait combinations. Blomberg and Garland (2002) <doi:10.1046/j.1420-9101.2002.00472.x> provided a widely accepted statistical definition of the phylogenetic signal, which is the "tendency for related species to resemble each other more than they resemble species drawn at random from the tree". The M statistic strictly adheres to the definition of phylogenetic signal, formulating an index and developing a method of testing in strict accordance with the definition, instead of relying on correlation analysis or evolutionary models. The novel method equivalently expressed the textual definition of the phylogenetic signal as an inequality equation of the phylogenetic and trait distances and constructed the M statistic. The M statistic implemented in this package is based on the methodology described in Yao and Yuan (2025) <doi:10.1002/ece3.71106>. If you use this method in your research, please cite the paper.

An implementation of the ternary plot for interpreting regression coefficients of trinomial regression models, as proposed in Santi, Dickson and Espa (2019) <doi:10.1080/00031305.2018.1442368>. Ternary plots can be drawn using either ggtern package (based on ggplot2') or Ternary package (based on standard graphics). The package and its features are illustrated in Santi, Dickson, Espa and Giuliani (2022) <doi:10.18637/jss.v103.c01>.