Multi-state models are essential tools in longitudinal data analysis. One primary goal of these models is the estimation of transition probabilities, a critical metric for predicting clinical prognosis across various stages of diseases or medical conditions. Traditionally, inference in multi-state models relies on the Aalen-Johansen (AJ) estimator which is consistent under the Markov assumption. However, in many practical applications, the Markovian nature of the process is often not guaranteed, limiting the applicability of the AJ estimator in more complex scenarios. This package extends the landmark Aalen-Johansen estimator (Putter, H, Spitoni, C (2018) <doi:10.1177/0962280216674497>) incorporating presmoothing techniques described by Soutinho, Meira-Machado and Oliveira (2020) <doi:10.1080/03610918.2020.1762895>, offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models with multiple states and potential reversible transitions.

This package provides functions for quantifying visible (VIS) and ultraviolet (UV) radiation in relation to the photoreceptors Phytochromes, Cryptochromes, and UVR8 which are present in plants. It also includes data sets on the optical properties of plants. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

This package implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph.

This package implements L1 and L2 penalized conditional logistic regression with penalty factors allowing for integration of multiple data sources. Implements stability selection for variable selection.

This package provides tools for modelling populations and demography using matrix projection models, with deterministic and stochastic model implementations. Includes population projection, indices of short- and long-term population size and growth, perturbation analysis, convergence to stability or stationarity, and diagnostic and manipulation tools.

This package implements Penalized Regression with Inferred Seasonality Module (PRISM) to generate forecast estimation of weekly unemployment initial claims using Google Trends data. It includes required data and tools for backtesting the performance in 2007-2020.

This package provides functions for computing fit indices for evaluating the path component of latent variable structural equation models. Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) <doi:10.1177/1094428117736137>. Also included are fit indices described by Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.

Fast and memory-less computation of the partial distance correlation for vectors and matrices. Permutation-based and asymptotic hypothesis testing for zero partial distance correlation are also performed. References include: Szekely G. J. and Rizzo M. L. (2014). "Partial distance correlation with methods for dissimilarities". The Annals Statistics, 42(6): 2382--2412. <doi:10.1214/14-AOS1255>. Shen C., Panda S. and Vogelstein J. T. (2022). "The Chi-Square Test of Distance Correlation". Journal of Computational and Graphical Statistics, 31(1): 254--262. <doi:10.1080/10618600.2021.1938585>. Szekely G. J. and Rizzo M. L. (2023). "The Energy of Data and Distance Correlation". Chapman and Hall/CRC. <ISBN:9781482242744>. Kontemeniotis N., Vargiakakis R. and Tsagris M. (2025). On independence testing using the (partial) distance correlation. <doi:10.48550/arXiv.2506.15659>.

Set of tools for reading, writing and transforming spatial and seasonal data, model selection and specific statistical tests for ecologists. It includes functions to interpolate regular positions of points between landmarks, to discretize polylines into regular point positions, link distant observations to points and convert a bounding box in a spatial object. It also provides miscellaneous functions for field ecologists such as spatial statistics and inference on diversity indexes, writing data.frame with Chinese characters.

This package provides functions which can be used to support the Multicriteria Decision Analysis (MCDA) process involving multiple criteria, by PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations).

Create PostgreSQL statements/scripts from R, optionally executing the SQL statements. Common SQL operations are included, although not every configurable option is available at this time. SQL output is intended to be compliant with PostgreSQL syntax specifications. PostgreSQL documentation is available here <https://www.postgresql.org/docs/current/index.html>.

Utilize the CDF penalty function to estimate a penalized linear model. It enables you to display some graphical representations and determine whether the Karush-Kuhn-Tucker conditions are met. For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) <arXiv:2212.08582>.

Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities.

This package provides tools for the evaluation of interim analysis plans for sequentially monitored trials on a survival endpoint; tools to construct efficacy and futility boundaries, for deriving power of a sequential design at a specified alternative, template for evaluating the performance of candidate plans at a set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.

Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).

This package implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.

Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for generating random data, fitting, predicting and check goodness of fit are prepared. The methods are developed from the theoretical framework of proportional hazard model and copula functions. Using this package, users can now simulate parametric time-dependent ROC and run experiment to understand the behavior of the curve under different scenario.

R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).

This package provides functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons.

This package provides path_chain class and functions, which facilitates loading and saving directory structure in YAML configuration files via config package. The file structure you created during exploration can be transformed into legible section in the config file, and then easily loaded for further usage.

Routines for state estimate in a linear Gaussian state space model and a simple stochastic volatility model using particle filtering. Parameter inference is also carried out in these models using the particle Metropolis-Hastings algorithm that includes the particle filter to provided an unbiased estimator of the likelihood. This package is a collection of minimal working examples of these algorithms and is only meant for educational use and as a start for learning to them on your own.

This package provides a database containing the names of the babies born in Quebec between 1980 and 2020.

This package provides tools to import, clean, filter, and prepare Project FeederWatch data for analysis. Includes functions for taxonomic rollup, easy filtering, zerofilling, merging in site metadata, and more. Project FeederWatch data comes from <https://feederwatch.org/explore/raw-dataset-requests/>.

Create and customize interactive phylogenetic trees using the phylocanvas JavaScript library and the htmlwidgets package. These trees can be used directly from the R console, from RStudio', in Shiny apps, and in R Markdown documents. See <http://phylocanvas.org/> for more information on the phylocanvas library.