This package provides functions and data sets inspired by data sharpening - data perturbation to achieve improved performance in nonparametric estimation, as described in Choi, E., Hall, P. and Rousson, V. (2000). Capabilities for enhanced local linear regression function and derivative estimation are included, as well as an asymptotically correct iterated data sharpening estimator for any degree of local polynomial regression estimation. A cross-validation-based bandwidth selector is included which, in concert with the iterated sharpener, will often provide superior performance, according to a median integrated squared error criterion. Sample data sets are provided to illustrate function usage.

Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.

This package provides monthly statistics on the number of monthly air passengers at SFO airport such as operating airline, terminal, geo, etc. Data source: San Francisco data portal (DataSF) <https://data.sfgov.org/Transportation/Air-Traffic-Passenger-Statistics/rkru-6vcg>.

Visualizes sulcal morphometry data derived from BrainVisa <https://brainvisa.info/> including width, depth, surface area, and length. The package enables mapping of statistical group results or subject-level values onto cortical surface maps, with options to focus on all sulci or only selected regions of interest. Users can display all four measures simultaneously or restrict plots to chosen measures, creating composite, publication-quality brain visualizations in R to support the analysis and interpretation of sulcal morphology.

This package contains several tools for nonlinear regression analyses and general data analysis in biology and agriculture. Contains also datasets for practicing and teaching purposes. Supports the blog: Onofri (2024) "Fixing the bridge between biologists and statisticians" <https://www.statforbiology.com> and the book: Onofri (2024) "Experimental Methods in Agriculture" <https://www.statforbiology.com/_statbookeng/>. The blog is a collection of short articles aimed at improving the efficiency of communication between biologists and statisticians, as pointed out in Kozak (2016) <doi:10.1590/0103-9016-2015-0399>, spreading a better awareness of the potential usefulness, beauty and limitations of biostatistic.

The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 <doi:10.1002/j.1538-7305.1948.tb01338.x>), Batty's spatial entropy (Batty, 1974 <doi:10.1111/j.1538-4632.1974.tb01014.x>), O'Neill's entropy (O'Neill et al., 1998 <doi:10.1007/BF00162741>), Li and Reynolds contagion index (Li and Reynolds, 1993 <doi:10.1007/BF00125347>), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 <https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-61351>), Leibovici's entropy (Leibovici, 2009 <doi:10.1007/978-3-642-03832-7_24>), Parresol and Edwards entropy (Parresol and Edwards, 2014 <doi:10.3390/e16041842>) and Altieri's entropy (Altieri et al., 2018, <doi:10.1007/s10651-017-0383-1>). Full references for all measures can be found under the topic SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated spatstat package (>= 3.0-2).

This package implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.

This package creates and fits staged event tree probability models, which are probabilistic graphical models capable of representing asymmetric conditional independence statements for categorical variables. Includes functions to create, plot and fit staged event trees from data, as well as many efficient structure learning algorithms. References: Carli F, Leonelli M, Riccomagno E, Varando G (2022). <doi: 10.18637/jss.v102.i06>. Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604). Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) <arXiv:1705.09457>. Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>. Barclay L. M., Hutton J. L. and Smith J. Q. (2013) <doi:10.1016/j.ijar.2013.05.006>. Smith J. Q. and Anderson P. E. (2008) <doi:10.1016/j.artint.2007.05.004>.

Mixed-effect proportional hazards models for multistage stratified, cluster-sampled, unequally weighted survey samples. Provides variance estimation by Taylor series linearisation or replicate weights.

Allows a Simile model saved as a compiled binary to be loaded, parameterized, executed and interrogated. This version works with Simile v6 on.

The computation of a seasonal index is a fundamental step in time-series forecasting when the data exhibits seasonality. Specifically, a seasonal index quantifies â for each season (e.g. month, quarter, week) â the relative magnitude of the seasonal effect compared to the overall average level of the series. This package has been developed to compute seasonal index for time series data and it also seasonalise and desesaonalise the time series data.

Evaluating probabilistic forecasts via proper scoring rules. scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families. Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score. For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.

Incorporate various statistics and layout customization options to enhance the efficiency and adaptability of the Kaplan-Meier plots.

Estimate the four parameters of stable laws using maximum likelihood method, generalised method of moments with finite and continuum number of points, iterative Koutrouvelis regression and Kogon-McCulloch method. The asymptotic properties of the estimators (covariance matrix, confidence intervals) are also provided.

This package provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.

Stock-and-flow models are a computational method from the field of system dynamics. They represent how systems change over time and are mathematically equivalent to ordinary differential equations. sdbuildR (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. sdbuildR simulates models in R and Julia', where Julia offers unit support and large-scale ensemble simulations. Additionally, sdbuildR can import models created in Insight Maker (<https://insightmaker.com/>).

This package provides tools to conduct interpretable sensitivity analyses for weighted estimators, introduced in Huang (2024) <doi:10.1093/jrsssa/qnae012> and Hartman and Huang (2024) <doi:10.1017/pan.2023.12>. The package allows researchers to generate the set of recommended sensitivity summaries to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts) or survey contexts.

This package provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.

Discovery of spatial patterns with Hidden Markov Random Field. This package is designed for spatial transcriptomic data and single molecule fluorescent in situ hybridization (FISH) data such as sequential fluorescence in situ hybridization (seqFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH). The methods implemented in this package are described in Zhu et al. (2018) <doi:10.1038/nbt.4260>.

Simplicially constrained regression models for proportions in both sides. The constraint is always that the betas are non-negative and sum to 1. References: Iverson S.J.., Field C., Bowen W.D. and Blanchard W. (2004) "Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets". Ecological Monographs, 74(2): 211-235. <doi:10.1890/02-4105>.

Runs SQL statements on in-memory data frames within a temporary in-memory duckdb data base.

This package provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including Latent Transition Analysis (LTA; Linda M. Collins and Stephanie T. Lanza, 2009) <doi:10.1002/9780470567333>, Latent Class Profile Analysis (LCPA; Hwan Chung et al., 2010) <doi:10.1111/j.1467-985x.2010.00674.x>, and Joint Latent Class Analysis (JLCA; Saebom Jeon et al., 2017) <doi:10.1080/10705511.2017.1340844>, and any other extended models involving multiple latent class variables.

Suns-Voc (or Isc-Voc) curves can provide the current-voltage (I-V) characteristics of the diode of photovoltaic cells without the effect of series resistance. Here, Suns-Voc curves can be constructed with outdoor time-series I-V curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to be measured in the lab. Time series of four different power loss modes can be calculated based on obtained Isc-Voc curves. This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0008172. Jennifer L. Braid is supported by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy administered by the Oak Ridge Institute for Science and Education (ORISE) for the DOE. ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-SC0014664. [1] Wang, M. et al, 2018. <doi:10.1109/PVSC.2018.8547772>. [2] Walters et al, 2018 <doi:10.1109/PVSC.2018.8548187>. [3] Guo, S. et al, 2016. <doi:10.1117/12.2236939>.

This package provides a very nice interface to Princeton's WordNet without rJava dependency. WordNet data is not included. Princeton University makes WordNet available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).