Use the leaflet-timeline plugin with a leaflet widget to add an interactive slider with play, pause, and step buttons to explore temporal geographic spatial data changes.

This package provides functions to estimate survival and a treatment effect using a landmark estimation approach.

This package provides a graph proposed by Rosenbaum is useful for checking some properties of various sorts of latent scale, this program generates commands to obtain the graph using dot from graphviz'.

European Commission's Labour Market Policy (LMP) database (<https://webgate.ec.europa.eu/empl/redisstat/databrowser/explore/all/lmp?lang=en&display=card&sort=category>) provides information on labour market interventions, which are government actions to help and support the unemployed and other disadvantaged groups in the transition from unemployment or inactivity to work. It covers the EU countries and Norway. This package provides functions for downloading and importing the LMP data and metadata (codelists).

This package provides functions to simulate data from large-scale educational assessments, including background questionnaire data and cognitive item responses that adhere to a multiple-matrix sampled design. The theoretical foundation can be found on Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018) <doi:10.1186/s40536-018-0068-8>.

This package provides functions that compute the lattice-based density and regression estimators for two-dimensional regions with irregular boundaries and holes. The density estimation technique is described in Barry and McIntyre (2011) <doi:10.1016/j.ecolmodel.2011.02.016>, while the non-parametric regression technique is described in McIntyre and Barry (2018) <doi:10.1080/10618600.2017.1375935>.

This package provides a complete framework for frequency analysis is provided by LMoFit'. It has functions related to the determination of sample L-moments as in Hosking, J.R.M. (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x>, the fitting of various distributions as in Zaghloul et al. (2020) <doi:10.1016/j.advwatres.2020.103720> and Hosking, J.R.M. (2019) <https://CRAN.R-project.org/package=lmom>, besides plotting and manipulating L-space diagrams as in Papalexiou, S.M. & Koutsoyiannis, D. (2016) <doi:10.1016/j.advwatres.2016.05.005> for two-shape parametric distributions on the L-moment ratio diagram. Additionally, the quantile, probability density, and cumulative probability functions of various distributions are provided in a user-friendly manner.

This package provides a collection of functions that calculate the log likelihood (support) for a range of statistical tests. Where possible the likelihood function and likelihood interval for the observed data are displayed. The evidential approach used here is based on the book "Likelihood" by A.W.F. Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall (2011) <doi:10.2105/AJPH.78.12.1568>, "Understanding Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), S. Glover & P. Dixon <doi:10.3758/BF03196706> and others. This package accompanies "Evidence-Based Statistics" by P. Cahusac (2020, ISBN-13 : 978-1119549802) <doi:10.1002/9781119549833>.

Local partial likelihood estimation by Fan, Lin and Zhou(2006)<doi:10.1214/009053605000000796> and simultaneous confidence band is a set of tools to test the covariates-biomarker interaction for survival data. Test for the covariates-biomarker interaction using the bootstrap method and the asymptotic method with simultaneous confidence band (Liu, Jiang and Chen (2015)<doi:10.1002/sim.6563>).

This package implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by McGrath et al. (2024) <doi:10.48550/arXiv.2412.20605>.

Data, scripts and code from chunks used as examples in the book "Learn R: As a Language" 1ed and 2ed by Pedro J. Aphalo. ISBN 9780367182533 (pbk 1ed); ISBN 9780367182557 (hbk 1ed); ISBN 9780429060342 (ebk 1ed).

This package provides a collection of tools for interactive manipulation of (spatial) data layers on leaflet web maps. Tools include editing of existing layers, creation of new layers through drawing of shapes (points, lines, polygons), deletion of shapes as well as cutting holes into existing shapes. Provides control over options to e.g. prevent self-intersection of polygons and lines or to enable/disable snapping to align shapes.

My PhD supervisor once told me that everyone doing newspaper analysis starts by writing code to read in files from the LexisNexis newspaper archive (retrieved e.g., from <https://www.lexisnexis.com/> or any of the partner sites). However, while this is a nice exercise I do recommend, not everyone has the time. This package takes files downloaded from the newspaper archive of LexisNexis', reads them into R and offers functions for further processing.

The log4r package is meant to provide a fast, lightweight, object-oriented approach to logging in R based on the widely-emulated log4j system and etymology.

This package provides a unified interface to large language models across multiple providers. Supports text generation, structured output with optional JSON Schema validation, and embeddings. Includes tidyverse-friendly helpers, chat session, consistent error handling, and parallel batch tools.

Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.

LimeSurvey is Free/Libre Open Source Software for the development and administrations of online studies, using sophisticated tailoring capabilities to support multiple study designs (see <https://www.limesurvey.org>). This package supports programmatic creation of surveys that can then be imported into LimeSurvey', as well as user friendly import of responses from LimeSurvey studies.

Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices.

Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc. REFERENCES i. Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>, ii. Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006>, iii. Imdadullah, Aslam, and Saima (2017), iv. Marquardt (1970) <doi:10.2307/1267205>.

This package provides a variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored R code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in C++ for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Constructs tables of counts and proportions out of data sets with possibility to insert tables to Excel, Word, HTML, and PDF documents. Transforms tables to data suitable for modelling. Features Gibbs sampling based log-linear (NB2) and power analyses (original by Oleksandr Ocheredko <doi:10.35566/isdsa2019c5>) for tabulated data.

Fast calculation of Area Under Curve (AUC) metric of a Receiver Operating Characteristic (ROC) curve, using the algorithm of Fawcett (2006) <doi:10.1016/j.patrec.2005.10.010>. Therefore it is appropriate for large-scale AUC metric calculations.

Set of the data science tools created by various members of the Long Term Ecological Research (LTER) community. These functions were initially written largely as standalone operations and have later been aggregated into this package.

The primary purpose of lavaan.mi is to extend the functionality of the R package lavaan', which implements structural equation modeling (SEM). When incomplete data have been multiply imputed, the imputed data sets can be analyzed by lavaan using complete-data estimation methods, but results must be pooled across imputations (Rubin, 1987, <doi:10.1002/9780470316696>). The lavaan.mi package automates the pooling of point and standard-error estimates, as well as a variety of test statistics, using a familiar interface that allows users to fit an SEM to multiple imputations as they would to a single data set using the lavaan package.