This package provides functions for the analysis of Latin Square and Semi-Latin Square (Latin Rectangle) experimental designs, including analysis of variance (ANOVA), least significant difference (LSD) tests, and calculation of summary statistics. The implemented methods follow Munzert (1992, ISBN: 3-489-53410-7).

This package provides R bindings to llama.cpp for running Large Language Models ('LLMs') locally with optional Vulkan GPU acceleration via ggmlR'. Supports model loading, text generation, tokenization', token-to-piece conversion, embeddings (single and batch), encoder-decoder inference, low-level batch management, chat templates, LoRA adapters, explicit backend/device selection, multi-GPU split, and NUMA optimization. Includes a high-level ragnar'-compatible embedding provider ('embed_llamar'). Built on top of ggmlR for efficient tensor operations.

This package provides a framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The akmedoids package is available from <https://github.com/MAnalytics/akmedoids>.

This package provides a shiny application to construct age-specific life tables and fertility schedules from individual female daily egg records. The application computes age-specific survival and fertility functions and estimates key demographic parameters including the net reproductive rate, mean generation time, intrinsic rate of increase, finite rate of increase and doubling time. Optional confidence intervals can be obtained using percentile bootstrap or delete-1 jackknife resampling at the female level. Methods and definitions follow Stevens (2009) <doi:10.1007/978-0-387-89882-7> and Rossini et al. (2024) <doi:10.1371/journal.pone.0299598>.

Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes. The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.

Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales and wavelet energy of Knight et al (2017) <doi:10.1007/s11222-016-9698-2>.

Genome-wide association (GWAS) analyses of a biomarker that account for the limit of detection.

Download and read data on lobbying in the United States Congress. Data is queried from the Senate's Application Programming Interface (<https://lda.senate.gov/api/>). This supports filings since 2008. Functions exist for all primary data endpoints, including queries by filings, contributions, registrations, clients, and lobbyists.

This package creates plots of peptides from shotgun proteomics analysis of secretome and lysate samples. These plots contain associated protein features and scores for potential secretion and truncation.

This package provides functions to automatically retrieve and deduplicate reference metadata based on saved search strings. Access to Web of Science and Scopus requires personal API keys, while PubMed can be queried without one. The optional deduplication functionality requires the package ASySD available from <https://github.com/camaradesuk/ASySD>.

This package contains 128 palettes from Color Lisa. All palettes are based on masterpieces from the worlds greatest artists. For more information, see <http://colorlisa.com/>.

The reference implementation of model equations and default parameters for the toxicokinetic-toxicodynamic (TKTD) model of the Lemna (duckweed) aquatic plant. Lemna is a standard test macrophyte used in ecotox effect studies. The model was described and published by the SETAC Europe Interest Group Effect Modeling. It is a refined description of the Lemna TKTD model published by Schmitt et al. (2013) <doi:10.1016/j.ecolmodel.2013.01.017>.

Brings together a comprehensive collection of R packages providing access to API functions and curated datasets from Argentina, Brazil, Chile, Colombia, and Peru. Includes real-time and historical data through public RESTful APIs ('Nager.Date', World Bank API, REST Countries API, and country-specific APIs) and extensive curated collections of open datasets covering economics, demographics, public health, environmental data, political indicators, social metrics, and cultural information. Designed to provide researchers, analysts, educators, and data scientists with centralized access to Latin American data sources, facilitating reproducible research, comparative analysis, and teaching applications focused on these five major Latin American countries. Included packages: - ArgentinAPI': API functions and curated datasets for Argentina covering exchange rates, inflation, political figures, national holidays and more. - BrazilDataAPI': API functions and curated datasets for Brazil covering postal codes, banks, economic indicators, holidays, company registrations and more. - ChileDataAPI': API functions and curated datasets for Chile covering financial indicators ('UF', UTM, Dollar, Euro, Yen, Copper, Bitcoin, IPSA index), holidays and more. - ColombiAPI': API functions and curated datasets for Colombia covering geographic locations, cultural attractions, economic indicators, demographic data, national holidays and more. - PeruAPIs': API functions and curated datasets for Peru covering economic indicators, demographics, national holidays, administrative divisions, electoral data, biodiversity and more. For more information on the APIs, see: Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, REST Countries API <https://restcountries.com/>, ArgentinaDatos API <https://argentinadatos.com/>, BrasilAPI <https://brasilapi.com.br/>, FINDIC <https://findic.cl/>, and API-Colombia <https://api-colombia.com/>.

Targeted Maximum Likelihood Estimation ('TMLE') of treatment/censoring specific mean outcome or marginal structural model for point-treatment and longitudinal data.

Helper functions to implement univariate and bivariate latent change score models in R using the lavaan package. For details about Latent Change Score Modeling (LCSM) see McArdle (2009) <doi:10.1146/annurev.psych.60.110707.163612> and Grimm, An, McArdle, Zonderman and Resnick (2012) <doi:10.1080/10705511.2012.659627>. The package automatically generates lavaan syntax for different model specifications and varying timepoints. The lavaan syntax generated by this package can be returned and further specifications can be added manually. Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications. Estimated model parameters and fit statistics can be extracted as data frames. Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects. This package combines the strengths of other R packages like lavaan', broom', and semPlot by generating lavaan syntax that helps these packages work together.

Affords an alternative, vector-based syntax to lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow.

Implementation of the three-step approach of (latent transition) cognitive diagnosis model (CDM) with covariates. This approach can be used for single time-point situations (cross-sectional data) and multiple time-point situations (longitudinal data) to investigate how the covariates are associated with attribute mastery. For multiple time-point situations, the three-step approach of latent transition CDM with covariates allows researchers to assess changes in attribute mastery status and to evaluate the covariate effects on both the initial states and transition probabilities over time using latent logistic regression. Because stepwise approaches often yield biased estimates, correction for classification error probabilities (CEPs) is considered in this approach. The three-step approach for latent transition CDM with covariates involves the following steps: (1) fitting a CDM to the response data without covariates at each time point separately, (2) assigning examinees to latent states at each time point and computing the associated CEPs, and (3) estimating the latent transition CDM with the known CEPs and computing the regression coefficients. The method was proposed in Liang et al. (2023) <doi:10.3102/10769986231163320> and demonstrated using mental health data in Liang et al. (in press; annotated R code and data utilized in this example are available in Mendeley data) <doi:10.17632/kpjp3gnwbt.1>.

Rapid satellite data streams in operational applications have clear benefits for monitoring land cover, especially when information can be delivered as fast as changing surface conditions. Over the past decade, remote sensing has become a key tool for monitoring and predicting environmental variables by using satellite data. This package presents the main applications in remote sensing for land surface monitoring and land cover mapping (soil, vegetation, water...). Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) <doi:10.1002/met.287>.

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

Sparklines are small plots (about one line of text high), made popular by Edward Tufte. This package is the interface from R to the LaTeX package sparklines by Andreas Loeffer and Dan Luecking (<http://www.ctan.org/pkg/sparklines>). It can work with Sweave or knitr or other engines that produce TeX. The package can be used to plot vectors, matrices, data frames, time series (in ts or zoo format).

Create custom labels, badges, certificates and other documents. Automate the production of potentially large numbers of herbarium and collection labels, accreditation badges, attendance and participation certificates, etc, and deliver them automatically. Documents are generated in PDF format, which requires a working installation of LaTeX', such as TinyTeX'.

This package provides tools for maximum likelihood estimation of parameters of scientific models. Based on Goffe et al (1994) <doi:10.1016/0304-4076(94)90038-8>.

Local Individual Conditional Expectation ('localICE') is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI). localICE is a model-agnostic XAI approach which provides three-dimensional local explanations for particular data instances. The approach is proposed in the master thesis of Martin Walter as an extension to ICE (see Reference). The three dimensions are the two features at the horizontal and vertical axes as well as the target represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. For classification models, the number of classes can be more than two and each class is added as a different color to the plot. The given instance is added to the plot as two dotted lines according to the feature values. The localICE-package can explain features of type factor and numeric of any machine learning model. Automatically supported machine learning packages are mlr', randomForest', caret or all other with an S3 predict function. For further model types from other libraries, a predict function has to be provided as an argument in order to get access to the model. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) <arXiv:1309.6392>.

When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.