This package provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types.

We provide a solution for performing permutation tests on linear and mixed linear regression models. It allows users to obtain accurate p-values without making distributional assumptions about the data. By generating a null distribution of the test statistics through repeated permutations of the response variable, permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023) <doi:10.1007/s10683-023-09799-6>). In this early version, we focus on the permutation tests over observed t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null distribution of the test statistic through repeated permutations of the response variable, each observed t values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop criterion (Anscombe (1953) <doi:10.1111/j.2517-6161.1953.tb00121.x>) is adopted to force permutation to stop if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so, we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate p-values.

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>.

Fits sparse generalized linear models using an adaptive ridge approximation to an L0 penalty. Supported model families include Gaussian, logistic, Poisson, gamma, and inverse Gaussian regression. The package also provides cross-validation for selecting the penalty parameter.

Back-end connections to LattE (<https://www.math.ucdavis.edu/~latte/>) for counting lattice points and integration inside convex polytopes and 4ti2 (<http://www.4ti2.de/>) for algebraic, geometric, and combinatorial problems on linear spaces and front-end tools facilitating their use in the R ecosystem.

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>.

Download Internet Protocol (IP) address location and more from the ip-api application programming interface (API) <https://ip-api.com/>. The package makes it easy to get the latitude, longitude, country, region, and organisation associated to the provided IP address. The information is conveniently returned in a rectangular format.

Data sets on various litter types like beach litter, riverain litter, floating litter, and seafloor litter are rapidly growing. This package offers a simple user interface to analyse these litter data in a consistent and reproducible way. It also provides functions to facilitate several kinds of litter analysis, e.g., trend analysis, power analysis, and baseline analysis. Under the hood, these functions are also used by the user interface. See Schulz et al. (2019) <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are advised to run litteR in RStudio'. See our vignette: Installation manual for RStudio and litteR'.

Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach. Based on FORTRAN program ld98 implemented by Reboussin, et al. (2000) <doi:10.1016/s0197-2456(00)00057-x>.

Alternate font rendering is useful when rendering text to novel graphics outputs where modern font rendering is not available or where bespoke text positioning is required. Bitmap and vector fonts allow for custom layout and rendering using pixel coordinates and line drawing. Formatted text is created as a data.frame of pixel coordinates (for bitmap fonts) or stroke coordinates (for vector fonts). All text can be easily previewed as a matrix or raster image. A selection of fonts is included with this package.

Software for computing a log-concave (maximum likelihood) estimator for independent and identically distributed data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).

The local diffusion exponent for undirected, unweighted networks characterizes the speed of local diffusion from a specified node and depends on the resistance distance.

An interface for the image processing program ImageJ', which allows a rapid digital image analysis for particle sizes. This package includes function to write an ImageJ macro which is optimized for a leaf area analysis by default.

This package implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.

This package provides tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. This is a revised version of the lineup package, to be more general and not tied to the qtl package.

Dimensionality reduction techniques for binary data including logistic PCA.

Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) <doi:10.1093/biostatistics/kxz052>.

Label-free bottom-up proteomics expression data is often affected by data heterogeneity and missing values. Normalization and missing value imputation are commonly used techniques to address these issues and make the dataset suitable for further downstream analysis. This package provides an optimal combination of normalization and imputation methods for the dataset. The package utilizes three normalization methods and three imputation methods.The statistical evaluation measures named pooled co-efficient of variance, pooled estimate of variance and pooled median absolute deviation are used for selecting the best combination of normalization and imputation method for the given dataset. The user can also visualize the results by using various plots available in this package. The user can also perform the differential expression analysis between two sample groups with the function included in this package. The chosen three normalization methods, three imputation methods and three evaluation measures were chosen for this study based on the research papers published by Välikangas et al. (2016) <doi:10.1093/bib/bbw095>, Jin et al. (2021) <doi:10.1038/s41598-021-81279-4> and Srivastava et al. (2023) <doi:10.2174/1574893618666230223150253>.This work has published by Sakthivel et al. (2025) <doi:10.1021/acs.jproteome.4c00552>.

This package provides a set of functions that allow stationary analysis and locally stationary time series analysis.

Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading. Main goal is to process information within "Decision Support System" to come up with analysis or predictions. There are several utilities such as dynamic and adaptive risk management using reinforcement learning and even functions to generate predictions of price changes using pattern recognition deep regression learning. Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, Market Type research of Van Tharp Institute: <https://vantharp.com/>, Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.

These functions take a gene expression value matrix, a primary covariate vector, an additional known covariates matrix. A two stage analysis is applied to counter the effects of latent variables on the rankings of hypotheses. The estimation and adjustment of latent effects are proposed by Sun, Zhang and Owen (2011). "leapp" is developed in the context of microarray experiments, but may be used as a general tool for high throughput data sets where dependence may be involved.

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is logConDens that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

This package provides three classes: Queue, PriorityQueue and Stack. Queue is just a "plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.

This package provides a bridge between the loon and ggplot2 packages. Extends the grammar of ggplot to add clauses to create interactive loon plots. Existing ggplot(s) can be turned into interactive loon plots and loon plots into static ggplot(s); the function loon.ggplot() is the bridge from one plot structure to the other.