Given the date column as an ascending entry, future errors are included in the sum of squares of error that should be minimized based on the number of steps and weights you determine. Thus, it is prevented that the variables affect each other's coefficients unrealistically.

The mia package implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.

RSeQC provides a number of modules that can comprehensively evaluate high throughput sequence data, especially RNA-seq data. Some basic modules inspect sequence quality, nucleotide composition bias, PCR bias and GC bias, while RNA-seq specific modules evaluate sequencing saturation, mapped reads distribution, coverage uniformity, strand specificity, etc.

This is a package for ratios of count data such as obtained from RNA-seq are modelled using Bayesian statistics to derive posteriors for effects sizes. This approach is described in Erhard & Zimmer (2015) <doi:10.1093/nar/gkv696> and Erhard (2018) <doi:10.1093/bioinformatics/bty471>.

This package provides basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.

This package offers a flexible, feature-rich yet light-weight logging framework based on R6 classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, JSON, (rotating) files, memory buffers, and databases, as well as email and push notifications.

RHash is a console utility for calculation and verification of magnet links and a wide range of hash sums like CRC32, MD4, MD5, SHA1, SHA256, SHA512, SHA3, AICH, ED2K, Tiger, DC++ TTH, BitTorrent BTIH, GOST R 34.11-94, RIPEMD-160, HAS-160, EDON-R, Whirlpool and Snefru.

Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians.

This package provides a collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.

This package implements cluster-polarization coefficient for measuring distributional polarization in single or multiple dimensions, as well as associated functions. Contains support for hierarchical clustering, k-means, partitioning around medoids, density-based spatial clustering with noise, and manually imposed cluster membership. Mehlhaff (2024) <doi:10.1017/S0003055423001041>.

This package provides density, distribution function, quantile function and random generation for the split normal and split-t distributions, and computes their mean, variance, skewness and kurtosis for the two distributions (Li, F, Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>).

Fuzzy set ordination is a multivariate analysis used in ecology to relate the composition of samples to possible explanatory variables. While differing in theory and method, in practice, the use is similar to constrained ordination. The package contains plotting and summary functions as well as the analyses.

Methodology for subgroup selection in the context of isotonic regression including methods for sub-Gaussian errors, classification, homoscedastic Gaussian errors and quantile regression. See the documentation of ISS(). Details can be found in the paper by Müller, Reeve, Cannings and Samworth (2023) <arXiv:2305.04852v2>.

Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the Python library PyLMD'.

This package provides functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by à yvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).

Building patient level networks for prediction of medical outcomes and draw the cluster of network. This package is based on paper Personalized disease networks for understanding and predicting cardiovascular diseases and other complex processes (See Cabrera et al. <http://circ.ahajournals.org/content/134/Suppl_1/A14957>).

We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.

This package provides bitmapped vectors of booleans (no NAs), coercion from and to logicals, integers and integer subscripts, fast boolean operators and fast summary statistics. With bit class vectors of true binary booleans, TRUE and FALSE can be stored with 1 bit only.

This package implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.

Reading and writing of files in the most commonly used formats of structural crystallography. It includes functions to work with a variety of statistics used in this field and functions to perform basic crystallographic computing. References: D. G. Waterman, J. Foadi, G. Evans (2011) <doi:10.1107/S0108767311084303>.

Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) <doi:10.1101/2022.10.28.513998>.

This package contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.

An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961.

Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) <doi:10.1111/j.1541-0420.2010.01550.x>. Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities.