This package provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon igraph'.

Interactive Shiny web application for comprehensive RNA-Seq data analysis, quality control, differential expression analysis with DESeq2', dimensionality reduction (PCA, t-SNE, UMAP), clustering, and integrated JBrowse 2 genome visualization. For differential expression analysis methods, see Love (2014) <doi:10.1186/s13059-014-0550-8>.

Annotated neuroblastoma copy number profiles, a benchmark data set for change-point detection algorithms, as described by Hocking et al. <doi:10.1186/1471-2105-14-164>.

This package contains a collection of functions for performing different kinds of calculation that are of interest to someone following a diet plan. Calculators for the Basal Metabolic Rate are based on Mifflin et al. (1990) <doi:10.1093/ajcn/51.2.241> and McArdle, W. D., Katch, F. I., & Katch, V. L. (2010, ISBN:9780812109917).

This package provides efficient methods to compute co-occurrence matrices, pointwise mutual information (PMI) and singular value decomposition (SVD). In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the RcppAlgos package and sparse matrices. Furthermore, the functions can be called on SQL databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.

Nowcasting using the Chain-Ladder method. Supports both non-cumulative delay-based estimation and model-based completeness fitting (e.g., using logistic or Gompertz curves) to predict final counts from partially reported data.

Introduces a novel network-constrained survival analysis framework for variable selection and parameter estimation in penalized survival models with convex penalties. The package extends two classical survival models, the Cox Proportional Hazards (PH) model and the Accelerated Failure Time (AFT) model, by incorporating prior biological knowledge from curated interaction networks (e.g., KEGG) into a double-penalty framework. The first penalty enforces variable selection through a LASSO penalty, while the second preserves gene-gene correlations by incorporating Laplacian-based constraints, ensuring that biologically relevant network structures are maintained. Using censored survival data, the method enables the identification of predictive biomarkers and pathways with potential relevance for target therapies. Model estimation is performed via proximal optimization algorithms combined with cross-validation for reliable tuning. To enhance interpretability, dedicated utility functions are implemented to consolidate results, yielding biologically coherent insights that can support personalized medicine and contribute to improved patient outcomes.

Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are best revealed by using Nonparametric and Unsupervised methods to "Design" the analysis of the given data ...rather than the collection of "designed data". Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences when X-Covariates are assumed to influence Treatment choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes. Obenchain and Young (2013) <doi:10.1080/15598608.2013.772821>; Obenchain, Young and Krstic (2019) <doi:10.1016/j.yrtph.2019.104418>.

Similarity measures for hierarchical clustering of objects characterized by nominal (categorical) variables. Evaluation criteria for nominal data clustering.

This package provides a set of functions to estimate outcomes of fourth down plays in the National Football League and obtain fourth down plays from <https://www.nfl.com/> and <https://www.espn.com/>.

Scrapes and cleans data from the NHL and ESPN APIs into data.frames and lists. Wraps 125+ endpoints documented in <https://github.com/RentoSaijo/nhlscraper/wiki> from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays.

Body Shape and related measurements from the US National Health and Nutrition Examination Survey (NHANES, 1999-2004). See <https://www.cdc.gov/nchs/nhanes/index.html> for details.

This package provides routines for plotting linkage and association results along a chromosome, with marker names displayed along the top border. There are also routines for generating BED and BedGraph custom tracks for viewing in the UCSC genome browser. The data reformatting program Mega2 uses this package to plot output from a variety of programs.

An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm that solves multiple optimization problems simultaneously by applying a non-dominated sorting technique. It uses a reference points based selection operator to explore solution space and preserve diversity. See the original paper by K. Deb and H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.

Wraps the nametag library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.

This package implements the nonparametric trend test for one or several samples as proposed by Bathke (2009) <doi:10.1007/s00184-008-0171-x>. The method provides a unified framework for analyzing trends in both independent and dependent data samples, making it a versatile tool for various study designs. The package allows for the evaluation of different trend alternatives, including two-sided (general trend), monotonic increasing, and monotonic decreasing trends. As a nonparametric procedure, it does not require the assumption of data normality, offering a robust alternative to parametric tests.

This package provides a suite of functions to work with data from the National Institutes of Health Brain Development Cohorts Data Hub. The package provides tools to create, clean, process, and filter datasets and associated metadata. These utilities are intended to simplify reproducible data-preparation for future research.

It names the R Markdown chunks of files based on the filename.

Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).

In the working paper titled "Why You Should Never Use the Hodrick-Prescott Filter", James D. Hamilton proposes a new alternative to economic time series filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.

Library to plot performance profiles (Dolan and More (2002) <doi:10.1007/s101070100263>) and nested performance profiles (Hekmati and Mirhajianmoghadam (2019) <doi:10.19139/soic-2310-5070-679>) for a given data frame.

This package provides functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.

User-friendly functions for extracting a data table (row for each match, column for each group) from non-tabular text data using regular expressions, and for melting columns that match a regular expression. Patterns are defined using a readable syntax that makes it easy to build complex patterns in terms of simpler, re-usable sub-patterns. Named R arguments are translated to column names in the output; capture groups without names are used internally in order to provide a standard interface to three regular expression C libraries ('PCRE', RE2', ICU'). Output can also include numeric columns via user-specified type conversion functions.

Create and manipulate numeric list ('nlist') objects. An nlist is an S3 list of uniquely named numeric objects. An numeric object is an integer or double vector, matrix or array. An nlists object is a S3 class list of nlist objects with the same names, dimensionalities and typeofs. Numeric list objects are of interest because they are the raw data inputs for analytic engines such as JAGS', STAN and TMB'. Numeric lists objects, which are useful for storing multiple realizations of of simulated data sets, can be converted to coda::mcmc and coda::mcmc.list objects.