This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with Dexcom', iPro 2', Diasend', Libre', or Carelink data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit <https://github.com/childhealthbiostatscore/R-Packages/> to download the new-user guide.

This package provides methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization ('jrSiCKLSNMF', pronounced "junior sickles NMF") on quality controlled single-cell multimodal omics count data. jrSiCKLSNMF specifically deals with dual-assay scRNA-seq and scATAC-seq data. This package contains functions to extract meaningful latent factors that are shared across omics modalities. These factors enable accurate cell-type clustering and facilitate visualizations. Methods for pre-processing, clustering, and mini-batch updates and other adaptations for larger datasets are also included. For further details on the methods used in this package please see Ellis, Roy, and Datta (2023) <doi:10.3389/fgene.2023.1179439>.

This package provides functions for simulating and estimating kinship-related dispersal. Based on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. Hoffmann (2019) <doi:10.1111/1755-0998.13043> "A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito". Assumes an additive variance model of dispersal in two dimensions, compatible with Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, as well as the functions needed to estimate parent-offspring dispersal for simulated or empirical data, and to undertake sampling design for future field studies of dispersal. For ease of use an integrated Shiny app is also included.

An HTML widget that randomly tours 2D projections of numerical data. A random walk through projections of the data is shown. The user can manipulate the plot to use specified axes, or turn on Guided Tour mode to find an informative projection of the data. Groups within the data can be hidden or shown, as can particular axes. Points can be brushed, and the selection can be linked to other widgets using crosstalk. The underlying method to produce the random walk and projection pursuit uses Langevin dynamics. The widget can be used from within R, or included in a self-contained R Markdown or Quarto document or presentation, or used in a Shiny app.

R6 classes to model traditional life insurance contracts like annuities, whole life insurances or endowments. Such life insurance contracts provide a guaranteed interest and are not directly linked to the performance of a particular investment vehicle, but they typically provide (discretionary) profit participation. This package provides a framework to model such contracts in a very generic (cash-flow-based) way and includes modelling profit participation schemes, dynamic increases or more general contract layers, as well as contract changes (like sum increases or premium waivers). All relevant quantities like premium decomposition, reserves and benefits over the whole contract period are calculated and potentially exported to Excel'. Mortality rates are given using the MortalityTables package.

This package provides a modern, tidy toolkit for lag sequential analysis and lag transition networks of categorical event and sequence data. It provides an accessible, unified workflow for fitting, inspecting, visualising, and comparing lagged transition patterns, with tidy outputs throughout. Includes confirmatory tools for uncertainty, robustness, and group differences, including bootstrap intervals, analytic certainty, split-half reliability, case-drop stability, permutation tests, and Bayesian group comparisons. Supports long-format event-log import, import from common sequence and state-sequence objects, multi-lag analysis, structural-zero constraints, transition and initial probabilities, plotting of transition structures, and a directed transfer-entropy measure. The lag sequential analysis framework follows Sackett and others (1979) <doi:10.3758/BF03205679>.

Labels are a common construct in statistical software providing a human readable description of a variable. While variable names are succinct, quick to type, and follow a language's naming conventions, labels may be more illustrative and may use plain text and spaces. R does not provide native support for labels. Some packages, however, have made this feature available. Most notably, the Hmisc package provides labelling methods for a number of different object. Due to design decisions, these methods are not all exported, and so are unavailable for use in package development. The labelVector package supports labels for atomic vectors in a light-weight design that is suitable for use in other packages.

The progressify() function rewrites (transpiles) calls to sequential and parallel map-reduce functions such as base::lapply(), purrr::map(), foreach::foreach(), and plyr::llply() to signal progress updates. By combining this function with R's native pipe operator, you have a straightforward way to report progress on iterative computations with minimal refactoring, e.g. lapply(x, fcn) |> progressify() and purrr::map(x, fcn) |> progressify()'. It is compatible with the parallel-processing map-reduce packages future.apply', furrr', crossmap', foreach', doFuture', and futurize'. It also supports domain-specific packages including boot', fwb', lme4', partykit', sandwich', and SimDesign', e.g. boot::boot(data, stat, R) |> progressify()'.

Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external.

Package for the access and distribution of long-term lake datasets from 28 lakes in the Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and biological parameters originally described in Farrell et al. 2018 <doi:10.1038/sdata.2018.59>. Water chemistry and nutrient records are extended through 2024 using data from the USGS AQ Samples database, including new columns for surface temperature, UV-254 absorbance, and a program flag distinguishing AEAP integrated samples from ALTM surface grabs. The underlying figshare archive <doi:10.6084/m9.figshare.32305479> additionally contains chemistry records for 25 ALTM-only lakes; the package restricts to the 28 originals for consistency with the published dataset.

Computation of key characteristics and plots for blinded sample size recalculation. Continuous as well as binary endpoints are supported in superiority and non-inferiority trials. See Baumann, Pilz, Kieser (2022) <doi:10.32614/RJ-2022-001> for a detailed description. The implemented methods include the approaches by Lu, K. (2016) <doi:10.1002/pst.1737>, Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>, Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>, Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.

Perform fuzzy joins on data frames using approximate string matching. Implements inner, left, right, full, semi, and anti joins with string distance metrics from the stringdist package, including Optimal String Alignment, Levenshtein, Damerau-Levenshtein, Jaro-Winkler, q-gram, cosine, Jaccard, and Soundex. Uses a data.table backend plus compiled C++ result assembly to reduce overhead in large joins, while adaptive candidate planning avoids unnecessary distance evaluations in single-column string joins. Suitable for reconciling misspellings, inconsistent labels, and other near-match identifiers while optionally returning the computed distance for each match. Bibliographic references include the stringdist package documentation (2014) <https://CRAN.R-project.org/package=stringdist> and Robinson, D. (2015) <https://github.com/dgrtwo/fuzzyjoin>.

Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their dynamic form. idopNetwork is an R interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) <doi:10.1080/19490976.2022.2106103>.

This package provides a method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network, which is originally developed by Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided. The sample size of the dataset used to train the LSTM model is 1,000,000. Each sample is a batch of simulated response data with a specific latent factor structure. The eigenvalues of these response data will be used as sequential data to train the LSTM. The pre-trained LSTM is capable of factor retention for real response data with a true latent factor number ranging from 1 to 10, that is, determining the number of factors.

This package performs Levins loop analysis of qualitatively-specified complex causal systems. Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means direct causal relationships and indirect causal effects are coded as sign only (i.e. increases, decreases, no change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as graphviz (<https://graphviz.org>). LoopAnalyst provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.

Estimates Shannon entropy, per gene and per genomic position, associated with non-synonymous mutation frequencies in viral populations, such as wastewater samples. The package uses codon translations for functional insights. Each amino acid can be treated as an individual state, resulting in a 20-state entropy computation, or grouped into one of six physicochemical classes, adding further functional context. Provides normalized values (0-1 scale) to facilitate the direct comparison of different genomic positions or total functional entropy across multiple metagenomes. Designed to analyze mutational data using tabular Single Nucleotide Variant (SNV) frequency tables generated by variant callers (e.g., iVar or LoFreq'), operating independently of consensus sequence estimation and multiple sequence alignment.

This package provides a unified framework for fitting, predicting, and interpreting nonlinear relationships in single-level, multilevel, and longitudinal regression models. Flexible functional forms are supported using natural cubic splines ('splines'), B-splines ('splines'), and GAM smooths ('mgcv'). Supports two-way and nested clustering via lme4', automatic knot selection by AIC or BIC, multilevel R-squared decomposition (Nakagawa-Schielzeth marginal and conditional R-squared with level-specific variance partitioning), a postestimation suite returning first and second derivatives with confidence bands, turning points and inflection regions, and a model comparison workflow contrasting linear, polynomial, and spline fits by AIC, BIC, and likelihood-ratio tests. Cluster heterogeneity in nonlinear effects is supported via random-slope spline terms.

This package provides details such as Morphine Equivalent Dose (MED), brand name and opioid content which are calculated of all oral opioids authorized for sale by Health Canada and the FDA based on their Drug Identification Number (DIN) or National Drug Code (NDC). MEDs are calculated based on recommendations by Canadian Institute for Health Information (CIHI) and Von Korff et al (2008) and information obtained from Health Canada's Drug Product Database's monthly data dump or FDA Daily database for Canadian and US databases respectively. Please note in no way should output from this package be a substitute for medical advise. All medications should only be consumed on prescription from a licensed healthcare provider.

This package provides a lightweight tool that provides a reproducible workflow for selecting and executing appropriate statistical analysis in one-way or two-way experimental designs. The package automatically checks for data normality, conducts parametric (ANOVA) or non-parametric (Kruskal-Wallis) tests, performs post-hoc comparisons with Compact Letter Displays (CLD), and generates publication-ready boxplots, faceted plots, and heatmaps. It is designed for researchers seeking fast, automated statistical summaries and visualization. Based on established statistical methods including Shapiro and Wilk (1965) <doi:10.2307/2333709>, Kruskal and Wallis (1952) <doi:10.1080/01621459.1952.10483441>, Tukey (1949) <doi:10.2307/3001913>, Fisher (1925) <ISBN:0050021702>, and Wickham (2016) <ISBN:978-3-319-24277-4>.

Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series. Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>. Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.

This package implements FiberMargin', a deterministic training-free operator for repairing categorical masks and spatial labels from coordinates and labels alone. Its primary multiclass operator uses rotated space-filling-curve charts and two-sided class enclosure at one fixed geometric transport range. A class-balanced, isolation-protected chart-disagreement rule provides pointwise repair decisions and audit scores. An auxiliary nearest-neighbour ballot handles binary masks. The C++ engine supports two- and three-dimensional coordinates, removes constant axes independently within each specimen, and reuses one deterministic CPU worker budget without nested process pools. Reproducible mask corruptions, planar and volumetric simulators, damage-aware evaluation, and compact licensed human dorsolateral prefrontal cortex and colorectal cancer benchmarks support assessment.

This package provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.

Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>, Amendola et al. (2015) <doi:10.1002/for.2322> and Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.

Visualization and reporting tools for sensitivity analysis to unmeasured confounding in observational studies. A common confoundsens object stores a sensitivity path (the treatment effect as a function of hypothetical confounder strength) regardless of the framework that produced it, so the same robustness curves, contour plots, covariate benchmark ("sensitivity Love") plots, and plain-language reports can be drawn for impact threshold analysis (Frank, 2000, <doi:10.1177/0049124100029002001>), partial R-squared omitted-variable bias analysis (Cinelli and Hazlett, 2020, <doi:10.1111/rssb.12348>), and E-values (VanderWeele and Ding, 2017, <doi:10.7326/M16-2607>). Paths can be computed directly from fitted linear models or converted from results produced by the sensemakr', konfound', and EValue packages.