This package contains tools to build deep neural network with flexible users define loss function and probability models. Several applications included in this package are, 1) The (deepAFT) model, a deep neural network model for accelerated failure time (AFT) model for survival data. 2) The (deepGLM) model, a deep neural network model for generalized linear model (glm) for continuous, categorical and Poisson data.

Function and data sets in the book entitled "R ile Temel Ekonometri", S.Guris, E.C.Akay, B. Guris(2020). The book published in Turkish. It is possible to makes Durbin two stage method for autocorrelation, generalized differencing method for correction autocorrelation, Hausman Test for identification and computes LM, LR and Wald test statistics for redundant variable by using the functions written in this package.

This package provides an interface to the Gibbs SeaWater ('TEOS-10') C library, version 3.06-16-0 (commit 657216dd4f5ea079b5f0e021a4163e2d26893371', dated 2022-10-11, available at <https://github.com/TEOS-10/GSW-C>, which stems from Matlab and other code written by members of Working Group 127 of SCOR'/'IAPSO (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans).

This package produces a group screening procedure that is based on maximum Lq-likelihood estimation, to simultaneously account for the group structure and data contamination in variable screening. The methods are described in Li, Y., Li, R., Qin, Y., Lin, C., & Yang, Y. (2021) Robust Group Variable Screening Based on Maximum Lq-likelihood Estimation. Statistics in Medicine, 40:6818-6834.<doi:10.1002/sim.9212>.

The purpose of this package is to share a collection of functions the author wrote during weekends for managing kitchen and garden tasks, e.g. making plant growth charts or Thanksgiving kitchen schedule charts, etc. Functions might include but not limited to: (1) aiding summarizing time related data; (2) generating axis transformation from data; and (3) aiding Markdown (with html output) and Shiny file editing.

Calculates spatial pattern analysis using a T-square sample procedure. This method is based on two measures "x" and "y". "x" - Distance from the random point to the nearest individual. "y" - Distance from individual to its nearest neighbor. This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species distribution (random, uniform or clumped patterns). Ludwig & Reynolds (1988, ISBN:0471832359).

Predicate helper functions for testing atomic vectors in R. All functions take a single argument x and check whether it's of the target type of base-R atomic vector (i.e. no class extensions nor attributes other than names'), returning TRUE or FALSE. Some additionally check for value (e.g. absence of missing values, infinities, blank characters, or names attribute; or having length 1).

The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.

Implementation of the categorical instrumental variable (CIV) estimator proposed by Wiemann (2023) <arXiv:2311.17021>. CIV allows for optimal instrumental variable estimation in settings with relatively few observations per category. To obtain valid inference in these challenging settings, CIV leverages a regularization assumption that implies existence of a latent categorical variable with fixed finite support achieving the same first stage fit as the observed instrument.

Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) <doi:10.1093/mnras/stab1216>).

Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize the model without losing any information, and fits the model on the standard fitting algorithm with no convergence issues.

Calculate NOS (node overlap and segregation) and the associated metrics described in Strona and Veech (2015) <doi:10.1111/2041-210X.12395> and Strona et al. (2018) <doi:10.1111/ecog.03447>. The functions provided in the package enable assessment of structural patterns ranging from complete node segregation to perfect nestedness in a variety of network types. In addition, they provide a measure of network modularity.

This package produces power spectral density estimates through iterative refinement of the optimal number of sine-tapers at each frequency. This optimization procedure is based on the method of Riedel and Sidorenko (1995), which minimizes the Mean Square Error (sum of variance and bias) at each frequency, but modified for computational stability. The same procedure can now be used to calculate the cross spectrum (multivariate analyses).

This package provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not. Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package VCA'. All kinds of, possibly transformed (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.

Calculates the Urban Centrality Index (UCI) as in Pereira et al., (2013) <doi:10.1111/gean.12002>. The UCI measures the extent to which the spatial organization of a city or region varies from extreme polycentric to extreme monocentric in a continuous scale from 0 to 1. Values closer to 0 indicate more polycentric patterns and values closer to 1 indicate a more monocentric urban form.

This package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. It also contains functions for identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data like gene expression/RNA sequencing/methylation/brain imaging data that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise.

This package includes tools for marginal maximum likelihood estimation and joint maximum likelihood estimation for unidimensional and multidimensional item response models. The package functionality covers the Rasch model, 2PL model, 3PL model, generalized partial credit model, multi-faceted Rasch model, nominal item response model, structured latent class model, mixture distribution IRT models, and located latent class models. Latent regression models and plausible value imputation are also supported.

Create videos from R Markdown documents, or images and audio files. These images can come from image files or HTML slides, and the audio files can be provided by the user or computer voice narration can be created using Amazon Polly'. The purpose of this package is to allow users to create accessible, translatable, and reproducible lecture videos. See <https://aws.amazon.com/polly/> for more information.

Cristin to Zotero ('c2z') aims at obtaining total dominion over Cristin ('Current Research Information SysTem in Norway') and Zotero'. The package enables manipulating Zotero libraries using R'. Import references from Cristin', Regjeringen', CRAN', ISBN ('Alma', LoC'), and DOI ('CrossRef', DataCite') to a Zotero library. Add, edit, copy, or delete items, including attachments and collections, and export references to BibLaTeX (and other formats).

This package provides a function composition operator to chain a series of calls into a single function, mimicking the math notion of (f o g o h)(x) = h(g(f(x))). Inspired by pipeOp ('|>') since R4.1 and magrittr pipe ('%>%'), the operator build a pipe without putting data through, which is best for anonymous function accepted by utilities such as apply() and lapply().

This package provides functions to help in fitting models to data, to perform Monte Carlo, sensitivity and identifiability analysis. It is intended to work with models be written as a set of differential equations that are solved either by an integration routine from package deSolve', or a steady-state solver from package rootSolve'. However, the methods can also be used with other types of functions.

An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.

Estimate the NNT using the proposed method in Yang and Yin's paper (2019) <doi:10.1371/journal.pone.0223301>, in which the NNT-RMST (number needed to treat based on the restricted mean survival time) is defined as the RMST (restricted mean survival time) in the control group divided by the difference in RMSTs between the treatment and control groups up to a chosen time t.

Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.