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20072026
most citedSciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

40.2k citations

Showing 2019Show all

7 papers · 1 filter

stat.AP2019★ 5 cited

Source-specific contributions of particulate matter to asthma-related pediatric emergency department utilization

Mohammad Alfrad Nobel Bhuiyan, Patrick Ryan, Farzan Oroumyeh +4

Few studies have linked specific sources of ambient particulate matter smaller than 2.5 m (PM2.5) and asthma. In this study, we estimated the contributions of specific sources t…

stat.ME2019★ 1 cited

Bayesian Shape Invariant Model for Latent Growth Curve with Time-Invariant Covariates

Mohammad Alfrad Nobel Bhuiyan, Heidi Sucharew, Rhonda Szczesniak +4

In the attention-deficit hyperactivity disorder (ADHD) study, children are prescribed different stimulant medications. The height measurements are recorded longitudinally along wit…

stat.ME2019

Efficiency of Observed Information Adaptive Designs

Adam Lane

In this work the primary objective is to maximize the precision of the maximum likelihood estimate in a linear regression model through the efficient design of the experiment. One…

q-bio.GN2019★ 54 cited

Guidelines for reporting single-cell RNA-Seq experiments

Anja Füllgrabe, Nancy George, Matthew Green +16

Single-cell RNA-Sequencing (scRNA-Seq) has undergone major technological advances in recent years, enabling the conception of various organism-level cell atlassing projects. With i…

stat.ME2019★ 3 cited

Conditional Information and Inference in Response-Adaptive Allocation Designs

Adam Lane

Response-adaptive allocation designs refer to a class of designs where the probability an observation is assigned to a treatment is changed throughout an experiment based on the ac…

cs.MS2019★ 40.2k cited

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant +32

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release…

stat.ME2019★ 2 cited

GPMatch: A Bayesian Doubly Robust Approach to Causal Inference with Gaussian Process Covariance Function As a Matching Tool

Bin Huang, Chen Chen, Jinzhong Liu

Gaussian process (GP) covariance function is proposed as a matching tool in GPMatch within a full Bayesian framework under relatively weaker causal assumptions. The matching is acc…